Repository: prakhar21/100-Days-of-ML Branch: master Commit: 6d33df79d6ec Files: 70 Total size: 5.9 MB Directory structure: gitextract_gjqzpqql/ ├── README.md ├── bonus/ │ └── Dockerfile ├── data/ │ ├── day1/ │ │ ├── iris.csv │ │ └── regression.csv │ ├── day11/ │ │ └── credit.csv │ ├── day15/ │ │ └── jobclass.csv │ ├── day2/ │ │ └── automobile.csv │ ├── day3/ │ │ └── haberman.csv │ ├── day6/ │ │ └── glass.csv │ ├── day7/ │ │ └── winequality-white.csv │ └── day9/ │ └── banknote_authentication.csv ├── day01/ │ ├── .ipynb_checkpoints/ │ │ ├── BlackBox Takeaways-checkpoint.ipynb │ │ └── Pandas(1-5)-checkpoint.ipynb │ ├── BlackBox Takeaways.ipynb │ ├── Linear Regression.ipynb │ ├── Pandas(1-5).ipynb │ └── README.md ├── day02/ │ ├── Data Spread.ipynb │ ├── Multivariate Regression.ipynb │ ├── Pandas(6-10).ipynb │ └── README.md ├── day03/ │ ├── Logistic Regression.ipynb │ ├── Pandas(11-15).ipynb │ ├── README.md │ └── Visualization.ipynb ├── day04/ │ ├── .ipynb_checkpoints/ │ │ └── Pandas(16-18)-checkpoint.ipynb │ ├── Pandas(16-18).ipynb │ └── README.md ├── day06/ │ ├── K-NearestNeighbours.ipynb │ ├── Pandas(23-26).ipynb │ └── README.md ├── day07/ │ ├── Numpy.ipynb │ └── README.md ├── day08/ │ ├── README.md │ └── Titanic.ipynb ├── day09/ │ ├── Lime.ipynb │ ├── README.md │ └── SVM.ipynb ├── day10/ │ ├── Average Ensemble Models.ipynb │ └── README.md ├── day11/ │ ├── Feature Scaling.ipynb │ └── README.md ├── day12/ │ ├── Decision Trees.ipynb │ └── README.md ├── day13/ │ ├── Missing Values (Basics).ipynb │ └── README.md ├── day15/ │ ├── Model Stacking.ipynb │ └── README.md ├── day16/ │ └── Missing Value Imputations + Accuracy Measure.ipynb ├── day18/ │ ├── Pandas(Scraper).ipynb │ └── README.md ├── day19/ │ ├── FeatureSelection.ipynb │ └── README.md ├── day20/ │ ├── .ipynb_checkpoints/ │ │ └── Tqdm-checkpoint.ipynb │ ├── README.md │ ├── Resampling.ipynb │ └── Tqdm.ipynb ├── day21/ │ ├── README.md │ └── Save-Load.ipynb ├── day22/ │ └── pytorch.py ├── day27/ │ ├── cartpole.py │ ├── cartpole_monitor.py │ └── gym_pre.py ├── day28/ │ ├── tensorboard_pre.py │ └── torch_pre.py ├── day31/ │ ├── cross-entropy-tensorboard.py │ └── cross-entropy.py ├── day33/ │ └── Matplotlib.ipynb └── day39/ ├── Agglomerative.ipynb └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # 50-Days-of-ML A day to day plan for this challenge. Covers both theoritical and practical aspects. I have build [ __Docker Image__](https://hub.docker.com/r/prakhar21/ml-utilities/) with all the required dependencies till __Day 21__. Feel free to use it by pulling it using -> __docker pull prakhar21/ml-utilities__ Please see [__Deep Work__](https://www.quora.com/What-is-the-one-skill-that-if-you-have-it-will-completely-change-your-life/answer/Shashank-Shekhar-221) which compliments our challenge and increases productivity. You can follow me on [__@Medium__](https://medium.com/@prakhar.mishra) for interesting blog articles. ## Day-1 (31st July, 2018) * Learn about Pandas. [See Videos(1-5)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Blackbox Machine Learning)](https://www.youtube.com/watch?v=MsD28INtSv8) * Read/Practice [Day-1 and Day-2](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * See [Intro to Linear Regression](https://www.youtube.com/watch?v=zPG4NjIkCjc) * Read [LR Docs](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) ## Day-2 (1st August, 2018) * Learn about Pandas. [See Videos(6-10)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Case Study: Churn Prediction)](https://www.youtube.com/watch?v=kE_t3Mm8Z50) * Read/Practice [Day-3](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * See [Data Spread](https://www.khanacademy.org/math/probability/data-distributions-a1/summarizing-spread-distributions/v/range-variance-and-standard-deviation-as-measures-of-dispersion) * Andrew Ng [See Videos (1-3)](https://www.youtube.com/watch?v=-la3q9d7AKQ&list=PLNeKWBMsAzboR8vvhnlanxCNr2V7ITuxy) ## Day-3 (2nd August, 2018) * Learn about Pandas. [See Videos(11-15)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Learn in general about ML [See Video (Statistical Learning Theory)](https://www.youtube.com/watch?v=rqJ8SrnmWu0) * Read/Practice [Day-4 and Day-8](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * Visualization in Python [See Official Docs](https://matplotlib.org/users/pyplot_tutorial.html) ## Day-4 (3rd August, 2018) * Learn about Pandas. [See Videos(16-18)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Read [KNN-1](https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/) * Read [KNN-2](https://medium.com/@adi.bronshtein/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7) ## Day-5 (4th August, 2018) * Learn about Pandas. [See Videos(19-22)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Read/Practice [Day-7](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * General read on [Medium](https://blog.usejournal.com/cracking-eaadhar-password-in-3-seconds-with-maths-9533c8e8f9c2) ## Day-6 (5th August, 2018) * Learn about Pandas. [See Videos(23-26)](https://www.dataschool.io/easier-data-analysis-with-pandas/) * Implementing KNN * Read/Practice [Day-12](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * KNN-Sklearn [See Official Docs](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) ## Day-7 (6th August, 2018) * Learn about Numpy. [Read this](https://www.dataquest.io/blog/numpy-tutorial-python/) * [Naive Bayes - 1](https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/) * [Naive Bayes - 2](https://medium.com/machine-learning-101/chapter-1-supervised-learning-and-naive-bayes-classification-part-1-theory-8b9e361897d5) * [Naive Bayes - 3](https://machinelearningmastery.com/naive-bayes-for-machine-learning/) * [Naive Bayes - 4](https://www.youtube.com/watch?v=6xBU74VWEuE) ## Day-8 (7th August, 2018) * [Lime](https://github.com/marcotcr/lime) * [Building Trust in ML models](https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/) * [Interpretable ML models](https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime) * Implementing Naive Bayes * Learn in general about ML [See Video (Stochastic Gradient Descent)](https://www.youtube.com/watch?v=5TZww5bTROE) - 10 mins onwards ## Day-9 (8th August, 2018) * Lime hands-on news dataset * Light read about [Averaging Ensemble Techniques](http://sebastianraschka.com/Articles/2014_ensemble_classifier.html) for more accurate predictions. * Light reading on [Ensemble Techniques](https://www.dataquest.io/blog/introduction-to-ensembles/) * Implementing Support Vector Machines * See [Ensemble learners](https://www.youtube.com/watch?v=Un9zObFjBH0) ## Day-10 (9th August, 2018) * Implement Average Voting Ensemble Meta Model * Read about [Stacking Ensemble Technique](https://www.kdnuggets.com/2017/02/stacking-models-imropved-predictions.html) * Read [Stacking from scratch](https://machinelearningmastery.com/implementing-stacking-scratch-python/) * Read [Stacking-concept-pictures-code](https://github.com/vecxoz/vecstack/blob/master/examples/00_stacking_concept_pictures_code.ipynb) ## Day-11 (10th August, 2018) * Read/Practice [Day-25](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%2025%20Decision%20Tree.md) * Read about [Feature Scaling](http://sebastianraschka.com/Articles/2014_about_feature_scaling.html) * Read [Why, How and When to Scale](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e) * Implementation of Feature scaling techniques * See [Decision Trees - MMDS](https://www.youtube.com/watch?v=NsUqRe-9tb4) * Glance through [Decision Trees - Coursera](https://www.coursera.org/learn/ml-classification/home/week/3) ## Day-12 (11th August, 2018) * Implementing of Decision Trees * See lectures from [Coursera - 2nd week](https://www.coursera.org/learn/ml-classification/home/week/2) and [Coursera - 4th week](https://www.coursera.org/learn/ml-classification/home/week/4) ## Day-13 (12th August, 2018) * Khan Academy [Vector's Section](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/vectors/v/vector-introduction-linear-algebra) * Light read on [Stacking Classifier](https://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/) * Implementing - Handeling missing values using pandas * General read on [EM for data imputation](https://www.theanalysisfactor.com/em-imputation-and-missing-data-is-mean-imputation-really-so-terrible/) ## Day-14 (13th August, 2018) * Read about [Model Evaluation](https://www.coursera.org/learn/ml-classification/home/week/6) * See Khan Academy [Linear combinatations & span](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/linear-combinations/v/linear-combinations-and-span) and [Linear Dependence/Independence](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/linear-independence/v/linear-algebra-introduction-to-linear-independence) * Explore a [Helper Lib](https://github.com/rasbt/mlxtend/) ## Day-15 (14th August, 2018) * See Khan Academy [Subspaces](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/subspace-basis/v/linear-subspaces) * Practice [Mlxtend](https://github.com/rasbt/mlxtend/) * Read/Practice [Day-33 & Day-34](https://github.com/Avik-Jain/100-Days-Of-ML-Code) ## Day-16 (15th August, 2018) * Light read on [Vector Quantization](https://machinelearningmastery.com/learning-vector-quantization-for-machine-learning/) * Reading about [Boosting Algorithms](https://www.youtube.com/watch?v=wPqtzj5VZus) * See all videos under [Ensembling](https://www.coursera.org/lecture/competitive-data-science/introduction-into-ensemble-methods-MJKCi) ## Day-17 (16th August, 2018) * Performance Metrics Hands-on * Khan Academy [Vector dot products](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/dot-cross-products/v/vector-dot-product-and-vector-length) * See [Metrics Optimization](https://www.coursera.org/learn/competitive-data-science/home/week/3) ## Day-18 (20th August, 2018) * General read on [Medium](https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e) * Read about [Text Classification](https://medium.com/@ageitgey/text-classification-is-your-new-secret-weapon-7ca4fad15788) * Read about [scrape method in Pandas](https://medium.com/@ageitgey/quick-tip-the-easiest-way-to-grab-data-out-of-a-web-page-in-python-7153cecfca58) * Read about [FastText](https://research.fb.com/fasttext/) ## Day-19 (21st August, 2018) * Glance through [Sklearn Docs on Feature Selection](http://scikit-learn.org/stable/modules/feature_selection.html) * Read [Feature Selection - Analytics Vidhya](https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/) * See [C2W1L4](https://www.youtube.com/watch?v=6g0t3Phly2M) and [C2W1L5](https://www.youtube.com/watch?v=NyG-7nRpsW8) * Implementing Feature Selection Methods ## Day-20 (22nd August, 2018) * Explore [A fast and simple progress bar](https://github.com/tqdm/tqdm) * Casual read on [Pandas - Tips/Tricks - 1](https://cambridgespark.com/content/tutorials/quick-panda-tricks/index.html) and [Pandas - Tips/Tricks - 2](https://towardsdatascience.com/pandas-tips-and-tricks-33bcc8a40bb9) * See [Day 35](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * Implement data resampling techniques ## Day-21 (23rd August, 2018) * See all videos under [C2W2](https://www.youtube.com/watch?v=SjQyLhQIXSM&list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc&index=2) * Implement saving/loading of ML models * Write Dockerfile ## Day-22 (24th August, 2018) * See and follow along [Introduction to PyTorch](https://www.youtube.com/watch?v=fJZew-fdNxw) * Push Dockerfile and update Docker Readme. ## Day-23 (25th August, 2018) * Read Chapter 6 (till 6.1.2) from the book Mining Massive Datasets * Read/Practice [Day-26](https://github.com/Avik-Jain/100-Days-Of-ML-Code) ## Day-24 (26th August, 2018) * Read Chapter 6 (till 6.1) from the book Mining Massive Datasets ## Day-25 (27th August, 2018) * Read/Practice [Day-27](https://github.com/Avik-Jain/100-Days-Of-ML-Code) and [Day-28](https://github.com/Avik-Jain/100-Days-Of-ML-Code) ## Day-26 (28th August, 2018) * See 1, 2, 3 videos from [Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) * See [Week-1 (Video by David Silver)](https://github.com/yandexdataschool/Practical_RL/tree/master/week1_intro) ## Day-27 (29th August, 2018) * Read about article on [RL 1, 2, 3, 4](https://medium.com/@prakhar.mishra) * Implement randomised cartpole balancer ## Day-28 (30th August, 2018) * Read [paper](https://arxiv.org/pdf/1808.07913.pdf) * Implement neural network in PyTorch * PyTorch + TensorBoard * Update Docker File/Image ## Day-29 (31st August, 2018) * See 4, 5, 6 videos from [Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) * See 1, 2, 3, 4 videos from [Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) ## Day-30 (1st September, 2018) * Implementing NN from scratch * See 5, 6 videos from [Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) ## Day-31 (3rd September, 2018) * Implement Cartpole using Cross Entropy method ## Day-32 (4th September, 2018) * Read about Q-Learning. * See 7, 8, 9 videos from [Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) * See 7, 8 videos from [Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) ## Day-33 (5th September, 2018) * Read/Practice [Day 51](https://github.com/Avik-Jain/100-Days-Of-ML-Code) * See [But what *is* a Neural Network?](https://www.youtube.com/watch?v=aircAruvnKk&t=7s) * Read [Grammar correction in text](http://ww.panl10n.net/english/final%20reports/pdf%20files/Bangladesh/BAN21.pdf) usecase ## Day-34 (6th September, 2018) * See [How Neural Networks learn](https://www.youtube.com/watch?v=IHZwWFHWa-w&t=0s&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=3) * Read [Text Summarization](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.19.5237&rep=rep1&type=pdf) * See 10, 11 videos from [Linear Algebra](https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) * Read [Neural Networks, Manifolds, and Topology](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/) ## Day-35 (7th September, 2018) * Implement Q-Learning ## Day-36 (10th September, 2018) * Complete [Equations/Graphs/Functions](https://courses.edx.org/courses/course-v1:Microsoft+DAT256x+2T2018/courseware/72190688919b4f72a3e81a7fdbc4ec19/be5df94381c74baf8fdd83c36f71e0f0/?child=first) * See 9, 10 videos from [Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) * See [What does Backpropagation really do ?](https://www.youtube.com/watch?v=Ilg3gGewQ5U&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=3) ## Day-37 (11th September, 2018) * See [Backpropagation Calculus](https://www.youtube.com/watch?v=tIeHLnjs5U8&index=4&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) * See 1, 2, 3 from [Statistics - Khan Academy](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566) ## Day-38 (12th September, 2018) * Read 7 in [Assignments](https://mlcourse.ai/assignments) * See 4, 5, 6 from [Statistics - Khan Academy](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566) ## Day-39 (13th September, 2018) * Read about Agglomerative Clustering ## Day-40 (14th September, 2018) * Read about Deep-Q-Networks and understand epsilon-greedy, replay buffer and target network in the same context. * See 7, 8 from [Statistics - Khan Academy](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566) ## Day-41 (15th September, 2018) * Read about Spectral Clustering * See 9, 10, 11, 12 [Statistics - Khan Academy](https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566) * Complete [Finance and Python](https://campus.datacamp.com/courses/importing-managing-financial-data-in-python/importing-stock-listing-data-from-excel) ## Day-42 (17th September, 2018) * Read [Autoencoders Notebook](https://www.kaggle.com/shivamb/how-autoencoders-work-intro-and-usecases?utm_medium=social&utm_source=linkedin.com&utm_campaign=Weekly-Kernel-Awards) * Complete [Week-1](https://www.coursera.org/learn/fundamentals-machine-learning-in-finance/home/week/1) ## Day-43 (18th September, 2018) * See [Neural Voice Cloning](https://www.youtube.com/watch?v=gVehTbi6Ipc&feature=youtu.be) * Complete [Week-2](https://www.coursera.org/learn/fundamentals-machine-learning-in-finance/home/week/2) * Read [Autoencoder in Text](https://www.doc.ic.ac.uk/~js4416/163/website/nlp/) ## Day-44 (19th September, 2018) * Read 1-10 pages of [A Primer on Neural Network Modelsfor Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-45 (20th Spetember, 2018) * Read 11-20 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-46 (21st September, 2018) * Read 21-30 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-47 (22nd Spetember, 2018) * Read 31-40 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-48 (22nd Spetember, 2018) * Read 41-50 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-49 (23rd September, 2018) * Read 51-60 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ## Day-50 (24th Spetember, 2018) * Read 61-76 pages of [A Primer on Neural Network Models for Natural Language Processing](http://u.cs.biu.ac.il/~yogo/nnlp.pdf) ================================================ FILE: bonus/Dockerfile ================================================ FROM ubuntu:16.04 # Base OS essentials RUN apt-get update && apt-get install -y RUN apt-get install python-pip -y RUN apt-get install python-dev -y # ML essentials requirements (till day 21) RUN pip install --upgrade pip RUN pip install pandas==0.20.3 && \ numpy==1.14.2 \ scipy==0.19.1 \ scikit_learn==0.19.1 \ lime==0.1.1.32 \ tqdm==4.23.0 \ xgboost==0.80 \ mlxtend==0.13.0 WORKDIR /usr/src ================================================ FILE: data/day1/iris.csv ================================================ feat1,feat2,feat3,feat4,class 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa 5.4,3.9,1.7,0.4,Iris-setosa 4.6,3.4,1.4,0.3,Iris-setosa 5.0,3.4,1.5,0.2,Iris-setosa 4.4,2.9,1.4,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 5.4,3.7,1.5,0.2,Iris-setosa 4.8,3.4,1.6,0.2,Iris-setosa 4.8,3.0,1.4,0.1,Iris-setosa 4.3,3.0,1.1,0.1,Iris-setosa 5.8,4.0,1.2,0.2,Iris-setosa 5.7,4.4,1.5,0.4,Iris-setosa 5.4,3.9,1.3,0.4,Iris-setosa 5.1,3.5,1.4,0.3,Iris-setosa 5.7,3.8,1.7,0.3,Iris-setosa 5.1,3.8,1.5,0.3,Iris-setosa 5.4,3.4,1.7,0.2,Iris-setosa 5.1,3.7,1.5,0.4,Iris-setosa 4.6,3.6,1.0,0.2,Iris-setosa 5.1,3.3,1.7,0.5,Iris-setosa 4.8,3.4,1.9,0.2,Iris-setosa 5.0,3.0,1.6,0.2,Iris-setosa 5.0,3.4,1.6,0.4,Iris-setosa 5.2,3.5,1.5,0.2,Iris-setosa 5.2,3.4,1.4,0.2,Iris-setosa 4.7,3.2,1.6,0.2,Iris-setosa 4.8,3.1,1.6,0.2,Iris-setosa 5.4,3.4,1.5,0.4,Iris-setosa 5.2,4.1,1.5,0.1,Iris-setosa 5.5,4.2,1.4,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 5.0,3.2,1.2,0.2,Iris-setosa 5.5,3.5,1.3,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa 4.4,3.0,1.3,0.2,Iris-setosa 5.1,3.4,1.5,0.2,Iris-setosa 5.0,3.5,1.3,0.3,Iris-setosa 4.5,2.3,1.3,0.3,Iris-setosa 4.4,3.2,1.3,0.2,Iris-setosa 5.0,3.5,1.6,0.6,Iris-setosa 5.1,3.8,1.9,0.4,Iris-setosa 4.8,3.0,1.4,0.3,Iris-setosa 5.1,3.8,1.6,0.2,Iris-setosa 4.6,3.2,1.4,0.2,Iris-setosa 5.3,3.7,1.5,0.2,Iris-setosa 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b,21.08,10.085,y,p,e,h,1.25,f,f,0,f,g,00260,0,- a,22.67,0.75,u,g,c,v,2,f,t,02,t,g,00200,394,- a,25.25,13.5,y,p,ff,ff,2,f,t,01,t,g,00200,1,- b,17.92,0.205,u,g,aa,v,0.04,f,f,0,f,g,00280,750,- b,35.00,3.375,u,g,c,h,8.29,f,f,0,t,g,00000,0,- ================================================ FILE: data/day15/jobclass.csv ================================================ ID,JobFamily,JobFamilyDescription,JobClass,JobClassDescription,PayGrade,EducationLevel,Experience,OrgImpact,ProblemSolving,Supervision,ContactLevel,FinancialBudget,PG 1,1,Accounting And Finance,1,Accountant I,5,3,1,3,3,4,3,5,PG05 2,1,Accounting And Finance,2,Accountant II,6,4,1,5,4,5,7,7,PG06 3,1,Accounting And Finance,3,Accountant III,8,4,2,6,5,6,7,10,PG08 4,1,Accounting And Finance,4,Accountant IV,10,5,5,6,6,7,8,11,PG10 5,2,Administrative Support,5,Admin Support I,1,1,0,1,1,1,1,1,PG01 6,2,Administrative Support,6,Admin Support II,2,1,1,1,1,1,2,3,PG02 7,2,Administrative Support,7,Admin Support III,3,1,2,1,2,1,3,3,PG03 8,2,Administrative Support,8,Administrative Support IV,4,4,0,1,2,1,3,5,PG04 9,2,Administrative Support,9,Administrative Support V,5,4,0,4,3,5,7,7,PG05 10,3,Baker,10,Baker I,4,2,0,1,4,1,1,2,PG04 11,3,Baker,11,Baker II,6,2,0,3,4,1,6,4,PG06 12,3,Baker,12,Baker III,9,2,0,4,5,5,6,10,PG09 13,4,Buildings And Facilities,13,Facilities I,5,1,0,3,4,3,7,4,PG05 14,4,Buildings And Facilities,14,Facilities II,6,3,0,3,4,5,6,7,PG06 15,4,Buildings And Facilities,15,Facilities III,9,4,0,4,5,7,6,10,PG09 16,4,Buildings And Facilities,16,Facilities IV,10,5,0,6,6,7,8,11,PG10 17,4,Buildings And Facilities,44,Maintenance Services I,2,1,0,1,2,1,1,1,PG02 18,4,Buildings And Facilities,45,Maintenance Services II,3,1,1,1,2,1,1,1,PG03 19,4,Buildings And Facilities,46,Maintenance Services III,4,1,1,3,3,4,3,2,PG04 20,4,Buildings And Facilities,47,Maintenance Services IV,5,3,2,3,4,5,8,4,PG05 21,5,Buyer,17,Buyer II,5,2,1,3,3,4,3,5,PG05 22,5,Buyer,18,Buyer III,8,4,0,6,5,6,7,8,PG08 23,5,Buyer,19,Buyer IV,9,4,10,6,5,6,7,9,PG09 24,6,Cashier,20,Cashier I,1,1,2,2,1,2,1,1,PG01 25,6,Cashier,21,Cashier II,3,3,3,2,3,4,2,1,PG03 26,6,Cashier,22,Cashier III,6,4,2,4,4,5,7,7,PG06 27,7,Communications And Media,23,Communications I,4,2,0,1,3,4,4,1,PG04 28,7,Communications And Media,24,Communications II,5,4,0,1,2,1,3,4,PG05 29,7,Communications And Media,25,Communications III,8,5,2,5,5,6,7,10,PG08 30,7,Communications And Media,26,Photographer I,2,1,0,1,1,1,1,1,PG02 31,7,Communications And Media,27,Photographer II,3,1,1,1,2,1,3,3,PG03 32,7,Communications And Media,28,Photographer III,5,4,0,1,3,1,4,3,PG05 33,7,Communications And Media,29,Photographer IV,7,4,0,3,4,5,7,7,PG07 34,7,Communications And Media,30,Printing II,2,1,0,1,2,1,1,1,PG02 35,7,Communications And Media,31,Printing III,4,1,0,3,3,4,5,4,PG04 36,7,Communications And Media,32,Printing IV,5,2,0,3,4,4,5,4,PG05 37,8,Corporate Research,33,Data Scientist,10,6,5,6,6,7,7,11,PG10 38,8,Corporate Research,34,Research Analyst I,2,1,0,1,1,1,1,1,PG02 39,8,Corporate Research,35,Research Analyst II,4,3,0,3,2,1,1,1,PG04 40,8,Corporate Research,36,Research Analyst Iii,5,4,2,3,4,4,1,2,PG05 41,8,Corporate Research,37,Research Analyst IV,8,6,5,4,5,6,7,11,PG08 42,9,Finance And Accounting,38,Financial Officer I,5,4,1,3,4,4,3,5,PG05 43,9,Finance And Accounting,39,Financial Officer II,6,4,2,3,4,5,6,7,PG06 44,9,Finance And Accounting,40,Financial Officer III,9,4,3,6,5,6,7,10,PG09 45,10,Human Resources,41,Human Resources I,5,4,2,3,3,4,3,1,PG05 46,10,Human Resources,42,Human Resources II,7,4,3,4,4,4,7,1,PG07 47,10,Human Resources,43,Human Resources III,8,5,5,6,5,4,7,10,PG08 48,11,Meat Cutter,48,Meat Cutter I,5,4,1,3,3,4,3,3,PG05 49,11,Meat Cutter,49,Meat Cutter II,7,4,3,4,4,4,7,7,PG07 50,11,Meat Cutter,50,Meat Cutter III,9,5,5,6,5,6,7,10,PG09 51,11,Meat Cutter,51,Meat Cutter IV,10,5,5,6,6,7,8,11,PG10 52,12,Produce,52,Produce I,3,1,1,3,3,4,4,2,PG03 53,12,Produce,53,Produce II,6,4,1,4,4,4,6,2,PG06 54,12,Produce,54,Produce III,7,4,3,4,4,5,6,7,PG07 55,12,Produce,55,Produce