Random_Forest_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random Forest Regression",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyM/st4Y39zPv6NcIDFItPRv",
      "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/Lakshaypahuja21/Machine-Learning-Basics/blob/master/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": "d0XOqdTPTTaj"
      },
      "source": [
        "# Random forest regression model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpicJ2G4TdzW"
      },
      "source": [
        "## lib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltmx0YLrT7xF"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kMPG8o3Tds7"
      },
      "source": [
        "## dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAeZppirT_QL"
      },
      "source": [
        "df = pd.read_csv('Position_Salaries.csv')\n",
        "x = df.iloc[:, 1:-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCNtsxFdThkr"
      },
      "source": [
        "## training regression model on full data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4nwnq6JUEke",
        "outputId": "d76972cb-d079-4fff-f506-06e9dcc4df16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)\n",
        "regressor.fit(x, y)"
      ],
      "execution_count": 4,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_vCfAcxThZw"
      },
      "source": [
        "## predicting new results\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwgBegiRUL-q",
        "outputId": "562dc814-17d2-44c9-e40b-c7aac846ed8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "regressor.predict([[6.5]])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([167000.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LwlKAGvKThWo"
      },
      "source": [
        "visualizing random forest - high resolution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PKDsk-zTgdw",
        "outputId": "6a457879-639a-4fde-efd0-0af28d52fed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "x_grid = np.arange(min(x), max(x), 0.1)\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)')\n",
        "plt.xlabel('position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "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"
          }
        }
      ]
    }
  ]
}