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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{
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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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"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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|
||
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|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x14717ff0>"
|
||
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|
]
|
||
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|
},
|
||
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"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
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}
|
||
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],
|
||
|
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"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
|
||
|
|
}
|