Lasso_Ridge_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lasso_Ridge_Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/udghosh/Lasso_Rigde_Regression/blob/main/Lasso_Ridge_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpydxBQfpLaD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "62302610-71ab-4428-873a-bb4d82596728"
      },
      "source": [
        "import pandas as pd\n",
        "BHNames= ['crim','zn','indus','chas','nox','rm',\n",
        "         'age','dis','rad','tax','ptratio','black','lstat','medv']\n",
        "\n",
        "\n",
        "url='https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'\n",
        "data = pd.read_csv(url, delim_whitespace=True, names=BHNames)\n",
        "\n",
        "print(data.head(20))\n",
        "\n",
        "\n",
        "'''\n",
        "CRIM: Per capita crime rate by town\n",
        "ZN: Proportion of residential land zoned for lots over 25,000 sq. ft\n",
        "INDUS: Proportion of non-retail business acres per town\n",
        "CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
        "NOX: Nitric oxide concentration (parts per 10 million)\n",
        "RM: Average number of rooms per dwelling\n",
        "AGE: Proportion of owner-occupied units built prior to 1940\n",
        "DIS: Weighted distances to five Boston employment centers\n",
        "RAD: Index of accessibility to radial highways\n",
        "TAX: Full-value property tax rate per $10,000\n",
        "PTRATIO: Pupil-teacher ratio by town\n",
        "B: 1000(Bk — 0.63)², where Bk is the proportion of [people of African American descent] by town\n",
        "LSTAT: Percentage of lower status of the population\n",
        "MEDV: Median value of owner-occupied homes in $1000s'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       crim    zn  indus  chas    nox  ...    tax  ptratio   black  lstat  medv\n",
            "0   0.00632  18.0   2.31     0  0.538  ...  296.0     15.3  396.90   4.98  24.0\n",
            "1   0.02731   0.0   7.07     0  0.469  ...  242.0     17.8  396.90   9.14  21.6\n",
            "2   0.02729   0.0   7.07     0  0.469  ...  242.0     17.8  392.83   4.03  34.7\n",
            "3   0.03237   0.0   2.18     0  0.458  ...  222.0     18.7  394.63   2.94  33.4\n",
            "4   0.06905   0.0   2.18     0  0.458  ...  222.0     18.7  396.90   5.33  36.2\n",
            "5   0.02985   0.0   2.18     0  0.458  ...  222.0     18.7  394.12   5.21  28.7\n",
            "6   0.08829  12.5   7.87     0  0.524  ...  311.0     15.2  395.60  12.43  22.9\n",
            "7   0.14455  12.5   7.87     0  0.524  ...  311.0     15.2  396.90  19.15  27.1\n",
            "8   0.21124  12.5   7.87     0  0.524  ...  311.0     15.2  386.63  29.93  16.5\n",
            "9   0.17004  12.5   7.87     0  0.524  ...  311.0     15.2  386.71  17.10  18.9\n",
            "10  0.22489  12.5   7.87     0  0.524  ...  311.0     15.2  392.52  20.45  15.0\n",
            "11  0.11747  12.5   7.87     0  0.524  ...  311.0     15.2  396.90  13.27  18.9\n",
            "12  0.09378  12.5   7.87     0  0.524  ...  311.0     15.2  390.50  15.71  21.7\n",
            "13  0.62976   0.0   8.14     0  0.538  ...  307.0     21.0  396.90   8.26  20.4\n",
            "14  0.63796   0.0   8.14     0  0.538  ...  307.0     21.0  380.02  10.26  18.2\n",
            "15  0.62739   0.0   8.14     0  0.538  ...  307.0     21.0  395.62   8.47  19.9\n",
            "16  1.05393   0.0   8.14     0  0.538  ...  307.0     21.0  386.85   6.58  23.1\n",
            "17  0.78420   0.0   8.14     0  0.538  ...  307.0     21.0  386.75  14.67  17.5\n",
            "18  0.80271   0.0   8.14     0  0.538  ...  307.0     21.0  288.99  11.69  20.2\n",
            "19  0.72580   0.0   8.14     0  0.538  ...  307.0     21.0  390.95  11.28  18.2\n",
            "\n",