IV,9,4,5,6,5,6,6,10,PG09 56,12,Produce,56,Produce V,10,5,8,6,6,7,8,11,PG10 57,13,Secretary,57,Secretary II,3,1,2,3,2,1,2,4,PG03 58,13,Secretary,58,Secretary III,5,2,3,3,3,4,3,5,PG05 59,14,Stockkeeping,59,StockKeeper I,4,3,0,1,2,1,3,1,PG04 60,14,Stockkeeping,60,StockKeeper II,5,3,0,3,3,1,7,5,PG05 61,14,Stockkeeping,61,StockKeeper III,8,4,0,4,5,6,7,7,PG08 62,15,Systems Analyst,62,Systems Analyst I,3,1,1,1,2,1,1,1,PG03 63,15,Systems Analyst,63,Systems Analyst II,5,4,1,3,3,4,3,4,PG05 64,15,Systems Analyst,64,Systems Analyst III,6,5,2,4,4,5,7,5,PG06 65,15,Systems Analyst,65,Systems Analyst IV,8,5,5,6,5,6,7,7,PG08 66,15,Systems Analyst,66,Systems Analyst V,10,5,5,6,6,7,8,11,PG10 ================================================ FILE: data/day2/automobile.csv ================================================ symboling,normalized-losses,make,fuel-type,aspiration,num-of-doors,body-style,drive-wheels,engine-location,wheel-base,length,width,height,curb-weight,engine-type,num-of-cylinders,engine-size,fuel-system,bore,stroke,compression-ratio,horsepower,peak-rpm,city-mpg,highway-mpg,price 2,164,audi,gas,std,four,sedan,fwd,front,99.80,176.60,66.20,54.30,2337,ohc,four,109,mpfi,3.19,3.40,10.00,102,5500,24,30,13950 2,164,audi,gas,std,four,sedan,4wd,front,99.40,176.60,66.40,54.30,2824,ohc,five,136,mpfi,3.19,3.40,8.00,115,5500,18,22,17450 1,158,audi,gas,std,four,sedan,fwd,front,105.80,192.70,71.40,55.70,2844,ohc,five,136,mpfi,3.19,3.40,8.50,110,5500,19,25,17710 1,158,audi,gas,turbo,four,sedan,fwd,front,105.80,192.70,71.40,55.90,3086,ohc,five,131,mpfi,3.13,3.40,8.30,140,5500,17,20,23875 2,192,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16430 0,192,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2395,ohc,four,108,mpfi,3.50,2.80,8.80,101,5800,23,29,16925 0,188,bmw,gas,std,two,sedan,rwd,front,101.20,176.80,64.80,54.30,2710,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,20970 0,188,bmw,gas,std,four,sedan,rwd,front,101.20,176.80,64.80,54.30,2765,ohc,six,164,mpfi,3.31,3.19,9.00,121,4250,21,28,21105 2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.40,141.10,60.30,53.20,1488,l,three,61,2bbl,2.91,3.03,9.50,48,5100,47,53,5151 1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.50,155.90,63.60,52.00,1874,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6295 0,81,chevrolet,gas,std,four,sedan,fwd,front,94.50,158.80,63.60,52.00,1909,ohc,four,90,2bbl,3.03,3.11,9.60,70,5400,38,43,6575 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 1,118,dodge,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1876,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6377 1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,mpfi,3.03,3.39,7.60,102,5500,24,30,7957 1,148,dodge,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,148,dodge,gas,std,four,sedan,fwd,front,93.70,157.30,63.80,50.60,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 -1,110,dodge,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.34,3.46,8.50,88,5000,24,30,8921 3,145,dodge,gas,turbo,two,hatchback,fwd,front,95.90,173.20,66.30,50.20,2811,ohc,four,156,mfi,3.60,3.90,7.00,145,5000,19,24,12964 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1713,ohc,four,92,1bbl,2.91,3.41,9.60,58,4800,49,54,6479 2,137,honda,gas,std,two,hatchback,fwd,front,86.60,144.60,63.90,50.80,1819,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,31,38,6855 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1837,ohc,four,79,1bbl,2.91,3.07,10.10,60,5500,38,42,5399 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1940,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,6529 1,101,honda,gas,std,two,hatchback,fwd,front,93.70,150.00,64.00,52.60,1956,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7129 0,110,honda,gas,std,four,sedan,fwd,front,96.50,163.40,64.00,54.50,2010,ohc,four,92,1bbl,2.91,3.41,9.20,76,6000,30,34,7295 0,78,honda,gas,std,four,wagon,fwd,front,96.50,157.10,63.90,58.30,2024,ohc,four,92,1bbl,2.92,3.41,9.20,76,6000,30,34,7295 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2236,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,7895 0,106,honda,gas,std,two,hatchback,fwd,front,96.50,167.50,65.20,53.30,2289,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,9095 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2304,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,8845 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,62.50,54.10,2372,ohc,four,110,1bbl,3.15,3.58,9.00,86,5800,27,33,10295 0,85,honda,gas,std,four,sedan,fwd,front,96.50,175.40,65.20,54.10,2465,ohc,four,110,mpfi,3.15,3.58,9.00,101,5800,24,28,12945 1,107,honda,gas,std,two,sedan,fwd,front,96.50,169.10,66.00,51.00,2293,ohc,four,110,2bbl,3.15,3.58,9.10,100,5500,25,31,10345 0,145,jaguar,gas,std,four,sedan,rwd,front,113.00,199.60,69.60,52.80,4066,dohc,six,258,mpfi,3.63,4.17,8.10,176,4750,15,19,32250 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1890,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,30,31,5195 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1900,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6095 1,104,mazda,gas,std,two,hatchback,fwd,front,93.10,159.10,64.20,54.10,1905,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6795 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1945,ohc,four,91,2bbl,3.03,3.15,9.00,68,5000,31,38,6695 1,113,mazda,gas,std,four,sedan,fwd,front,93.10,166.80,64.20,54.10,1950,ohc,four,91,2bbl,3.08,3.15,9.00,68,5000,31,38,7395 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8845 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,8495 1,129,mazda,gas,std,two,hatchback,fwd,front,98.80,177.80,66.50,53.70,2385,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10595 0,115,mazda,gas,std,four,sedan,fwd,front,98.80,177.80,66.50,55.50,2410,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,10245 0,115,mazda,gas,std,four,hatchback,fwd,front,98.80,177.80,66.50,55.50,2425,ohc,four,122,2bbl,3.39,3.39,8.60,84,4800,26,32,11245 0,118,mazda,gas,std,four,sedan,rwd,front,104.90,175.00,66.10,54.40,2670,ohc,four,140,mpfi,3.76,3.16,8.00,120,5000,19,27,18280 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,110.00,190.90,70.30,56.50,3515,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,25552 -1,93,mercedes-benz,diesel,turbo,four,wagon,rwd,front,110.00,190.90,70.30,58.70,3750,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28248 0,93,mercedes-benz,diesel,turbo,two,hardtop,rwd,front,106.70,187.50,70.30,54.90,3495,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,28176 -1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,115.60,202.60,71.70,56.30,3770,ohc,five,183,idi,3.58,3.64,21.50,123,4350,22,25,31600 3,142,mercedes-benz,gas,std,two,convertible,rwd,front,96.60,180.30,70.50,50.80,3685,ohcv,eight,234,mpfi,3.46,3.10,8.30,155,4750,16,18,35056 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1918,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,37,41,5389 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,1944,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6189 2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.70,157.30,64.40,50.80,2004,ohc,four,92,2bbl,2.97,3.23,9.40,68,5500,31,38,6669 1,161,mitsubishi,gas,turbo,two,hatchback,fwd,front,93,157.30,63.80,50.80,2145,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7689 3,153,mitsubishi,gas,turbo,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2370,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9959 3,153,mitsubishi,gas,std,two,hatchback,fwd,front,96.30,173.00,65.40,49.40,2328,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8499 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2365,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,6989 1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2405,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,25,32,8189 1,125,mitsubishi,gas,turbo,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 -1,137,mitsubishi,gas,std,four,sedan,fwd,front,96.30,172.40,65.40,51.60,2403,ohc,four,110,spdi,3.17,3.46,7.50,116,5500,23,30,9279 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1889,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,5499 1,128,nissan,diesel,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,2017,ohc,four,103,idi,2.99,3.47,21.90,55,4800,45,50,7099 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1918,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6649 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1938,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,6849 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2024,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7349 1,128,nissan,gas,std,two,sedan,fwd,front,94.50,165.30,63.80,54.50,1951,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7299 1,128,nissan,gas,std,two,hatchback,fwd,front,94.50,165.60,63.80,53.30,2028,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7799 1,122,nissan,gas,std,four,sedan,fwd,front,94.50,165.30,63.80,54.50,1971,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7499 1,103,nissan,gas,std,four,wagon,fwd,front,94.50,170.20,63.80,53.50,2037,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,7999 2,168,nissan,gas,std,two,hardtop,fwd,front,95.10,162.40,63.80,53.30,2008,ohc,four,97,2bbl,3.15,3.29,9.40,69,5200,31,37,8249 0,106,nissan,gas,std,four,hatchback,fwd,front,97.20,173.40,65.20,54.70,2324,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,8949 0,106,nissan,gas,std,four,sedan,fwd,front,97.20,173.40,65.20,54.70,2302,ohc,four,120,2bbl,3.33,3.47,8.50,97,5200,27,34,9549 0,128,nissan,gas,std,four,sedan,fwd,front,100.40,181.70,66.50,55.10,3095,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,13499 0,108,nissan,gas,std,four,wagon,fwd,front,100.40,184.60,66.50,56.10,3296,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,17,22,14399 0,108,nissan,gas,std,four,sedan,fwd,front,100.40,184.60,66.50,55.10,3060,ohcv,six,181,mpfi,3.43,3.27,9.00,152,5200,19,25,13499 3,194,nissan,gas,std,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3071,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,17199 3,194,nissan,gas,turbo,two,hatchback,rwd,front,91.30,170.70,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,7.80,200,5200,17,23,19699 1,231,nissan,gas,std,two,hatchback,rwd,front,99.20,178.50,67.90,49.70,3139,ohcv,six,181,mpfi,3.43,3.27,9.00,160,5200,19,25,18399 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3020,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,11900 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3197,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,13200 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,2.19,8.40,95,5000,19,24,15580 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,16900 0,161,peugot,gas,std,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3075,l,four,120,mpfi,3.46,3.19,8.40,97,5000,19,24,16630 0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.90,186.70,68.40,56.70,3252,l,four,152,idi,3.70,3.52,21.00,95,4150,28,33,17950 0,161,peugot,gas,turbo,four,sedan,rwd,front,108.00,186.70,68.30,56.00,3130,l,four,134,mpfi,3.61,3.21,7.00,142,5600,18,24,18150 1,119,plymouth,gas,std,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,1918,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,37,41,5572 1,119,plymouth,gas,turbo,two,hatchback,fwd,front,93.70,157.30,63.80,50.80,2128,ohc,four,98,spdi,3.03,3.39,7.60,102,5500,24,30,7957 1,154,plymouth,gas,std,four,hatchback,fwd,front,93.70,157.30,63.80,50.60,1967,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6229 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,1989,ohc,four,90,2bbl,2.97,3.23,9.40,68,5500,31,38,6692 1,154,plymouth,gas,std,four,sedan,fwd,front,93.70,167.30,63.80,50.80,2191,ohc,four,98,2bbl,2.97,3.23,9.40,68,5500,31,38,7609 -1,74,plymouth,gas,std,four,wagon,fwd,front,103.30,174.60,64.60,59.80,2535,ohc,four,122,2bbl,3.35,3.46,8.50,88,5000,24,30,8921 3,186,porsche,gas,std,two,hatchback,rwd,front,94.50,168.90,68.30,50.20,2778,ohc,four,151,mpfi,3.94,3.11,9.50,143,5500,19,27,22018 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2695,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,12170 3,150,saab,gas,std,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2707,ohc,four,121,mpfi,2.54,2.07,9.30,110,5250,21,28,15040 2,104,saab,gas,std,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2758,ohc,four,121,mpfi,3.54,3.07,9.30,110,5250,21,28,15510 3,150,saab,gas,turbo,two,hatchback,fwd,front,99.10,186.60,66.50,56.10,2808,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18150 2,104,saab,gas,turbo,four,sedan,fwd,front,99.10,186.60,66.50,56.10,2847,dohc,four,121,mpfi,3.54,3.07,9.00,160,5500,19,26,18620 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,156.90,63.40,53.70,2050,ohcf,four,97,2bbl,3.62,2.36,9.00,69,4900,31,36,5118 2,83,subaru,gas,std,two,hatchback,fwd,front,93.70,157.90,63.60,53.70,2120,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7053 2,83,subaru,gas,std,two,hatchback,4wd,front,93.30,157.30,63.80,55.70,2240,ohcf,four,108,2bbl,3.62,2.64,8.70,73,4400,26,31,7603 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2145,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4800,32,37,7126 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2190,ohcf,four,108,2bbl,3.62,2.64,9.50,82,4400,28,33,7775 0,102,subaru,gas,std,four,sedan,fwd,front,97.20,172.00,65.40,52.50,2340,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,26,32,9960 0,102,subaru,gas,std,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2385,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,24,25,9233 0,102,subaru,gas,turbo,four,sedan,4wd,front,97.00,172.00,65.40,54.30,2510,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,24,29,11259 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2290,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,28,32,7463 0,89,subaru,gas,std,four,wagon,fwd,front,97.00,173.50,65.40,53.00,2455,ohcf,four,108,mpfi,3.62,2.64,9.00,94,5200,25,31,10198 0,85,subaru,gas,std,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2420,ohcf,four,108,2bbl,3.62,2.64,9.00,82,4800,23,29,8013 0,85,subaru,gas,turbo,four,wagon,4wd,front,96.90,173.60,65.40,54.90,2650,ohcf,four,108,mpfi,3.62,2.64,7.70,111,4800,23,23,11694 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,1985,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,35,39,5348 1,87,toyota,gas,std,two,hatchback,fwd,front,95.70,158.70,63.60,54.50,2040,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6338 1,74,toyota,gas,std,four,hatchback,fwd,front,95.70,158.70,63.60,54.50,2015,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,38,6488 0,77,toyota,gas,std,four,wagon,fwd,front,95.70,169.70,63.60,59.10,2280,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,31,37,6918 0,81,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,2290,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,7898 0,91,toyota,gas,std,four,wagon,4wd,front,95.70,169.70,63.60,59.10,3110,ohc,four,92,2bbl,3.05,3.03,9.00,62,4800,27,32,8778 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2081,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,6938 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2109,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,30,37,7198 0,91,toyota,diesel,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,34,36,7898 0,91,toyota,diesel,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2275,ohc,four,110,idi,3.27,3.35,22.50,56,4500,38,47,7788 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,53.00,2094,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,38,47,7738 0,91,toyota,gas,std,four,hatchback,fwd,front,95.70,166.30,64.40,52.80,2122,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,8358 0,91,toyota,gas,std,four,sedan,fwd,front,95.70,166.30,64.40,52.80,2140,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,28,34,9258 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2169,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8058 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2204,ohc,four,98,2bbl,3.19,3.03,9.00,70,4800,29,34,8238 1,168,toyota,gas,std,two,sedan,rwd,front,94.50,168.70,64.00,52.60,2265,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9298 1,168,toyota,gas,std,two,hatchback,rwd,front,94.50,168.70,64.00,52.60,2300,dohc,four,98,mpfi,3.24,3.08,9.40,112,6600,26,29,9538 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2540,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,8449 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2536,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9639 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2551,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,9989 2,134,toyota,gas,std,two,hardtop,rwd,front,98.40,176.20,65.60,52.00,2679,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11199 2,134,toyota,gas,std,two,hatchback,rwd,front,98.40,176.20,65.60,52.00,2714,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,11549 2,134,toyota,gas,std,two,convertible,rwd,front,98.40,176.20,65.60,53.00,2975,ohc,four,146,mpfi,3.62,3.50,9.30,116,4800,24,30,17669 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2326,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,29,34,8948 -1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2480,ohc,four,110,idi,3.27,3.35,22.50,73,4500,30,33,10698 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,9988 -1,65,toyota,gas,std,four,sedan,fwd,front,102.40,175.60,66.50,54.90,2414,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,10898 -1,65,toyota,gas,std,four,hatchback,fwd,front,102.40,175.60,66.50,53.90,2458,ohc,four,122,mpfi,3.31,3.54,8.70,92,4200,27,32,11248 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,2976,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,20,24,16558 3,197,toyota,gas,std,two,hatchback,rwd,front,102.90,183.50,67.70,52.00,3016,dohc,six,171,mpfi,3.27,3.35,9.30,161,5200,19,24,15998 -1,90,toyota,gas,std,four,sedan,rwd,front,104.50,187.80,66.50,54.10,3131,dohc,six,171,mpfi,3.27,3.35,9.20,156,5200,20,24,15690 2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2261,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7775 2,122,volkswagen,gas,std,two,sedan,fwd,front,97.30,171.70,65.50,55.70,2209,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,7975 2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2264,ohc,four,97,idi,3.01,3.40,23.00,52,4800,37,46,7995 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2212,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8195 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2275,ohc,four,109,mpfi,3.19,3.40,9.00,85,5250,27,34,8495 2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2319,ohc,four,97,idi,3.01,3.40,23.00,68,4500,37,42,9495 2,94,volkswagen,gas,std,four,sedan,fwd,front,97.30,171.70,65.50,55.70,2300,ohc,four,109,mpfi,3.19,3.40,10.00,100,5500,26,32,9995 3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.50,165.70,64.00,51.40,2221,ohc,four,109,mpfi,3.19,3.40,8.50,90,5500,24,29,9980 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2912,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,12940 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3034,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,13415 -2,103,volvo,gas,std,four,sedan,rwd,front,104.30,188.80,67.20,56.20,2935,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,15985 -1,74,volvo,gas,std,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3042,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,24,28,16515 -2,103,volvo,gas,turbo,four,sedan,rwd,front,104.30,188.80,67.20,56.20,3045,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18420 -1,74,volvo,gas,turbo,four,wagon,rwd,front,104.30,188.80,67.20,57.50,3157,ohc,four,130,mpfi,3.62,3.15,7.50,162,5100,17,22,18950 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,2952,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,23,28,16845 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.80,55.50,3049,ohc,four,141,mpfi,3.78,3.15,8.70,160,5300,19,25,19045 -1,95,volvo,gas,std,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3012,ohcv,six,173,mpfi,3.58,2.87,8.80,134,5500,18,23,21485 -1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3217,ohc,six,145,idi,3.01,3.40,23.00,106,4800,26,27,22470 -1,95,volvo,gas,turbo,four,sedan,rwd,front,109.10,188.80,68.90,55.50,3062,ohc,four,141,mpfi,3.78,3.15,9.50,114,5400,19,25,22625 ================================================ FILE: data/day3/haberman.csv ================================================ Age,Year,AuxNodes,Status 30,64,1,1 30,62,3,1 30,65,0,1 31,59,2,1 31,65,4,1 33,58,10,1 33,60,0,1 34,59,0,2 34,66,9,2 34,58,30,1 34,60,1,1 34,61,10,1 34,67,7,1 34,60,0,1 35,64,13,1 35,63,0,1 36,60,1,1 36,69,0,1 37,60,0,1 37,63,0,1 37,58,0,1 37,59,6,1 37,60,15,1 37,63,0,1 38,69,21,2 38,59,2,1 38,60,0,1 38,60,0,1 38,62,3,1 38,64,1,1 38,66,0,1 38,66,11,1 38,60,1,1 38,67,5,1 39,66,0,2 39,63,0,1 39,67,0,1 39,58,0,1 39,59,2,1 39,63,4,1 40,58,2,1 40,58,0,1 40,65,0,1 41,60,23,2 41,64,0,2 41,67,0,2 41,58,0,1 41,59,8,1 41,59,0,1 41,64,0,1 41,69,8,1 41,65,0,1 41,65,0,1 42,69,1,2 42,59,0,2 42,58,0,1 42,60,1,1 42,59,2,1 42,61,4,1 42,62,20,1 42,65,0,1 42,63,1,1 43,58,52,2 43,59,2,2 43,64,0,2 43,64,0,2 43,63,14,1 43,64,2,1 43,64,3,1 43,60,0,1 43,63,2,1 43,65,0,1 43,66,4,1 44,64,6,2 44,58,9,2 44,63,19,2 44,61,0,1 44,63,1,1 44,61,0,1 44,67,16,1 45,65,6,2 45,66,0,2 45,67,1,2 45,60,0,1 45,67,0,1 45,59,14,1 45,64,0,1 45,68,0,1 45,67,1,1 46,58,2,2 46,69,3,2 46,62,5,2 46,65,20,2 46,62,0,1 46,58,3,1 46,63,0,1 47,63,23,2 47,62,0,2 47,65,0,2 47,61,0,1 47,63,6,1 47,66,0,1 47,67,0,1 47,58,3,1 47,60,4,1 47,68,4,1 47,66,12,1 48,58,11,2 48,58,11,2 48,67,7,2 48,61,8,1 48,62,2,1 48,64,0,1 48,66,0,1 49,63,0,2 49,64,10,2 49,61,1,1 49,62,0,1 49,66,0,1 49,60,1,1 49,62,1,1 49,63,3,1 49,61,0,1 49,67,1,1 50,63,13,2 50,64,0,2 50,59,0,1 50,61,6,1 50,61,0,1 50,63,1,1 50,58,1,1 50,59,2,1 50,61,0,1 50,64,0,1 50,65,4,1 50,66,1,1 51,59,13,2 51,59,3,2 51,64,7,1 51,59,1,1 51,65,0,1 51,66,1,1 52,69,3,2 52,59,2,2 52,62,3,2 52,66,4,2 52,61,0,1 52,63,4,1 52,69,0,1 52,60,4,1 52,60,5,1 52,62,0,1 52,62,1,1 52,64,0,1 52,65,0,1 52,68,0,1 53,58,4,2 53,65,1,2 53,59,3,2 53,60,9,2 53,63,24,2 53,65,12,2 53,58,1,1 53,60,1,1 53,60,2,1 53,61,1,1 53,63,0,1 54,60,11,2 54,65,23,2 54,65,5,2 54,68,7,2 54,59,7,1 54,60,3,1 54,66,0,1 54,67,46,1 54,62,0,1 54,69,7,1 54,63,19,1 54,58,1,1 54,62,0,1 55,63,6,2 55,68,15,2 55,58,1,1 55,58,0,1 55,58,1,1 55,66,18,1 55,66,0,1 55,69,3,1 55,69,22,1 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0.40614,1.3492,-1.4501,-0.55949,1 -1.3887,-4.8773,6.4774,0.34179,1 -3.7503,-13.4586,17.5932,-2.7771,1 -3.5637,-8.3827,12.393,-1.2823,1 -2.5419,-0.65804,2.6842,1.1952,1 ================================================ FILE: day01/.ipynb_checkpoints/BlackBox Takeaways-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Bbox ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day01/.ipynb_checkpoints/Pandas(1-5)-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day01/BlackBox Takeaways.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Bbox ML" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day01/Linear Regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19.1\n", "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import sklearn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import train_test_split\n", "%matplotlib inline\n", "\n", "print sklearn.__version__\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " independent dependent\n", "0 24 21.549452\n", "1 50 47.464463\n", "2 15 17.218656\n", "3 38 36.586398\n", "4 87 87.288984" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read data with read_table\n", "# put sep=',' as data is ',' seperated\n", "# renames the 0th row which is header with easy to read names\n", "# display top 5\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR,'day1/regression.csv')), \n", " sep=',', \n", " header=0, \n", " names=['independent', 'dependent']\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False]\n", "[False]\n" ] } ], "source": [ "# checking for any NaN value in the dataset\n", "print df['dependent'].map(lambda x: np.isnan(x)).unique()\n", "print df['independent'].map(lambda x: np.isnan(x)).unique()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = df.iloc[:,:1].values\n", "Y = df.iloc[:,1].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Split" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(799, 1) (200, 1)\n" ] } ], "source": [ "print X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression Functionality" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Regression:\n", " \n", " def __init__(self):\n", " self.regressor = LinearRegression()\n", " \n", " def train(self, X_train, Y_train):\n", " model = self.regressor.fit(X_train, Y_train)\n", " return model\n", "\n", " def predict(self, model, X_test):\n", " return model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "regress = Regression()\n", "model = regress.train(X_train, Y_train)\n", "predictions = regress.predict(model, X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_train , Y_train, color = 'red')\n", "plt.scatter(X_train , model.predict(X_train), color ='blue')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X_test , Y_test, color = 'red')\n", "plt.scatter(X_test , predictions, color ='blue')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day01/Pandas(1-5).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Conventional Import" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Reading tabular data (Vid-2)\n", "\n", "* csv\n", "* excel\n", "* etc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DATA_DIR = '../data'\n", "\n", "# reading table\n", "# making seperator as comma\n", "# renaming column names for 0th row of the file\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR,'day1/iris.csv')), \n", " sep=',',\n", " header=0,\n", " names=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']\n", " )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal_lensepal_widpetal_lenpetal_widclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
