ridge_regression and lasso_regression with and without outliers.ipynb

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      "language": "python",
      "name": "python3"
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      "version": "3.6.4"
    },
    "colab": {
      "name": "ridge_regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shuaib11-Github/shuaib/blob/master/ridge_regression%20and%20lasso_regression%20with%20and%20without%20outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glZcyx5Kg0ju"
      },
      "source": [
        "### Ridge regression without outlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "2O4Ng0Ona-XG",
        "outputId": "f4fe317f-7fb3-45a5-a998-9b1b7a832cb0"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "train = [ [4.0,50],[4.5,55],[4.7,60],[5.0,70]]\n",
        "test = [[5.5,68], [5.7,65], [5.9,69], [6.2,70], [7.0,73]]\n",
        "X_train = [4.0,4.5,4.7,5.0]\n",
        "y_train = [50,55,60,70]\n",
        "X_test = [5.5,5.7,5.9,6.2,7.0]\n",
        "y_test = [68,65,69,70,73]\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\n",
        "plt.plot([min(X_train), max(X_train)], [min(y_train), max(y_train)], color='red') # predicted\n",
        "plt.xlabel('Height in ft')\n",
        "plt.ylabel('Weight in kgs')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UQ5hl7C9/gfvvD333u5D0ARa2M/qivXIRKT+51Pifdffd2yozsxfd/WvtfK8aeArY192fNrOJhF8ap7r7Fi32e9/dM7bzp7rG/8kn8PWvhzb9uXOhR48uHU41fpH06EqNv7eZfb6OX/S+d7T5aYbvLQIWufvT0fZUYCiwNGriIXpdlkMM6XXllaF9f9KkLid9gHHjoKJi/bKKilAuIumQS+I/A3jczP5uZrOAx4Azo2ab29r7kru/DbxpZuva74cBLwHTgeOisuOAezsZe/lbtAguuQQOPxy+8528HLKmBurqQg3fLLzW1enBrkiaZG3qATCzTYCdos1X2nug28b3hgCTCV1B5wMnEH7Z3A0MBBqBo7JN+pbapp5jjglLKb70UlhLV0SkA9pr6sk0gOsgd/+bmf2o1Uc7mBnu/qdsJ3X3OcAGJyXU/iWTRx+Fu+6CX/1KSV9E8irTlA0HAH8DftDGZw5kTfzSSatXh/l4Bg6EczTEQUTyK9MArrHR6wmFC0cAuOGGMAPn1KkbPokVEemiXNbc7W9mvzOzB6PtwWb2i/hDS6mmJrjgAhg2DH7UupVNRKTrcunVcyvwEPDv0farhKmaJQ5jxsCHH4bum1pgRURikEvi7+vudxMtwuLuq4E1sUaVVg0NMHkyjBgBgwcnHY2IlKlcEv8qM9uaaL4eM9sLzc+ff2vXwvDhsM02YWoGEZGY5LIQyxmEQVc7mNkTQD/gyFijSqPbb4enn4Zbb4XNNks6GhEpY5n68Y8E/gk8R+jaOQgwwgCuzwoTXkqsWBG6be61F/z0p0lHIyJlLlONfzvgasKI3ReAJwi/CN4CMo60lQ769a9Db54HHgiro4iIxChTP/4zAaIFVKqBfQhTLtSZ2XJ319PHfHjppbCi1oknwm67JR2NiKRALm38PYHNgM2jn7cIfwFIV7mHHjy9e2t6TBEpmExt/HXA1wiLrjxNaOa50t3fL1Bs5e9Pf4KZM0ONv1+/pKMRkZTI1KA8ENiEsErWYsL8+ssLEVQqNDfDqFGw885w8slJRyMiKZKpjf//mZkRav37ELp1ft3M3gOeXDeXj3TSZZeF9Q5nzYLuubS4iYjkR8aMEy2SPs/MlhMGba0Avg/sASjxd9b8+SHxH3MMHHBA0tGISMpkauMfQajp7wN8Rmjj/ydwM3q42zWjRoVa/oQJSUciIimUqcZfBdwDnO7uSwoTTgo89BDcey/85jew7bZJRyMiKZTT0otJK5ulFz