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Repository: MLEveryday/100-Days-Of-ML-Code
Branch: master
Commit: 04e7076df2c8
Files: 45
Total size: 11.5 MB
Directory structure:
gitextract_grz_jkps/
├── .gitignore
├── Code/
│ ├── Day 11_K-NN.ipynb
│ ├── Day 11_K-NN.md
│ ├── Day 11_k-NN.py
│ ├── Day 13_SVM.ipynb
│ ├── Day 13_SVM.md
│ ├── Day 13_SVM.py
│ ├── Day 1_Data_Preprocessing.ipynb
│ ├── Day 1_Data_Preprocessing.md
│ ├── Day 1_Data_Preprocessing.py
│ ├── Day 25_Decision_Tree.ipynb
│ ├── Day 25_Decision_Tree.md
│ ├── Day 25_Decision_Tree.py
│ ├── Day 2_Simple_Linear_Regression.ipynb
│ ├── Day 2_Simple_Linear_Regression.md
│ ├── Day 2_Simple_Linear_Regression.py
│ ├── Day 34_Random_Forests.ipynb
│ ├── Day 34_Random_Forests.md
│ ├── Day 34_Random_Forests.py
│ ├── Day 39.ipynb
│ ├── Day 3_Multiple_Linear_Regression.ipynb
│ ├── Day 3_Multiple_Linear_Regression.md
│ ├── Day 3_Multiple_Linear_Regression.py
│ ├── Day 40.ipynb
│ ├── Day 41.ipynb
│ ├── Day 42.ipynb
│ ├── Day 6_Logistic_Regression.ipynb
│ ├── Day 6_Logistic_Regression.md
│ ├── Day 6_Logistic_Regression.py
│ ├── KafkaProducer.py
│ ├── TestKafka.py
│ └── my/
│ ├── Data_age_salary.csv
│ └── LinerTest.py
├── FAQ.MD
├── LICENSE
├── Other Docs/
│ ├── README.md
│ └── 速查手册/
│ └── README.md
├── README.md
├── Translation specification.MD
└── datasets/
├── 50_Startups.csv
├── Data.csv
├── Social_Network_Ads.csv
├── mnist.npz
├── readme.md
└── studentscores.csv
================================================
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FILE: .gitignore
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================================================
FILE: Code/Day 11_K-NN.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#机器学习100天——第十一天：K近邻法（K-NN）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第一步：导入相关库"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第二步：导入数据集"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" User ID Gender Age EstimatedSalary Purchased\n",
"0 15624510 Male 19 19000 0\n",
"1 15810944 Male 35 20000 0\n",
"2 15668575 Female 26 43000 0\n",
"3 15603246 Female 27 57000 0\n",
"4 15804002 Male 19 76000 0\n",
"5 15728773 Male 27 58000 0\n",
"6 15598044 Female 27 84000 0\n",
"7 15694829 Female 32 150000 1\n",
"8 15600575 Male 25 33000 0\n",
"9 15727311 Female 35 65000 0\n",
"10 15570769 Female 26 80000 0\n",
"11 15606274 Female 26 52000 0\n",
"12 15746139 Male 20 86000 0\n",
"13 15704987 Male 32 18000 0\n",
"14 15628972 Male 18 82000 0\n",
"15 15697686 Male 29 80000 0\n",
"16 15733883 Male 47 25000 1\n",
"17 15617482 Male 45 26000 1\n",
"18 15704583 Male 46 28000 1\n",
"19 15621083 Female 48 29000 1\n",
"20 15649487 Male 45 22000 1\n",
"21 15736760 Female 47 49000 1\n",
"22 15714658 Male 48 41000 1\n",
"23 15599081 Female 45 22000 1\n",
"24 15705113 Male 46 23000 1\n",
"25 15631159 Male 47 20000 1\n",
"26 15792818 Male 49 28000 1\n",
"27 15633531 Female 47 30000 1\n",
"28 15744529 Male 29 43000 0\n",
"29 15669656 Male 31 18000 0\n",
".. ... ... ... ... ...\n",
"370 15611430 Female 60 46000 1\n",
"371 15774744 Male 60 83000 1\n",
"372 15629885 Female 39 73000 0\n",
"373 15708791 Male 59 130000 1\n",
"374 15793890 Female 37 80000 0\n",
"375 15646091 Female 46 32000 1\n",
"376 15596984 Female 46 74000 0\n",
"377 15800215 Female 42 53000 0\n",
"378 15577806 Male 41 87000 1\n",
"379 15749381 Female 58 23000 1\n",
"380 15683758 Male 42 64000 0\n",
"381 15670615 Male 48 33000 1\n",
"382 15715622 Female 44 139000 1\n",
"383 15707634 Male 49 28000 1\n",
"384 15806901 Female 57 33000 1\n",
"385 15775335 Male 56 60000 1\n",
"386 15724150 Female 49 39000 1\n",
"387 15627220 Male 39 71000 0\n",
"388 15672330 Male 47 34000 1\n",
"389 15668521 Female 48 35000 1\n",
"390 15807837 Male 48 33000 1\n",
"391 15592570 Male 47 23000 1\n",
"392 15748589 Female 45 45000 1\n",
"393 15635893 Male 60 42000 1\n",
"394 15757632 Female 39 59000 0\n",
"395 15691863 Female 46 41000 1\n",
"396 15706071 Male 51 23000 1\n",
"397 15654296 Female 50 20000 1\n",
"398 15755018 Male 36 33000 0\n",
"399 15594041 Female 49 36000 1\n",
"\n",
"[400 rows x 5 columns]\n"
]
}
],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"print(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"为了方便理解，这里我们只取Age年龄和EstimatedSalary估计工资作为特征"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第三步：将数据划分成训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第四步：特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, _DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"##第五步：使用K-NN对训练集数据进行训练"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"从sklearn的neighbors类中导入KNeighborsClassifier学习器"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"设置好相关的参数\n",
"n_neighbors = 5(K值的选择，默认选择5)、\n",
"metric = 'minkowski'(距离度量的选择，这里选择的是闵氏距离(默认参数))、\n",
"p = 2 (距离度量metric的附属参数，只用于闵氏距离和带权重闵氏距离中p值的选择，p=1为曼哈顿距离， p=2为欧式距离。默认为2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第六步：对测试集进行预测"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
" 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1]\n"
]
}
],
"source": [
"y_pred = classifier.predict(X_test)\n",
"print(y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##第七步：生成混淆矩阵"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"混淆矩阵可以对一个分类器性能进行分析，由此可以计算出许多指标，例如：ROC曲线、正确率等"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[64 4]\n",
" [ 3 29]]\n"
]
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"print(cm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" 预测值\n",
" 0 1\n",
" 实0 64 4 \n",
" 际1 3 29\n",
" 值\n",
"\n",
"预测集中的0总共有68个，1总共有32个。\n",
"在这个混淆矩阵中，实际有68个0，但K-NN预测出有67(64+3)个0，其中有3个实际上是1。\n",
"同时K-NN预测出有33(4+29)个1，其中4个实际上是0。"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
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},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 11_K-NN.md
================================================
# K近邻法 (K-NN)
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
</p>
## 数据集 | 社交网络
<p align="center">
<img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
</p>
## 导入相关库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## 导入数据集
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
## 将数据划分成训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
## 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
## 使用K-NN对训练集数据进行训练
```python
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
```
## 对测试集进行预测
```python
y_pred = classifier.predict(X_test)
```
## 生成混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
================================================
FILE: Code/Day 11_k-NN.py
================================================
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(classification_report(y_test, y_pred))
================================================
FILE: Code/Day 13_SVM.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第13天：支持向量机 (SVM)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第一步：导入库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第二步：导入数据"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第三步：拆分数据集为训练集合和测试集合"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第四步：特征量化"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.fit_transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第五步：适配SVM到训练集合"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
