Linear, Ridge, Lasso, ElasticNet analysis with and without outliers.ipynb

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      "language": "python",
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      "pygments_lexer": "ipython3",
      "version": "3.6.4"
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    "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/Linear%2C%20Ridge%2C%20Lasso%2C%20ElasticNet%20analysis%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": "Gbnrck0hxEJQ"
      },
      "source": [
        "### Analysis of Linear, Ridge, Lasso, ElasticNet without outliers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ouXwumKqt6CV",
        "outputId": "5f8ddfd3-4c18-4de1-c47d-195f557d406f"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "train = [ [4.0,52],[4.2,52],[4.4,62],[4.6,68], [4.8,65], [5,75]]\r\n",
        "test = [[5,68], [4.3,53], [4.7,69], [4,55], [4.5,58]]\r\n",
        "X_train = [4.0, 4.2, 4.4, 4.6, 4.8, 5]\r\n",
        "y_train = [52, 52, 62, 68, 65, 75]\r\n",
        "X_test = [5, 4.3, 4.7, 4, 4.5]\r\n",
        "y_test = [68, 53, 69, 55, 58]\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='green') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cCG7tLCKZrdy+knbj2jFr9SwaxTVixE0jqH5Kda9jSZgJ5AKuKmbWA8DMigOfoQu4RELqcMZh+n/Tn1pDa7Fo4yJG3DSCGW1mqOjLcQlkjP9hINlf/BsBk5xzA4IbS0SO+HHzjySkJLDgjwW0PK8lQ5oN4cwyZ3odS8LYMQu/mdXNtDgIGA7MBWabWV3n3KJghxOJZgcOH6DvnL70/bovJ5c4mf+0+g93XngnZuZ1NAlzOfX4+2dZ3gHU8Lc74NrcPtzMygEjgZr+9zwM3Ai0A7b6V/uXc25i3mKLRLbv1n9HQkoCy7Yu475a9zHgxgFUiKngdSzPJCdDz56wdi3ExvruOZ5f9xmORscs/M65/LjGexAw2Tl3u5kVA2LwFf4BzrnX8uHzRSLK3oN7+feX/2bgtwM5s8yZjL9nPM3Pbe51LE8lJ0NiIqSl+ZbXrPEtg4r/8Qra7XbMrCy+c/5HATjnDjrn/grW9kTC3czfZ1JrWC0GfDuADvEdWNZpWdQXffD19I8U/SPS0nztcnyCeZ+1aviGc942s+/NbKT/oi+ALma2xMxG+2/n+A9mlmhmqWaWunXr1uxWEYkIf+3/i3Yp7Wj8XmMKWSFmPTCLIc2HUKZ4Ga+jFQhr1+atXXIXzMJfBKgLDHXOXQzsBZ4BhgLVgTrARv55LAEA51yScy7eORdfsaLmDpfI9MVPX1BjcA1GLx7NUw2fYkmHJVwdd7XXsQqU2Ni8tUvuArrZupk1BOIyr++cey+Xt60H1jvnvvMvfwI845zbnOlzRwDj8xJYJBJs2buFrpO6MmbZGGqdVouUe1KIPyPe61gFUp8+fx/jB4iJ8bXL8Qnkyt338fXQFwPp/mYH5Fj4nXObzGydmZ3nnPsZaAwsN7NKzrmN/tVuBZYed3qRMOOcI/nHZB6d/Ch7Du7hpUYv8fTlT1O0cFGvoxVYRw7g6qye/BNIjz8eqOGcc8fx+Y/gu/irGLAKeAh4w8zq4PvlsRpofxyfKxJ21u1cR4cJHZj460QaVG7AqJajqFGxhtexwkLr1ir0+SmQwr8UOB3feHyeOOcW4/vFkdn9ef0ckXCW4TIYnjqcp6c/TbpLZ+CNA+lSv4smVRPPBFL4K+AbopkPHJ1/3znXMmipRCLEL3/+QtuUtsxZO4frzrqOpBZJVDu5mtexJMoFUvifD3YIkUhzOOMwr897nedmPUeJIiUY3XI0D9Z5UNMtSIEQyHz8s0MRRCRS/LDpBx5OeZhFGxdx6/m3MrjZYCqVruR1LJGjcpqk7Wvn3BVmthvfgdijLwHOOaerS0QyOXD4AL2/6s3Lc1/mlJNOYewdY2l1QSv18qXAyWmuniv8j6VDF0ckPM1bN4+ElARWbFtBm9pteP2G1ykfU97rWCLZCugCLhHJ3p6De+g1sxdvfPcGVcpWYVLrSTQ5u4nXsURypMIvcpym/TaNxPGJrP5rNV3qdaFv476ULq4/kKXgU+EXyaMd+3bw+NTHeXvx25xX/jzmPDSHK2Kv8DqWSMBynaTNzPoF0iYSDT5b8Rk1htTgvR/eo8cVPVjcYbGKvoSdQGbnvD6btqb5HUSkINu0ZxN3jL2D2z6+jdNLnc78dvPp27gvJYqU8DqaSJ7ldDpnR6ATcJaZLcn0Uml8994ViXjOOd774T0em/IYaYfS6HttX55o+IQmVZOwltMY/4fAJOD/8M2jf8Ru59z2oKYSKQDW/LWG9uPbM+W3KTSs0pBRLUdxfoXzvY4lcsJyOo9/J7ATuMfMCgOn+dcvZWalnHO6/41EpAyXwZAFQ3hmuq+/82bTN+lUrxOFLJj3LRIJnUDm4++Cb76ezUCGv9kBtYIXS8QbP2/7mYSUBOaum8uN1W9keIvhVC1X1etYIvkqkNM5uwHnOef+DHYYEa8cSj/Ea9+8xguzXyCmaAzv3PwObWq30XQLEpECKfzr8A35iESk7zd+T0JKAt9v+p7ba9zOm03f5PRSp3sdSyRocjqrp7v/6SpglplN4O/z8b8e5GwiQbX/8H5enP0ir8x9hQoxFfj0zk+57YLbvI4lEnQ5Ha0q7f9ZC0wDimVq03XpUiAkJ0NcHBQq5HtMTg7sfV+v/Zraw2rzf1//H21qt2FF5xUq+hI1cjqr54VQBhHJq+RkSEyEtDTf8po1vmU49v1Zdx/YTY8ZPRi8YDBx5eKYct8Ubqh+Q2gCixQQlts91M1sHH+fjx98Y/6pwHDn3P4gZTsqPj7epaamBnszEmbi4nzFPquqVWH16n+2T1k5hcTxiazbuY5H6j9Cn8Z9KFWsVLBjinjGzBY657Le9zygg7urgIrAR/7lu4DdwLnACHTzdPHI2mNcSZK1ffu+7Tw25THe++E9zq9wPl8//DUNqzQMfkCRAiqQwt/QOVcv0/I4M1vgnKtnZsuCFUwkN7Gx2ff4Y2N9j845Pl3xKZ0ndmb7vu30vLInva7qpfl1JOoFciliKTOLPbLgf37k7+ODQUklEoA+fSAm5u9tMTG+9o27N9Lq41bcMfYOKpepzIJ2C+h9bW8VfREC6/E/DnxtZr/hu99uNaCTmZUE3g1mOJGcHDmA27Onb3gnNhZ693YcrPEONYZ0Z//h/fS7