linear_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "linear_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN+dUuHluzs/mbb4HgEHJRX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shreykinshu/Bus-Reservation-System/blob/master/linear_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": "lG09oNJXWZns"
      },
      "source": [
        "Problem 1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39NOUhKFWdk8",
        "outputId": "0817a9f3-c40e-415d-d37f-97e1f072025c"
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWyP43r7aEpA",
        "outputId": "40f78bb1-3ba4-4d38-fe59-e4921fcd9927"
      },
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/lab_file/Car_Buy.csv\")\n",
        "inputs = df.drop('Buy',axis='columns')\n",
        "target = df['Buy']\n",
        "print(inputs)\n",
        "print(target)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Age Mileage Road tested\n",
            "0  Recent     Low         Yes\n",
            "1  Recent    High         Yes\n",
            "2     Old     Low          No\n",
            "3  Recent    High          No\n",
            "0    1\n",
            "1    1\n",
            "2    0\n",
            "3    0\n",
            "Name: Buy, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBEgwz2RfgaF",
        "outputId": "79abcfcd-b3cf-4eff-85e4-dcb9b5a7f67c"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le_Age = LabelEncoder()\n",
        "le_Mileage = LabelEncoder()\n",
        "le_RoadTested = LabelEncoder()\n",
        "input_n=pd.DataFrame()\n",
        "input_n['Age_n'] = le_Age.fit_transform(inputs['Age'])\n",
        "input_n['Mileage_n'] = le_Mileage.fit_transform(inputs['Mileage'])\n",
        "input_n['RoadTested_n'] = le_RoadTested.fit_transform(inputs['Road tested'])\n",
        "print(input_n)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Age_n  Mileage_n  RoadTested_n\n",
            "0      1          1             1\n",
            "1      1          0             1\n",
            "2      0          1             0\n",
            "3      1          0             0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnaqatHiieX6",
        "outputId": "03da15e9-ba0c-4f6a-8023-b50df21f329f"
      },
      "source": [
        "target"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    0\n",
              "3    0\n",
              "Name: Buy, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOsiXsk6imgR",
        "outputId": "5443e0b8-1c36-4473-ab92-7ab2e1bd5e5a"
      },
      "source": [
        "from sklearn import tree\n",
        "model = tree.DecisionTreeClassifier(criterion='entropy')\n",
        "model.fit(input_n, target)\n",
        "model.score(input_n,target)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "Vsv8sfECkKOm",
        "outputId": "1dd08700-ee73-4b5b-b6ad-819b26ba1e54"
      },
      "source": [
        "from sklearn.tree import plot_tree\n",
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize=(16,12))\n",
        "a = plot_tree(model, feature_names=df.columns, fontsize=12, filled=True, \n",
        "              class_names=['1', '0'])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}