Python/machine_learning/Random Forest Regression/Random Forest Regression.ipynb
2018-10-27 08:08:03 +05:30

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Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Importing the libraries\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Importing the dataset\n",
"dataset = pd.read_csv('Position_Salaries.csv')\n",
"X = dataset.iloc[:, 1:2].values\n",
"y = dataset.iloc[:, 2].values"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=300, n_jobs=1, oob_score=False, random_state=0,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting Random Forest Regression to the dataset\n",
"regressor = RandomForestRegressor(n_estimators = 300, random_state = 0)\n",
"regressor.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Predicting a new result\n",
"y_pred = regressor.predict(6.5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 160333.33333333]\n"
]
}
],
"source": [
"print(y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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94bYm67wwK63TUR1mZluMDRuchDoUEY+SdTB4Ang6xTAN+DZwRupgsBNwdVrlamCnVH4G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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa760ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualising the Random Forest Regression results (higher resolution)\n",
"X_grid = np.arange(min(X), max(X), 0.01)\n",
"X_grid = X_grid.reshape((len(X_grid), 1))\n",
"plt.scatter(X, y, color = 'red')\n",
"plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
"plt.title('Truth or Bluff (Random Forest Regression)')\n",
"plt.xlabel('Position level')\n",
"plt.ylabel('Salary')\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
}