" max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import SVC\n",
"classifier = SVC(kernel = 'linear', random_state = 0)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第六步：预测测试集合结果"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第七步：创建混淆矩阵"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第八步：训练集合结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qZTO7lWMO2kLSD1GcOibV3991tpaJblQ8FYKIvKfaB1X1k9GLM4twM5dEYBoJ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\nAZhCMAzDMPL8f2I4Lp2zH4gCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62a991e7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('SVM (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第九步：测试集合结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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InwD+E/gpEGEGL0lVJb2U1KMpXBxTkEettpvBZIbFy3BKsDa1lsCsXpSEMMvd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\n1pPjhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62a991c940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('SVM (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 13_SVM.md
================================================
# Day 13 | 支持向量机 (SVM)
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2012.jpg">
</p>
## 导入库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## 导入数据
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
## 拆分数据集为训练集合和测试集合
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
## 特征量化
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
```
## 适配SVM到训练集合
```python
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
```
## 预测测试集合结果
```python
y_pred = classifier.predict(X_test)
```
## 创建混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
## 训练集合结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/SVM_training%20set.png">
</p>
## 测试集合结果可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/SVM_test%20set.png">
</p>
================================================
FILE: Code/Day 13_SVM.py
================================================
#Day13: Support Vector Machine (SVM)
#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#Fitting SVM to the Training set
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
#Predicting the Test set results
y_pred = classifier.predict(X_test)
#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(classification_report(y_test, y_pred))
#Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
#Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('SVM (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
================================================
FILE: Code/Day 1_Data_Preprocessing.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第1天：数据预处理（Data Preprocessing）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"搭建anaconda环境，参考 https://zhuanlan.zhihu.com/p/33358809\n",
"\n",
"## 第一步：导入需要的库\n",
"这两个是我们每次都需要导入的库。NumPy包含数学计算函数。Pandas用于导入和管理数据集。"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[ 7. 2. 3. ]\n [ 4. 3.5 6. ]\n [10. 3.5 9. ]]\nSklearn verion is 0.23.1\n"
]
}
],
"source": [
"import sklearn\n",
"from sklearn.impute import SimpleImputer\n",
"#This block is an example used to learn SimpleImputer\n",
"imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
"imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])\n",
"X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]\n",
"print(imp_mean.transform(X))\n",
"print(\"Sklearn verion is {}\".format(sklearn.__version__))"
]
},
{
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"enc = OneHotEncoder(handle_unknown='ignore')\n",
"X = [['Male', 1], ['Female', 3], ['Female', 2]]\n",
">>> enc.fit(X)\n",
"OneHotEncoder(handle_unknown='ignore')\n",
">>> enc.categories_\n",
"[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]\n",
">>> enc.transform([['Female', 1], ['Male', 4]]).toarray()\n",
"array([[1., 0., 1., 0., 0.],\n",
" [0., 1., 0., 0., 0.]])\n",
">>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])\n",
"array([['Male', 1],\n",
" [None, 2]], dtype=object)\n",
">>> enc.get_feature_names(['gender', 'group'])\n",
"array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'],\n",
" dtype=object)"
],
"cell_type": "code",
"metadata": {},
"execution_count": 4,
"outputs": [
{
"output_type": "error",
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-4-44f585aeb41d>, line 4)",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-4-44f585aeb41d>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m >>> enc.fit(X)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第二步：导入数据集\n",
"数据集通常是.csv格式。CSV文件以文本形式保存表格数据。文件的每一行是一条数据记录。我们使用Pandas的read_csv方法读取本地csv文件为一个数据帧。然后，从数据帧中制作自变量和因变量的矩阵和向量。"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Step 2: Importing dataset\nX\n[['France' 44.0 72000.0]\n ['Spain' 27.0 48000.0]\n ['Germany' 30.0 54000.0]\n ['Spain' 38.0 61000.0]\n ['Germany' 40.0 nan]\n ['France' 35.0 58000.0]\n ['Spain' nan 52000.0]\n ['France' 48.0 79000.0]\n ['Germany' 50.0 83000.0]\n ['France' 37.0 67000.0]]\nY\n['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']\n[[44.0 72000.0]\n [27.0 48000.0]\n [30.0 54000.0]\n [38.0 61000.0]\n [40.0 nan]\n [35.0 58000.0]\n [nan 52000.0]\n [48.0 79000.0]\n [50.0 83000.0]\n [37.0 67000.0]]\n"
]
}
],
"source": [
"dataset = pd.read_csv('../datasets/Data.csv')\n",
"# 不包括最后一列的所有列\n",
"X = dataset.iloc[ : , :-1].values\n",
"#取最后一列\n",
"Y = dataset.iloc[ : , 3].values\n",
"print(\"Step 2: Importing dataset\")\n",
"print(\"X\")\n",
"print(X)\n",
"print(\"Y\")\n",
"print(Y)\n",
"print(X[ : , 1:3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第三步：处理丢失数据\n",
"我们得到的数据很少是完整的。数据可能因为各种原因丢失，为了不降低机器学习模型的性能，需要处理数据。我们可以用整列的平均值或中间值替换丢失的数据。我们用sklearn.preprocessing库中的Imputer类完成这项任务。"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 3: Handling the missing data\nstep2\nX\n[['France' 44.0 72000.0]\n ['Spain' 27.0 48000.0]\n ['Germany' 30.0 54000.0]\n ['Spain' 38.0 61000.0]\n ['Germany' 40.0 63777.77777777778]\n ['France' 35.0 58000.0]\n ['Spain' 38.77777777777778 52000.0]\n ['France' 48.0 79000.0]\n ['Germany' 50.0 83000.0]\n ['France' 37.0 67000.0]]\n"
]
}
],
"source": [
"# If you use the newest version of sklearn, use the lines of code commented out\n",
"from sklearn.impute import SimpleImputer\n",
"imputer = SimpleImputer(missing_values=np.nan, strategy=\"mean\")\n",
"#from sklearn.preprocessing import Imputer\n",
"# axis=0表示按列进行\n",
"#imputer = Imputer(missing_values = \"NaN\", strategy = \"mean\", axis = 0)\n",
"#print(imputer)\n",
"#\n",
"# print(X[ : , 1:3])\n",
"imputer = imputer.fit(X[ : , 1:3]) #put the data we want to process in to this imputer\n",
"X[ : , 1:3] = imputer.transform(X[ : , 1:3]) #replace the np.nan with mean\n",
"#print(X[ : , 1:3])\n",
"print(\"---------------------\")\n",
"print(\"Step 3: Handling the missing data\")\n",
"print(\"step2\")\n",
"print(\"X\")\n",
"print(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第四步：解析分类数据\n",
