Random_Forest_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random_Forest_regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ansarimoiz/MachineLearning/blob/main/Random_Forest_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeamvpPVXuS_"
      },
      "source": [
        "# Random Forest Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2wvZ7SKXzVC"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVmESEFZX4Ig"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgbK_F8-X7em"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adBE4tjQX_Bh"
      },
      "source": [
        "dataset = pd.read_csv('Position_Salaries.csv')\n",
        "X = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4S2fyIBYDcu"
      },
      "source": [
        "## Training the Random Forest Regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbliSbXuOb6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "669bf964-2885-4bd8-e7dc-464c54109dc0"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor=RandomForestRegressor(n_estimators=10,random_state=0)\n",
        "regressor.fit(X,y)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=10, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IbsXbK3YM4M"
      },
      "source": [
        "## Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTXrS8FEYQlJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6b4da6b-989b-4acb-a0c9-ac7d3f99cce0"
      },
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([167000.])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLqF9yMbYTon"
      },
      "source": [
        "## Visualising the Random Forest Regression results (higher resolution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMlTBifVYWNr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "25fa7449-0749-42bb-c598-b8a2ed137c00"
      },
      "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(' Random Forest Regression ')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('Salary')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ]
}