/9NDzMdQ/z7W+ySdIRiUgZ6/DSixKDiRPh1VfhL39R0heRxGh+gEJ56y246CL4/vfhu99NOhoRSTEl/kI555zQ1HPVVUlHIiIpp8RfCE88AVOmwJlnwo47Jh2NiKScEn/c1qwJC6xstx2cd17S0YiI6OFu7G66CebMgbvugl69ko5GREQ1/li9+25YPP3AA+Goo5KORkQEUOKP1/nnh9W1Jk0Cs6SjEREBlPjj8/zzcOONcMopYdCWiEiRUOKPgzuceir07RuWVRQRKSJ6uBuH+vrQhXPyZNhii6SjERFZj2r8+bZyJZx9Nuy+O5xwQtLRiIhsQDX+fLv4YliyBKZNg276vSoixUeZKZ9eeQWuvjrU9PfcM+loRETapMSfL+5w2mnQs2eYa19EpEipqSdfpk8Pi6xcdRX07590NCIi7Yq1xm9mC8zsBTObY2YNUdmFZrY4KptjZqU/R/FHH8HIkTB4cOi3n2f19VBVFR4ZVFWFbRGRzipEjf+b7v5Oq7Kr3P3yApy7MCZMgAULYOZM2HjjvB66vh5qa6G5OWw3NoZtgJqavJ5KRFJCbfxd1dgY2vR//GM46KC8H37MmC+S/jrNzaFcRKQz4k78DjxsZrPNrLZF+XAzm2tmN5vZlm190cxqzazBzBqamppiDrMLzjgjzMNzeTx/wCxc2LFyEZFs4k78+7n7UOAQ4BQz2x+4HtgBGAIsAa5o64vuXufu1e5e3a9fv5jD7KSZM+GPfwzz7A8cGMsp2jtsTKcTkRSINfG7++LodRkwDdjD3Ze6+xp3XwvcBOwRZwyx+eyzMB/P9tuHlbViMm4cVFSsX1ZREcpFRDojtsRvZr3MrM+698B3gHlmNqDFbj8E5sUVQ6yuvRZefjl039x009hOU1MDdXVQWRlalCorw7Ye7IpIZ5m7x3Ngs+0JtXwIvYfudPdxZnYHoZnHgQXASe6+JNOxqqurvaGhIZY4O2XpUvjqV2GffeCBBzTXvogUJTOb7e7Vrctj687p7vOBb7RR/tO4zlkwo0eHvvsTJyrpi0jJUXfOjnrySbj1Vjj99FDrFxEpMUr8HbFmTXigO2BAWFZRRKQEaa6ejrj5Zpg9G6ZMgT59ko5GRKRTVOPP1fvvh/76++0HP/lJ0tGIiHSaEn+ufvUreO+90I1TD3RFpIQp8edi7ly47jo4+WT4xgYdlURESooSfzbu4YHulluGZRVFREqcHu5m84c/wD/+ATfcAFttlXQ0IiJdphp/Jh9+GObhGToUTjwx6WhERPJCNf5Mxo+HxYvh7rtho42SjkZEJC9U42/Pa6+FOfZ/+tMwJ4+ISJlQ4m/PyJFh1s3LLks6EhGRvFJTT1vuvz/MujlhQpieQUSkjKjG39rHH4fa/k47wYgRSUcjIpJ3qvG3duWV8MYb8PDD0KNH0tGIiOSdavwtvflmWNPwhz+Eb3876WhERGKhxN/SWWfB2rWh1i8iUqaU+NeZNSuM0j3nHKiqSjoaEZHYKPEDrF4dHuRWVobELyJSxvRwF+D66+GFF+CPf4SePZOORkQkVqrxL1sGF1wA3/pWeKgrIlLmlPjPOw9WrYJJk7TAioikQroT/7PPhnV0R4yA//iPpKMRESmI9Cb+tWth+HDYZhsYOzbpaERECia9D3dvuw2eeSa8brZZ0tGIiBRMOmv8K1bA6NGw995w7LFJRyMiUlDprPFfeCE0NcGDD0K3dP7uE5H0Sl/We/FFuOYa+OUvw5KKIiIpk67E7x568Gy2WZiMTUQkhdLV1DN1Kvztb3DttdC3b9LRiIgkIj01/lWr4IwzYJdd4KSTko5GRCQx6anxX3ppmG9/yhTonp7LFhFpLR01/vnzw/q5xxwD+++fdDQiIolKR+I//fRQy58wIelIREQSF2viN7MFZvaCmc0xs4aobCsz+6uZvRa9bhnHuevrw3oqh9gMmD6d5793Pmy7bRynEhEpKYWo8X/T3Ye4e3W0PRqY6e5fAWZG23lVXw+1tfBW46dczWm8ylc46L7Tqa/P95lEREpPEk09hwG3Re9vAw7P9wnGjIHmZhjJ1QziVU5jIss/2oQxY/J9JhGR0hN34nfgYTObbWa1UVl/d18SvX8b6N/WF82s1swazKyhqampQydduDC8LmEAN3MCMzhkvXIRkTQzd4/v4GbbuvtiM9sG+CtwKjDd3bdosc/77p6xnb+6utobGhpyPm9VFTQ2blheWQkLFuR8GBGRkmZms1s0s38u1hq/uy+OXpcB04A9gKVmNiAKagCwLN/nHTcOKirWL6uo0CwNIiIQY+I3s15m1mfde+A7wDxgOnBctNtxwL35PndNDdTVhRq+WXitqwvlIiJpF+cQ1v7ANAvr2HYH7nT3GWb2LHC3mf0CaASOiuPkNTVK9CIibYkt8bv7fOAbbZS/CwyL67wiIpJZOkbuiojI55T4RURSRolfRCRllPhFRFIm1gFc+WJmTYQeQJ3RF3gnj+EkSddSfMrlOkDXUqy6ci2V7t6vdWFJJP6uMLOGtkaulSJdS/Epl+sAXUuxiuNa1NQjIpIySvwiIimThsRfl3QAeaRrKT7lch2gaylWeb+Wsm/jFxGR9aWhxi8iIi0o8YuIpExZJX4z28jMnjez+9v4bBMz+4OZvW5mT5tZVeEjzE2W6zjezJqiBeznmNmJScSYCzNbYGYvRHFusJKOBZOiezLXzIYmEWcucriWA81sRYv78qsk4syFmW1hZlPN7F9m9rKZ7d3q81K6L9mupejvi5kNahHfHDP7wMxGttonr/ckzmmZk3Aa8DKwWRuf/QJ43913NLOjgcuA/yxkcB2Q6ToA/uDuwwsYT1d8093bG3xyCPCV6GdP4ProtVhluhaAx9z9+wWLpvMmAjPc/Ugz6wG0WraopO5LtmuBIr8v7v4KMARCpQ9YTFi4qqW83pOyqfGb2XbA94DJ7ezScpH3qcAwixYLKCY5XEc5OQy43YOngC3Wrc4m8TCzzYH9gd8BuPun7r681W4lcV9yvJZSMwx4w91bz1SQ13tSNokfuBo4G1jbzufbAm8CuPtqYAWwdWFC65Bs1wFwRPTn3lQz+1KB4uoMBx42s9lmVtvG55/fk8iiqKwYZbsWgL3N7H/N7EEz+1ohg+uALwNNwC1Rc+LkaIW8lkrlvuRyLVAa92Wdo4Hft1Ge13tSFonfzL4PLHP32UnH0hU5Xsd9QJW770JYwP62DPsmbT93H0r4M/UUM9s/6YC6INu1PEeYF+UbwDXAnwsdYI66A0OB6919V2AVMDrZkDotl2splftC1FR1KHBP3Ocqi8QP7AscamYLgLuAg8xsSqt9FgNfAjCz7sDmwLuFDDIHWa/D3d9190+izcnAboUNMXfuvjh6XUZos9yj1S6f35PIdlFZ0cl2Le7+gbt/GL1/ANjYzPoWPNDsFgGL3P3paHsqIXm2VCr3Jeu1lNB9gVCpeM7dl7bxWV7vSVkkfnc/1923c/cqwp9Kf3P3Y1vt1nKR9yOjfYpq9Fou19GqXe9QwkPgomNmvcysz7r3wHeAea12mw78LOqxsBewwt2XFDjUrHK5FjP7t3XPjMxsD8L/rWKrWODubwNvmtmgqGgY8FKr3UrivuRyLaVyXyLH0HYzD+T5npRbr571mNlFQIO7Tyc8ALrDzF4H3iMk1pLQ6jpGmNmhwGrCdRyfZGwZ9AemRf/nugN3uvsMMzsZwN1vAB4Avgu8DjQDJyQUaza5XMuRwH+Z2WrgI+DoYqtYtHAqUB81LcwHTijR+wLZr6Uk7ktUofg2cFKLstjuiaZsEBFJmbJo6hERkdwp8YuIpIwSv4hIyijxi4ikjBK/iEjKKPFL2TGzD1ttH29m12b5zqFmlnEEazTT4wYzpkafjTSztiYII5pKYHC2uFvsv4mZPRLN1PifZnZert8VyUVZ9+MXyVU0RmJ6Fw4xEphC6GPd+tgdnTp71+h762Zs/BAY34XYRNajGr+kipn1M7M/mtmz0c++UfnnfxWY2Q5m9pSF+fcvafUXRG/7Yv73+mgk5Qjg34G/m9nf2zjnLDOrjt5/aGbjoknDnjKz/q323YbwC2T3qMZ/D9Azel8fz7+KpI0Sv5SjdYlyjpnNAS5q8dlE4Cp33x04granv54ITHT3nQnzwbS0K6F2PxjYHtjX3ScBbxHm6/9mlth6AU9Fk4b9A/hlyw+juYBOJMwhP8Tdfwx8FL2vyX7pItmpqUfK0Ufrmkkg1OaB6mjzW8Bg+2Iphs3MrHer7+8NHB69vxO4vMVnz7j7oui4c4Aq4PEOxPYpsO45wWzCMH2RglLil7TpBuzl7h+3LLTc1+T5pMX7NXT8/9BnLeaK6cz3RbpMTT2SNg8TJvYCwMyGtLHPU4RmIMh9Mr+VQJ+uhdauz8xs45iOLSmkxC9pMwKotrCC2UvAyW3sMxIYZWZzgR0Jq7VlUwfMaOvhbh7UAXP1cFfyRbNzirQS9cf/yN3dzI4GjnH3w5KOSyRf1L4osqHdgGujBTyWAz9POB6RvFKNX0QkZdTGLyKSMkr8IiIpo8QvIpIySvwiIimjxC8ikjL/B77aqzbR91ZTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Y7qCwZpFa-XX",
        "outputId": "45e6c00f-418a-4f5c-83fe-8afee2c44391"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\n",
        "X_train\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "model_ridge = Ridge(alpha=1)\n",
        "model_ridge.fit(X_train,y_train)\n",
        "model_lnr = LinearRegression()\n",
        "model_lnr.fit(X_train,y_train)\n",
        "\n",
        "x_gen = np.linspace(3,7,100)\n",
        "x_gen = x_gen.reshape(-1,1)\n",
        "y_rid= model_ridge.predict(x_gen)\n",
        "y_lnr = model_lnr.predict(x_gen)\n",
