random_forest_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "random_forest_regression.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeamvpPVXuS_",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2wvZ7SKXzVC",
        "colab_type": "text"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVmESEFZX4Ig",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgbK_F8-X7em",
        "colab_type": "text"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adBE4tjQX_Bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4S2fyIBYDcu",
        "colab_type": "text"
      },
      "source": [
        "## Training the Random Forest Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8dOCoJ1YKMc",
        "colab_type": "code",
        "outputId": "8894f460-d2de-44ab-d79b-9e69dffb12cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
        "regressor.fit(X, y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IbsXbK3YM4M",
        "colab_type": "text"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTXrS8FEYQlJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6bfe8b5e-125c-43ce-ee32-d2f53477e0ee"
      },
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([167000.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLqF9yMbYTon",
        "colab_type": "text"
      },
      "source": [
        "## Visualising the Random Forest Regression results (higher resolution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMlTBifVYWNr",
        "colab_type": "code",
        "outputId": "71439b75-32b1-444b-a9ee-7ae0672cea82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "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()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}