"分类数据指的是含有标签值而不是数字值的变量。取值范围通常是固定的。例如\"Yes\"和\"No\"不能用于模型的数学计算，所以需要解析成数字。为实现这一功能，我们从sklearn.preprocessing库导入LabelEncoder类。"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 4: Encoding categorical data\nX\n[[1.0 0.0 0.0 44.0 72000.0]\n [0.0 0.0 1.0 27.0 48000.0]\n [0.0 1.0 0.0 30.0 54000.0]\n [0.0 0.0 1.0 38.0 61000.0]\n [0.0 1.0 0.0 40.0 63777.77777777778]\n [1.0 0.0 0.0 35.0 58000.0]\n [0.0 0.0 1.0 38.77777777777778 52000.0]\n [1.0 0.0 0.0 48.0 79000.0]\n [0.0 1.0 0.0 50.0 83000.0]\n [1.0 0.0 0.0 37.0 67000.0]]\nY\n[0 1 0 0 1 1 0 1 0 1]\n"
]
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer \n",
"#labelencoder_X = LabelEncoder()\n",
"#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])\n",
"#Creating a dummy variable\n",
"#print(X)\n",
"ct = ColumnTransformer([(\"\", OneHotEncoder(), [0])], remainder = 'passthrough')\n",
"X = ct.fit_transform(X)\n",
"#onehotencoder = OneHotEncoder(categorical_features = [0])\n",
"#X = onehotencoder.fit_transform(X).toarray()\n",
"labelencoder_Y = LabelEncoder()\n",
"Y = labelencoder_Y.fit_transform(Y)\n",
"print(\"---------------------\")\n",
"print(\"Step 4: Encoding categorical data\")\n",
"print(\"X\")\n",
"print(X)\n",
"print(\"Y\")\n",
"print(Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第五步：拆分数据集为测试集合和训练集合\n",
"把数据集拆分成两个：一个是用来训练模型的训练集合，另一个是用来验证模型的测试集合。两者比例一般是80:20。我们导入sklearn.model_selection库中的train_test_split()方法。"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 5: Splitting the datasets into training sets and Test sets\nX_train\n[[0.0 1.0 0.0 40.0 63777.77777777778]\n [1.0 0.0 0.0 37.0 67000.0]\n [0.0 0.0 1.0 27.0 48000.0]\n [0.0 0.0 1.0 38.77777777777778 52000.0]\n [1.0 0.0 0.0 48.0 79000.0]\n [0.0 0.0 1.0 38.0 61000.0]\n [1.0 0.0 0.0 44.0 72000.0]\n [1.0 0.0 0.0 35.0 58000.0]]\nX_test\n[[0.0 1.0 0.0 30.0 54000.0]\n [0.0 1.0 0.0 50.0 83000.0]]\nY_train\n[1 1 1 0 1 0 0 1]\nY_test\n[0 0]\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)\n",
"print(\"---------------------\")\n",
"print(\"Step 5: Splitting the datasets into training sets and Test sets\")\n",
"print(\"X_train\")\n",
"print(X_train)\n",
"print(\"X_test\")\n",
"print(X_test)\n",
"print(\"Y_train\")\n",
"print(Y_train)\n",
"print(\"Y_test\")\n",
"print(Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第六步：特征量化\n",
"大部分模型算法使用两点间的欧氏距离表示，但此特征在幅度、单位和范围姿态问题上变化很大。在距离计算中，高幅度的特征比低幅度特征权重更大。可用特征标准化或Z值归一化解决。导入sklearn.preprocessing库的StandardScalar类。"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"---------------------\nStep 6: Feature Scaling\nX_train\n[[-1. 2.64575131 -0.77459667 0.26306757 0.12381479]\n [ 1. -0.37796447 -0.77459667 -0.25350148 0.46175632]\n [-1. -0.37796447 1.29099445 -1.97539832 -1.53093341]\n [-1. -0.37796447 1.29099445 0.05261351 -1.11141978]\n [ 1. -0.37796447 -0.77459667 1.64058505 1.7202972 ]\n [-1. -0.37796447 1.29099445 -0.0813118 -0.16751412]\n [ 1. -0.37796447 -0.77459667 0.95182631 0.98614835]\n [ 1. -0.37796447 -0.77459667 -0.59788085 -0.48214934]]\nX_test\n[[-1. 2.64575131 -0.77459667 -1.45882927 -0.90166297]\n [-1. 2.64575131 -0.77459667 1.98496442 2.13981082]]\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc_X = StandardScaler()\n",
"X_train = sc_X.fit_transform(X_train)\n",
"X_test = sc_X.transform(X_test) #we should not use fit_transfer cause the u and z is determined from x_train\n",
"print(\"---------------------\")\n",
"print(\"Step 6: Feature Scaling\")\n",
"print(\"X_train\")\n",
"print(X_train)\n",
"print(\"X_test\")\n",
"print(X_test)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.3 64-bit (conda)",
"metadata": {
"interpreter": {
"hash": "1b78ff499ec469310b6a6795c4effbbfc85eb20a6ba0cf828a15721670711b2c"
}
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3-final"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 1_Data_Preprocessing.md
================================================
# 数据预处理
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%201.jpg">
</p>
如图所示，通过6步完成数据预处理。
此例用到的[数据](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Data.csv)，[代码](https://github.com/MLEveryday/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.py)。
## 第1步：导入库
```Python
import numpy as np
import pandas as pd
```
## 第2步：导入数据集
```python
//随后一列是label
dataset = pd.read_csv('Data.csv')//读取csv文件
X = dataset.iloc[ : , :-1].values//.iloc[行，列]
Y = dataset.iloc[ : , 3].values // : 全部行 or 列；[a]第a行 or 列
// [a,b,c]第 a,b,c 行 or 列
```
## 第3步：处理丢失数据
```python
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
```
## 第4步：解析分类数据
```python
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
```
### 创建虚拟变量
```python
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
```
## 第5步：拆分数据集为训练集合和测试集合
```python
#from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
```
## 第6步：特征量化
```python
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
```
================================================
FILE: Code/Day 1_Data_Preprocessing.py
================================================
#Day 1: Data Prepocessing
#Step 1: Importing the libraries
import numpy as np
import pandas as pd
#Step 2: Importing dataset
dataset = pd.read_csv('../datasets/Data.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : , 3].values
print("Step 2: Importing dataset")
print("X")
print(X)
print("Y")
print(Y)
#Step 3: Handling the missing data
# If you use the newest version of sklearn, use the lines of code commented out
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
#from sklearn.preprocessing import Imputer
# axis=0表示按列进行
#imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
print("---------------------")
print("Step 3: Handling the missing data")
print("step2")
print("X")
print(X)
#Step 4: Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
#labelencoder_X = LabelEncoder()
#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
#Creating a dummy variable
#print(X)
ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)
#onehotencoder = OneHotEncoder(categorical_features = [0])
#X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
print("---------------------")
print("Step 4: Encoding categorical data")
print("X")
print(X)
print("Y")
print(Y)
#Step 5: Splitting the datasets into training sets and Test sets
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
print("---------------------")
print("Step 5: Splitting the datasets into training sets and Test sets")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
print("Y_train")
print(Y_train)
print("Y_test")
print(Y_test)
#Step 6: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
print("---------------------")
print("Step 6: Feature Scaling")
print("X_train")
print(X_train)
print("X_test")
print(X_test)
================================================
FILE: Code/Day 25_Decision_Tree.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第25天：决策树（Decision Tree）"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第1步：导入需要用到的python库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第2步：导入数据集"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
"X = dataset.iloc[:, [2, 3]].values\n",
"y = dataset.iloc[:, 4].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第3步：将数据集拆分为训练集和测试集"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第4步：特征缩放"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