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" ], "text/plain": [ " sepal_len sepal_wid petal_len petal_wid class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print top 5 records only\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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user_idagegenderoccupationzip
0124Mtechnician85711
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2323Mwriter32067
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4533Fother15213
\n", "
" ], "text/plain": [ " user_id age gender occupation zip\n", "0 1 24 M technician 85711\n", "1 2 53 F other 94043\n", "2 3 23 M writer 32067\n", "3 4 24 M technician 43537\n", "4 5 33 F other 15213" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# data is seperated by '|' so our sep paramater is also '|'\n", "# header=None avoids data first row to be header incase of header not present in the data\n", "# create column names\n", "df = pd.read_table(\n", " 'http://bit.ly./movieusers', \n", " sep='|', \n", " header=None, \n", " names=['user_id', 'age', 'gender','occupation','zip']\n", " )\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "### By default:\n", "1. __read_table__ - takes sep='\\t'\n", "2. __read_table__ - assumes 1st row is header row\n", "\n", "### Useful\n", "1. __skiprows__ - skip 'x' rows from the top\n", "2. __skipfooter__ - skip 'x' rows from the bottom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# --------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Selecting Pandas Series from DataFrame (Vid-3)\n", "\n", "* Series is basically column in a dataframe\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", "3 Abilene NaN DISK KS 6/1/1931 13:00\n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table('http://bit.ly/uforeports', sep=',')\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 NY\n", "1 NJ\n", "2 CO\n", "3 KS\n", "4 NY\n", "Name: State, dtype: object\n", "\n", "\n", "0 NY\n", "1 NJ\n", "2 CO\n", "3 KS\n", "4 NY\n", "Name: State, dtype: object\n", "\n" ] } ], "source": [ "# Case sensitive - Keep in mind\n", "\n", "# Bracket notation\n", "print df['State'].head(5)\n", "print type(df['State'])\n", "\n", "print \n", "\n", "# Alternative ['.' notation]\n", "print df.State.head(5)\n", "print type(df.State)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CityColors ReportedShape ReportedStateTimeLocation
0IthacaNaNTRIANGLENY6/1/1930 22:00Ithaca, NY
1WillingboroNaNOTHERNJ6/30/1930 20:00Willingboro, NJ
2HolyokeNaNOVALCO2/15/1931 14:00Holyoke, CO
3AbileneNaNDISKKS6/1/1931 13:00Abilene, KS
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00New York Worlds Fair, NY
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time \\\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00 \n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00 \n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00 \n", "3 Abilene NaN DISK KS 6/1/1931 13:00 \n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00 \n", "\n", " Location \n", "0 Ithaca, NY \n", "1 Willingboro, NJ \n", "2 Holyoke, CO \n", "3 Abilene, KS \n", "4 New York Worlds Fair, NY " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# creating new series in a dataframe\n", "df['Location'] = df['City'] + ', ' + df['State']\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTimeLocationDate
0IthacaNaNTRIANGLENY6/1/1930 22:00Ithaca, NY6/1/1930
1WillingboroNaNOTHERNJ6/30/1930 20:00Willingboro, NJ6/30/1930
2HolyokeNaNOVALCO2/15/1931 14:00Holyoke, CO2/15/1931
3AbileneNaNDISKKS6/1/1931 13:00Abilene, KS6/1/1931
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00New York Worlds Fair, NY4/18/1933
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time \\\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00 \n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00 \n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00 \n", "3 Abilene NaN DISK KS 6/1/1931 13:00 \n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00 \n", "\n", " Location Date \n", "0 Ithaca, NY 6/1/1930 \n", "1 Willingboro, NJ 6/30/1930 \n", "2 Holyoke, CO 2/15/1931 \n", "3 Abilene, KS 6/1/1931 \n", "4 New York Worlds Fair, NY 4/18/1933 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# creating new series in a dataframe\n", "# map is used to apply a function to each row of the series in a dataframe\n", "# row is the variable representing each row instance which is then splitted by 'space' and '0th' element is picked as date\n", "date = df['Time'].map(lambda row: row.split()[0])\n", "df['Date'] = date\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. If space in the column name then '.' notation does not work. Use bracket notation\n", "2. Column names that are already builtin methods of pandas, '.' notation will not work.\n", "2. Use bracket notation bydefualt.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# --------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Some Pandas command end with paranthesis others don't (Vid-4)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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star_ratingtitlecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read_csv is alternate to read_table if sep=','\n", "df = pd.read_csv('http://bit.ly/imdbratings')\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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star_ratingduration
count979.000000979.000000
mean7.889785120.979571
std0.33606926.218010
min7.40000064.000000
25%7.600000102.000000
50%7.800000117.000000
75%8.100000134.000000
max9.300000242.000000
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" ], "text/plain": [ " star_rating duration\n", "count 979.000000 979.000000\n", "mean 7.889785 120.979571\n", "std 0.336069 26.218010\n", "min 7.400000 64.000000\n", "25% 7.600000 102.000000\n", "50% 7.800000 117.000000\n", "75% 8.100000 134.000000\n", "max 9.300000 242.000000" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# describe() method shows the statistics of all numeric column in a dataframe\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "star_rating float64\n", "title object\n", "content_rating object\n", "genre object\n", "duration int64\n", "actors_list object\n", "dtype: object" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dtypes is a attribute not a method, that's why no ()\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(979, 6)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shape is a attribute not a method, that's why no ()\n", "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. read_csv is alternate to read_table if sep=','\n", "2. methods are action oriented.\n", "3. attributes are properties.\n", "4. (shift+tab)[4 times] inside method parenthesis to see documentation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# --------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Rename Columns in Pandas Dataframe (Vid-5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'star_rating', u'title', u'content_rating', u'genre', u'duration',\n", " u'actors_list'],\n", " dtype='object')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# column names\n", "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ratingmovie_namecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " rating movie_name content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.rename(columns={'star_rating':'rating', 'title':'movie_name'})\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 2" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# shown in Vid-2 (above)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Experiment 3" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ratingmovie namecontent ratinggenredurationactors list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " rating movie name content rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TIP: Always replace ' ' with a undescore for easy reading\n", "# we will see how to replace '_' with space for this exp.\n", "df.columns = df.columns.str.replace('_',' ')\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Always convert spaces in column names to underscore for better reading ability." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day01/README.md ================================================ # Hosted Notebooks 1. [Pandas(1-5)](https://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day01/Pandas%281-5%29.ipynb) 2. [Linear Regression](https://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day01/Linear%20Regression.ipynb) ================================================ FILE: day02/Data Spread.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Spread\n", "\n", "1. Mean \n", "2. Stardard Deviation\n", "3. Variance\n", "\n", "##### ---------------------------------------------" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.14.2\n", "0.20.3\n" ] } ], "source": [ "from __future__ import division\n", "\n", "import numpy as np\n", "import math\n", "import pandas as pd\n", "\n", "print np.__version__\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python Way" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mean(x):\n", " return sum(x) / len(x)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def variance(x):\n", " return (standard_deviation(x))**2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def standard_deviation(lst):\n", " m = mean(lst)\n", " return math.sqrt(float(reduce(lambda x, y: x + y, map(lambda x: (x - m) ** 2, lst))) / len(lst))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean: 3.0\n", "Variance: 2.0\n", "Standard Deviation: 1.41421356237\n" ] } ], "source": [ "X = [1, 2, 3, 4, 5]\n", "print 'Mean: {}'.format(mean(X))\n", "print 'Variance: {}'.format(variance(X))\n", "print 'Standard Deviation: {}'.format(standard_deviation(X))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy Way" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def mean(x):\n", " return np.mean(x)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def variance(x):\n", " return np.var(x)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def standard_deviation(x):\n", " return np.std(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean: 3.0\n", "Variance: 2.0\n", "Standard Deviation: 1.41421356237\n" ] } ], "source": [ "X = [1, 2, 3, 4, 5]\n", "print 'Mean: {}'.format(mean(X))\n", "print 'Variance: {}'.format(variance(X))\n", "print 'Standard Deviation: {}'.format(standard_deviation(X))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day02/Multivariate Regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19.1\n", "0.20.3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import sklearn\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import train_test_split\n", "import os\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "print sklearn.__version__\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-base...engine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
02164audigasstdfoursedanfwdfront99.8...109mpfi3.193.410.01025500243013950
12164audigasstdfoursedan4wdfront99.4...136mpfi3.193.48.01155500182217450
21158audigasstdfoursedanfwdfront105.8...136mpfi3.193.48.51105500192517710
31158audigasturbofoursedanfwdfront105.8...131mpfi3.133.48.31405500172023875
42192bmwgasstdtwosedanrwdfront101.2...108mpfi3.502.88.81015800232916430
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5 rows × 26 columns

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" ], "text/plain": [ " symboling normalized-losses make fuel-type aspiration num-of-doors \\\n", "0 2 164 audi gas std four \n", "1 2 164 audi gas std four \n", "2 1 158 audi gas std four \n", "3 1 158 audi gas turbo four \n", "4 2 192 bmw gas std two \n", "\n", " body-style drive-wheels engine-location wheel-base ... engine-size \\\n", "0 sedan fwd front 99.8 ... 109 \n", "1 sedan 4wd front 99.4 ... 136 \n", "2 sedan fwd front 105.8 ... 136 \n", "3 sedan fwd front 105.8 ... 131 \n", "4 sedan rwd front 101.2 ... 108 \n", "\n", " fuel-system bore stroke compression-ratio horsepower peak-rpm city-mpg \\\n", "0 mpfi 3.19 3.4 10.0 102 5500 24 \n", "1 mpfi 3.19 3.4 8.0 115 5500 18 \n", "2 mpfi 3.19 3.4 8.5 110 5500 19 \n", "3 mpfi 3.13 3.4 8.3 140 5500 17 \n", "4 mpfi 3.50 2.8 8.8 101 5800 23 \n", "\n", " highway-mpg price \n", "0 30 13950 \n", "1 22 17450 \n", "2 25 17710 \n", "3 20 23875 \n", "4 29 16430 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATA_DIR = '../data'\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day2/automobile.csv')),\n", " sep=','\n", " \n", ")\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(159, 26)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 205 rows, 26 cols\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "symboling int64\n", "normalized-losses int64\n", "make object\n", "fuel-type object\n", "aspiration object\n", "num-of-doors object\n", "body-style object\n", "drive-wheels object\n", "engine-location object\n", "wheel-base float64\n", "length float64\n", "width float64\n", "height float64\n", "curb-weight int64\n", "engine-type object\n", "num-of-cylinders object\n", "engine-size int64\n", "fuel-system object\n", "bore float64\n", "stroke float64\n", "compression-ratio float64\n", "horsepower int64\n", "peak-rpm int64\n", "city-mpg int64\n", "highway-mpg int64\n", "price int64\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# datatypes\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 1\n", "\n", "For the first experiment we will just use numerical features as our features for prediction\n", "\n", "So, to summarize\n", "\n", "__Input__: Numerical Values \n", "__Output__: Price\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
0216499.8176.666.254.323371093.193.410.01025500243013950
1216499.4176.666.454.328241363.193.48.01155500182217450
21158105.8192.771.455.728441363.193.48.51105500192517710
31158105.8192.771.455.930861313.133.48.31405500172023875
42192101.2176.864.854.323951083.502.88.81015800232916430
\n", "
" ], "text/plain": [ " symboling normalized-losses wheel-base length width height \\\n", "0 2 164 99.8 176.6 66.2 54.3 \n", "1 2 164 99.4 176.6 66.4 54.3 \n", "2 1 158 105.8 192.7 71.4 55.7 \n", "3 1 158 105.8 192.7 71.4 55.9 \n", "4 2 192 101.2 176.8 64.8 54.3 \n", "\n", " curb-weight engine-size bore stroke compression-ratio horsepower \\\n", "0 2337 109 3.19 3.4 10.0 102 \n", "1 2824 136 3.19 3.4 8.0 115 \n", "2 2844 136 3.19 3.4 8.5 110 \n", "3 3086 131 3.13 3.4 8.3 140 \n", "4 2395 108 3.50 2.8 8.8 101 \n", "\n", " peak-rpm city-mpg highway-mpg price \n", "0 5500 24 30 13950 \n", "1 5500 18 22 17450 \n", "2 5500 19 25 17710 \n", "3 5500 17 20 23875 \n", "4 5800 23 29 16430 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numerics_dtypes = ['int64', 'float64']\n", "df_rel = df.select_dtypes(include=numerics_dtypes)\n", "df_rel.loc[:,'price'] = df.price\n", "df_rel.head(5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(159, 16)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we have only 16 columns of 26 that are numeric\n", "df_rel.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make input and output" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(159, 15)\n", "(159,)\n" ] } ], "source": [ "X = df_rel.iloc[ : , :-1].values\n", "Y = df_rel.iloc[:,-1].values\n", "\n", "print X.shape\n", "print Y.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train/Test Split" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((127, 15), (32, 15))" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_test.shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Regression:\n", " \n", " def __init__(self):\n", " self.regressor = LinearRegression()\n", " \n", " def train(self, X_train, Y_train):\n", " model = self.regressor.fit(X_train, Y_train)\n", " return model\n", "\n", " def predict(self, model, X_test):\n", " return model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "regress = Regression()\n", "model = regress.train(X_train, Y_train)\n", "predictions_train = regress.predict(model, X_train)\n", "predictions_test = regress.predict(model, X_test)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day02/Pandas(6-10).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Removing Columns/Rows (Vid-6)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal_lensepal_widpetal_lenpetal_widclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
\n", "
" ], "text/plain": [ " sepal_len sepal_wid petal_len petal_wid class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATA_DIR = '../data'\n", "# reading table\n", "# making seperator as comma\n", "# renaming column names for 0th row of the file\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR,'day1/iris.csv')), \n", " sep=',',\n", " header=0,\n", " names=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(150, 5)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# see dimension of the dataset\n", "# 150 rows, 5 columns\n", "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Column Drop" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal_lensepal_widpetal_lenpetal_wid
05.13.51.40.2
14.93.01.40.2
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
\n", "
" ], "text/plain": [ " sepal_len sepal_wid petal_len petal_wid\n", "0 5.1 3.5 1.4 0.2\n", "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop method takes the column names in array\n", "# axis=1 corresponds to columns\n", "# inplace=True does not require you to hold it in other variable, memory efficient\n", "df.drop(['class'], axis=1, inplace=True)\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Row Drop" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sepal_lensepal_widpetal_lenpetal_wid
24.73.21.30.2
34.63.11.50.2
45.03.61.40.2
55.43.91.70.4
64.63.41.40.3
\n", "
" ], "text/plain": [ " sepal_len sepal_wid petal_len petal_wid\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", "4 5.0 3.6 1.4 0.2\n", "5 5.4 3.9 1.7 0.4\n", "6 4.6 3.4 1.4 0.3" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop method takes the row names in array\n", "# axis=0 corresponds to rows, bydefault axis=0 in drop method\n", "# inplace=True does not require you to hold it in other variable, memory efficient\n", "df.drop([0, 1], axis=0, inplace=True)\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Takeaways\n", "\n", "1. Keep in practice to always specify 'axis' parameter in drop method or other necessary methods for better understanding." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# -----------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sorting (Vid-7)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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star_ratingtitlecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table(\n", " 'http://bit.ly/imdbratings', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "978 7.4\n", "950 7.4\n", "949 7.4\n", "948 7.4\n", "947 7.4\n", "Name: star_rating, dtype: float64\n", "6 8.9\n", "3 9.0\n", "2 9.1\n", "1 9.2\n", "0 9.3\n", "Name: star_rating, dtype: float64\n" ] } ], "source": [ "# sort_values() method returns bydefault by ascending order\n", "# sort_values() can take 'inplace=True/False' for changing the values inplace\n", "print df['star_rating'].sort_values().head(5)\n", "print df['star_rating'].sort_values().tail(5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 9.3\n", "1 9.2\n", "2 9.1\n", "3 9.0\n", "6 8.9\n", "Name: star_rating, dtype: float64\n", "947 7.4\n", "948 7.4\n", "949 7.4\n", "950 7.4\n", "978 7.4\n", "Name: star_rating, dtype: float64\n" ] } ], "source": [ "# ascending=True/False parameter in sort_values() can decide the sorting order\n", "print df['star_rating'].sort_values(ascending=False).head(5)\n", "print df['star_rating'].sort_values(ascending=False).tail(5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
3898.0FreaksUNRATEDDrama64[u'Wallace Ford', u'Leila Hyams', u'Olga Bacla...