        "\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\n",
        "plt.plot(x_gen,y_lnr,color='orange')\n",
        "plt.plot(x_gen,y_rid,color='green')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySWfPo2Rdd4y"
      },
      "source": [
        ""
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYiuG-PLgoHG"
      },
      "source": [
        "### Ridge regression with introduction of outlier and its effect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "R8C0rOxGgUth",
        "outputId": "2c5c0b83-dfbc-4a3c-f4e5-38fc164c13ae"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "train = [ [4.0,50],[4.5,55],[4.7,60],[5.0,70], [7,80], [5.5, 100]]\r\n",
        "test = [[5.5,68], [5.7,65], [5.9,69], [6.2,70], [7.0,73]]\r\n",
        "X_train = [4.0,4.5,4.7,5.0,7, 5.5]\r\n",
        "y_train = [50,55,60,70,80,100]\r\n",
        "X_test = [5.5,5.7,5.9,6.2,7.0]\r\n",
        "y_test = [68,65,69,70,73]\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot([min(X_train), max(X_train)], [min(y_train), max(y_train)], color='red') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "985YQOSOgUvI",
        "outputId": "c01fddff-375e-41f9-fe32-80079b8de3ab"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_train\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Ridge\r\n",
        "model_ridge = Ridge(alpha=1)\r\n",
        "model_ridge.fit(X_train,y_train)\r\n",
        "model_lnr = LinearRegression()\r\n",
        "model_lnr.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(3,7,100)\r\n",
        "x_gen = x_gen.reshape(-1,1)\r\n",
        "y_rid= model_ridge.predict(x_gen)\r\n",
        "y_lnr = model_lnr.predict(x_gen)\r\n",
        "\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot(x_gen,y_lnr,color='orange')\r\n",
        "plt.plot(x_gen,y_rid,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nySgAzZJhYDY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJz6nqESimUA"
      },
      "source": [
        "### Lasso regression without outlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "_9oLzo41gUy0",
        "outputId": "f6adcc65-1e40-4317-bddd-cf5cb8a38778"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "train = [ [4.0,50],[4.5,55],[4.7,60],[5.0,70]]\r\n",
        "test = [[5.5,68], [5.7,65], [5.9,69], [6.2,70], [7.0,73]]\r\n",
        "X_train = [4.0,4.5,4.7,5.0]\r\n",
        "y_train = [50,55,60,70]\r\n",
        "X_test = [5.5,5.7,5.9,6.2,7.0]\r\n",
        "y_test = [68,65,69,70,73]\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot([min(X_train), max(X_train)], [min(y_train), max(y_train)], color='red') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "H4LY7IA1hdAT",