" warnings.warn(msg, DataConversionWarning)\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第5步：对测试集进行决策树分类拟合"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
" max_features=None, 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, presort=False, random_state=0,\n",
" splitter='best')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
"classifier.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第6步：预测测试集的结果"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = classifier.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第7步：制作混淆矩阵"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第8步：将训练集结果进行可视化"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GYcTIlfOvZELzhJK2Cc0TuHJ+tPLXAFctuYpFr17Epic2seCFC/jO7d+J3GcxFlQ2DMOI\nkULg+MbuG9m+dzszJs/gyvlXxhJQvmH5DZH7qIYZBMMwEqFnbw8bn91IfjBPS1MLHUd10D453iMf\ns8pFcy/KfEaRF2YQjLqmEZROI4yhnJ69PazvXc+QDgGQH8yzvnc9QN2PrZExg2DUjLgVXyMonUYY\ngxcbn914eEwFhnSIjc9urMtxDTGEqiIiaYtSFVVliKGRL/TBDIJRE5JQfGGVThZn4o2mOAvkB/Oh\n2rPO9v7ttO1po6W1JbNGQVXJ78mzvX/06a1mEIyakITiC6N0sjoTbwTF6WVoW5paPMfQ0tSSgoTR\nuXPLnVzGZcyYOINcRpMzhxhie/927txy56j7MINg1IQkFF8YpZPVmXi9K04/Qzt90nR27NtR8p7n\nJEfHUR1piRqJvYN7uWXTLWmLkTjZNHVGw+Gn4KIovo6jOshJ6VfYT+mENUg9e3vo3tpN15NddG/t\npmdvz6jlrEaYMWQRP0Pb299LZ1vn4c+3pamFzrbOunaDjQVshWDUhI6jOkpmkhBd8RWUS5C4QJiZ\neC3dS2HGkEWqGdr2ye0V40g7jpP2/bOOGQSjJiSl+LyUjhdhDFKt3UtBx5BFsmpovUj7/vWAGQSj\nZqSp+MIYpDjiHbWaiT6wqSv2PsOghT+ktDE/kK+QTT2Sc2oZx8lqHClLmEEwxgxBDVLUQG+tZ6JD\nyyL+jAcHQXX4sQg0NQV++YoXDLL05cqWKTC7D5bdLyx+rPL1snSg1HC41CqjqhEyupLGDIJhlBE1\n3lHzmeg554z+tT09sH59pUE48URoDybrYmDx6qKGowBPkbo8X1+rjKp6z+iqBakZBBGZBXwdaMdZ\neS5X1ZvSkscwCkSNdyQ5Ey13RenIL6nOxo0wVLazdWjIaQ9oEMKQk1xqqahJJDY0GmmuEAaA/6uq\nj4pIK/BbEblPVf+UokyGAUSLdyQ1E/VyRQGsmNbD4p2jVN75PCtOgaXnM+zyWQWL10Q3Xium9bC0\nYyNbWvLMzjtj72zrTC3Lp94zumpBagZBVbcD292/94jIWuBYwAxCHdGoaXxRxpXUTNTLFYXA5fPW\ncsW8taPq84b9sPQVsH+883jzVFhyETwzEa5a0DVqWcuDzZsn5A83zp81f9T9RqWeM7pqQSZiCCIy\nBzgN+LXHc0uAJQAtbebryxKNmsYXdVxJzUR9XU4CC+YsHFWf177yQfY3D5S07R8P176ymQUnjD42\n0b21u1JewTJ6Mk7qBkFEJgN3Ae9X1efKn1fV5cBygNYTWiO7TI34iCN4GmYmXqvVSBzjSmImmoQr\nqq/MGIzUHhTL6KlPUjUIIjIOxxisUNXvpimLEZ6oP/owM/FarkbCjmvDrg1s27vt8OOZk2cy9+i5\nscoEybiiwhiZB7c86NnHZb8f5PqfKrP6YOsU+NgrBE7xv5+RXdLMMhLgK8BaVU32XDgjEaLOWMPM\nxGuZyhlmXOXGADj8OIpROP9XPbzzro1M682zs62FWy7tYNVZ8bui2ia2VchfaC/mgU1dNA3B5IOl\n1136GPzHvXCEu6A4vg++9ANl6yR44AQqNqxFjaM0aswqK6S5QjgbuAJYIyKFLOarVfXuFGUyQhB1\nxhpmJl5LF0SYcXkp00L7aA3C+b/q4QO3rWfCQef+03vzfOA2ZzW06qx4XVG9/b2B2w/9W3Plnofu\nbhgo/QyOGICub7aw4uKOkiyjzS35MX8gUtYR1fpxy8tMUVmSthRGMV7fnqDHh6jfxVrZHObaOAg6\nriTk2vh5mNNX2f7kFOh4f2X7ScfMG/WsuevJLt/nFhYFqh/Y1OXsiC43CF3+r2fhwpKHuQVdLDhh\noeelQfAMVOOs3NLMXKoHut7a9VtVPWOk61IPKofh9D2TeeSBEcdk1AkrpvWwpHM9+5uGZ+JHDOZY\nvr6zIq8+zLW1pPllXQx6aP4mhYGfLxxdp31dns1z+mDogdI+ZUFXpFlzkzQxqIOe7eXklg5Qvtt4\n4++qGK8IaateWKA6eerKIBiNRUGRF7sVlm3s8FTwYa6tJUu2zeS/j91W4Stfsm3m6DttaWHF3Hzl\nZrEN3rGZKLEVEfFcDpUfE+k3s7/970rdWwAHxue4/e86WXBC9rOsjFLqymV0RmurPnKGrRAyx+rV\n5N63O20pUiOK28yLM7fC6unQP364beJBOHUHPDTL494+N1sYYG9CUJdRNaoFwOOkPIYATmzHDt4Z\nmYZ0GRnZJop/2Bimu7nSV94/Hh49oYUFZb7yan71IMQx6151VnsiBqAcKz2RPGYQDCNjhPGVR830\nqreCb1Z6IlnMIBhjmizmtYeZtUedNdus2yjGDIIxZunZ28O6XetQNwqQH8yzbtc6IN289rCz9qiz\n5qzOurNorBudXNoCGEZaPP6Xxw8bgwKK8vhfHk9JIof2ye10tnUeXhG0NLWMucBpIYBcWCkV0ml7\n9vakLFljYysEI3bqZWY3MORdwM2vvZZkddZeK+z843Qwg2DEylgsL1AvBrCesE1o6WAuIyNWqs3s\nsobXbtxq7V6YayMZ/NJebRNasphBMGIl6fOEu7d20/VkF91buyMr3blt3sXn/Nq9qCcDWE90HNVB\nTkrVU5bTYRsFcxkZsVLL84SjuqLiSLlsBNdGFl1elg6bDiMaBBH5Z+B2VX22BvIYWaanBzZuhHwe\nWlqgowPaS3+gcWx08lJQWQ0y1nt9nSzHfMZ6YD0NgqwQ2oHfiMijwFeBn2g9FUAy4qGnB9avhyFX\nKefzzuMyos7s/BRUxeHyLlFm4nEow6zu9A0668+qoTXSYUSDoKofE5FrgFcCbwO+ICLfAr6iqn9O\nWkAjI2zcOGwMCgwNOe0TJ5Y0h5nZlRdGO+k9gww1VyooP8IEgMuJ6+zkQl9ZcW2EMXSN4PIy4iNQ\nDEFVVUR2ADuAAeAo4Dsicp+qfihJAY2MkPdREPl8hUEIitfJYM+F1O/lZZrDEJcyzJprI4yhq3eX\nlxEvQWII7wP+HtgF3AJ8UFUPiUgOeBwwgzAWaGnxNgotw4rjgU1dobq89ZswoeyM3tl9sHmqx8WK\nZ5nngcGBivs2NTVzzuxzKi8uo1GVYS2L41WjVmWxjfgIskI4Cnijqm4ublTVIRF5XTJiGZmjo6M0\nhgCQyx0OLA89MIo+PU4GW7YKllwE+4vOAjhiMMfEwRy94yt3EB+/v5kn/6PpcKD7+pfm+fj5lSeA\neRFWGWYxG8fXCPsc6+k1hs62zlDjCmL4F62BK1fCpEPO4+m9ea68dS2AGYUMU9UgiEgTcJmqXuv1\nvKquTUIoI4MUsolGyDIKhceqY/EaoLmJpa9qLjkZDag8QnNAWHbvAORdQ5HP84FfwdpjlG1zAgwp\nhP8/y9k45cdqeh43egj2N+M5hs62ztBnEpffs4LubjhU+tlOOgTvvGujGYQMU9UgqOqgiKwXkdmq\nuqVWQhkZpb09mgEox2fVsfjAXBY/5H2fkiM0fzLA4jWlq4EJg84q4zXnBZvNB/X/11M2judxo/fn\nuPzC/tqNwSfmNK3XgtVZJqjL6DEReRjYV2hU1dcnJpUxNgi56li8s730DOXfdXle9+Dsypnw2l1r\nWbdrHU250WUlDQwOeLph8gN5HtzyoO/rgsQykqDivVq/mssv7Pe8NpGMIp+Y0862+o7PNDpBDMI1\niUthjF2irDp8lM5HLvBJVR1SJvePrpLppEPw9JGV7cfugX3jvPvsmwCrd6zm1OmnjuqetSKRILrH\n6m/fOLjlUis9kWWC7EMYTbjQMJLHQ+kcaIKnW32uF3j21wtHdasVvV2Vwe6D8Jn7YHGbd5/jzu0a\n1b2SJCe52myi81j9LXltnm0WP8g0QdJOzwRuBuYB44EmYJ+qesyXDKOGeCidz700751hA8ze3+wE\nO0cRFF+8oQVW5ll6PmyZ4qTHLlvltoeLx1ZQq+wlgdAZRZEoW/3dcUoXCzwuy0L2VhZkyAJBXEZf\nAC4Dvg2cgbMnIXg5SMOowoppPaXBz40dpb7vkShTOqvmPABoxUy4aYiKjKTDpTeCGIWODhY/tp7F\na8rSbju9Z9crpvUwkIPdB3bTvbU7M9lLWdtEl4XsrSzIkBUClb9W1SeAJlUdVNVbgQuTFcsYCxTS\nIzdPyKMCmyfkWdK5nhXTopW1FqTiCMrP3eumtBZTKL0RhPZ26Owc3ojX0uI8bm9nxbQe5pzZTW5B\nF3PO7Ob/PH8DSzrXH16pVDsjYayXz87C+LMgQ1YIskLYLyLjgdUi8llgOymdo/Db1r3kFnSlcWsj\nARQq3Dv7m4a4fN5arpg3+i0uTbnmipnwvzzc5XntUD5P86i+U3lgLYorpzuOzRPy/Pex2yrG5Zfe\nOdZrCWVh/FmQISsEMQhX4MQN/gm4EpgFXBrHzUXkq8DrgJ2qevJI109umcwZJ5wRx62NDND1ZJf3\nEwIL5iyM9V4721qY7pEDv7OthQUnjD4I0L21u1Jx+MQw/MpkNGL5DD/KffXNuWbPM6z9xp+Er3+s\nfQbVGHGmr6qbVbVfVZ9T1etU9SrXhRQHt2HupzFLLY9JvOXSDg6ML/26Hxifi5wGGWYW6TWusXQy\nmELFcaNexgCgbWJbRVtSx5WOpc9gJHxXCCKyBndV74WqvjDqzVX15yIyJ2o/Rn1Sy7MECuUS4i62\n5je7LMdvXFksn50k1UqZF9Pb31vRltRu8fbJ7fQd6GPb3m2H26ZPmt6wn0E1qrmMMlG4TkSWAEsA\nWmyXY0NRa2W46qz22Ovo+Bm16ZOm09vfG2hcWcv8yQJeRjasrz+oe6lnbw879u0oaduxbwdTJkwZ\nc5+Lr0Eor26aFqq6HFgO0HpCq53U1mCEUYZZzBUfazP8WuHlXgvj6w+TSlpPdaqSxjamGXVBlnPF\nbYYfnPL9IeCkCWuRd9rPvRbGxRhGyVuW0TBB0ke/ACzCOQxnIvBO4D+TFMowyrFc8fqnsFO6eH/I\nvKPncdLRJ5W0dbZ1+lamLX+937VhlHwtkxuyTtAjNJ8QkSZVHQRuFZHfAR+NenMRuQNYCBwtIk8B\nn1DVr0Tt12g8bBbXGPitpsKcYR3k2jDupVomN2SdVDemqeqiOPoxGp84csWzGIMwkiGMkrc40DBB\nN6blSGBjmmEEJeosLskYRCYNTU9P6TkTOf85XCblj0hYJW9xIIcg5a83A4jIIPBD4GlV3Zm0YIZR\nTNRZXFKZJJkMdvf0lJYFd8+MWLQGtp1QdmkW5TdSo9rGtC8CN6vqYyIyBegGBoHnicgHVPWOWglp\nGBBtFpdUDCKTKYsbN5YeS+qybBW8reycw0zKHwNm6EZHtRXCuar6bvfvtwEbVPUNIjIduAcwg2DU\nDWFjEEHdKJkMdvucZzy7z+PSLMofA41q6JKmWnD4YNHfFwDfB1DVHd6XG0Z2CVOvJkzNnEymLLZ4\n33vLFI9Lsyh/DDSqoUuaagZht4i8TkROA84G7gUQkWac/QiGUTe0T25n+qTpJW1+9WrC7HnIZGG0\njg7PIPLS8z0uzaL8MdCohi5pqrmM3gX8BzAdeH/RyuB84MdJC2akx/m/6glcBC7MtWkSpl5NmNll\nHCmLUbN8vM4IWbTGiRnM7nNWBkvPhztOoeIIy9HKn/VzSWxvwegQ1fopD9R6Qqueca2dh5Ak5/+q\nhw/ctp4JB4sOrh+f43Nv7axQ9H7X3nP2dOb/oXfUBgXir0rqeW4Bzoxx/qz5o742KuXBT3AUl98O\n3FpS7+mo9S5/nHS9teu3qjqi8qwrgyAzRWVJ2lI0Nhs/D3M8go9PToGO9we7dohSX+S+cbDkImeG\nWsyiNbB8JUw6NNyWbwJVmDA08uvD4HU6W+GJ8mYFcrlcTZR0UsYnqjLs2dvD2l2Vp9bNnDwzcBVX\nIzsENQiBSldkhdP3TOaRB2yFkCh9XZ7Nc/pg6IGFga4t915POgQrftzCir+UKbjubjhUqgxbBiv7\n8319COa85EE2T6o8jOX4/c08+ZtzStpyC7robOuMfXbppaSTCH7GkXK5oXeDZ3vxmQGWytl41JVB\naDjKd5N2dDiHuadJczMMeJxi1dxcKW9TEwx6aHAvvFIhfdIjA78+BMt+qix5NewfP9x2xEGnHY/s\nm7h3rvqsxGX5AAAgAElEQVQp6bBHSAYhjpTLQQ32uVoqZ2NRbWPaVdVeqKo3xC/OGMJrN+l6Z7aV\nqlHwcyEODlbKKz6HB3vhlQrZ0hJc0fukUgZl8e8GYcAJrm6Z4gRbl62CxWsGnfKKCeOnpAWpKAkd\nNfhZ65RLS+VsHKqtEFrd/zuBl+CUrQC4CHg4SaHGBF67SYeGnPY0DYLfjF+10lioOiuHpqbhVcPE\nibB7d+Xr2yrPyKWjo9TIgGNkyu+TyznXRqGlhcVr8ixeU9leC/yU5qAOMu/oebG6p+IoBOi3cvG7\nn9EYVDsx7ToAEfk58GJV3eM+vhZLO42O38w4omskMmFm7eC4l84p8sE/+KD3dTt3wpQplS6yzs7K\nNojfleZlfOIwNAGppqTjdk/FkXJ54vNOZN2udSUH15QfZDOafo1sEySG0E7pruWDbpsRBT/FW6MZ\nqy9+itOjNo4nXvGHQruXi6yzE+Z7BIvjXiUV+kspZpNUXvwDm7o828sdf0M6xLpn1gYyPA9ueZDB\nwYGKPsqNQXG/656pzEjyYsEJCwNdZ6RDEIPwdeBhEfme+/gNwNeSE2mMEHbGumEDbBvO8GDmTJg7\nN/j9ggaw/RTn2mA/+Kqk7SJrbw98Lz9FGwU/JR1UmfpRkf0FnuWvcx/qD9znlAPw7K89+o1AbkGX\n7Q3IOEHKXy8TkXuAc92mt6nq75IVawwQZsZabgxg+HEQoxA2gO2lOAtyllO+ogmTeVSQJWMMLauj\n5LvJk+HUsrYQ5a9riYJVIM04Qb/5RwDPqeqtInKMiJygqpuSFGxMEHTGWm4MituDGISwAWyv1URb\nm7cc5cHiuXO9VxPV0lm7u7OVenvOOd7vAURzOfmt0uJOPw5R/rrWWAXSbDOiQRCRTwBn4GQb3QqM\nA27HKXhn1ANhAtg9PbBu3XCmTz7vPPZj585So9TeDn19le6tKVO8M4oGBoYNRdKpt0EVr9cMu/g9\nKZcVRu63p6fUUObzzuO+PtixI1r6cfm48nlWnFKZYruoPMMqI1jaanYIskK4BDgNeBRAVbeJSGv1\nlxiZIkwA+/HHvdNL/Sif9ff0OAqumB07HINQnlE0MFDpXkoqrhDGbbZxIyteMFSmULUyZXVoyHHn\nqVb229cHvb3DYz14EE+8Vl1h3gOPcd1+CrzrouFNeJunOqU/nslojWJLW80O1cpfFzioTsEjpxyM\nyKRkRTIqmDkzXHs5XuWQ/QLYfllCQRnJPTV/Pixc6PzvF2tIIq5QTa4yVszNs+QiR5GqDCvUFV61\nlAYHvfvdtm14HPl8daPqRdD3wGNcHzu/dEc2OI+vfWX6sZFGLLXdSAT5hnxLRL4ETBWRfwTeDtyS\nrFhGCQWXzGizjJJMuWxqKn0cxj3lt3JJIq4QQq4PX+CtUJeeT8UqQfGumReZoOnHHvJ7HYQD0Ncc\n0dhHRCCRGlFhsCyn6gTJMvqciFwAPIcTR/i4qt6XuGRGKXPnhkszLSdoANsvS8hrB3FBrmKquafK\nfd1tbaX+88J9kogrhDA+T/s4RMsV7YHxOQ6Mz/HjEwY8SmIElMvv/Q66Yc5jXLP7nFVNxaUZcM3E\nvQkvDHbO8sgECSp/RlU/DNzn0WbUiloVwvPLEjrpJOf/kWTw21/R1lbpw9+xA6ZPL/W1JxVX8MuS\n8jA+R/XDs0dUXjq13ykDPruPw+c0/H5iH3dP2lbhr4cARkHEGdP27aXGNkyNKI/3++P3w7svgkNF\nv+6mIcir9yqpfNY8OOS+/1ksvuhDkJm/nbM8MkFcRhcA5cr/1R5tRlLUshDeSO6lke7n93o/H35v\nb+lO5a4u736jxhV6e4NdNzTEzfc4Sr28MurN9wpzTltAbkEXC05wZO7eupF8mf3ycy8hAuPHV7wv\nK07WygD2hoAG0OP9fvszbbT8YFvFquXuEyv3IXjNmgFetJ1sFl/0IOjM385ZHplq1U7fA/wfoENE\n/lD0VCvwy6QFM4oYKSAa9yzOz70UZrdzebvfTuekNqZ5pGIGpaDIK91AWlEZNT+Q9wwiePrxVUsD\nzU8+eTiAXbHCWJlncVCBy9/v7m4Wb6s0SGdvqdyH4DVrBnjiaBLZWZ6EDz/ozD+Oon+NTrUVwv8A\n9wD/BnykqH2Pqv4lUamMUqoFRGs1i4u6Sqll7SafnbphWLzGx+XT1cVgFwxKF186HR5/GTx9ZOVl\nz9sPc95falDesqbUdmh/P1f7ZAR9+AK4orUrkKwVpSt8xjvb43Q7v9nxNr/E8ggGPKmdykFn/nbO\n8shUq3baB/QBiwBEZBowAZgsIpNVdUttRByDhDmIplb1gaKW6w5au8lvrOXZTGFlDYpbyM9rY1fB\nQAjQrPDeR2BqvtK9NH4AnmuBXjdB2y+uIMBWn4ygp1sjZC/5GF+vVYvfrHnmnip9R8BrJr+hd0Ok\nVUPQmX+hT8sy8idIUPki4AZgJrATOB5YC7wg6s1F5ELgJqAJuEVVPx21z7rHa3brFWSsVoE0CTdM\n1HLdQVNf/QKqfu1ebqyw4y8oUPf1K5rXertxqFw1eLmX9o4bNgYF/OIKfhlBx+dbePKh+ZVPBMHL\n+LoyVlzqMWsGeP4uKr9jCZULH9RBBt1JwGhWDWFm/mlmOdUDQYLK1wNnAj9V1dNE5OXA5VFvLCJN\nwH/iBK2fAn4jIj9U1T9F7buu8Zrdeh1EUwjU1soNE4fLJ0jqa7Xy2eX4ubH86ib5UVZ+++pT14ba\nh1DuXsp9wvs2XjP0Zavg8jdS5kuCnbk8R/1N14iiP/v/mkvPowBv45vLcccp/Swoe73XrPng4EF+\nP0O9z6qoQUA5bOaPzfzjI4hBOKSqvSKSE5Gcqt4vIp+P4d4vBZ5Q1Y0AInIncDEwtg2C3+y2/CAa\nqKwZVMDrdLKo1OqAmTAuIz83lkjwMxw8Vh5+bhy/DV/l+M36vXz4b/4jXHFp6cEzIsKhCU0cCnCv\n3NIBhh7weKLc+K5eDXiXvy6fNT+45UFgIFS58KCUHxfqR9jMH5v5x0MQg7BbRCYDPwdWiMhOYF8M\n9z4W2Fr0+Cngb8ovEpElwBKA2WkfHlMLwszE/VIpg6ZYhqFWB8yEcRn5Gc/BQZg3r3ITnJfxLOyv\nKGLiocpAL8CsMoWuwMaZE+nY1l8ywV+2Ct55MRwo+nW1DArXryrd2DcEfPYf57FgzujfwyTObUgK\nr53KgzroeVSnZf6kQxCDcDFwALgSWAxMAT6ZpFDFqOpyYDnAGa2tIQvC1CFhZuJhq5hGVeYJzBgr\nCOMyqmY8vWT1OsLTYzz7xzkbuQaLyu40DcHLN8KAQJPCoMCXTod/fm0/N/8Y3vXb4fbdLXCgiZIT\ncfI55Z4T4ZwtzkphyxTHBXXHjLWwKYbDh+qE8pl8+R4CsMyfNAlSumIfgIgcCayM8d5PA7OKHh/n\nto1twszEg64marmxLSphXEYJubH0pqms6Myz9OwDbGlVZu8Rlv1yAov/AM7cSGlGeO9jE3jvE+57\n7bqTmoH3PgHv/WweDhxw4j8iMGGC87kIMBXmACt+5vxj3z44VOQgGjcOJgWsIXlq+ek4MVKDncrm\n/88WQbKM3gVch/NLGML5SisQ1YT/BjhRRE7AMQSXAW+J2GdjEHQmHlQhRk0ZrSVhXEZhjGcYo3jq\nqSwGFj9a1PZcD+TXD5eYKGwymzPH++yD9QGv3bABdu8ubTt0CI44Ilrtqohc+hg1m0SY/z87BHEZ\nfQA4WVV3xXljVR0QkX8CfoKTdvpVVX0szns0PEEVYtSU0VoSxmUEwY2nn1F8/PFgBiWMUQ1zr6in\n4SXEx39O/UwijNgIYhD+DOxP4uaqejdwdxJ9jxmCKMRa7hIOS7lbwi9lNKqs1bK3glRWDWNUw94r\ngxz3nM8TWZxEGLERxCB8FPiViPwaOPxtUNV/SUwqI15qlTI6EiOVvy4om/JS23HIGrSekd8sOExZ\n76D7IEa7m7oGPHUkzPYyClmYRBiJEcQgfAn4GbAGJ4Zg1Bu1ShmthpcP389dkss5SjWIrEEDn35p\np154Kf6JE73bc7nIdZM8CXoaXkJ88mVwy9212akcBjvgJlmCGIRxqnpV4pIYyVKLlNECGzZUnu7W\n2xt8Rjw4COeeO/J1YQLFPT3B7g3eGU3lgd8C/d6bvQLjtUcizGl4fpQbyvIjVEfgrhfALX9OZ6ey\nH3bATfIEMQj3uJvDVlLqMrKKp0Yl5cYAgs/MCwQ9QjNMoNevOKAXYQ6oiUpbW/TT8Mrxqfa6aE3l\neQhVqeUkIgB2wE3yBDEIi9z/P1rUFkfaqdEIRDh3wJMwR2gmlT01MFA5rqRIYle5T7XXZasqz0Oo\nJ+yAm+QJsjEtzJzCGEuEPXfAq3pmlCM0k8qeampKJi7gRQ0r03rVUqon7ICb5Kl2Ytp5qvozEXmj\n1/Oq+t3kxDIiEWaHaZTdqGHPHQhSPTPMEZpJZE/lcs4qJYyLKQo1rEwbtDhfVrEDbpKn2gphAU52\n0UUezylgBiGLhA20RtmNGmZ2O3Nm/HsmwmRPlaey+jF9eriYx9Sp8Nxzo0shLRgvryB8lJhCiPMQ\n6gkrc5E81U5MK1R1/6Sqbip+zi03YWSROHbUBt2N6ld3qFz5hlFwYWf9QQOfTU3B9gbs2FH9hLpy\n+vsrVz7VDGXZYTyeJcwLj0drFEKch1BvWJmLZAkSVL4LeHFZ23eA0+MXx4hMHDtqg878/bJxmpoq\nz24ISlJ7JoIemDM05GQ5BT1PIZ/3PnvAK0116tTKYnRrfSqdRi1dEeI8BMMoUC2GcBLOMZlTyuII\nR+KcrWxkkTAul7BB2fJ4Q9i6Q0FJO91xYMBZ1RTP3P1cTl7v1amnVhqFqVNhxozKdFrDyBDVVgid\nwOuAqZTGEfYA/5ikUEYEwrhcwlwbJqOoluUNkijRLOK4joJc56fUy1cC1eI1NcR2+hrVqBZD+AHw\nAxGZr6rdNZTJiEIYl0uYa4NmFNWyvEGYoHiYPRKqlasBr9VBkCB1Ab94jR8BN8flFnQFu/8CJxNk\n7a5hF1V+MH/4sRkFA4LFEC4RkcdwHJD3Ai8ErlTV2xOVzCglzEw4jMsl6LVhAqW1cveECYqHqWUU\nhscfjz8jCzyP9vRjwQkLA133i82/YFArg+UbejeYQTCAYAbhlar6IRG5BHgSeCPO+cpmEGpFFk48\nqxZvmD+/NjKUEyYonsSOYAgeL6n2/nV01KRmkJcxqNZujD0CFbdz/38t8G1V7ZNa1noxsnHiWRyb\nwOL294cJioedoQfNMgpKtfcv7SB6g2LxkvAEMQgrRWQdjsvoPSJyDM5xmkatyMKJZ1HTQZNY5YQx\nUmFiCF6z9oMHvWMGXpVRvchCCXLFOQDXo/2BTV21k6MGhK2MasbDIUgto4+IyGeBPlUdFJH9wMXJ\ni2YcJisnnkWZySaxygmjZL2Mh1cqqd+svafHe89AisdcFgiizFbvWM3Ufth9ROXr2/ph18MLayNs\njQhTGdXKag9TbR/Ch1T1s+7D81X12wCquk9ElgJX10LAhiaoCyUrJ54FxWtcSa1yghqp9vbKXcEz\nZsCUKfFnZHmRUBxIIbAyu/keePvFcKjoVz9uAG66B2gLeMMk0nwTIExlVCurPUy1FcJlQMEgfBT4\ndtFzF2IGIRphFEQW3A0QTBn4jSups5L9ZPI7rrOYHTscgxA0KJ61FVKhm4DK7NwtcOsPnJpGW6Y4\n1U+XrYLFG1ogyFuQheSGgISpjGpltYepZhDE52+vx0ZYwiqItAOPQZWB37j8ArQTJ8YvU19f5VnN\nXimncQXmgxSnq3EcyEuZfezl8I0f5li8pmyl2RlwpRmDUatVrOKwI1BKG1+8Kc+tn+tidp9jFJee\nDy2nWVntAtUMgvr87fXYCEsWAsVhCKoMwsrvdzRlFJnC7DeI+n5XOyGu2CjUOA7kpczuPAW+sb6y\nBPmKU2BpRzdbWvLMzrewbGMHi3d6KPiI39mhm6aGGUJkVnTmWXr2Aba0KrP3CMu6mln86KHDz8/p\ng+Ur4dHjDrHueZWvb5sY1I/WOFQzCC8SkedwbOxE92/cx1bLKCpZCRQHJagyiOPUtKDEcZ+o77ef\n8SkvTpdgHCgnueBnBJStNFdM62FJ53r2Nzmv3zwhz5JOZ+VXYRSifmfLy3kkzGJg8aNFDY9VFlyY\ndAj2NHuvXnv7E9q7kmGqla4ImE9njIp6CxQHVQZ+44ozpz8uqtUiipu44kBlsZG3PA9+d15noJTJ\nwRyMO7erpG0gR4UDeH/TEJfPW8sVJ61Fi547ew78ZIWjRAvsGwfvfnWeO08p7TcOBoWS+4tCUwTf\nRL4Lch7t21p9rrcYglEzshIoDkpQA+Y3Lq+6/+D425Og3Ah5pZiGqUUUB1HjQB4xk+Ur4cajYdVZ\n1aPCp04/ldU7Vle07z7g47IrU8YAv5wDr33rOL7+7QGO2608NVX45Ksm8KPTWpjs0UV+IM+BgQMo\niiBMaJ5AS3Ow1UR+IM/AQGm5bhUY1zwxcB/lPDW1j9m7Kz/zY/fAU0dWXm8xBKO2pB0oDkPYonl+\nmVJxngwWphzEwYPefWzYEO0zKC+TXdweNx4xk0mH4J13bWTVWSOP4dTplS6b7q3doWbCD83O8Q+f\nLz1mx8sRVMjtVzfcqCj5wTxzps4JlMrZvdW7nuaQDnmOIwhf/9sePnDbeiYcHH4PD4zPcebgdL4r\nO+xoTswgGGEIasD8UkHnzo13I1eYchB+ZzVHPTu5MJ44DZ0fPjGTab2jd234nVNcnsp6WISAxiNq\nbn8SqaAFo/nOuzYyrTfPzrYWbrm0g12ntdO5d4rtVCYlgyAibwauBeYBL1XVR9KQo66okw1BNc1V\nz4rbLW5D54fPimhn2+hdG37nFBcel9Ocaz68qqimOKMq9DD7CMKw6qx2z9WUHc3p4BVjqQV/ZLhq\nqjESBSVbUAYFJdvTk65cXlRLT02C9nZnY9nChc7/fsag2Wfu49eeRTo6nBVQEfvGwS2Xxu/a6Diq\ng5yU3ksQBoYGDivqwq7onr2V30M/xR1UoXvdf6y6cWpJKr8GVV0LYFVTA5KFaqdByer+ihNPhHXr\nSgPJIk57veCxIlry2jzbAsQPCpTXPWqb2MaOfTsqSl90tnXS2VaavTQwNFBRKtvPDeTnigqq0P1W\nLknN4q24nUMdTY/GMFlVsl5kdX+Fn3sJKs859ip9kRUXXVls5I5TulhQ5fJivIq4bdtbGRAvKPn5\ns+aXKMWuJ7s8+/Vy7cSh0MO4caIodCtuN0xiBkFEfgpM93hqqXs8Z9B+lgBLAGanrVTSIkklG0bx\nBbm2o8N7Jp6F/RVeFUyDlr7IaM0eP7wUpFeg1w8//30Yv36t/PJRFboVtxsmMYOgqq+IqZ/lwHKA\nM1pbx2bJjKQ2sYUJAFdTnr29pUXkapnvH2UmH6b0RVZddB74KcigxsCPqG6gpMhiRlO9Yi6jeiCp\nbJowsYkgytOviFzh9XEr06gZTWFdblFddDVyQ/kpyKjU2q8flKxmNNUjaaWdXgLcDBwD/FhEVqvq\nq9KQpW5IYhNbmNhEVGWYRLwjarA9bN2lKC66GqbjxjGzbRLvyjVZTM8Mo9C9XGlZXfmkQVpZRt8D\nvpfGvY0iwsQmohata2mJf4YcNdju54qbPr00hlBoj+Kiq2GmmJ+CDEu9ZN4EVeh+rjSvjKqsjjVp\nzGXUiIQ5iS1oANhLeQYll3NiC3HPkP0O3fHbW+D1vnRWloOmvT34SWpBqWGmWNvENs/soTAM6mDd\nZN4EdWVVizWUZ1SNVcwgNBphXRNBA8BecYzCKWReM+ziQHOhrlDcM2Q/Wb3a/d6Xzk7vE9PidtEl\nmClWPpMv3yswWmqVeVOrlYgFj0fGDEIWSSJzxi9Q7NdH0KJ1QWfSXgfUQ7QZsl8dIq/2tDf3JZQp\n5nWmcpLE3X8cewCC9mHB45Exg5A1ksqcCRMoDqOkg86kk5ghh+kz7c19CdZdCppB1CRNDOnQ4Qqk\nI13rtdKIW3nGsQcgaB8WPB4ZMwhZw28mu2FDMGUSR6A4iQ2AScyQ6+2QoRTLnQuCiKAe7rQmaaI5\n11zisgECK88oLp843DjV+igvxGfB4+qYQcgafjPWwcFhV0i1VUMYJdnW5r1voC2Bs2STmCFnpdpp\nHaAoA0MeAXicAPK5s871fG4k5RnV5ROHG6daVlV5Ib7Otk7mz/KIGRmAGYTsETS908//HUZJ9vqc\nGdvTUxkUjkPJJjFDrqdDhuqMIHsOorp84nDjePXhxVgtRxEGMwhZwysV1A8/wxFUSUZdjRiZQJDS\nuIBScU5y1fYIVJuZB3ElxVUEr7wPyygaHWYQskjQ2j9Rff1RVyP1RFarsMZARZDYR+k3qXNwfTlh\n3DPlSt4v+Nycaw7sSopj93N5H35Hg1pGUXXSOiDH8CPoQTJxBE87OpyNaEHIYqntMHgcLpPpAHRU\nymzEEQdhoc9Xq21isJhRIV5Q7Jf3ctPkJIeq+rqSaoEdsDM6bIWQNaop3sIsN44y1QVqtRoJg5/8\nUfZnjLEAdNt+mHwItkyB2X2wbBV8+ALva3v7fWJJZXjFCxSlOddMkzSVuHzW7vLed1Irl01WC/Fl\nHTMI9YTXjtpywuxjCLsa8VLIhX5Gq2TL+yzf/RznGQUNGoDOSa5EUTcNwU33wuI1pddd/kbv1wdV\n0n7XDQwNcM6cc0ra/M5krqXLJouF+LKOGYR6p1yhDgwE35EbZjUCpbuN8/nK3cdhlbSX8fI7i6DO\nzyhICoGK3Pq85lk8MA9air4XuRzQ79lHUCUdJkXUNoHVJ2YQskaY4KeXQvXDr0+/9vLVyC9+4d93\nMWGUtNcmvLDUe2wjBspnwg9s6qpcDa1eDfRXrCbCKOkwSt5cNvWJGYSsEWZjWRiF6mVQwtzLr26Q\nF0GVdBzKvAGyhGqF12oijJIOq+TNZVN/mEHIGmGCn0EVqp+STyrQGreSTuqMgjFIVCVtSr6xMYOQ\nRaIWjGtuhqamYEo+7kBrXEraK6Mq7jMKDMMowQxCPePn8jnxxNoqyqDpsH6v82qvxRkFhmGUYAah\nnqllbv3Mmd6ZPjNnwty5o+uz3qqVGkaDYwah3qnVrLmg9IuNQhRjAGNus5hhZB0zCEZw5s6NZgC8\nMDeQYWQGq2VkGIZhAGYQDMMwDBczCIZhGAZgBsEwDMNwMYNgGIZhAGYQDMMwDBczCIZhGAaQkkEQ\nkX8XkXUi8gcR+Z6ITE1DDsMwDGOYtFYI9wEnq+oLgQ3AR1OSwzAMw3BJxSCo6v+q6oD78CHguDTk\nMAzDMIbJQgzh7cA9fk+KyBIReUREHnnm0KEaimUYhjG2SKyWkYj8FJju8dRSVf2Be81SYABY4deP\nqi4HlgOc0dqqCYhqGIZhkKBBUNVXVHteRN4KvA44X1VN0RuGYaRMKtVOReRC4EPAAlXdn4YMhmEY\nRilpxRC+ALQC94nIahH5YkpyGIZhGC6prBBU9flp3NcwDMPwJwtZRoZhGEYGMINgGIZhAGYQDMMw\nDBczCIZhGAZgBsEwDMNwMYNgGIZhAGYQDMMwDBczCIZhGAZgBsEwDMNwMYNgGIZhAGYQDMMwDBcz\nCIZhGAZgBsEwDMNwMYNgGIZhAGYQDMMwDBczCIZhGAYAUk/HGYvIHmB92nIkwNHArrSFSIBGHRc0\n7tgadVzQuGMLMq7jVfWYkTpK5cS0CKxX1TPSFiJuROQRG1d90ahja9RxQeOOLc5xmcvIMAzDAMwg\nGIZhGC71ZhCWpy1AQti46o9GHVujjgsad2yxjauugsqGYRhGctTbCsEwDMNICDMIhmEYBlBnBkFE\nPiUifxCR1SLyvyIyM22Z4kJE/l1E1rnj+56ITE1bpjgQkTeLyGMiMiQidZ/yJyIXish6EXlCRD6S\ntjxxISJfFZGdIvLHtGWJExGZJSL3i8if3O/h+9KWKS5EZIKIPCwiv3fHdl3kPusphiAiR6rqc+7f\n/wL8taq+O2WxYkFEXgn8TFUHROQzAKr64ZTFioyIzAOGgC8BH1DVR1IWadSISBOwAbgAeAr4DbBI\nVf+UqmAxICIvA/YCX1fVk9OWJy5EZAYwQ1UfFZFW4LfAGxrkMxNgkqruFZFxwIPA+1T1odH2WVcr\nhIIxcJkE1I81GwFV/V9VHXAfPgQcl6Y8caGqa1W1UXaXvxR4QlU3qupB4E7g4pRligVV/Tnwl7Tl\niBtV3a6qj7p/7wHWAsemK1U8qMNe9+E4918knVhXBgFARJaJyFZgMfDxtOVJiLcD96QthFHBscDW\nosdP0SDKZSwgInOA04BfpytJfIhIk4isBnYC96lqpLFlziCIyE9F5I8e/y4GUNWlqjoLWAH8U7rS\nhmOksbnXLAUGcMZXFwQZl2GkiYhMBu4C3l/maahrVHVQVU/F8Si8VEQiufsyV8tIVV8R8NIVwN3A\nJxIUJ1ZGGpuIvBV4HXC+1lFwJ8RnVu88Dcwqenyc22ZkGNe/fhewQlW/m7Y8SaCqu0XkfuBCYNSJ\nAZlbIVRDRE4sengxsC4tWeJGRC4EPgS8XlX3py2P4clvgBNF5AQRGQ9cBvwwZZmMKriB168Aa1X1\nhrTliRMROaaQjSgiE3GSHSLpxHrLMroL6MTJWtkMvFtVG2KGJiJPAC1Ar9v0UCNkUInIJcDNwDHA\nbmC1qr4qXalGj4i8Bvg80AR8VVWXpSxSLIjIHcBCnFLKPcAnVPUrqQoVAyJyDvALYA2O3gC4WlXv\nTk+qeBCRFwJfw/ku5oBvqeonI/VZTwbBMAzDSI66chkZhmEYyWEGwTAMwwDMIBiGYRguZhAMwzAM\nwAyCYRiG4WIGwTACIiJvEBEVkZPSlsUwksAMgmEEZxFORclFaQtiGElgBsEwAuDWwjkHeAfODmVE\nJCci/+WeY3GfiNwtIm9ynztdRB4Qkd+KyE/cMsyGkWnMIBhGMC4G7lXVDUCviJwOvBGYA/w1cAUw\nH/KyQtoAAAEPSURBVA7XzrkZeJOqng58FWiIHc1GY5O54naGkVEWATe5f9/pPm4Gvq2qQ8AOt7gY\nOOVVTgbuc0rp0ARsr624hhEeMwiGMQIi8jzgPOAUEVEcBa/A9/xeAjymqvNrJKJhxIK5jAxjZN4E\nfENVj1fVOe55HJtwThi71I0ltOMUhwNYDxwjIoddSCLygjQEN4wwmEEwjJFZROVq4C5gOs6paX8C\nbgceBfrc4zXfBHxGRH4PrAbOqp24hjE6rNqpYURARCa7h5y3AQ8DZ6vqjrTlMozRYDEEw4jGj9xD\nSsYDnzJjYNQztkIwDMMwAIshGIZhGC5mEAzDMAzADIJhGIbhYgbBMAzDAMwgGIZhGC7/HyA3zwQk\nnsJsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0c9e6b37f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_train, y_train\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Decision Tree Classification (Training set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第9步：将测试集结果进行可视化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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t5fCI40aZsysogURtF6m3OL2M/tnMzgOeBLqBf3T3O1JPWTOIqq6pdYqGtHrj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\nifLpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0c9d686e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.colors import ListedColormap\n",
"X_set, y_set = X_test, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
"plt.title('Decision Tree Classification (Test set)')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Estimated Salary')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>完整的项目请前往Github项目<a href=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code\">100-Days-Of-ML-Code</a>查看。有任何的建议或者意见欢迎在issue中提出~</b>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: Code/Day 25_Decision_Tree.md
================================================
# 决策树分类
<p align="center">
<img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/raw/master/Info-graphs/Day%2023%20-%20Chinese.jpg">
</p>
### 导入需要用到的python库
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
### 导入数据集
```python
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
```
### 将数据集拆分为训练集和测试集
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
```
### 特征缩放
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
### 对测试集进行决策树分类拟合
```python
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
```
### 预测测试集的结果
```python
y_pred = classifier.predict(X_test)
```
### 制作混淆矩阵
```python
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
```
### 将训练集结果进行可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
### 将测试集结果进行可视化
```python
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
```
================================================
FILE: Code/Day 25_Decision_Tree.py
================================================
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Decision Tree Classification to the Training set
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Decision Tree Classification (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
================================================
FILE: Code/Day 2_Simple_Linear_Regression.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 机器学习100天——第二天：简单线性回归\n",
"## 第一步：数据预处理"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这里导入我们需要的库，值得注意的是，这里比第一天多了一个matplotlib.pyplot,matplotlib是python上的一个2D绘图库,\n",
"matplotlib下的模块pyplot是一个有命令样式的函数集合，\n",