3388.0Battleship PotemkinUNRATEDHistory66[u'Aleksandr Antonov', u'Vladimir Barsky', u'G...
2588.1The Cabinet of Dr. CaligariUNRATEDCrime67[u'Werner Krauss', u'Conrad Veidt', u'Friedric...
2938.1Duck SoupPASSEDComedy68[u'Groucho Marx', u'Harpo Marx', u'Chico Marx']
888.4The KidNOT RATEDComedy68[u'Charles Chaplin', u'Edna Purviance', u'Jack...
\n", "
" ], "text/plain": [ " star_rating title content_rating genre \\\n", "389 8.0 Freaks UNRATED Drama \n", "338 8.0 Battleship Potemkin UNRATED History \n", "258 8.1 The Cabinet of Dr. Caligari UNRATED Crime \n", "293 8.1 Duck Soup PASSED Comedy \n", "88 8.4 The Kid NOT RATED Comedy \n", "\n", " duration actors_list \n", "389 64 [u'Wallace Ford', u'Leila Hyams', u'Olga Bacla... \n", "338 66 [u'Aleksandr Antonov', u'Vladimir Barsky', u'G... \n", "258 67 [u'Werner Krauss', u'Conrad Veidt', u'Friedric... \n", "293 68 [u'Groucho Marx', u'Harpo Marx', u'Chico Marx'] \n", "88 68 [u'Charles Chaplin', u'Edna Purviance', u'Jack... " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# relatively better way to do is to use the below mentioned technique\n", "# to sort by multiple fields, just populate the array inside sort_values()\n", "df.sort_values(['duration'], ascending=True).head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Pandas dataframe is table having rows and columns.\n", "2. Pandas Series is just one column in the dataframe.\n", "3. sort_values() method returns bydefault by ascending order." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# -----------------------\n", "\n", "# Single Filter (Vid-8)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table(\n", " 'http://bit.ly/imdbratings', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 1" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
358.6Modern TimesGComedy87[u'Charles Chaplin', u'Paulette Goddard', u'He...
368.6Saving Private RyanRAction169[u'Tom Hanks', u'Matt Damon', u'Tom Sizemore']
378.6Raiders of the Lost ArkPGAction115[u'Harrison Ford', u'Karen Allen', u'Paul Free...
388.6Rear WindowAPPROVEDMystery112[u'James Stewart', u'Grace Kelly', u'Wendell C...
398.6PsychoRHorror109[u'Anthony Perkins', u'Janet Leigh', u'Vera Mi...
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "35 8.6 Modern Times G Comedy 87 \n", "36 8.6 Saving Private Ryan R Action 169 \n", "37 8.6 Raiders of the Lost Ark PG Action 115 \n", "38 8.6 Rear Window APPROVED Mystery 112 \n", "39 8.6 Psycho R Horror 109 \n", "\n", " actors_list \n", "35 [u'Charles Chaplin', u'Paulette Goddard', u'He... \n", "36 [u'Tom Hanks', u'Matt Damon', u'Tom Sizemore'] \n", "37 [u'Harrison Ford', u'Karen Allen', u'Paul Free... \n", "38 [u'James Stewart', u'Grace Kelly', u'Wendell C... \n", "39 [u'Anthony Perkins', u'Janet Leigh', u'Vera Mi... " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we need movies above 8.5\n", "df_rating_bools = df['star_rating'].map(lambda row: row>8.5)\n", "df[df_rating_bools].tail(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 2" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
358.6Modern TimesGComedy87[u'Charles Chaplin', u'Paulette Goddard', u'He...
368.6Saving Private RyanRAction169[u'Tom Hanks', u'Matt Damon', u'Tom Sizemore']
378.6Raiders of the Lost ArkPGAction115[u'Harrison Ford', u'Karen Allen', u'Paul Free...
388.6Rear WindowAPPROVEDMystery112[u'James Stewart', u'Grace Kelly', u'Wendell C...
398.6PsychoRHorror109[u'Anthony Perkins', u'Janet Leigh', u'Vera Mi...
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "35 8.6 Modern Times G Comedy 87 \n", "36 8.6 Saving Private Ryan R Action 169 \n", "37 8.6 Raiders of the Lost Ark PG Action 115 \n", "38 8.6 Rear Window APPROVED Mystery 112 \n", "39 8.6 Psycho R Horror 109 \n", "\n", " actors_list \n", "35 [u'Charles Chaplin', u'Paulette Goddard', u'He... \n", "36 [u'Tom Hanks', u'Matt Damon', u'Tom Sizemore'] \n", "37 [u'Harrison Ford', u'Karen Allen', u'Paul Free... \n", "38 [u'James Stewart', u'Grace Kelly', u'Wendell C... \n", "39 [u'Anthony Perkins', u'Janet Leigh', u'Vera Mi... " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we need movies above 8.5\n", "boolean = list()\n", "for row in df['star_rating']:\n", " if row > 8.5: boolean.append(True)\n", " else: boolean.append(False)\n", "\n", "# boolean is a list, and since column in pandas is a series, so we need to convert list to series\n", "df_rating_bools = pd.Series(boolean)\n", "df[df_rating_bools].tail(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 3" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
358.6Modern TimesGComedy87[u'Charles Chaplin', u'Paulette Goddard', u'He...
368.6Saving Private RyanRAction169[u'Tom Hanks', u'Matt Damon', u'Tom Sizemore']
378.6Raiders of the Lost ArkPGAction115[u'Harrison Ford', u'Karen Allen', u'Paul Free...
388.6Rear WindowAPPROVEDMystery112[u'James Stewart', u'Grace Kelly', u'Wendell C...
398.6PsychoRHorror109[u'Anthony Perkins', u'Janet Leigh', u'Vera Mi...
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "35 8.6 Modern Times G Comedy 87 \n", "36 8.6 Saving Private Ryan R Action 169 \n", "37 8.6 Raiders of the Lost Ark PG Action 115 \n", "38 8.6 Rear Window APPROVED Mystery 112 \n", "39 8.6 Psycho R Horror 109 \n", "\n", " actors_list \n", "35 [u'Charles Chaplin', u'Paulette Goddard', u'He... \n", "36 [u'Tom Hanks', u'Matt Damon', u'Tom Sizemore'] \n", "37 [u'Harrison Ford', u'Karen Allen', u'Paul Free... \n", "38 [u'James Stewart', u'Grace Kelly', u'Wendell C... \n", "39 [u'Anthony Perkins', u'Janet Leigh', u'Vera Mi... " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# df['star_rating'] > 8.5 automatically searches/iterates through all the rows satisying this condition\n", "df[df['star_rating'] > 8.5].tail(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Try practicing, Experiment 3 while coding." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# -----------------------\n", "\n", "# Multiple Filter (Vid-9)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
09.3The Shawshank RedemptionRCrime142[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...
19.2The GodfatherRCrime175[u'Marlon Brando', u'Al Pacino', u'James Caan']
29.1The Godfather: Part IIRCrime200[u'Al Pacino', u'Robert De Niro', u'Robert Duv...
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
48.9Pulp FictionRCrime154[u'John Travolta', u'Uma Thurman', u'Samuel L....
\n", "
" ], "text/plain": [ " star_rating title content_rating genre duration \\\n", "0 9.3 The Shawshank Redemption R Crime 142 \n", "1 9.2 The Godfather R Crime 175 \n", "2 9.1 The Godfather: Part II R Crime 200 \n", "3 9.0 The Dark Knight PG-13 Action 152 \n", "4 8.9 Pulp Fiction R Crime 154 \n", "\n", " actors_list \n", "0 [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt... \n", "1 [u'Marlon Brando', u'Al Pacino', u'James Caan'] \n", "2 [u'Al Pacino', u'Robert De Niro', u'Robert Duv... \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "4 [u'John Travolta', u'Uma Thurman', u'Samuel L.... " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table(\n", " 'http://bit.ly/imdbratings', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 1" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
78.9The Lord of the Rings: The Return of the KingPG-13Adventure201[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...
178.7Seven SamuraiUNRATEDDrama207[u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...
\n", "
" ], "text/plain": [ " star_rating title content_rating \\\n", "7 8.9 The Lord of the Rings: The Return of the King PG-13 \n", "17 8.7 Seven Samurai UNRATED \n", "\n", " genre duration actors_list \n", "7 Adventure 201 [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK... \n", "17 Drama 207 [u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K... " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we want movies that have rating above 8.5 and duration above 200mins\n", "df[(df['star_rating'] > 8.5) & (df['duration'] > 200)].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 2" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
78.9The Lord of the Rings: The Return of the KingPG-13Adventure201[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...
178.7Seven SamuraiUNRATEDDrama207[u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K...
\n", "
" ], "text/plain": [ " star_rating title content_rating \\\n", "7 8.9 The Lord of the Rings: The Return of the King PG-13 \n", "17 8.7 Seven Samurai UNRATED \n", "\n", " genre duration actors_list \n", "7 Adventure 201 [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK... \n", "17 Drama 207 [u'Toshir\\xf4 Mifune', u'Takashi Shimura', u'K... " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_rating_bools = df['star_rating'].map(lambda row: row>8.5)\n", "df_duration_bools = df['duration'].map(lambda row: row>200)\n", "df[df_rating_bools & df_duration_bools].tail(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 3" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
star_ratingtitlecontent_ratinggenredurationactors_list
39.0The Dark KnightPG-13Action152[u'Christian Bale', u'Heath Ledger', u'Aaron E...
58.912 Angry MenNOT RATEDDrama96[u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals...
98.9Fight ClubRDrama139[u'Brad Pitt', u'Edward Norton', u'Helena Bonh...
118.8InceptionPG-13Action148[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...
128.8Star Wars: Episode V - The Empire Strikes BackPGAction124[u'Mark Hamill', u'Harrison Ford', u'Carrie Fi...
\n", "
" ], "text/plain": [ " star_rating title \\\n", "3 9.0 The Dark Knight \n", "5 8.9 12 Angry Men \n", "9 8.9 Fight Club \n", "11 8.8 Inception \n", "12 8.8 Star Wars: Episode V - The Empire Strikes Back \n", "\n", " content_rating genre duration \\\n", "3 PG-13 Action 152 \n", "5 NOT RATED Drama 96 \n", "9 R Drama 139 \n", "11 PG-13 Action 148 \n", "12 PG Action 124 \n", "\n", " actors_list \n", "3 [u'Christian Bale', u'Heath Ledger', u'Aaron E... \n", "5 [u'Henry Fonda', u'Lee J. Cobb', u'Martin Bals... \n", "9 [u'Brad Pitt', u'Edward Norton', u'Helena Bonh... \n", "11 [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'... \n", "12 [u'Mark Hamill', u'Harrison Ford', u'Carrie Fi... " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this appoarch is inspired by python 'if in [1,2]' functionality\n", "bools = df['genre'].isin(['Drama', 'Action'])\n", "df[bools].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Use & when putting and filter\n", "2. Use | when putting or filter\n", "3. Remember to put parenthesis as shown in [Vid-9 Experiment 1], it helps pandas to set priority to the evaluations\n", "4. When in situation to use multiple | conditions, try using Vid-9 Experiement 3" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day02/README.md ================================================ # Hosted Notebooks 1. [Pandas(6-10)](https://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day02/Pandas%286-10%29.ipynb) 2. [Multivariate Regression](https://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day02/Multivariate%20Regression.ipynb) 3. [Data Spread](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day02/Data%20Spread.ipynb) ================================================ FILE: day03/Logistic Regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Import" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19.1\n", "1.14.2\n", "0.20.3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import sklearn\n", "import os\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn import preprocessing\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "print sklearn.__version__\n", "print np.__version__\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading and Describe" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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feat1feat2feat3feat4class
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" ], "text/plain": [ " feat1 feat2 feat3 feat4 class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATA_DIR = '../data'\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day1/iris.csv')),\n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "class\n", "Iris-setosa 50\n", "Iris-versicolor 50\n", "Iris-virginica 50\n", "Name: class, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# examples per class\n", "df.groupby('class')['class'].count()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# feat1/2/3/4 are considered as input (features) to the model, whereas class is considered as output of the model\n", "X = df.iloc[:, :-1].values\n", "Y = df.iloc[:, -1].values\n", "# encode the class with integers\n", "le = preprocessing.LabelEncoder()\n", "Y = le.fit_transform(Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train/Test Split" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((120, 4), (30, 4))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Normalizer Class" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this class takes care for scaling the features to the scale of 0-1\n", "# we are doing the scaling with this cap because we use sigmoid activation fxn in logistic which \n", "# also has the range from 0-1\n", "class Normalizer:\n", "\n", " def __init__(self):\n", " self.sc = StandardScaler()\n", " \n", " def scale(self, X, dtype):\n", " if dtype=='train':\n", " XX = self.sc.fit_transform(X)\n", " elif dtype=='test':\n", " XX = self.sc.transform(X)\n", " else:\n", " return None\n", " return XX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Logistic Model Class" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class LogisticModel:\n", " \n", " def __init__(self):\n", " self.classifier = LogisticRegression()\n", "\n", " def train(self, X_train, Y_train):\n", " model = self.classifier.fit(X_train, Y_train)\n", " return model\n", " \n", " def predict(self, model, X_test):\n", " return model.predict(X_test)\n", " \n", " def evaluate(self, Y_test, Y_pred, measure):\n", " if measure=='matrix':\n", " cm = confusion_matrix(Y_test, Y_pred, labels=[0, 1, 2])\n", " return cm\n", " elif measure=='accuracy':\n", " return accuracy_score(Y_test, Y_pred)*100\n", " else: return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Starting to Train and Predict" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# cap to range of 0-1\n", "norm = Normalizer()\n", "X_train = norm.scale(X_train, 'train')\n", "X_test = norm.scale(X_test, 'test')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# train the model\n", "logit = LogisticModel()\n", "model = logit.train(X_train, Y_train)\n", "predictions = logit.predict(model, X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluating" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[10 0 0]\n", " [ 0 10 3]\n", " [ 0 0 7]]\n", "\n", "90.0\n" ] } ], "source": [ "print logit.evaluate(Y_test, predictions, 'matrix')\n", "print \n", "print logit.evaluate(Y_test, predictions, 'accuracy')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# iris versicolor verginica\n", "# iris - 10 10 0\n", "# versicolor - 0 10 3\n", "# verginica - 0 0 7\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day03/Pandas(11-15).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import matplotlib\n", "\n", "%matplotlib inline\n", "\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Axis parameter in Pandas (Vid-11)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/drinksbycountry', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcohol
0Afghanistan0000.0
1Albania89132544.9
2Algeria250140.7
3Andorra24513831212.4
4Angola21757455.9
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol \n", "0 0.0 \n", "1 4.9 \n", "2 0.7 \n", "3 12.4 \n", "4 5.9 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# in-context with drop() method for column\n", "# since inplace=T/F not used. So it is not saved for now.\n", "df.drop('continent', axis=1).head(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
4Angola21757455.9Africa
5Antigua & Barbuda102128454.9North America
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "4 Angola 217 57 45 \n", "5 Antigua & Barbuda 102 128 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "4 5.9 Africa \n", "5 4.9 North America " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# in-context with drop() method for row\n", "# since inplace=T/F not used. So it is not saved for now.\n", "df.drop(3, axis=0).head(5)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "beer_servings True\n", "spirit_servings True\n", "wine_servings True\n", "total_litres_of_pure_alcohol True\n", "dtype: bool" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# mean() method bydefault takes axis=0, if not specified\n", "df.mean(axis=0) == df.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. axis=1 (Y-axis/Column) == axis='columns'\n", "2. axis=0 (X-axis/Row) == axis='index'\n", "\n", "# -----------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# String Method in Pandas (Vid-12)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
\n", "
" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/drinksbycountry', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we would like to make continent to all small letters\n", "df['continent'] = df['continent'].str.lower()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0asia
12Bahrain426372.0asia
13Bangladesh0000.0asia
19Bhutan23000.4asia
24Brunei31210.6asia
\n", "
" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "12 Bahrain 42 63 7 \n", "13 Bangladesh 0 0 0 \n", "19 Bhutan 23 0 0 \n", "24 Brunei 31 2 1 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 asia \n", "12 2.0 asia \n", "13 0.0 asia \n", "19 0.4 asia \n", "24 0.6 asia " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we would now like to filter the table where continent is asia\n", "df[df.continent.str.contains('asia')].head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. To apply any of the string methods on pandas series, first typecast them as string by using .str then use the relevant method\n", "\n", "# -----------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data type change (Vid-13)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
\n", "
" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/drinksbycountry', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "country object\n", "beer_servings int64\n", "spirit_servings int64\n", "wine_servings int64\n", "total_litres_of_pure_alcohol float64\n", "continent object\n", "dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# country and continent are strings, rest all are numeric\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.total_litres_of_pure_alcohol = df.total_litres_of_pure_alcohol.astype('int64')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000Asia
1Albania89132544Europe
2Algeria250140Africa
3Andorra24513831212Europe
4Angola21757455Africa
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0 Asia \n", "1 4 Europe \n", "2 0 Africa \n", "3 12 Europe \n", "4 5 Africa " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# notice the change under `total_litres_of_pure_alcohol` column name\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Sometimes while importing dataset, you numbers can be of object type. There you might want to changes the types for applying mathematical operations.\n", "\n", "# -----------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using GroupBy (Vid-14)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
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" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/drinksbycountry', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "continent\n", "Africa 3.007547\n", "Asia 2.170455\n", "Europe 8.617778\n", "North America 5.995652\n", "Oceania 3.381250\n", "South America 6.308333\n", "Name: total_litres_of_pure_alcohol, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we will group by each continent to get on average total_liters_of_pure_alcohol\n", "df.groupby('continent')['total_litres_of_pure_alcohol'].mean()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.170454545454545" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for continent asia, what is the mean value of `total_litres_of_pure_alcohol`\n", "df[df.continent.str.contains('Asia')]['total_litres_of_pure_alcohol'].mean()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countminmaxmean
continent
Africa530.09.13.007547
Asia440.011.52.170455
Europe450.014.48.617778
North America232.211.95.995652
Oceania160.010.43.381250
South America123.88.36.308333
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" ], "text/plain": [ " count min max mean\n", "continent \n", "Africa 53 0.0 9.1 3.007547\n", "Asia 44 0.0 11.5 2.170455\n", "Europe 45 0.0 14.4 8.617778\n", "North America 23 2.2 11.9 5.995652\n", "Oceania 16 0.0 10.4 3.381250\n", "South America 12 3.8 8.3 6.308333" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# keeping eye to all necessary stats at once using `agg` method by passing list of necessary attributes\n", "df.groupby('continent')['total_litres_of_pure_alcohol'].agg(['count', 'min', 'max', 'mean'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.groupby('continent')['total_litres_of_pure_alcohol'].agg(['count', 'min', 'max', 'mean']).plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. \"agg\" method can be used to generate stats in one shot\n", "\n", "# -----------------------" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day03/README.md ================================================ # Hosted Notebooks 1. [Pandas(11-15)](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day03/Pandas%2811-15%29.ipynb) 2. [Visualization](https://github.com/prakhar21/100-Days-of-ML/blob/master/day03/Visualization.ipynb) 3. [Logistic Regression](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day03/Logistic%20Regression.ipynb) ================================================ FILE: day03/Visualization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0Asia
1Albania89132544.9Europe
2Algeria250140.7Africa
3Andorra24513831212.4Europe
4Angola21757455.9Africa
\n", "
" ], "text/plain": [ " country beer_servings spirit_servings wine_servings \\\n", "0 Afghanistan 0 0 0 \n", "1 Albania 89 132 54 \n", "2 Algeria 25 0 14 \n", "3 Andorra 245 138 312 \n", "4 Angola 217 57 45 \n", "\n", " total_litres_of_pure_alcohol continent \n", "0 0.0 Asia \n", "1 4.9 Europe \n", "2 0.7 Africa \n", "3 12.4 Europe \n", "4 5.9 Africa " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/drinksbycountry', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pie Chart" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Africa': 53,\n", " 'Asia': 44,\n", " 'Europe': 45,\n", " 'North America': 23,\n", " 'Oceania': 16,\n", " 'South America': 12}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for countries per continent in our dataset, we will draw a pie chart\n", "continent_groupby_country = df.groupby('continent')['country'].count().to_dict()\n", "continent_groupby_country" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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A8UATck/NykwkmIwBREhJwOfJoY4cqWMk2445xBj8AWRXI4kHqk1a3S5ymzeb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\nUipKaEJWSqkooQlZKaWihCZkpZSKEv8ffjnfLqeaCs0AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = continent_groupby_country.keys()\n", "sizes = continent_groupby_country.values()\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)\n", "ax1.axis('equal')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bar Chart" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we would like to see the mean of all 4 numeric variables per continent\n", "df.groupby('continent').mean().plot(kind='bar')\n", "\n", "# beer consumption is most in europe, which looks true" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day04/.ipynb_checkpoints/Pandas(16-18)-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n", "2.2.2\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import matplotlib\n", "\n", "%matplotlib inline\n", "\n", "print pd.__version__\n", "print matplotlib.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Missing Values in Pandas (Vid-16)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", "3 Abilene NaN DISK KS 6/1/1931 13:00\n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/uforeports', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTime
0FalseTrueFalseFalseFalse
1FalseTrueFalseFalseFalse
2FalseTrueFalseFalseFalse
3FalseTrueFalseFalseFalse
4FalseTrueFalseFalseFalse
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 False True False False False\n", "1 False True False False False\n", "2 False True False False False\n", "3 False True False False False\n", "4 False True False False False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# isnull() method returns a df of T/F considering if NaN occurs in a particular cell\n", "df.isnull().head()\n", "# notnull() is just inverse of isnull()\n", "# df.notnull().head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "City 25\n", "Colors Reported 15359\n", "Shape Reported 2644\n", "State 0\n", "Time 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pandas converts T to 1 and F to 0\n", "# since isnull() returns T to whereever NaN exists\n", "# .sum() over it gives the total number of NaN in the dataframe\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CityColors ReportedShape ReportedStateTime
12BeltonREDSPHERESC6/30/1939 20:00
19Bering SeaREDOTHERAK4/30/1943 23:00
36PortsmouthREDFORMATIONVA7/10/1945 1:30
44BlairsdenGREENSPHERECA6/30/1946 19:00
82San JoseBLUECHEVRONCA7/15/1947 21:00
84ModestoBLUEDISKCA8/8/1947 22:00
91ScipioREDSPHEREIN5/10/1948 19:00
111Tarrant CityORANGECIRCLEAL8/15/1949 22:00
129NapaGREENDISKCA6/10/1950 0:00
138Coeur d'AleneORANGECIGARID7/2/1950 13:00
152IrvingBLUEDISKKS4/15/1951 0:30
157GreenvilleGREENDISKMS6/15/1951 20:30
163Green RiverGREENSPHEREWY7/3/1951 12:00
164ProvoBLUEDISKUT7/10/1951 23:30
174GreenvilleORANGETRIANGLETX4/15/1952 16:00
178NorfolkREDFIREBALLVA6/1/1952 22:00
202ArlingtonGREENOVALVA7/13/1952 21:00
226CambridgeREDSPHEREMA4/1/1953 18:00
229Midwest CityYELLOWFIREBALLOK4/15/1953 16:00
238ClevelandREDFIREBALLOH6/30/1953 0:00
249ArtesiaORANGEOTHERNM8/15/1953 19:00
256PendletonGREENDISKIN11/21/1953 22:30
288St. Louis AirportREDOVALMO7/1/1954 21:00
289Los AngelesREDCIRCLECA7/1/1954 22:00
304BeaumontREDDISKTX9/9/1954 12:30
311Red BankORANGECIRCLENJ12/15/1954 23:10
314HolbrookYELLOWEGGMA5/1/1955 15:00
323Terre HauteORANGECIRCLEIN6/15/1955 0:00
354MemphisORANGECYLINDERTN6/1/1956 20:00
363VistaORANGECIGARCA6/15/1956 19:15
..................
18110GilbertREDLIGHTAZ12/11/2000 23:45
18115MadisonRED GREENTEARDROPOH12/12/2000 18:45
18117ConcordRED GREENCIRCLENH12/12/2000 23:10
18121Redwood ValleyBLUELIGHTCA12/15/2000 1:09
18123WaldorfGREENLIGHTMD12/15/2000 5:30
18127North PoleORANGESPHEREAK12/15/2000 16:36
18129BeaverREDDISKPA12/15/2000 18:25
18134GrahamGREENLIGHTWA12/16/2000 0:20
18141SebastopolYELLOWCIRCLECA12/17/2000 18:00
18144MedinaORANGETRIANGLEOH12/17/2000 19:30
18148Highland ParkBLUEVARIOUSNJ12/18/2000 2:30
18158WoodlandORANGELIGHTCA12/19/2000 23:30
18167GilbertREDCIRCLEAZ12/21/2000 19:05
18170MM 110BLUECIRCLEAZ12/22/2000 3:00
18172ToomsubaREDOVALMS12/23/2000 4:00
18177ChandlerREDLIGHTAZ12/23/2000 22:00
18181FortunaORANGECIRCLECA12/24/2000 18:00
18184PlymouthGREENFIREBALLOH12/24/2000 22:00
18191FallstonREDVARIOUSMD12/25/2000 19:15
18192AtlantaORANGELIGHTGA12/25/2000 20:30
18194WalpoleGREENFIREBALLNH12/26/2000 18:20
18195WalpoleGREENFIREBALLNH12/26/2000 18:20
18196BroctonGREENOVALMA12/26/2000 18:23
18197GreenfiledGREENFIREBALLMA12/26/2000 18:30
18210Monument ValleyBLUEOTHERUT12/28/2000 17:51
18213PasadenaGREENFIREBALLCA12/28/2000 19:10
18216Garden GroveORANGELIGHTCA12/29/2000 16:10
18220Shasta LakeBLUEDISKCA12/29/2000 20:30
18233AnchorageREDVARIOUSAK12/31/2000 21:00
18239Eagle RiverREDLIGHTWI12/31/2000 23:45
\n", "

2486 rows × 5 columns

\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State \\\n", "12 Belton RED SPHERE SC \n", "19 Bering Sea RED OTHER AK \n", "36 Portsmouth RED FORMATION VA \n", "44 Blairsden GREEN SPHERE CA \n", "82 San Jose BLUE CHEVRON CA \n", "84 Modesto BLUE DISK CA \n", "91 Scipio RED SPHERE IN \n", "111 Tarrant City ORANGE CIRCLE AL \n", "129 Napa GREEN DISK CA \n", "138 Coeur d'Alene ORANGE CIGAR ID \n", "152 Irving BLUE DISK KS \n", "157 Greenville GREEN DISK MS \n", "163 Green River GREEN SPHERE WY \n", "164 Provo BLUE DISK UT \n", "174 Greenville ORANGE TRIANGLE TX \n", "178 Norfolk RED FIREBALL VA \n", "202 Arlington GREEN OVAL VA \n", "226 Cambridge RED SPHERE MA \n", "229 Midwest City YELLOW FIREBALL OK \n", "238 Cleveland RED FIREBALL OH \n", "249 Artesia ORANGE OTHER NM \n", "256 Pendleton GREEN DISK IN \n", "288 St. Louis Airport RED OVAL MO \n", "289 Los Angeles RED CIRCLE CA \n", "304 Beaumont RED DISK TX \n", "311 Red Bank ORANGE CIRCLE NJ \n", "314 Holbrook YELLOW EGG MA \n", "323 Terre Haute ORANGE CIRCLE IN \n", "354 Memphis ORANGE CYLINDER TN \n", "363 Vista ORANGE CIGAR CA \n", "... ... ... ... ... \n", "18110 Gilbert RED LIGHT AZ \n", "18115 Madison RED GREEN TEARDROP OH \n", "18117 Concord RED GREEN CIRCLE NH \n", "18121 Redwood Valley BLUE LIGHT CA \n", "18123 Waldorf GREEN LIGHT MD \n", "18127 North Pole ORANGE SPHERE AK \n", "18129 Beaver RED DISK PA \n", "18134 Graham GREEN LIGHT WA \n", "18141 Sebastopol YELLOW CIRCLE CA \n", "18144 Medina ORANGE TRIANGLE OH \n", "18148 Highland Park BLUE VARIOUS NJ \n", "18158 Woodland ORANGE LIGHT CA \n", "18167 Gilbert RED CIRCLE AZ \n", "18170 MM 110 BLUE CIRCLE AZ \n", "18172 Toomsuba RED OVAL MS \n", "18177 Chandler RED LIGHT AZ \n", "18181 Fortuna ORANGE CIRCLE CA \n", "18184 Plymouth GREEN FIREBALL OH \n", "18191 Fallston RED VARIOUS MD \n", "18192 Atlanta ORANGE LIGHT GA \n", "18194 Walpole GREEN FIREBALL NH \n", "18195 Walpole GREEN FIREBALL NH \n", "18196 Brocton GREEN OVAL MA \n", "18197 Greenfiled GREEN FIREBALL MA \n", "18210 Monument Valley BLUE OTHER UT \n", "18213 Pasadena GREEN FIREBALL CA \n", "18216 Garden Grove ORANGE LIGHT CA \n", "18220 Shasta Lake BLUE DISK CA \n", "18233 Anchorage RED VARIOUS AK \n", "18239 Eagle River RED LIGHT WI \n", "\n", " Time \n", "12 6/30/1939 20:00 \n", "19 4/30/1943 23:00 \n", "36 7/10/1945 1:30 \n", "44 6/30/1946 19:00 \n", "82 7/15/1947 21:00 \n", "84 8/8/1947 22:00 \n", "91 5/10/1948 19:00 \n", "111 8/15/1949 22:00 \n", "129 6/10/1950 0:00 \n", "138 7/2/1950 13:00 \n", "152 4/15/1951 0:30 \n", "157 6/15/1951 20:30 \n", "163 7/3/1951 12:00 \n", "164 7/10/1951 23:30 \n", "174 4/15/1952 16:00 \n", "178 6/1/1952 22:00 \n", "202 7/13/1952 21:00 \n", "226 4/1/1953 18:00 \n", "229 4/15/1953 16:00 \n", "238 6/30/1953 0:00 \n", "249 8/15/1953 19:00 \n", "256 11/21/1953 22:30 \n", "288 7/1/1954 21:00 \n", "289 7/1/1954 22:00 \n", "304 9/9/1954 12:30 \n", "311 12/15/1954 23:10 \n", "314 5/1/1955 15:00 \n", "323 6/15/1955 0:00 \n", "354 6/1/1956 20:00 \n", "363 6/15/1956 19:15 \n", "... ... \n", "18110 12/11/2000 23:45 \n", "18115 12/12/2000 18:45 \n", "18117 12/12/2000 23:10 \n", "18121 12/15/2000 1:09 \n", "18123 12/15/2000 5:30 \n", "18127 12/15/2000 16:36 \n", "18129 12/15/2000 18:25 \n", "18134 12/16/2000 0:20 \n", "18141 12/17/2000 18:00 \n", "18144 12/17/2000 19:30 \n", "18148 12/18/2000 2:30 \n", "18158 12/19/2000 23:30 \n", "18167 12/21/2000 19:05 \n", "18170 12/22/2000 3:00 \n", "18172 12/23/2000 4:00 \n", "18177 12/23/2000 22:00 \n", "18181 12/24/2000 18:00 \n", "18184 12/24/2000 22:00 \n", "18191 12/25/2000 19:15 \n", "18192 12/25/2000 20:30 \n", "18194 12/26/2000 18:20 \n", "18195 12/26/2000 18:20 \n", "18196 12/26/2000 18:23 \n", "18197 12/26/2000 18:30 \n", "18210 12/28/2000 17:51 \n", "18213 12/28/2000 19:10 \n", "18216 12/29/2000 16:10 \n", "18220 12/29/2000 20:30 \n", "18233 12/31/2000 21:00 \n", "18239 12/31/2000 23:45 \n", "\n", "[2486 rows x 5 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Handling Missing Values\n", "## Drop Missing Values\n", "\n", "# dropna() method drops al those rows from the dataset where anyof the columns holds a NaN values\n", "df.dropna(how='any')\n", "\n", "# (how='all') - drop only if all the values in the row are missing" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2877, 5)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop a row only if any of the City, Colors Reported columns have NaN value\n", "df.dropna(subset=['City', 'Colors Reported'], how='any').shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seattle 187\n", "New York City 161\n", "Name: City, dtype: int64\n", "\n", "25\n", "\n", "Seattle 212\n", "New York City 161\n", "Name: City, dtype: int64\n", "\n", "\n", "187+25 = 212\n" ] } ], "source": [ "print df.City.value_counts().head(2)\n", "print \n", "print df.City.isnull().sum()\n", "print \n", "print df.City.fillna(value='Seattle', inplace=False).value_counts().head(2)\n", "\n", "print \"\\n\\n187+25 = 212\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. For any value that is missing in the dataset. Pandas makes it NaN bydefault\n", "2. Missing values are excluded while making a count using value_counts()\n", "\n", "# -----------------------\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day04/Pandas(16-18).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n", "2.2.2\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import matplotlib\n", "\n", "%matplotlib inline\n", "\n", "print pd.__version__\n", "print matplotlib.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Missing Values in Pandas (Vid-16)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
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" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", "3 Abilene NaN DISK KS 6/1/1931 13:00\n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table\n", "# making seperator as comma\n", "df = pd.read_table(\n", " 'http://bit.ly/uforeports', \n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTime
0FalseTrueFalseFalseFalse
1FalseTrueFalseFalseFalse
2FalseTrueFalseFalseFalse
3FalseTrueFalseFalseFalse
4FalseTrueFalseFalseFalse
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" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 False True False False False\n", "1 False True False False False\n", "2 False True False False False\n", "3 False True False False False\n", "4 False True False False False" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# isnull() method returns a df of T/F considering if NaN occurs in a particular cell\n", "df.isnull().head()\n", "# notnull() is just inverse of isnull()\n", "# df.notnull().head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "City 25\n", "Colors Reported 15359\n", "Shape Reported 2644\n", "State 0\n", "Time 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pandas converts T to 1 and F to 0\n", "# since isnull() returns T to whereever NaN exists\n", "# .sum() over it gives the total number of NaN in the dataframe\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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CityColors ReportedShape ReportedStateTime
12BeltonREDSPHERESC6/30/1939 20:00
19Bering SeaREDOTHERAK4/30/1943 23:00
36PortsmouthREDFORMATIONVA7/10/1945 1:30
44BlairsdenGREENSPHERECA6/30/1946 19:00
82San JoseBLUECHEVRONCA7/15/1947 21:00
84ModestoBLUEDISKCA8/8/1947 22:00
91ScipioREDSPHEREIN5/10/1948 19:00
111Tarrant CityORANGECIRCLEAL8/15/1949 22:00
129NapaGREENDISKCA6/10/1950 0:00
138Coeur d'AleneORANGECIGARID7/2/1950 13:00
152IrvingBLUEDISKKS4/15/1951 0:30
157GreenvilleGREENDISKMS6/15/1951 20:30
163Green RiverGREENSPHEREWY7/3/1951 12:00
164ProvoBLUEDISKUT7/10/1951 23:30
174GreenvilleORANGETRIANGLETX4/15/1952 16:00
178NorfolkREDFIREBALLVA6/1/1952 22:00
202ArlingtonGREENOVALVA7/13/1952 21:00
226CambridgeREDSPHEREMA4/1/1953 18:00
229Midwest CityYELLOWFIREBALLOK4/15/1953 16:00
238ClevelandREDFIREBALLOH6/30/1953 0:00
249ArtesiaORANGEOTHERNM8/15/1953 19:00
256PendletonGREENDISKIN11/21/1953 22:30
288St. Louis AirportREDOVALMO7/1/1954 21:00
289Los AngelesREDCIRCLECA7/1/1954 22:00
304BeaumontREDDISKTX9/9/1954 12:30
311Red BankORANGECIRCLENJ12/15/1954 23:10
314HolbrookYELLOWEGGMA5/1/1955 15:00
323Terre HauteORANGECIRCLEIN6/15/1955 0:00
354MemphisORANGECYLINDERTN6/1/1956 20:00
363VistaORANGECIGARCA6/15/1956 19:15
..................