        "outputId": "cb07719e-2073-42fe-c669-e385b249cf84"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_train\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Lasso\r\n",
        "model_lasso = Lasso(alpha=1)\r\n",
        "model_lasso.fit(X_train,y_train)\r\n",
        "model_lnr = LinearRegression()\r\n",
        "model_lnr.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(3,7,100)\r\n",
        "x_gen = x_gen.reshape(-1,1)\r\n",
        "y_las= model_lasso.predict(x_gen)\r\n",
        "y_lnr = model_lnr.predict(x_gen)\r\n",
        "\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot(x_gen,y_lnr,color='blue')\r\n",
        "plt.plot(x_gen,y_las,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jpFJxMKiGFc"
      },
      "source": [
        ""
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrD8yxaHixfx"
      },
      "source": [
        "### Lasso regression with introduction of outlier and its effect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "hwmPyqG6h4pu",
        "outputId": "736f4e5b-b9c0-40d1-f5e3-151558ef07e4"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "train = [ [4.0,50],[4.5,55],[4.7,60],[5.0,70], [7,80], [5.5, 100]]\r\n",
        "test = [[5.5,68], [5.7,65], [5.9,69], [6.2,70], [7.0,73]]\r\n",
        "X_train = [4.0,4.5,4.7,5.0,7, 5.5]\r\n",
        "y_train = [50,55,60,70,80,100]\r\n",
        "X_test = [5.5,5.7,5.9,6.2,7.0]\r\n",
        "y_test = [68,65,69,70,73]\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot([min(X_train), max(X_train)], [min(y_train), max(y_train)], color='red') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hJgpoxuXiKnB",
        "outputId": "aa07b0eb-10e8-4f28-8649-5d4fc94428f7"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_train\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Lasso\r\n",
        "model_lasso = Lasso(alpha=1)\r\n",
        "model_lasso.fit(X_train,y_train)\r\n",
        "model_lnr = LinearRegression()\r\n",
        "model_lnr.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(3,7,100)\r\n",
        "x_gen = x_gen.reshape(-1,1)\r\n",
        "y_las= model_lasso.predict(x_gen)\r\n",
        "y_lnr = model_lnr.predict(x_gen)\r\n",
        "\r\n",
        "plt.scatter(np.array(X_train),y_train,color='blue')\r\n",
        "plt.scatter(np.array(X_test),y_test,color='red')\r\n",
        "plt.plot(x_gen,y_lnr,color='blue')\r\n",
        "plt.plot(x_gen,y_las,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7SnLIXxiLTv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}