"matplotlib.pyplot是为我们对结果进行图像化作准备的。"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"导入相关数据"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Hours Scores\n0 2.5 21\n1 5.1 47\n2 3.2 27\n3 8.5 75\n4 3.5 30\n5 1.5 20\n6 9.2 88\n7 5.5 60\n8 8.3 81\n9 2.7 25\n10 7.7 85\n11 5.9 62\n12 4.5 41\n13 3.3 42\n14 1.1 17\n15 8.9 95\n16 2.5 30\n17 1.9 24\n18 6.1 67\n19 7.4 69\n20 2.7 30\n21 4.8 54\n22 3.8 35\n23 6.9 76\n24 7.8 86\n25 2.1 93\n26 2.2 93\n27 2.5 93\n Hours Scores\n15 8.9 95\n27 2.5 93\n26 2.2 93\n25 2.1 93\n6 9.2 88\n24 7.8 86\n10 7.7 85\n8 8.3 81\n23 6.9 76\n3 8.5 75\n19 7.4 69\n18 6.1 67\n11 5.9 62\n7 5.5 60\n21 4.8 54\n1 5.1 47\n13 3.3 42\n12 4.5 41\n22 3.8 35\n20 2.7 30\n4 3.5 30\n16 2.5 30\n2 3.2 27\n9 2.7 25\n17 1.9 24\n0 2.5 21\n5 1.5 20\n14 1.1 17\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Hours Scores\n",
"0 2.5 21\n",
"1 5.1 47\n",
"2 3.2 27\n",
"3 8.5 75\n",
"4 3.5 30\n",
"5 1.5 20\n",
"6 9.2 88\n",
"7 5.5 60\n",
"8 8.3 81\n",
"9 2.7 25\n",
"10 7.7 85\n",
"11 5.9 62\n",
"12 4.5 41\n",
"13 3.3 42\n",
"14 1.1 17\n",
"15 8.9 95\n",
"16 2.5 30\n",
"17 1.9 24\n",
"18 6.1 67\n",
"19 7.4 69\n",
"20 2.7 30\n",
"21 4.8 54\n",
"22 3.8 35\n",
"23 6.9 76\n",
"24 7.8 86\n",
"25 2.1 93\n",
"26 2.2 93\n",
"27 2.5 93"
],
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Hours</th>\n <th>Scores</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2.5</td>\n <td>21</td>\n </tr>\n <tr>\n <th>1</th>\n <td>5.1</td>\n <td>47</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3.2</td>\n <td>27</td>\n </tr>\n <tr>\n <th>3</th>\n <td>8.5</td>\n <td>75</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3.5</td>\n <td>30</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1.5</td>\n <td>20</td>\n </tr>\n <tr>\n <th>6</th>\n <td>9.2</td>\n <td>88</td>\n </tr>\n <tr>\n <th>7</th>\n <td>5.5</td>\n <td>60</td>\n </tr>\n <tr>\n <th>8</th>\n <td>8.3</td>\n <td>81</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2.7</td>\n <td>25</td>\n </tr>\n <tr>\n <th>10</th>\n <td>7.7</td>\n <td>85</td>\n </tr>\n <tr>\n <th>11</th>\n <td>5.9</td>\n <td>62</td>\n </tr>\n <tr>\n <th>12</th>\n <td>4.5</td>\n <td>41</td>\n </tr>\n <tr>\n <th>13</th>\n <td>3.3</td>\n <td>42</td>\n </tr>\n <tr>\n <th>14</th>\n <td>1.1</td>\n <td>17</td>\n </tr>\n <tr>\n <th>15</th>\n <td>8.9</td>\n <td>95</td>\n </tr>\n <tr>\n <th>16</th>\n <td>2.5</td>\n <td>30</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1.9</td>\n <td>24</td>\n </tr>\n <tr>\n <th>18</th>\n <td>6.1</td>\n <td>67</td>\n </tr>\n <tr>\n <th>19</th>\n <td>7.4</td>\n <td>69</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2.7</td>\n <td>30</td>\n </tr>\n <tr>\n <th>21</th>\n <td>4.8</td>\n <td>54</td>\n </tr>\n <tr>\n <th>22</th>\n <td>3.8</td>\n <td>35</td>\n </tr>\n <tr>\n <th>23</th>\n <td>6.9</td>\n <td>76</td>\n </tr>\n <tr>\n <th>24</th>\n <td>7.8</td>\n <td>86</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2.1</td>\n <td>93</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2.2</td>\n <td>93</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2.5</td>\n <td>93</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 90
}
],
"source": [
"dataset = pd.read_csv('../datasets/studentscores.csv')\n",
"print(dataset)\n",
"df = dataset.sort_values(\"Scores\",ascending=False)\n",
"print(df)\n",
"dataset.head(30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这里我们需要使用pandas的iloc(区分于loc根据index来索引，iloc利用行号来索引)方法来对数据进行处理，第一个参数为行号，:表示全部行，第二个参数 ：1表示截到第1列(也就是取第0列)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"X: [[2.5]\n [5.1]\n [3.2]\n [8.5]\n [3.5]\n [1.5]\n [9.2]\n [5.5]\n [8.3]\n [2.7]\n [7.7]\n [5.9]\n [4.5]\n [3.3]\n [1.1]\n [8.9]\n [2.5]\n [1.9]\n [6.1]\n [7.4]\n [2.7]\n [4.8]\n [3.8]\n [6.9]\n [7.8]]\nY: [[21]\n [47]\n [27]\n [75]\n [30]\n [20]\n [88]\n [60]\n [81]\n [25]\n [85]\n [62]\n [41]\n [42]\n [17]\n [95]\n [30]\n [24]\n [67]\n [69]\n [30]\n [54]\n [35]\n [76]\n [86]]\n"
]
}
],
"source": [
"X = dataset.iloc[ 0: 25, : 1 ].values\n",
"Y = dataset.iloc[ 0: 25, -1: ].values\n",
"print(\"X:\",X)\n",
"print(\"Y:\",Y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"导入sklearn库的cross_validation类来对数据进行训练集、测试集划分"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[7.8]\n [6.9]\n [1.1]\n [5.1]\n [7.7]\n [3.3]\n [8.3]\n [9.2]\n [6.1]\n [3.5]\n [2.7]\n [5.5]\n [2.7]\n [8.5]\n [2.5]\n [4.8]\n [8.9]\n [4.5]] [[1.5]\n [3.2]\n [7.4]\n [2.5]\n [5.9]\n [3.8]\n [1.9]]\n[[86]\n [76]\n [17]\n [47]\n [85]\n [42]\n [81]\n [88]\n [67]\n [30]\n [25]\n [60]\n [30]\n [75]\n [21]\n [54]\n [95]\n [41]] [[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"#拆分数据，0.25作为测试集\n",
"X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) \n",
"print(X_train,X_test)\n",
"print(Y_train,Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 训练线性回归"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"#使用训练集对模型进行训练\n",
"regressor = LinearRegression()\n",
"regressor = regressor.fit(X_train, Y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 预测结果"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[16.84472176]\n [33.74557494]\n [75.50062397]\n [26.7864001 ]\n [60.58810646]\n [39.71058194]\n [20.8213931 ]]\n[[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n"
]
}
],
"source": [
"Y_pred = regressor.predict(X_test)\n",
"print(Y_pred)\n",
"print(Y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 可视化"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 训练集结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"#散点图\n",
"plt.scatter(X_train , Y_train, color = 'red')\n",
"#线图\n",
"plt.plot(X_train , regressor.predict(X_train), 'bo-')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 测试集结果可视化"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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gitextract_grz_jkps/
├── .gitignore
├── Code/
│ ├── Day 11_K-NN.ipynb
│ ├── Day 11_K-NN.md
│ ├── Day 11_k-NN.py
│ ├── Day 13_SVM.ipynb
│ ├── Day 13_SVM.md
│ ├── Day 13_SVM.py
│ ├── Day 1_Data_Preprocessing.ipynb
│ ├── Day 1_Data_Preprocessing.md
│ ├── Day 1_Data_Preprocessing.py
│ ├── Day 25_Decision_Tree.ipynb
│ ├── Day 25_Decision_Tree.md
│ ├── Day 25_Decision_Tree.py
│ ├── Day 2_Simple_Linear_Regression.ipynb
│ ├── Day 2_Simple_Linear_Regression.md
│ ├── Day 2_Simple_Linear_Regression.py
│ ├── Day 34_Random_Forests.ipynb
│ ├── Day 34_Random_Forests.md
│ ├── Day 34_Random_Forests.py
│ ├── Day 39.ipynb
│ ├── Day 3_Multiple_Linear_Regression.ipynb
│ ├── Day 3_Multiple_Linear_Regression.md
│ ├── Day 3_Multiple_Linear_Regression.py
│ ├── Day 40.ipynb
│ ├── Day 41.ipynb
│ ├── Day 42.ipynb
│ ├── Day 6_Logistic_Regression.ipynb
│ ├── Day 6_Logistic_Regression.md
│ ├── Day 6_Logistic_Regression.py
│ ├── KafkaProducer.py
│ ├── TestKafka.py
│ └── my/
│ ├── Data_age_salary.csv
│ └── LinerTest.py
├── FAQ.MD
├── LICENSE
├── Other Docs/
│ ├── README.md
│ └── 速查手册/
│ └── README.md
├── README.md
├── Translation specification.MD
└── datasets/
├── 50_Startups.csv
├── Data.csv
├── Social_Network_Ads.csv
├── mnist.npz
├── readme.md
└── studentscores.csv
Condensed preview — 45 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (540K chars).