            "[20 rows x 14 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nCRIM: Per capita crime rate by town\\nZN: Proportion of residential land zoned for lots over 25,000 sq. ft\\nINDUS: Proportion of non-retail business acres per town\\nCHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\nNOX: Nitric oxide concentration (parts per 10 million)\\nRM: Average number of rooms per dwelling\\nAGE: Proportion of owner-occupied units built prior to 1940\\nDIS: Weighted distances to five Boston employment centers\\nRAD: Index of accessibility to radial highways\\nTAX: Full-value property tax rate per $10,000\\nPTRATIO: Pupil-teacher ratio by town\\nB: 1000(Bk — 0.63)², where Bk is the proportion of [people of African American descent] by town\\nLSTAT: Percentage of lower status of the population\\nMEDV: Median value of owner-occupied homes in $1000s'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0zotxMep0eA"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMiYKr_jp722",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e060add4-bad9-499a-a896-0914720756d0"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = data.drop('medv', axis = 1)\n",
        "print('X shape = ',X.shape)\n",
        "Y = data['medv']\n",
        "print('Y shape = ',Y.shape)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X shape =  (506, 13)\n",
            "Y shape =  (506,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bql01wWq32H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b5874948-b00f-42be-ee7f-a219920546c4"
      },
      "source": [
        "from sklearn import linear_model\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "names = data.drop('medv', axis =1).columns\n",
        "print(names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
            "       'ptratio', 'black', 'lstat'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wUtPjbSsG83"
      },
      "source": [
        "lasso = linear_model.Lasso(alpha=0.2)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ax6quoUMtkPP"
      },
      "source": [
        "lasso_coef = lasso.fit(X,Y).coef_\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0TO-n4qu7md",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "19cfa6a8-ac0e-42e3-9eaf-6161228dfe2b"
      },
      "source": [
        "plt.plot(range(len(names)),lasso_coef )\n",
        "plt.xticks(range(len(names)), names, rotation=60)\n",
        "plt.ylabel(\"coefficient\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AVX1TRMpD2YZJTAvrGuifm0FRnv+nmwwrzCUjLcUWgk3C2VUAGKCql4rIfFW9Nyo96kZE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\nwBhjkpQFAGOMSVL/H/ytTFG9tVBCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fiKe-cBu9r6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoT_Av3gv0cE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_ffHZlQwCn8"
      },
      "source": [
        "#For Ridge\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "x_std = scaler.fit_transform(X)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lABoF1ZPwHUM"
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "ridge = Ridge(alpha=0.2)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGL6vMvMxFAL"
      },
      "source": [
        "model = ridge.fit(x_std,Y).coef_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0E_12wDSXjW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "295767dd-b36f-414d-e20f-3dd108960bb8"
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.92646068,  1.07849275,  0.13607368,  0.68243527, -2.05110798,\n",
              "        2.6759721 ,  0.01817939, -3.09866587,  2.64775331, -2.06318543,\n",
              "       -2.05894196,  0.84918485, -3.74101393])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOFxMZAUSXzT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0adb5-9JSYA7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTueOnm7SYPT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jhg13ZcHSYck"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3Y2JzB7SYoj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00V8r3TiSY0j"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLRt2ezoSZAz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSGXG63ZSZOT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VV4baOgxFOZ"
      },
      "source": [
        "'''\n",
        "rr = Ridge(alpha=0.4)\n",
        "rr.fit(X_std, Y)\n",
        "\n",
        "plt.scatter(X_std, Y)\n",
        "plt.plot(X_std, c='red')\n",
        "\n",
        "plt.plot(model.coef_,linestyle='none',marker='o',markersize=10,color='green')\n",
        "plt.xlabel('Coefficient Index',fontsize=16)\n",
        "plt.ylabel('Coefficient Magnitude',fontsize=16)\n",
        "plt.legend(fontsize=13,loc=4)\n",
        "plt.show()'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw-PNnunyXxY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}