regression_05_decision_tree.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "regression_05_decision_tree.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPO1CZNmWU1LkvAyjX/XPKD",
      "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/jihadkamil/MLearning/blob/master/regression_05_decision_tree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWat_K4NwSZP",
        "colab_type": "text"
      },
      "source": [
        "#decision tree regression "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWboM1b2wUxH",
        "colab_type": "text"
      },
      "source": [
        "importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYVL1ivJw2xz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uSqvnW6wo5c",
        "colab_type": "text"
      },
      "source": [
        "importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvoQ0f6CxLcA",
        "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": "nW3nzYUSwsxG",
        "colab_type": "text"
      },
      "source": [
        "training the decision tree regression model on the whole dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r4HRZltEyRNT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "952fa48e-56ea-4595-e4fb-e5636c600735"
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "dsregressor = DecisionTreeRegressor()\n",
        "dsregressor.fit(x,y)\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ipmATmgwxNZ",
        "colab_type": "text"
      },
      "source": [
        "predicting new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRBy_2ORzI8g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "cdc4cb35-fafa-453c-c5f0-3f9c00d13c4a"
      },
      "source": [
        "hissalaries = dsregressor.predict(x)\n",
        "# print(\"hisssalaries\",hissalaries)\n",
        "# print(\"yyyy\",y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hisssalaries [  45000.   50000.   60000.   80000.  110000.  150000.  200000.  300000.\n",
            "  500000. 1000000.]\n",
            "yyyy [  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yrM9Px_Kw80E",
        "colab_type": "text"
      },
      "source": [
        "visualizing the decision tree regression results (higher resolution)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKOEsVxG0fme",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "aa69624d-f6c7-4f3b-9395-ec0f07cadf8d"
      },
      "source": [
        "x_plot = np.arange(min(x),max(x),0.1)\n",
        "x_plot = x_plot.reshape((len(x_plot),1))\n",
        "\n",
        "plt.scatter(x,y,color='orange')\n",
        "plt.plot(x_plot,dsregressor.predict(x_plot),color='blue')\n",
        "plt.title('decision tree regression',color='pink')\n",
        "plt.ylabel('salary', color='yellow')\n",
        "plt.xlabel('level',color='yellow')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "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"
          }
        }
      ]
    }
  ]
}