[
{
"path": ".gitignore",
"chars": 1226,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "Code/Day 11_K-NN.ipynb",
"chars": 9652,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#机器学习100天——第十一天：K近邻法（K-NN）\"\n ]\n "
},
{
"path": "Code/Day 11_K-NN.md",
"chars": 1224,
"preview": "# K近邻法 (K-NN)\n\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info"
},
{
"path": "Code/Day 11_k-NN.py",
"chars": 1100,
"preview": "# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the datase"
},
{
"path": "Code/Day 13_SVM.ipynb",
"chars": 45136,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第13天：支持向量机 (SVM)\"\n ]\n"
},
{
"path": "Code/Day 13_SVM.md",
"chars": 2951,
"preview": "# Day 13 | 支持向量机 (SVM)\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/mas"
},
{
"path": "Code/Day 13_SVM.py",
"chars": 2793,
"preview": "#Day13: Support Vector Machine (SVM)\r\n\r\n#Importing the libraries\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\ni"
},
{
"path": "Code/Day 1_Data_Preprocessing.ipynb",
"chars": 10891,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第1天：数据预处理（Data Preproce"
},
{
"path": "Code/Day 1_Data_Preprocessing.md",
"chars": 1668,
"preview": "# 数据预处理\n\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graph"
},
{
"path": "Code/Day 1_Data_Preprocessing.py",
"chars": 2252,
"preview": "#Day 1: Data Prepocessing\n\n#Step 1: Importing the libraries\nimport numpy as np\nimport pandas as pd\n\n#Step 2: Importing d"
},
{
"path": "Code/Day 25_Decision_Tree.ipynb",
"chars": 46952,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第25天：决策树（Decision Tree）"
},
{
"path": "Code/Day 25_Decision_Tree.md",
"chars": 2775,
"preview": "# 决策树分类\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/raw/master/Info-graphs/"
},
{
"path": "Code/Day 25_Decision_Tree.py",
"chars": 2706,
"preview": "# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the datase"
},
{
"path": "Code/Day 2_Simple_Linear_Regression.ipynb",
"chars": 68020,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第二天：简单线性回归\\n\",\n \"## "
},
{
"path": "Code/Day 2_Simple_Linear_Regression.md",
"chars": 1052,
"preview": "# 简单线性回归模型\n\n\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-g"
},
{
"path": "Code/Day 2_Simple_Linear_Regression.py",
"chars": 905,
"preview": "# Data Preprocessing\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndataset = pd.read_csv('../d"
},
{
"path": "Code/Day 34_Random_Forests.ipynb",
"chars": 46429,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"导入库\"\n ]\n },\n {\n \"cell_type\": "
},
{
"path": "Code/Day 34_Random_Forests.md",
"chars": 3087,
"preview": "# 随机森林\n\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs"
},
{
"path": "Code/Day 34_Random_Forests.py",
"chars": 2714,
"preview": "# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the datase"
},
{
"path": "Code/Day 39.ipynb",
"chars": 36683,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"## 安装库\\n\""
},
{
"path": "Code/Day 3_Multiple_Linear_Regression.ipynb",
"chars": 18408,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第3天：多元线性回归（Multiple Lin"
},
{
"path": "Code/Day 3_Multiple_Linear_Regression.md",
"chars": 1071,
"preview": "# 多元线性回归\n\n\n<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-gra"
},
{
"path": "Code/Day 3_Multiple_Linear_Regression.py",
"chars": 1071,
"preview": "# Importing the libraries\nimport pandas as pd\nimport numpy as np\n\n# Importing the dataset\ndataset = pd.read_csv('../data"
},
{
"path": "Code/Day 40.ipynb",
"chars": 103087,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 准备\\n\",\n \"\\n\",\n \"- 数据集下载地址请"
},
{
"path": "Code/Day 41.ipynb",
"chars": 4357,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 基础知识\\n\",\n \"基本的CNN结构如下： Convolu"
},
{
"path": "Code/Day 42.ipynb",
"chars": 9572,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"这是Python，TensorFlow和Keras教程系列的深度学习基"
},
{
"path": "Code/Day 6_Logistic_Regression.ipynb",
"chars": 45025,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 机器学习100天——第6天：逻辑回归（Linear Regress"
},
{
"path": "Code/Day 6_Logistic_Regression.md",
"chars": 3492,
"preview": "<p align=\"center\">\n <img src=\"https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%204"
},
{
"path": "Code/Day 6_Logistic_Regression.py",
"chars": 2611,
"preview": "# Importing the Libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the datase"
},
{
"path": "Code/KafkaProducer.py",
"chars": 341,
"preview": "#!/usr/bin/python\n\nfrom kafka import KafkaProducer\n\nkafkaHosts=[\"kafka01.paas.longfor.sit:9092\"\n ,\"kafka02.pa"
},
{
"path": "Code/TestKafka.py",
"chars": 598,
"preview": "#!/usr/bin/python\n\nfrom kafka import KafkaConsumer;\n\n\nkafkaHosts=[\"kafka01.paas.longfor.sit:9092\"\n ,\"kafka02."
},
{
"path": "Code/my/Data_age_salary.csv",
"chars": 101,
"preview": "Age,Salary\n44,72000\n27,48000\n30,54000\n38,61000\n40,78000\n35,58000\n35,52000\n48,79000\n50,83000\n37,67000\n"
},
{
"path": "Code/my/LinerTest.py",
"chars": 134,
"preview": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndataset = pd.read_csv('Data_age_salary.csv');\nda"
},
{
"path": "FAQ.MD",
"chars": 1512,
"preview": "\n# 常见问题解答\n欢迎到Issues提问\n \n### 1. “拆分数据”和“特征缩放”的顺序\n可以先“拆分数据”，再“特征缩放”，但需要使用训练集的fit参数，去transform测试集，以保证参数相同。\n\n详见[issue#41](ht"
},
{
"path": "LICENSE",
"chars": 1075,
"preview": "MIT License\n\nCopyright (c) 2018 MachineLearning100\n\nPermission is hereby granted, free of charge, to any person obtainin"
},
{
"path": "Other Docs/README.md",
"chars": 5,
"preview": "演示图片\n"
},
{
"path": "Other Docs/速查手册/README.md",
"chars": 5481,
"preview": "# 14张速查表，带你玩转 Python 数据科学\n译自 DataCamp 的速查表，有兴趣的朋友可以在这里查看[英文原版](https://www.datacamp.com/community/data-science-cheatshee"
},
{
"path": "README.md",
"chars": 13094,
"preview": "# 机器学习100天\n\n英文原版请移步[Avik-Jain](https://github.com/Avik-Jain/100-Days-Of-ML-Code)。数据在[这里](https://github.com/MachineLearn"
},
{
"path": "Translation specification.MD",
"chars": 640,
"preview": "## 翻译规范`v20180809`\n### 交付物\n>- 图片（.jpg），质量选择最高，源文件自己保留好，文件命名`Day xxx.jpg`\n>- 代码，含md、py、notebook等，要求调试通过，数据引用datasets文件夹`."
},
{
"path": "datasets/50_Startups.csv",
"chars": 2436,
"preview": "R&D Spend,Administration,Marketing Spend,State,Profit\r\n165349.2,136897.8,471784.1,New York,192261.83\r\n162597.7,151377.59"
},
{
"path": "datasets/Data.csv",
"chars": 226,
"preview": "Country,Age,Salary,Purchased\r\nFrance,44,72000,No\r\nSpain,27,48000,Yes\r\nGermany,30,54000,No\r\nSpain,38,61000,No\r\nGermany,40"
},
{
"path": "datasets/Social_Network_Ads.csv",
"chars": 10926,
"preview": "User ID,Gender,Age,EstimatedSalary,Purchased\r\n15624510,Male,19,19000,0\r\n15810944,Male,35,20000,0\r\n15668575,Female,26,430"
},
{
"path": "datasets/readme.md",
"chars": 160,
"preview": "## 代码中使用的数据\n\n 50 50_Startups.csv\n\n 10 Data.csv\n\n 400 Social_Network_Ads.csv\n\n 40966 mnist.npz\n\n "
},
{
"path": "datasets/studentscores.csv",
"chars": 236,
"preview": "Hours,Scores\r\n2.5,21\r\n5.1,47\r\n3.2,27\r\n8.5,75\r\n3.5,30\r\n1.5,20\r\n9.2,88\r\n5.5,60\r\n8.3,81\r\n2.7,25\r\n7.7,85\r\n5.9,62\r\n4.5,41\r\n3."
}
]
// ... and 1 more files (download for full content)
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