mirror of
https://github.com/TheAlgorithms/Python.git
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197 lines
45 KiB
Plaintext
197 lines
45 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
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" from numpy.core.umath_tests import inner1d\n"
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]
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}
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],
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"source": [
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"# Importing the libraries\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.metrics import confusion_matrix\n",
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"from matplotlib.colors import ListedColormap\n",
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"from sklearn.ensemble import RandomForestClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Importing the dataset\n",
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"dataset = pd.read_csv('Social_Network_Ads.csv')\n",
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"X = dataset.iloc[:, [2, 3]].values\n",
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"y = dataset.iloc[:, 4].values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Splitting the dataset into the Training set and Test set\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
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" warnings.warn(msg, DataConversionWarning)\n"
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]
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}
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],
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"source": [
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"# Feature Scaling\n",
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"sc = StandardScaler()\n",
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"X_train = sc.fit_transform(X_train)\n",
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"X_test = sc.transform(X_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[63 5]\n",
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" [ 3 29]]\n"
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]
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}
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],
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"source": [
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"# Fitting classifier to the Training set\n",
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"# Create your classifier here\n",
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"classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
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"classifier.fit(X_train,y_train)\n",
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"# Predicting the Test set results\n",
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"y_pred = classifier.predict(X_test)\n",
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"\n",
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"# Making the Confusion Matrix\n",
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"cm = confusion_matrix(y_test, y_pred)\n",
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"print(cm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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UfCOTG/JOphvTROQaYAlwhIg8BnxCVeNWAjBGITaLGx0Eraai9LAOc24U91JD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\nF2GyjN4FfApvlTCI1z1P8X7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"text/plain": [
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"<matplotlib.figure.Figure at 0x14150b50>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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cyZzJc2jJ6XP0EENs2rmJKzdeOeZzKCBIQ+ic0TmsDQGgxVronNGZ+LXUXiGl+gb7uHTD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\nAABJRU5ErkJggg==\n",
|
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"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x14717ff0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Visualising the Training set results\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('Random Forest Classifier (Training set)')\n",
|
|
"plt.xlabel('Age')\n",
|
|
"plt.ylabel('Estimated Salary')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Visualising the Test set results\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('Random Forest Classifier (Test set)')\n",
|
|
"plt.xlabel('Age')\n",
|
|
"plt.ylabel('Estimated Salary')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|