18110GilbertREDLIGHTAZ12/11/2000 23:45
18115MadisonRED GREENTEARDROPOH12/12/2000 18:45
18117ConcordRED GREENCIRCLENH12/12/2000 23:10
18121Redwood ValleyBLUELIGHTCA12/15/2000 1:09
18123WaldorfGREENLIGHTMD12/15/2000 5:30
18127North PoleORANGESPHEREAK12/15/2000 16:36
18129BeaverREDDISKPA12/15/2000 18:25
18134GrahamGREENLIGHTWA12/16/2000 0:20
18141SebastopolYELLOWCIRCLECA12/17/2000 18:00
18144MedinaORANGETRIANGLEOH12/17/2000 19:30
18148Highland ParkBLUEVARIOUSNJ12/18/2000 2:30
18158WoodlandORANGELIGHTCA12/19/2000 23:30
18167GilbertREDCIRCLEAZ12/21/2000 19:05
18170MM 110BLUECIRCLEAZ12/22/2000 3:00
18172ToomsubaREDOVALMS12/23/2000 4:00
18177ChandlerREDLIGHTAZ12/23/2000 22:00
18181FortunaORANGECIRCLECA12/24/2000 18:00
18184PlymouthGREENFIREBALLOH12/24/2000 22:00
18191FallstonREDVARIOUSMD12/25/2000 19:15
18192AtlantaORANGELIGHTGA12/25/2000 20:30
18194WalpoleGREENFIREBALLNH12/26/2000 18:20
18195WalpoleGREENFIREBALLNH12/26/2000 18:20
18196BroctonGREENOVALMA12/26/2000 18:23
18197GreenfiledGREENFIREBALLMA12/26/2000 18:30
18210Monument ValleyBLUEOTHERUT12/28/2000 17:51
18213PasadenaGREENFIREBALLCA12/28/2000 19:10
18216Garden GroveORANGELIGHTCA12/29/2000 16:10
18220Shasta LakeBLUEDISKCA12/29/2000 20:30
18233AnchorageREDVARIOUSAK12/31/2000 21:00
18239Eagle RiverREDLIGHTWI12/31/2000 23:45
\n", "

2486 rows × 5 columns

\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State \\\n", "12 Belton RED SPHERE SC \n", "19 Bering Sea RED OTHER AK \n", "36 Portsmouth RED FORMATION VA \n", "44 Blairsden GREEN SPHERE CA \n", "82 San Jose BLUE CHEVRON CA \n", "84 Modesto BLUE DISK CA \n", "91 Scipio RED SPHERE IN \n", "111 Tarrant City ORANGE CIRCLE AL \n", "129 Napa GREEN DISK CA \n", "138 Coeur d'Alene ORANGE CIGAR ID \n", "152 Irving BLUE DISK KS \n", "157 Greenville GREEN DISK MS \n", "163 Green River GREEN SPHERE WY \n", "164 Provo BLUE DISK UT \n", "174 Greenville ORANGE TRIANGLE TX \n", "178 Norfolk RED FIREBALL VA \n", "202 Arlington GREEN OVAL VA \n", "226 Cambridge RED SPHERE MA \n", "229 Midwest City YELLOW FIREBALL OK \n", "238 Cleveland RED FIREBALL OH \n", "249 Artesia ORANGE OTHER NM \n", "256 Pendleton GREEN DISK IN \n", "288 St. Louis Airport RED OVAL MO \n", "289 Los Angeles RED CIRCLE CA \n", "304 Beaumont RED DISK TX \n", "311 Red Bank ORANGE CIRCLE NJ \n", "314 Holbrook YELLOW EGG MA \n", "323 Terre Haute ORANGE CIRCLE IN \n", "354 Memphis ORANGE CYLINDER TN \n", "363 Vista ORANGE CIGAR CA \n", "... ... ... ... ... \n", "18110 Gilbert RED LIGHT AZ \n", "18115 Madison RED GREEN TEARDROP OH \n", "18117 Concord RED GREEN CIRCLE NH \n", "18121 Redwood Valley BLUE LIGHT CA \n", "18123 Waldorf GREEN LIGHT MD \n", "18127 North Pole ORANGE SPHERE AK \n", "18129 Beaver RED DISK PA \n", "18134 Graham GREEN LIGHT WA \n", "18141 Sebastopol YELLOW CIRCLE CA \n", "18144 Medina ORANGE TRIANGLE OH \n", "18148 Highland Park BLUE VARIOUS NJ \n", "18158 Woodland ORANGE LIGHT CA \n", "18167 Gilbert RED CIRCLE AZ \n", "18170 MM 110 BLUE CIRCLE AZ \n", "18172 Toomsuba RED OVAL MS \n", "18177 Chandler RED LIGHT AZ \n", "18181 Fortuna ORANGE CIRCLE CA \n", "18184 Plymouth GREEN FIREBALL OH \n", "18191 Fallston RED VARIOUS MD \n", "18192 Atlanta ORANGE LIGHT GA \n", "18194 Walpole GREEN FIREBALL NH \n", "18195 Walpole GREEN FIREBALL NH \n", "18196 Brocton GREEN OVAL MA \n", "18197 Greenfiled GREEN FIREBALL MA \n", "18210 Monument Valley BLUE OTHER UT \n", "18213 Pasadena GREEN FIREBALL CA \n", "18216 Garden Grove ORANGE LIGHT CA \n", "18220 Shasta Lake BLUE DISK CA \n", "18233 Anchorage RED VARIOUS AK \n", "18239 Eagle River RED LIGHT WI \n", "\n", " Time \n", "12 6/30/1939 20:00 \n", "19 4/30/1943 23:00 \n", "36 7/10/1945 1:30 \n", "44 6/30/1946 19:00 \n", "82 7/15/1947 21:00 \n", "84 8/8/1947 22:00 \n", "91 5/10/1948 19:00 \n", "111 8/15/1949 22:00 \n", "129 6/10/1950 0:00 \n", "138 7/2/1950 13:00 \n", "152 4/15/1951 0:30 \n", "157 6/15/1951 20:30 \n", "163 7/3/1951 12:00 \n", "164 7/10/1951 23:30 \n", "174 4/15/1952 16:00 \n", "178 6/1/1952 22:00 \n", "202 7/13/1952 21:00 \n", "226 4/1/1953 18:00 \n", "229 4/15/1953 16:00 \n", "238 6/30/1953 0:00 \n", "249 8/15/1953 19:00 \n", "256 11/21/1953 22:30 \n", "288 7/1/1954 21:00 \n", "289 7/1/1954 22:00 \n", "304 9/9/1954 12:30 \n", "311 12/15/1954 23:10 \n", "314 5/1/1955 15:00 \n", "323 6/15/1955 0:00 \n", "354 6/1/1956 20:00 \n", "363 6/15/1956 19:15 \n", "... ... \n", "18110 12/11/2000 23:45 \n", "18115 12/12/2000 18:45 \n", "18117 12/12/2000 23:10 \n", "18121 12/15/2000 1:09 \n", "18123 12/15/2000 5:30 \n", "18127 12/15/2000 16:36 \n", "18129 12/15/2000 18:25 \n", "18134 12/16/2000 0:20 \n", "18141 12/17/2000 18:00 \n", "18144 12/17/2000 19:30 \n", "18148 12/18/2000 2:30 \n", "18158 12/19/2000 23:30 \n", "18167 12/21/2000 19:05 \n", "18170 12/22/2000 3:00 \n", "18172 12/23/2000 4:00 \n", "18177 12/23/2000 22:00 \n", "18181 12/24/2000 18:00 \n", "18184 12/24/2000 22:00 \n", "18191 12/25/2000 19:15 \n", "18192 12/25/2000 20:30 \n", "18194 12/26/2000 18:20 \n", "18195 12/26/2000 18:20 \n", "18196 12/26/2000 18:23 \n", "18197 12/26/2000 18:30 \n", "18210 12/28/2000 17:51 \n", "18213 12/28/2000 19:10 \n", "18216 12/29/2000 16:10 \n", "18220 12/29/2000 20:30 \n", "18233 12/31/2000 21:00 \n", "18239 12/31/2000 23:45 \n", "\n", "[2486 rows x 5 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Handling Missing Values\n", "## Drop Missing Values\n", "\n", "# dropna() method drops al those rows from the dataset where anyof the columns holds a NaN values\n", "df.dropna(how='any')\n", "\n", "# (how='all') - drop only if all the values in the row are missing" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2877, 5)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop a row only if any of the City, Colors Reported columns have NaN value\n", "df.dropna(subset=['City', 'Colors Reported'], how='any').shape" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Seattle 187\n", "New York City 161\n", "Name: City, dtype: int64\n", "\n", "25\n", "\n", "Seattle 212\n", "New York City 161\n", "Name: City, dtype: int64\n", "\n", "\n", "187+25 = 212\n" ] } ], "source": [ "print df.City.value_counts().head(2)\n", "print \n", "print df.City.isnull().sum()\n", "print \n", "print df.City.fillna(value='Seattle', inplace=False).value_counts().head(2)\n", "\n", "print \"\\n\\n187+25 = 212\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. For any value that is missing in the dataset. Pandas makes it NaN bydefault\n", "2. Missing values are excluded while making a count using value_counts()\n", "\n", "# -----------------------\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day04/README.md ================================================ # Hosted Notebooks 1. [Pandas(16-18)](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day04/Pandas%2816-18%29.ipynb) ================================================ FILE: day06/K-NearestNeighbours.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n", "1.14.2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "print pd.__version__\n", "print np.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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idrinamgalsikcabafeclass
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\n", "
" ], "text/plain": [ " id ri na mg al si k ca ba fe class\n", "0 1 1.52101 13.64 4.49 1.10 71.78 0.06 8.75 0.0 0.0 1\n", "1 2 1.51761 13.89 3.60 1.36 72.73 0.48 7.83 0.0 0.0 1\n", "2 3 1.51618 13.53 3.55 1.54 72.99 0.39 7.78 0.0 0.0 1\n", "3 4 1.51766 13.21 3.69 1.29 72.61 0.57 8.22 0.0 0.0 1\n", "4 5 1.51742 13.27 3.62 1.24 73.08 0.55 8.07 0.0 0.0 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DATA_DIR = \"../data\"\n", "\n", "df = pd.read_csv(\n", " os.path.abspath(os.path.join(DATA_DIR, \"day6/glass.csv\")), \n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we will first drop the id column; since it is of no use to us\n", "# remember axis=1 (column); axis=0 (row); default value of axis is 0\n", "df.drop(['id'], inplace=True, axis=1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'ri', u'na', u'mg', u'al', u'si', u'k', u'ca', u'ba', u'fe', u'class'], dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ri 0\n", "na 0\n", "mg 0\n", "al 0\n", "si 0\n", "k 0\n", "ca 0\n", "ba 0\n", "fe 0\n", "class 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking for any NaN value in the dataset across any of the remaining columns in our df\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 5, 6, 7])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# total classes for prediction\n", "df['class'].unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = df.iloc[:,:-1].values\n", "Y = df.iloc[:,-1].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Split" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " X_train, X_validation, Y_train, Y_validation = train_test_split(X_train, Y_train, test_size=0.2, random_state = 10)\n", " return X_train, X_validation, X_test, Y_train, Y_validation, Y_test\n", "\n", "X_train, X_validation, X_test, Y_train, Y_validation, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(136, 9)\n", "(35, 9)\n", "(43, 9)\n" ] } ], "source": [ "print X_train.shape\n", "print X_validation.shape\n", "print X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuning of K using validation" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Neighbour: 5 - Score: 0.571428571429\n", "Neighbour: 6 - Score: 0.6\n", "Neighbour: 7 - Score: 0.628571428571\n" ] } ], "source": [ "n = [5, 6, 7, 8, 9, 10, 11, 12]\n", "maximum = 0\n", "for neighbor in n:\n", " knn = KNeighborsClassifier(n_neighbors=neighbor)\n", " knn.fit(X_train, Y_train)\n", " score = knn.score(X_validation, Y_validation)\n", " if score > maximum:\n", " maximum = score\n", " n_final = neighbor\n", " print 'Neighbour: {} - Score: {}'.format(neighbor, maximum)\n", " else:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.4883720930232558" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn = KNeighborsClassifier(n_neighbors=n_final)\n", "knn.fit(X_train, Y_train)\n", "knn.score(X_test, Y_test)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day06/Pandas(23-26).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Removing duplicate rows (Vid-26)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agegenderoccupationzipcode
user_id
124Mtechnician85711
253Fother94043
323Mwriter32067
424Mtechnician43537
533Fother15213
\n", "
" ], "text/plain": [ " age gender occupation zipcode\n", "user_id \n", "1 24 M technician 85711\n", "2 53 F other 94043\n", "3 23 M writer 32067\n", "4 24 M technician 43537\n", "5 33 F other 15213" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_table(\n", " \"http://bit.ly/movieusers\",\n", " sep=\"|\",\n", " header=None,\n", " names=[\"user_id\", \"age\", \"gender\", \"occupation\", \"zipcode\"],\n", " index_col=\"user_id\"\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(943, 4)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# number of rows that are duplicate\n", "df.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
agegenderoccupationzipcode
user_id
49621Fstudent55414
57251Meducator20003
62117Mstudent60402
68428Mstudent55414
73344Fother60630
80527Fother20009
89032Mstudent97301
\n", "
" ], "text/plain": [ " age gender occupation zipcode\n", "user_id \n", "496 21 F student 55414\n", "572 51 M educator 20003\n", "621 17 M student 60402\n", "684 28 M student 55414\n", "733 44 F other 60630\n", "805 27 F other 20009\n", "890 32 M student 97301" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we are trying to location of duplicated elements\n", "df.loc[df.duplicated()]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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agegenderoccupationzipcode
user_id
6717Mstudent60402
8551Meducator20003
19821Fstudent55414
35032Mstudent97301
42828Mstudent55414
43727Fother20009
46044Fother60630
49621Fstudent55414
57251Meducator20003
62117Mstudent60402
68428Mstudent55414
73344Fother60630
80527Fother20009
89032Mstudent97301
\n", "
" ], "text/plain": [ " age gender occupation zipcode\n", "user_id \n", "67 17 M student 60402\n", "85 51 M educator 20003\n", "198 21 F student 55414\n", "350 32 M student 97301\n", "428 28 M student 55414\n", "437 27 F other 20009\n", "460 44 F other 60630\n", "496 21 F student 55414\n", "572 51 M educator 20003\n", "621 17 M student 60402\n", "684 28 M student 55414\n", "733 44 F other 60630\n", "805 27 F other 20009\n", "890 32 M student 97301" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# but if you notice, we can just locate one of the occurence that was duplicate of some other.\n", "# keep=False let's you see all the utterences\n", "df.loc[df.duplicated(keep=False)]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "943-7 = 936\n" ] } ], "source": [ "# we started with (943, 4) and now it is (936, 4)\n", "df.drop_duplicates().shape\n", "\n", "print \n", "print \"943-7 = 936\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. 'keep' paramater let's you see all the duplicated pairs.\n", "\n", "# -------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Date & Time in Pandas (Vid-25)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY6/1/1930 22:00
1WillingboroNaNOTHERNJ6/30/1930 20:00
2HolyokeNaNOVALCO2/15/1931 14:00
3AbileneNaNDISKKS6/1/1931 13:00
4New York Worlds FairNaNLIGHTNY4/18/1933 19:00
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State Time\n", "0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00\n", "1 Willingboro NaN OTHER NJ 6/30/1930 20:00\n", "2 Holyoke NaN OVAL CO 2/15/1931 14:00\n", "3 Abilene NaN DISK KS 6/1/1931 13:00\n", "4 New York Worlds Fair NaN LIGHT NY 4/18/1933 19:00" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\n", " \"http://bit.ly/uforeports\"\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "City object\n", "Colors Reported object\n", "Shape Reported object\n", "State object\n", "Time object\n", "dtype: object" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# anything that has a dtype as object in pandas is stored as a string\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CityColors ReportedShape ReportedStateTime
0IthacaNaNTRIANGLENY1930-06-01 22:00:00
1WillingboroNaNOTHERNJ1930-06-30 20:00:00
2HolyokeNaNOVALCO1931-02-15 14:00:00
3AbileneNaNDISKKS1931-06-01 13:00:00
4New York Worlds FairNaNLIGHTNY1933-04-18 19:00:00
\n", "
" ], "text/plain": [ " City Colors Reported Shape Reported State \\\n", "0 Ithaca NaN TRIANGLE NY \n", "1 Willingboro NaN OTHER NJ \n", "2 Holyoke NaN OVAL CO \n", "3 Abilene NaN DISK KS \n", "4 New York Worlds Fair NaN LIGHT NY \n", "\n", " Time \n", "0 1930-06-01 22:00:00 \n", "1 1930-06-30 20:00:00 \n", "2 1931-02-15 14:00:00 \n", "3 1931-06-01 13:00:00 \n", "4 1933-04-18 19:00:00 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Time = pd.to_datetime(df.Time)\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "City object\n", "Colors Reported object\n", "Shape Reported object\n", "State object\n", "Time datetime64[ns]\n", "dtype: object" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1930-06-01\n", "1 1930-06-30\n", "Name: Time, dtype: object" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Time.dt.date.head(2)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 6\n", "1 6\n", "Name: Time, dtype: int64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.Time.dt.month.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Takeaways\n", "\n", "1. Read the docs for many other attributes that exist for pandas datetime." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day06/README.md ================================================ # Hosted Notebooks 1. [KNN](https://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day06/K-NearestNeighbours.ipynb) 2. [Pandas(23-26)](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day06/Pandas%2823-26%29.ipynb) ================================================ FILE: day07/Numpy.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "1.14.2\n" ] } ], "source": [ "import os\n", "import numpy as np\n", "import csv\n", "\n", "print csv.__version__\n", "print np.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# open file\n", "# set delimiter as ';'\n", "# typecast to list and store to variable \"wine\"\n", "\n", "DATA_DIR = '../data'\n", "\n", "with open(os.path.abspath(os.path.join(DATA_DIR, 'day7/winequality-white.csv')), 'r') as datafile:\n", " reader = csv.reader(datafile, delimiter=\";\")\n", " wines = list(reader)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['fixed acidity',\n", " 'volatile acidity',\n", " 'citric acid',\n", " 'residual sugar',\n", " 'chlorides',\n", " 'free sulfur dioxide',\n", " 'total sulfur dioxide',\n", " 'density',\n", " 'pH',\n", " 'sulphates',\n", " 'alcohol',\n", " 'quality']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wines[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total records: 4898\n" ] } ], "source": [ "# -1 to avoid header from counting into records\n", "print 'Total records: {}'.format(len(wines)-1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.87790935076\n" ] } ], "source": [ "# average of quality variable\n", "qualities = [float(item[-1]) for item in wines[1:]]\n", "print sum(qualities)/len(qualities)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4898, 12)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# converting python array to numpy array and typecasting the cell values to float\n", "wines = wines[1:]\n", "wines_np = np.array(wines, dtype='float')\n", "wines_np.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7. , 0.27, 0.36, ..., 0.45, 8.8 , 6. ],\n", " [ 6.3 , 0.3 , 0.34, ..., 0.49, 9.5 , 6. ],\n", " [ 8.1 , 0.28, 0.4 , ..., 0.44, 10.1 , 6. ],\n", " ...,\n", " [ 6.5 , 0.24, 0.19, ..., 0.46, 9.4 , 6. ],\n", " [ 5.5 , 0.29, 0.3 , ..., 0.38, 12.8 , 7. ],\n", " [ 6. , 0.21, 0.38, ..., 0.32, 11.8 , 6. ]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# printing out wines 2-D array\n", "wines_np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0. 0.]\n", " [0. 0.]]\n" ] } ], "source": [ "# creating numpy array with all zero elements\n", "print np.zeros((2,2), dtype='float')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.79301112 0.95735914]\n", " [0.29987175 0.96456405]]\n" ] } ], "source": [ "# creating numpy array with all random numbers\n", "print np.random.rand(2,2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# using numpy to read dataset\n", "wines = np.genfromtxt(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day7/winequality-white.csv')), \n", " delimiter=\";\", \n", " skip_header=1\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ True, True, True, ..., True, True, True],\n", " [ True, True, True, ..., True, True, True],\n", " [ True, True, True, ..., True, True, True],\n", " ...,\n", " [ True, True, True, ..., True, True, True],\n", " [ True, True, True, ..., True, True, True],\n", " [ True, True, True, ..., True, True, True]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wines == wines_np" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.28\n", "True\n" ] } ], "source": [ "# accessing the value for 3rd row 2nd column of wines\n", "print wines[2, 1]\n", "print wines[2, 1] == wines_np[2, 1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0.36, 0.34, 0.4 , 0.32])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we would like to access 4rows from top of 3rd column\n", "# wines[start:end, column_index]\n", "# since the index start from zero; so slicing excludes 4 and finds out result from 0, 1, 2, 3\n", "wines[:4,2]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([10., 10., 10., 10.])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we will override the existing value of 2nd column to 10.0 for all the rows\n", "wines[:, 2] = 10.0\n", "wines[:4, 2]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0.28855438, 0.95129591, 0.80747318, 0.89765623, 0.98632739])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# creating 1-D array in numpy\n", "random_1d = np.random.rand(5)\n", "random_1d" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# creating 3-D numpy array\n", "random_3d = np.random.rand(2,4,3)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 4, 3)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# take this shape as any thing for 2 years across 4 quarters per month in that quarter\n", "#2x4x3 = 24 months\n", "random_3d.shape" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 7, 0, 10, ..., 0, 8, 6],\n", " [ 6, 0, 10, ..., 0, 9, 6],\n", " [ 8, 0, 10, ..., 0, 10, 6],\n", " ...,\n", " [ 6, 0, 10, ..., 0, 9, 6],\n", " [ 5, 0, 10, ..., 0, 12, 7],\n", " [ 6, 0, 10, ..., 0, 11, 6]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Data types in numpy\n", "# converting wines to type=int\n", "wines.astype('int')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6. 6. 6. ... 6. 7. 6.]\n", "[7. 7. 7. ... 7. 8. 7.]\n" ] } ], "source": [ "# addition to any column across all rows\n", "# as shows below all the remaining mathematical operations can be done\n", "print wines[:, 11]\n", "print wines[:, 11] + 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([36., 36., 36., ..., 36., 49., 36.])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# multiplying 2 columns\n", "# examples show the square of 12th column\n", "wines[:, 11] * wines[: , 11]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "28790.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sum any column across all rows\n", "wines[:, 11].sum(axis=0)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5.87790935075541" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wines[:, 11].mean() #std, min, max are many other methods for fast stats computation" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day07/README.md ================================================ # Hosted Notebooks 1. [Numpy](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day07/Numpy.ipynb) ================================================ FILE: day08/README.md ================================================ # Hosted Notebooks 1. [Titanic Challenge](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day08/Titanic.ipynb) ================================================ FILE: day08/Titanic.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "# Imports libraries\n", "import sklearn\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.naive_bayes import GaussianNB\n", "import pandas as pd\n", "import os\n", "\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S \n", "2 1 0 PC 17599 71.2833 C85 C \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", "4 1 0 113803 53.1000 C123 S \n", "5 0 0 373450 8.0500 NaN S " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading table as dataframe\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day8/titanic.csv')),\n", " sep=',', \n", " index_col='PassengerId'\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(891, 11)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Survived 0\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# identifiying the missing values across all the colums\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# dropping name, cabin, Fare, Embarked ticket columns\n", "# Name does not give any information if a person will live or not\n", "# cabin, Ticket, Fare are correlated to eachother and to PClass; so removing\n", "df.drop(['Cabin', 'Ticket', 'Name', 'Fare', 'Embarked'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# fill age mean to NaN value\n", "df[df['Age'].isnull()] = df['Age'].mean()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Survived 0\n", "Pclass 0\n", "Sex 0\n", "Age 0\n", "SibSp 0\n", "Parch 0\n", "dtype: int64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check for any other NaN value\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Survived Pclass Sex Age SibSp Parch\n", "PassengerId \n", "1 0.0 3.0 male 22.0 1.0 0.0\n", "2 1.0 1.0 female 38.0 1.0 0.0\n", "3 1.0 3.0 female 26.0 0.0 0.0\n", "4 1.0 1.0 female 35.0 1.0 0.0\n", "5 0.0 3.0 male 35.0 0.0 0.0" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(5)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# converting Sex to numbers\n", "replacements_sex = {'male': 0, 'female': 1}\n", "df['Sex'].replace(replacements_sex, inplace=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Survived Pclass Sex Age SibSp Parch\n", "PassengerId \n", "1 0.000000 3.000000 0.000000 22.000000 1.000000 0.000000\n", "2 1.000000 1.000000 1.000000 38.000000 1.000000 0.000000\n", "3 1.000000 3.000000 1.000000 26.000000 0.000000 0.000000\n", "4 1.000000 1.000000 1.000000 35.000000 1.000000 0.000000\n", "5 0.000000 3.000000 0.000000 35.000000 0.000000 0.000000\n", "6 29.699118 29.699118 29.699118 29.699118 29.699118 29.699118" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(6)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# removing duplicates\n", "# redundancy does not help our model to generalize better; introduces biasness\n", "df.drop_duplicates(keep=False, inplace=True)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0 202\n", "1.0 197\n", "Name: Survived, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check if there is class imbalance ?