07_random_forest_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LeamvpPVXuS_"
   },
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O2wvZ7SKXzVC"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PVmESEFZX4Ig"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zgbK_F8-X7em"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "adBE4tjQX_Bh"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Position_Salaries.csv')\n",
    "X = dataset.iloc[:, 1:-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v4S2fyIBYDcu"
   },
   "source": [
    "## Training the Random Forest Regression model on the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "o8dOCoJ1YKMc",
    "outputId": "8894f460-d2de-44ab-d79b-9e69dffb12cb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8IbsXbK3YM4M"
   },
   "source": [
    "## Predicting a new result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "pTXrS8FEYQlJ",
    "outputId": "6bfe8b5e-125c-43ce-ee32-d2f53477e0ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([167000.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.predict([[6.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLqF9yMbYTon"
   },
   "source": [
    "## Visualising the Random Forest Regression results (higher resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "BMlTBifVYWNr",
    "outputId": "71439b75-32b1-444b-a9ee-7ae0672cea82"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "random_forest_regression.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}