\n", "df['Survived'].value_counts()\n", "\n", "# this would work; otherwise you can try stratified split instead of random data split" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = df.iloc[:, 1:].values\n", "Y = df.iloc[:, 0].values" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((399, 5), (399,))" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape, Y.shape" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.3, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(279, 5) (120, 5)\n" ] } ], "source": [ "print X_train.shape, X_test.shape" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this class takes care for scaling the features to the scale of 0-1\n", "# we are doing the scaling with this cap because we use sigmoid activation fxn in logistic which \n", "# also has the range from 0-1\n", "class Normalizer:\n", "\n", " def __init__(self):\n", " self.sc = StandardScaler()\n", " \n", " def scale(self, X, dtype):\n", " if dtype=='train':\n", " XX = self.sc.fit_transform(X)\n", " elif dtype=='test':\n", " XX = self.sc.transform(X)\n", " else:\n", " return None\n", " return XX" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "norm = Normalizer()\n", "X_train = norm.scale(X_train, 'train')\n", "X_test = norm.scale(X_test, 'test')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NaiveBayes:\n", " \n", " def __init__(self):\n", " self.classifier = GaussianNB()\n", "\n", " def train(self, X_train, Y_train):\n", " model = self.classifier.fit(X_train, Y_train)\n", " return model\n", " \n", " def predict(self, model, X_test):\n", " return model.predict(X_test)\n", " \n", " def evaluate(self, Y_test, Y_pred, measure):\n", " if measure=='matrix':\n", " cm = sklearn.metrics.confusion_matrix(Y_test, Y_pred, labels=[0, 1])\n", " return cm\n", " elif measure=='accuracy':\n", " return sklearn.metrics.accuracy_score(Y_test, Y_pred)*100\n", " else: return None" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "71.66666666666667\n" ] } ], "source": [ "nb = NaiveBayes()\n", "model = nb.train(X_train, Y_train)\n", "predictions = nb.predict(model, X_test)\n", "print nb.evaluate(Y_test, predictions, 'accuracy')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day09/Lime.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n", "0.19.1\n", "1.14.2\n" ] } ], "source": [ "# Imports\n", "\n", "import os\n", "import lime\n", "import sklearn\n", "import sklearn.ensemble\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.datasets import fetch_20newsgroups\n", "\n", "print pd.__version__\n", "print sklearn.__version__\n", "print np.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']\n" ] } ], "source": [ "# lists out all the classes possible in the newsgroup dataset\n", "newsgroups_train = fetch_20newsgroups(subset='train')\n", "print newsgroups_train.target_names" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['description', 'DESCR', 'filenames', 'target_names', 'data', 'target']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we will be exploring \"comp.sys.ibm.pc.hardware\", \"comp.sys.mac.hardware\"\n", "categories = [\"comp.sys.ibm.pc.hardware\", \"comp.sys.mac.hardware\"]\n", "newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)\n", "newsgroups_test = fetch_20newsgroups(subset='test', categories=categories)\n", "\n", "newsgroups_train.keys()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From: blakey@ug.cs.dal.ca (Jason \"Fish\" Blakey)\n", "Subject: Newlife 25 and hard drives\n", "Nntp-Posting-Host: ug.cs.dal.ca\n", "Organization: Math, Stats & CS, Dalhousie University, Halifax, NS, Canada\n", "Lines: 12\n", "\n", " Giday netters! Just got a used Newlife 25 accelerator, with FPU, and i \n", "was wondering about a few points. \n", "-Anyone know the current driver version for it??\n", "-Can it handle the 16-bit grayscale card, if i get the video option\n", "-Why would it be hating my hard drive?(can't use the accelerator and \n", "\thard drive at the same time). Do i need a new driver on my drive?\n", "\tWhat make?\n", "-Thanks,\n", "\tJason\n", "-- \n", " ............................................................................ \n", " blakey@ug.cs.dal.ca -> He's big! He's purple! He's your best friend!\n", "\n", "++++++++++++++++++\n", "comp.sys.ibm.pc.hardware\n" ] } ], "source": [ "# Sample\n", "print newsgroups_test.get('data')[0]\n", "print \"++++++++++++++++++\"\n", "print newsgroups_test.get('target_names')[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we will be vectorizing the text using TF-IDF vectorization technique\n", "# we will discuss this TF-IDF in future as part of this challenge itself;\n", "vectorizer = sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False)\n", "train_vectors = vectorizer.fit_transform(newsgroups_train.data)\n", "test_vectors = vectorizer.transform(newsgroups_test.data)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((1168, 21486), (777, 21486))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_vectors.shape, test_vectors.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forest" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model 1\n", "rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n", "rf.fit(train_vectors, newsgroups_train.target)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8854568854568855" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# do prediction\n", "pred = rf.predict(test_vectors)\n", "sklearn.metrics.accuracy_score(newsgroups_test.target, pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Bayes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model 2\n", "from sklearn.naive_bayes import MultinomialNB\n", "nb = MultinomialNB()\n", "nb.fit(train_vectors, newsgroups_train.target)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.9124839124839125" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# do prediction\n", "pred = nb.predict(test_vectors)\n", "sklearn.metrics.accuracy_score(newsgroups_test.target, pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Classifier" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# model 3\n", "from sklearn.linear_model import LogisticRegression\n", "lr = LogisticRegression()\n", "lr.fit(train_vectors, newsgroups_train.target)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8867438867438867" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# do prediction\n", "pred = lr.predict(test_vectors)\n", "sklearn.metrics.accuracy_score(newsgroups_test.target, pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lime in Action" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lime import lime_text\n", "from sklearn.pipeline import make_pipeline\n", "\n", "crf = make_pipeline(vectorizer, rf)\n", "cnb = make_pipeline(vectorizer, nb)\n", "clr = make_pipeline(vectorizer, lr)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from lime.lime_text import LimeTextExplainer\n", "explainer = LimeTextExplainer(class_names=['ibm', 'mac'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# picking on random example from the test dataset; and seeing the top 6 features learnt by each different\n", "# classifier for predicting the actual class of the example data point\n", "idx = np.random.randint(1, len(newsgroups_test.data))\n", "exp_crf = explainer.explain_instance(newsgroups_test.data[idx], crf.predict_proba, num_features=6)\n", "exp_clr = explainer.explain_instance(newsgroups_test.data[idx], clr.predict_proba, num_features=6)\n", "exp_cnb = explainer.explain_instance(newsgroups_test.data[idx], cnb.predict_proba, num_features=6)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp_crf.show_in_notebook(text=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp_cnb.show_in_notebook(text=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp_clr.show_in_notebook(text=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day09/README.md ================================================ # Hosted Notebooks 1. [Lime Hands-On](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day09/Lime.ipynb) 2. [SVM](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day09/SVM.ipynb) ================================================ FILE: day09/SVM.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Import" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import confusion_matrix\n", "\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " f1 f2 f3 f4 target\n", "0 3.62160 8.6661 -2.8073 -0.44699 0\n", "1 4.54590 8.1674 -2.4586 -1.46210 0\n", "2 3.86600 -2.6383 1.9242 0.10645 0\n", "3 3.45660 9.5228 -4.0112 -3.59440 0\n", "4 0.32924 -4.4552 4.5718 -0.98880 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data into a dataframe\n", "DATA_DIR = 'data'\n", "\n", "df = pd.read_csv(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day9/banknote_authentication.csv'))\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1372, 5)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# (rows, columns)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "f1 0\n", "f2 0\n", "f3 0\n", "f4 0\n", "target 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# look for NaN values\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 762\n", "1 610\n", "Name: target, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's figure out the distribution of target variable\n", "# there are many options to handle this imbalance; of which one is to add false data to class 1; secondly we can \n", "# delete records from 0 to make it equal to 1\n", "# we will leave it for now\n", "df['target'].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = df.iloc[:, :-1].values\n", "Y = df.iloc[:, -1].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Split" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.3, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((960, 4), (412, 4))" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Define" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class SVMModel:\n", " \n", " def __init__(self):\n", " self.classifier = SVC()\n", "\n", " def train(self, X_train, Y_train):\n", " model = self.classifier.fit(X_train, Y_train)\n", " return model\n", " \n", " def predict(self, model, X_test):\n", " return model.predict(X_test)\n", " \n", " def evaluate(self, Y_test, Y_pred, measure):\n", " if measure=='matrix':\n", " cm = confusion_matrix(Y_test, Y_pred, labels=[0, 1])\n", " return cm\n", " elif measure=='accuracy':\n", " return accuracy_score(Y_test, Y_pred)*100\n", " else: return None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# train the model\n", "svm = SVMModel()\n", "model = svm.train(X_train, Y_train)\n", "predictions = svm.predict(model, X_test)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[226 0]\n", " [ 0 186]]\n", "\n", "100.0\n" ] } ], "source": [ "# evaluating the model\n", "print svm.evaluate(Y_test, predictions, 'matrix')\n", "print \n", "print svm.evaluate(Y_test, predictions, 'accuracy')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day10/Average Ensemble Models.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import sklearn\n", "import os\n", "from sklearn import preprocessing\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.cross_validation import train_test_split" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " feat1 feat2 feat3 feat4 class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data into pandas dataframe\n", "DATA_DIR = 'data'\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day1/iris.csv')),\n", " sep=','\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# encoding the class to integers\n", "X = df.iloc[:, :-1].values\n", "Y = df.iloc[:, -1].values\n", "# encode the class with integers\n", "le = preprocessing.LabelEncoder()\n", "Y = le.fit_transform(Y)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(105, 4) (45, 4)\n" ] } ], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.30, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)\n", "print X_train.shape, X_test.shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this class takes care for scaling the features to the scale of 0-1\n", "# we are doing the scaling with this cap because we use sigmoid activation fxn in logistic which \n", "# also has the range from 0-1\n", "class Normalizer:\n", "\n", " def __init__(self):\n", " self.sc = StandardScaler()\n", " \n", " def scale(self, X, dtype):\n", " if dtype=='train':\n", " XX = self.sc.fit_transform(X)\n", " elif dtype=='test':\n", " XX = self.sc.transform(X)\n", " else:\n", " return None\n", " return XX" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "norm = Normalizer()\n", "X_train = norm.scale(X_train, 'train')\n", "X_test = norm.scale(X_test, 'test')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 1 (Logistic)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8666666666666667\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "# train the model\n", "classifier = LogisticRegression()\n", "model = classifier.fit(X_train, Y_train)\n", "predictions_lr = model.predict_proba(X_test)\n", "print sklearn.metrics.accuracy_score(Y_test, np.argmax(predictions_lr, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 2 (Decision Tree)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9777777777777777\n" ] } ], "source": [ "from sklearn import tree\n", "# train the model\n", "classifier = tree.DecisionTreeClassifier()\n", "model = classifier.fit(X_train, Y_train)\n", "predictions_dtree = model.predict_proba(X_test)\n", "print sklearn.metrics.accuracy_score(Y_test, np.argmax(predictions_dtree, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model 3 (KNN)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9555555555555556\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "knn = KNeighborsClassifier(n_neighbors=3)\n", "model = knn.fit(X_train, Y_train)\n", "predictions_knn = model.predict_proba(X_test)\n", "print sklearn.metrics.accuracy_score(Y_test, np.argmax(predictions_knn, axis=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Meta Model (Ensemble)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Ensemble(object):\n", " \"\"\"\n", " Implements averaging voting ensemble technique\n", " Each model is given equal weight\n", " \"\"\"\n", " def __init__(self, samples=None, classes=None, classifiers=None):\n", " self.classes = classes\n", " self.samples = samples\n", " self.classifiers = classifiers\n", " \n", " def mixmatch(self, predictions):\n", " if not self.classifiers:\n", " self.classifiers = len(predictions)\n", " \n", " if not self.samples:\n", " self.samples = len(predictions[0])\n", " \n", " if not self.classes:\n", " self.classes = len(predictions[0][0])\n", " \n", " final_pred = np.array([0]*self.classes)\n", " for s in range(self.samples):\n", " s_pred = np.array([0]*self.classes)\n", " for c in range(self.classifiers):\n", " pred = predictions[c][s]\n", " s_pred = np.vstack((s_pred, pred))\n", " s_pred = s_pred[1:, :]\n", " s_pred_avg = np.average(s_pred, axis=0)\n", " final_pred = np.vstack((final_pred, s_pred_avg))\n", " return final_pred[1:, :]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "ensemble = Ensemble(45, 3, 3)\n", "pred = np.argmax(ensemble.mixmatch([predictions_lr, predictions_dtree, predictions_knn]), axis=1)\n", "print sklearn.metrics.accuracy_score(Y_test, pred)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day10/README.md ================================================ # Hosted Notebooks 1. [Ensemble Model](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day10/Average%20Ensemble%20Models.ipynb) ================================================ FILE: day11/Feature Scaling.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19.1\n", "1.14.2\n" ] } ], "source": [ "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "import sklearn\n", "import numpy as np\n", "np.random.seed(10)\n", "\n", "print sklearn.__version__\n", "print np.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Data Generation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rows = 5\n", "column = 2\n", "data = np.random.rand(rows, column)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standardisation \n", "\n", "* Mean = 0; Stdev = 1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "\n", "[[ 1.3365329 -1.08044519]\n", " [ 0.7564286 1.1806552 ]\n", " [ 0.18698985 -0.44674652]\n", " [-1.07897872 1.21707505]\n", " [-1.20097264 -0.87053854]]\n" ] } ], "source": [ "def py_standardisation(X):\n", " rows, features = X.shape\n", " data = np.zeros((rows, ), dtype='float')\n", " for f in xrange(features):\n", " X[:, f] = (X[:,f] - X[:,f].mean(axis=0)) / X[:,f].std()\n", " return X\n", "\n", "def skl_standardisation(X):\n", " scaler = StandardScaler()\n", " return scaler.fit_transform(data)\n", "\n", "\n", "print skl_standardisation(data).all() == py_standardisation(data).all()\n", "print \n", "print skl_standardisation(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mean Normalisation\n", "\n", "* Mean = 0; Range = [-1, 1]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.52671132 -0.4702658 ]\n", " [ 0.29809929 0.51388239]\n", " [ 0.07369042 -0.19444726]\n", " [-0.42521236 0.5297342 ]\n", " [-0.47328868 -0.37890353]]\n" ] } ], "source": [ "def py_mean_normalisation(X):\n", " rows, features = X.shape\n", " data = np.zeros((rows, ), dtype='float')\n", " for f in xrange(features):\n", " X[:, f] = (X[:,f] - X[:,f].mean(axis=0)) / (X[:,f].max() - X[:,f].min())\n", " return X\n", "\n", "print py_mean_normalisation(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Min-Max Scaling\n", "\n", "* Range = [0, 1]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "\n", "[[1. 0. ]\n", " [0.77138797 0.98414819]\n", " [0.5469791 0.27581854]\n", " [0.04807631 1. ]\n", " [0. 0.09136227]]\n" ] } ], "source": [ "def py_min_max_scaling(X):\n", " rows , features = X.shape\n", " data = np.zeros((rows, ), dtype='float')\n", " for f in xrange(features):\n", " X[:, f] = (X[:,f] - X[:,f].min()) / (X[:,f].max() - X[:,f].min())\n", " return X\n", "\n", "def skl_min_max_scaling(X):\n", " min_max_scaler = MinMaxScaler()\n", " return min_max_scaler.fit_transform(X)\n", "\n", "print skl_min_max_scaling(data).all() == py_min_max_scaling(data).all()\n", "print \n", "print skl_min_max_scaling(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### I would recommend the reader/practitioner to solve them on copy pen once to get a feel of it" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day11/README.md ================================================ # Hosted Notebooks 1. [Feature Scaling](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day11/Feature%20Scaling.ipynb) ================================================ FILE: day12/Decision Trees.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn import preprocessing\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.cross_validation import train_test_split\n", "\n", "print pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n", "0 b 30.83 0.000 u g w v 1.25 t t 1 f g 00202 0 +\n", "1 a 58.67 4.460 u g q h 3.04 t t 6 f g 00043 560 +\n", "2 a 24.50 0.500 u g q h 1.50 t f 0 f g 00280 824 +\n", "3 b 27.83 1.540 u g w v 3.75 t t 5 t g 00100 3 +\n", "4 b 20.17 5.625 u g w v 1.71 t f 0 f s 00120 0 +" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data to pandas dataframe\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day11/credit.csv')),\n", " sep=',',\n", " header=None\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(690, 16)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# (rows, columns)\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking for NaN in the entire df\n", "df.isnull().sum()\n", "\n", "# None of the columns have missing values in them" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 object\n", "1 object\n", "2 float64\n", "3 object\n", "4 object\n", "5 object\n", "6 object\n", "7 float64\n", "8 object\n", "9 object\n", "10 int64\n", "11 object\n", "12 object\n", "13 object\n", "14 int64\n", "15 object\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# object in pandas means string; we need to convert all to numerical\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prune rows" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# but here is something we found unusual; a '?' we will prune out all the rows \n", "# where in any of the column if '?' exists\n", "\n", "# figuring out all the columns where '?' exists\n", "for columns in range(16):\n", " if '?' in df[columns].unique().tolist():\n", " df = df[df[columns]!='?']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(653, 16)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# new data after removing the rows having '?' in them\n", "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transform datapoints" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we will now encode the objects to float dtype for features\n", "# 0, 3, 4, 5, 6, 8, 9, 11\n", "for column in [0, 3, 4, 5, 6, 8, 9, 11, 12]:\n", " possible_values = df[column].unique().tolist()\n", " encoded_inp = {v:idx for idx, v in enumerate(possible_values)}\n", " df[column].replace(encoded_inp, inplace=True)\n", "\n", "# we will now encode the objects to float dtype for target\n", "# 15\n", "encoded_inp = {'+':1, '-':0}\n", "df[15].replace(encoded_inp, inplace=True)\n", "\n", "# we will not convert the remaining object dtypes to float dtype\n", "# 1, 13; i am not sure what 13 column is about; will drop it for now\n", "df.drop([13], axis = 1, inplace = True)\n", "df[1] = df[1].astype(float)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Sep. features and target" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = df.iloc[:, :-1].values\n", "Y = df.iloc[:, -1].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train/Test split" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((522, 14), (131, 14))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, X_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train / Evaluate" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DecisionTrees(object):\n", " \n", " def __init__(self):\n", " self.classifier = DecisionTreeClassifier(random_state=10)\n", "\n", " def train(self, X_train, Y_train):\n", " model = self.classifier.fit(X_train, Y_train)\n", " return model\n", " \n", " def predict(self, model, X_test):\n", " return model.predict(X_test)\n", " \n", " def evaluate(self, Y_test, Y_pred):\n", " return accuracy_score(Y_test, Y_pred)*100" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "81.67938931297711\n" ] } ], "source": [ "# train the model and tuning depth paramater of the classifier over validation set\n", "dtree = DecisionTrees()\n", "model_dtree = dtree.train(X_train, Y_train)\n", "predictions = dtree.predict(model_dtree, X_test)\n", "print dtree.evaluate(Y_test, predictions)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day12/README.md ================================================ # Hosted Notebooks 1. [Decision Trees](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day12/Decision%20Trees.ipynb) ================================================ FILE: day13/Missing Values (Basics).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.14.2\n", "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "\n", "print np.__version__\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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1a58.674.460ugqh3.04tt6fg43.0560+
2a24.500.500ugqh1.50tf0fg280.0824+
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n", "0 b 30.83 0.000 u g w v 1.25 t t 1 f g 202.0 0 +\n", "1 a 58.67 4.460 u g q h 3.04 t t 6 f g 43.0 560 +\n", "2 a 24.50 0.500 u g q h 1.50 t f 0 f g 280.0 824 +\n", "3 b 27.83 1.540 u g w v 3.75 t t 5 t g 100.0 3 +\n", "4 b 20.17 5.625 u g w v 1.71 t f 0 f s 120.0 0 +" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data into pandas dataframe\n", "# also we know that the missing values in this dataset is denoted by '?', so we are telling pandas beforehand \n", "# to treat '?' as NaN values\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_table(\n", " os.path.abspath(os.path.join(DATA_DIR, 'day11/credit.csv')),\n", " sep = ',',\n", " header=None,\n", " na_values = '?'\n", " )\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 12\n", "1 12\n", "2 0\n", "3 6\n", "4 6\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 13\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# list out number of missing value in each of the series of the df\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 object\n", "1 float64\n", "2 float64\n", "3 object\n", "4 object\n", "5 object\n", "6 object\n", "7 float64\n", "8 object\n", "9 object\n", "10 int64\n", "11 object\n", "12 object\n", "13 float64\n", "14 int64\n", "15 object\n", "dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# just have look at the data types to get an idea of what imputation to make for any particular series\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deleting Rows that have missing values" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "5 0\n", "6 0\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_ = df.dropna(inplace=False)\n", "df_.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputing with Mean value - (For Continuous Data)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 12\n", "1 0\n", "2 0\n", "3 6\n", "4 6\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 13\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we will just show example of doing it with column number 1; this can be scaled to 'n' number of columns\n", "df[1].fillna(df[1].mean(), inplace=True)\n", "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputing with Mode value - (For Categorical Data)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 6\n", "4 6\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 13\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0].fillna(df[0].mode()[0], inplace=True)\n", "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputing by adding one more category - (For Categorical Data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['u', 'y', nan, 'l'], dtype=object)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3rd column can have these number of possibilities\n", "df[3].unique()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 6\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 13\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# treating it as new category all together tells model to learn it's dependency with other features for \n", "# making prediction; adds to one more column; if one hot representation; else another integer value if label encoding\n", "df[3].fillna('UNK', inplace=True)\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15\n", "206 a 71.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n", "270 b 37.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n", "330 b 20.42 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 -\n", "456 b 34.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 -\n", "592 b 23.17 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +\n", "622 a 25.58 0.0 UNK NaN NaN NaN 0.0 f f 0 f p NaN 0 +" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df[3] == 'UNK']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputing by back filling " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 6\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we can specify a back-fill to propagate the next values backward\n", "df[13].fillna(method='bfill', inplace=True)\n", "df.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputing by forward filling " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "5 9\n", "6 9\n", "7 0\n", "8 0\n", "9 0\n", "10 0\n", "11 0\n", "12 0\n", "13 0\n", "14 0\n", "15 0\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can specify a forward-fill to propagate the previous value forward\n", "df[4].fillna(method='ffill', inplace=True)\n", "df.isnull().sum()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day13/README.md ================================================ # Hosted Notebooks 1. [Handeling Missing Data (Basics)](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day13/Missing%20Values%20%28Basics%29.ipynb) ================================================ FILE: day15/Model Stacking.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19.1\n", "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "import sklearn\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from xgboost import XGBClassifier\n", "from mlxtend.classifier import StackingClassifier\n", "from sklearn.cross_validation import train_test_split\n", "\n", "print sklearn.__version__\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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IDJobFamilyJobFamilyDescriptionJobClassJobClassDescriptionPayGradeEducationLevelExperienceOrgImpactProblemSolvingSupervisionContactLevelFinancialBudgetPG
011Accounting And Finance1Accountant I53133435PG05
121Accounting And Finance2Accountant II64154577PG06
231Accounting And Finance3Accountant III842656710PG08
341Accounting And Finance4Accountant IV1055667811PG10
452Administrative Support5Admin Support I11011111PG01
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" ], "text/plain": [ " ID JobFamily JobFamilyDescription JobClass JobClassDescription \\\n", "0 1 1 Accounting And Finance 1 Accountant I \n", "1 2 1 Accounting And Finance 2 Accountant II \n", "2 3 1 Accounting And Finance 3 Accountant III \n", "3 4 1 Accounting And Finance 4 Accountant IV \n", "4 5 2 Administrative Support 5 Admin Support I \n", "\n", " PayGrade EducationLevel Experience OrgImpact ProblemSolving \\\n", "0 5 3 1 3 3 \n", "1 6 4 1 5 4 \n", "2 8 4 2 6 5 \n", "3 10 5 5 6 6 \n", "4 1 1 0 1 1 \n", "\n", " Supervision ContactLevel FinancialBudget PG \n", "0 4 3 5 PG05 \n", "1 5 7 7 PG06 \n", "2 6 7 10 PG08 \n", "3 7 8 11 PG10 \n", "4 1 1 1 PG01 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# reading data to df\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_csv(os.path.abspath(os.path.join(DATA_DIR, 'day15/jobclass.csv')))\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# target is to predict the column PG using rest (all to one)\n", "target = df['PG']\n", "\n", "# dropping unnecessary columns and keeping just the concerned features\n", "df.drop(['ID', 'JobFamilyDescription', 'JobClassDescription', 'PG'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " JobFamily JobClass PayGrade EducationLevel Experience OrgImpact \\\n", "0 1 1 5 3 1 3 \n", "1 1 2 6 4 1 5 \n", "\n", " ProblemSolving Supervision ContactLevel FinancialBudget \n", "0 3 4 3 5 \n", "1 4 5 7 7 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "JobFamily 0\n", "JobClass 0\n", "PayGrade 0\n", "EducationLevel 0\n", "Experience 0\n", "OrgImpact 0\n", "ProblemSolving 0\n", "Supervision 0\n", "ContactLevel 0\n", "FinancialBudget 0\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check for NaN (Missing values)\n", "df.isnull().sum()\n", "\n", "# Luckily not a single missing values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = df[:].values\n", "Y = target.values" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# ideal practice is to use test as 20% - 30% of training data\n", "# defined by test_size in train_test_split()\n", "# random_state is required to avoid sequential biasness in the data distribution\n", "def data_split(X, Y):\n", " X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state = 10)\n", " return X_train, X_test, Y_train, Y_test\n", "\n", "X_train, X_test, Y_train, Y_test = data_split(X, Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model - 1 [Random Forest]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf1 = RandomForestClassifier(random_state=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model - 2 [XGBoost]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clf2 = XGBClassifier()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Meta Model [Logit]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lr = LogisticRegression()\n", "sclf = StackingClassifier(\n", " classifiers = [clf1, clf2], \n", " meta_classifier=lr,\n", " use_probas=True,\n", " average_probas=False,\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold cross validation:\n", "\n", "Accuracy: 0.70 (+/- 0.10) [Random Forest]\n", "Accuracy: 0.92 (+/- 0.09) [XGBoost]\n", "Accuracy: 0.86 (+/- 0.14) [StackingClassifier]\n" ] } ], "source": [ "print('5-fold cross validation:\\n')\n", "\n", "for clf, label in zip([clf1, clf2, sclf], \n", " ['Random Forest', \n", " 'XGBoost',\n", " 'StackingClassifier']):\n", "\n", " scores = sklearn.model_selection.cross_val_score(clf, X_train, Y_train, \n", " cv=5, scoring='accuracy')\n", " print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" \n", " % (scores.mean(), scores.std(), label))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day15/README.md ================================================ # Hosted Notebooks 1. [Model Stacking](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day15/Model%20Stacking.ipynb) ================================================ FILE: day16/Missing Value Imputations + Accuracy Measure.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.20.3\n" ] } ], "source": [ "import pandas as pd\n", "import random\n", "random.seed(10)\n", "import numpy as np\n", "from sklearn.utils import shuffle\n", "import os\n", "\n", "print pd.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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175.13.51.40.3Iris-setosa
1015.82.75.11.9Iris-virginica
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374.93.11.50.1Iris-setosa
474.63.21.40.2Iris-setosa
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" ], "text/plain": [ " feat1 feat2 feat3 feat4 class\n", "17 5.1 3.5 1.4 0.3 Iris-setosa\n", "101 5.8 2.7 5.1 1.9 Iris-virginica\n", "104 6.5 3.0 5.8 2.2 Iris-virginica\n", "37 4.9 3.1 1.5 0.1 Iris-setosa\n", "47 4.6 3.2 1.4 0.2 Iris-setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# read csv to a df object\n", "DATA_DIR = '../data'\n", "\n", "df = pd.read_csv(os.path.abspath(os.path.join(DATA_DIR, 'day1/iris.csv')))\n", "df = shuffle(df)\n", "df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inducing Missing Values" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# we will intentionally creating missing values in feat3 column\n", "# thereafter will notice the affect in accuraccy based on the imputation methods\n", "index_missing = [random.randint(0, df.shape[0]) for i in xrange(0, 10)]\n", "\n", "for i in index_missing:\n", " df.set_value(index=i, col='feat3', value=np.nan)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "feat1 0\n", "feat2 0\n", "feat3 10\n", "feat4 0\n", "class 0\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# only feeat3 column has null values; since we introduced it there only\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Method: 1\n", "### Removing the rows" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Method: 2\n", "### Removing the column" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Method: 3\n", "### Filling missing values with Zeros" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 4\n", "### Filling missing values with Global Mean" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 5\n", "### Filling missing values with Global Median" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 6\n", "### Filling missing values with Global Max" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 7\n", "### Filling missing values with Global Min" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 8\n", "### Filling missing values with Class Mean" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 9\n", "### Filling missing values with Class Median" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 10\n", "### Filling missing values with Class Max" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 11\n", "### Filling missing values with Class Min" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Method: 12\n", "### Filling missing values by training a regressor model" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day18/Pandas(Scraper).ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Imports\n", "import pandas as pd\n", "import requests" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# acting like a browser and getting the html content of the `url` page\n", "url = 'https://simple.wikipedia.org/wiki/List_of_U.S._states'\n", "header = {\n", " \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36\",\n", " \"X-Requested-With\": \"XMLHttpRequest\"\n", "}\n", "r = requests.get(url, headers=header)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# using pandas `read_html` method to extract any tabular data in the html source code and returning a dataframe \n", "# out of it\n", "# df will be the list of all tables found\n", "\n", "df = pd.read_html(url , header=0)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# df[i], where i>=0 199: print("Solved!") break writer.close() ================================================ FILE: day31/cross-entropy.py ================================================ #!/usr/bin/env python # use when # - short episodes # - frequent reward system # it is # - model free # - policy based # - on policy import gym from collections import namedtuple import numpy as np import torch import torch.nn as nn import torch.optim as optim EPISODE = namedtuple('Episode', ['reward', 'steps']) EPISODESTEP = namedtuple('EpisodeStep', field_names=['observation', 'action']) class MyNet(nn.Module): def __init__(self, obs_dim, actions): super(MyNet, self).__init__() self.pipe = nn.Sequential( nn.Linear(in_features = obs_dim, out_features = 128), nn.ReLU(), nn.Linear(in_features = 128, out_features = 128), nn.ReLU(), nn.Linear(in_features = 128, out_features = actions) ) def forward(self, x): return self.pipe(x) def iterate_batches(env, net, batch_size): batch = [] episode_reward = 0.0 episode_steps = [] obs = env.reset() sm = nn.Softmax(dim=1) while True: obs_v = torch.FloatTensor([obs]) act_probs_v = sm(net(obs_v)) act_probs = act_probs_v.data.numpy()[0] action = np.random.choice(len(act_probs), p=act_probs) next_obs, reward, is_done, _ = env.step(action) episode_reward += reward episode_steps.append(EPISODESTEP(observation=obs, action=action)) if is_done: batch.append(EPISODE(reward=episode_reward, steps=episode_steps)) episode_reward = 0.0 episode_steps = [] next_obs = env.reset() if len(batch) == batch_size: yield batch batch = [] obs = next_obs def filter_batch(batch, percent): rewards = list(map(lambda s: s.reward, batch)) reward_bound = np.percentile(rewards, percent) reward_mean = float(np.mean(rewards)) train_obs = [] train_act = [] for example in batch: if example.reward < reward_bound: continue train_obs.extend(map(lambda step: step.observation, example.steps)) train_act.extend(map(lambda step: step.action, example.steps)) train_obs_v = torch.FloatTensor(train_obs) train_act_v = torch.LongTensor(train_act) return train_obs_v, train_act_v, reward_bound, reward_mean if __name__ == '__main__': env = gym.make('CartPole-v0') env = gym.wrappers.Monitor(env, directory="cross_entropy", force=True) # observation size obs_size = env.observation_space.shape[0] # possible action types n_actions = env.action_space.n # initial observation observation = env.reset() net = MyNet(obs_size, n_actions) objective = nn.CrossEntropyLoss() optimizer = optim.Adam(params=net.parameters(), lr=0.001) for iter_no, batch in enumerate(iterate_batches(env, net, 64)): print 'Iterating {} time with a size of {}'.format(iter_no, len(batch)) obs_v, acts_v, reward_b, reward_m = filter_batch(batch, 70) optimizer.zero_grad() # training model with just cream of data action_scores_v = net(obs_v) # calculating loss loss_v = objective(action_scores_v, acts_v) # back propogating the error to the intermediate nodes loss_v.backward() optimizer.step() print("%d: loss=%.3f, reward_mean=%.1f, reward_bound=%.1f" % ( iter_no, loss_v.item(), reward_m, reward_b)) if reward_m > 199: print("Solved!") break ================================================ FILE: day33/Matplotlib.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Styling in Matplotlib" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "mpl.rcParams['font.family'] = 'Ubuntu'\n", "plt.style.use('tableau-colorblind10')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2FrlVfRSbOLQa+sXDgJFaK7Eas1nyzv6TzE4dSJCfl/0unLYELlXAiS/td00n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\nIWUJwBhjQuq/R6NaxnyyMwkAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0, 10, 100)\n", "\n", "plt.plot(x, np.sin(x))\n", "plt.plot(x, np.cos(x))\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lnda+vDImfbqWRbtzGdYung9uGkxyfON7QxK1JODPUWWNjb/P+4VXF+2kVXQI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\n2SLaJZ8eq2psfLX5MO+t2sv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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plotting multiple sub plots\n", "fig, ax = plt.subplots(3) # 3 tells us on how many sub plots we would be needing\n", "\n", "# ax is the array of length 3 where each is a 2axis object\n", "ax[0].plot(x, np.sin(x));\n", "ax[1].plot(x, np.cos(x));\n", "ax[2].plot(x, np.tan(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Chart" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "plt.style.use('seaborn-whitegrid')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ls6EzzJwpbz+PPlrmad70eZaFp/PMzst22o+0/fh2vcbrNfRp26a5JMNSGEVYcXCFzgT0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\n4JMhn3CjuFG9sBd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1iFG/jlIfKQQyM/+FF9TLcYJGNRrxfPzzhvZlLhEDQonjo+I5/a/T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\nfRpAax0PVFRK1VBKhQLtvccHnFLqYuBNoL3W+vfs+5RSX/t8U2sB/BD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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# always the first steps\n", "fig = plt.figure()\n", "ax = plt.axes()\n", "\n", "# all the plots that you make will be part of axis\n", "x = np.linspace(0, 20, 100)\n", "ax.plot(x, np.sin(x), color='red', linestyle='dashed');\n", "ax.plot(x, np.cos(x), color='green', linestyle='dashed');\n", "ax.set_xlim(0, 20);\n", "ax.set_ylim(-1.5, 1.5);\n", "ax.set_title('Sin(x) & Cos(x)');\n", "ax.set_xlabel('x');\n", "ax.set_ylabel('range');\n", "\n", "# linestyle takes (dashed, dotted, solid, dashdot)\n", "# color: takes care of what chart has to be made\n", "\n", "#xlim, ylim is used to set min/max range in x and y axis respectively\n", "\n", "#title, xlabel, ylabel are used to set title, x label and y label of the plot in making." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatter Chart" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "plt.style.use('fast')\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vVAe3goefP0Rje+1fkLqmFF/9vZ/lR19/l0KmwFOvHuPxn3p0Vfsf/eRDBIEk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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "features = iris.data.T\n", "\n", "plt.scatter(features[0], features[1], alpha=0.3,\n", " s=100*features[3], c=iris.target, cmap='viridis')\n", "plt.xlabel(iris.feature_names[0])\n", "plt.ylabel(iris.feature_names[1]);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aCxH1TVj+evtAlawfceVImtVQi8P/SeAjwH6i57kvAX9bT6PqSaoxyX0fuYeZ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\n6YpuUrIFii9A/itAI9zYRERIMLZBcI7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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.preprocessing import MinMaxScaler\n", "scaler = MinMaxScaler()\n", "\n", "iris = load_iris()\n", "feat = scaler.fit_transform(iris.data)\n", "features = feat.T\n", "\n", "plt.scatter(features[0], features[1], alpha=0.3,\n", " s=100*features[3], c=iris.target, cmap='viridis')\n", "plt.xlabel(iris.feature_names[0])\n", "plt.ylabel(iris.feature_names[1]);\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day39/Agglomerative.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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feat1feat2feat3feat4class
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
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" ], "text/plain": [ " feat1 feat2 feat3 feat4 class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import StandardScaler\n", "%matplotlib inline\n", "\n", "data = pd.read_csv('../data/day1/iris.csv')\n", "data.head(5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Normalizer:\n", "\n", " def __init__(self):\n", " self.sc = StandardScaler()\n", " \n", " def scale(self, X, dtype):\n", " if dtype=='train':\n", " XX = self.sc.fit_transform(X)\n", " elif dtype=='test':\n", " XX = self.sc.transform(X)\n", " else:\n", " return None\n", " return XX\n", " \n", "data_ = data.iloc[:, :-1].values\n", "\n", "norm = Normalizer()\n", "data_ = norm.scale(data_, 'train')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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M7jaz65PfnWxmd5pZJf68orvFBAAA6E/N9EydJ+moKr8/3d2H4s+POlssAACAwdAwTLn7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\nB4YZO87Qw9seftR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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.cluster.hierarchy as shc\n", "\n", "plt.figure(figsize=(10, 7)) \n", "plt.title(\"Flower Dendogram\") \n", "dend = shc.dendrogram(shc.linkage(data_, method='ward')) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## in the above diagram, if we draw a horizontal line cutting the longest line with no already horontal line\n", "## (i.e left blue) in this case. If we make a horizontal line cutting it we will witness 3 cuts overall which means \n", "## 3 clusters" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 0, 2, 0,\n", " 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 2, 0, 0, 2,\n", " 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.cluster import AgglomerativeClustering\n", "\n", "cluster = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward') \n", "cluster.fit_predict(data_) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reference\n", "\n", "1. https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/\n", "2. http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: day39/README.md ================================================ # Hosted Notebooks 1. [Agglomerative](http://nbviewer.jupyter.org/github/prakhar21/100-Days-of-ML/blob/master/day39/Agglomerative.ipynb)