rh/dL+tOkUK69YTIEbke3AUws+LAkdmpfg7FAd3MdHBXAvH7jt9JHJ/I9FXTuTL2Ska2HMm55c/1OpaIZ/J8cNfMrnXOzTSzrCc3VzcznHP/zfeUIschPSOdwQsG02NGDwpZIYY0G0L7+PaaVE3kGHL6+/dqYCZwUzavOUCFXzy3YusKElISmLd+Hk3PbsqwFsOILRub+xtFolhOF3A95398KHRxRAJzKP0Q/eb246WvXqJUsVK8f+v7tL6otSZVEwlAINMynwb0Bc5wzjU1sxrAZc65UUFPJ5KNhX8s5OGUh1myeQl3XngnbzZ9k1NLnup1LJGwEcgg6DvAFOAM//Iv+KZqFgmpfYf28fS0p6k/sj5b927ls7s+Y8ztY1T0RfIokHPcKjjnPjazHgDOucNmlh7kXCJ/89War2ib0pZft/9K24vb8uoNr1KuRDmvY4mEpUAK/14zK49/vh4za4Dm55cQ2XVgF89Mf4ahqUOpVq4a0++fTuOzGnsdSySsBXoBVwq+0zjn4pu35/agphIBJv46kfbj27Nh1wYea/AYLzV6iZLFSnodSyTs5XQefzfgG2ARvlM7z8N35e7PzrlDoYkn0Whb2ja6Te5G8o/J1KhYg7EJY2lQuYHXsUQiRk4HdysDA4EtwAygNRBHHm7CYmblzOwTM/vJzFaY2WVmdoqZTTOzX/2PJ5/IDkjkcM4xZukYagyuwZhlY3j2qmdZlLhIRV8knx2z8DvnnnDONQROB3oA24GHgKVmtjzAzx8ETHbOnQ/UBlYAzwAznHPn4PuF8swJ5JcI8cfuP7hlzC3c/endVC1XlYWJC3mh0QsUL1Lc62giESeQMf6TgDJAWf/PH8CPub3JzMoCVwEPAjjnDgIHzexm4Br/au8Cs4Cn8xZbIoVzjlHfj+KJqU9wIP0Ar13/Go82eFSTqokEUU5j/EnAhfhuuvIdvvH+151zOwL87GrAVuBtM6sNLAQeBU5zzm30r7MJOO0Y208EEgFiY3UJfiRatWMV7ca1Y+bvM7m66tWMbDmSs0852+tYIhEvpzH+WKA4vuK8AVgP/JWHzy4C1AWGOucuBvaSZVjH+aYGzXZ6UOdcknMu3jkXX7FixTxsVgq69Ix0BswbQM0hNVmwYQHDWwxn5gMzVfRFQiSnuXqamG/ikwuBhvhO66xpZtuBeUfm8snBemC9c+47//In+Ar/ZjOr5JzbaGaV8B08liixdMtSElISmL9hPs3Pac6wFsOoXKay17FEokqOUzY4n6XARGASMBeojm/IJkfOuU3AOjM7z9/UGFiO75qAB/xtDwBfHF90CScH0w/ywqwXqDu8Lqt2rOLD2z5k3D3jVPRFPJDTGH9XfD39hsAhfGP83wCjCeDgrt8jQLKZFcN30/aH8P2y+djMEoA1wJ3HnV7CwoINC3g45WGWblnKvRfdy8AbB1KxpIbvRLyS06kTccBY4LFMB2PzxDm3GPjH3V/w9f4lwqUdSuPZL59lwLcDqFSqEil3p3DTednd3kFEQimnMf7uoQwikWXW6lm0TWnLbzt+o/0l7el3XT/KlijrdSwRIbDz+EUCtnP/Tp6a9hRJi5KofnJ1ZraZSaNqjbyOJSKZqPBLvhn38zg6TOjApj2beOKyJ3ih0QvEFI3xOpaIZKHCLyds696tPDr5UT5a+hEXnXoRn9/1OfXOrOd1LBE5BhV+OW7OOT5a+hFdJ3Vl14FdvHDNCzxzxTMUK1zM62gikgMVfjku63etp+OEjoz/ZTyXnnkpo1qO4sJTL/Q6logEQIVf8iTDZTBi4QienPYkhzMO8/oNr9P10q4ULlTY62giEiAVfgnYr3/+Srtx7Zi9ZjbXVruWETeN4KyTz/I6lojkUY5TNogAHM44zGvfvEatYbX4ftP3jLhpBNPvnx7dRT85GeLioFAh32NysteJRAKmHr/kaMnmJSSkJJD6Ryotz2vJkGZDOLPMmV7H8lZyMiQmQlqab3nNGt8yQOvW3uUSCZB6/JKtA4cP8NyXz3FJ0iWs+WsNY24fw+d3fa6iD9Cz5/+K/hFpab52kTCgHr/8w7frvyUhJYHlW5dzX637GHjjQMrHlPc6VsGxdm3e2kUKGPX45ai9B/fSfUp3Go5qyK4Du5hw7wTev/V9Ff2sjnVHON0pTsKECr8AMGPVDC4aehEDvh1Ah/gOLOu0jGbnNPM6VsHUpw/EZJmKIibG1y4SBlT4o9xf+/+ibUpbrnv/OooUKsLsB2czpPkQyhQv43W0gqt1a0hKgqpVwcz3mJSkA7sSNjTGH8W++OkLOk7oyJa9W3j68qd57urnOKnoSV7HCg+tW6vQS9hS4Y9Cm/dspuvkrny87GNqn1abcfeM45IzLvE6loiEiAp/FHHO8cGSD+g2pRt7Du6hd6PePHX5UxQtXNTraCISQir8UWLtzrV0GN+BSSsncVnlyxjVchQXVLzA61gi4gEV/giX4TIYljqMp6c/TYbLYFCTQXSu11mTqolEMRX+CPbLn7/QNqUtc9bO4bqzriOpRRLVTq7mdSwR8ZgKfwQ6nHGY/t/057lZvrN0RrcczYN1HsTMvI4mIgWACn+E+WHTDzyc8jCLNi7i1vNvZXCzwVQqXcnrWCJSgKjwR4j9h/fT+6ve9Jvbj/InleeTOz6hVY1WXscSkQJIhT8CfLPuGxJSEvhp2088UPsBXr/xdU456RSvY4lIAaXCH8b2HNzDv2b8i7fmv0WVslWY3HoyN559o9exRKSAU+EPU1N/m0riuETW7lxL53qd6du4L6WLl/Y6loiEARX+MLNj3w66T+3OO4vf4bzy5/HVQ19xRewVXscSkTCiwh9G/rviv3Se2Jmte7fS44oePHv1s5QoUsLrWCISZlT4w8CmPZvoMrELn674lDqn12HivRO5uNLFXscSkTClwl+AOed494d36T6lO2mH0uh7bV+eaPiEJlUTkROiwl9Arf5rNe3Ht2fqb1O5vMrljGw5kvMrnO91LBGJACr8BUyGy2Dw/MH0mNEDM+Otpm/RsV5HCpluliYi+UOFvwD5adtPtE1py9x1c7mx+o0MbzGcquWqeh1LRCJMULuRZrbazH40s8Vmlupve97MNvjbFptZcO7onZwMcXFQqJDvMTk5KJvJD4fSD9F3Tl9qD6vN8q3LefeWd5nUepKKvogERSh6/I2cc9uytA1wzr0WtC0mJ0NiIqSl+ZbXrPEtQ4G7T+qijYtISElg8abF3F7jdt5q+hanlTrN61giEsEic+C4Z8//Ff0j0tJ87QXEvkP76DG9B/VH1GfTnk18euenjL1jrIq+iARdsAu/A6aa2UIzS8zU3sXMlpjZaDM7Obs3mlmimaWaWerWrVvzttW1a/PWHmJfr/2aOsPr8PLcl2lTuw3LOy3ntgtu8zqWiESJYBf+K5xzdYGmQGczuwoYClQH6gAbgf7ZvdE5l+Sci3fOxVesWDFvW42NzVt7iOw+sJsuE7tw5dtXcjD9IFPvm8romwQZTw8AAAp0SURBVEdz8knZ/u4TEQmKoBZ+59wG/+MW4DOgvnNus3Mu3TmXAYwA6uf7hvv0gZiYv7fFxPjaPTJ55WRqDq3JkAVDePTSR/mx449cX/16z/KISPQKWuE3s5JmVvrIc+AGYKmZZb4d1K3A0nzfeOvWkJQEVauCme8xKcmTA7t/pv3JA58/QNPkppQsWpK5D89lYJOBlCpWKuRZREQguGf1nAZ85r/PaxHgQ+fcZDN738zq4Bv/Xw20D8rWW7f29Awe5xyfLP+ELpO6sH3fdnpd2YteV/WieJHinmUSEYEgFn7n3Cqgdjbt9wdrmwXFxt0b6TSxE5//9DmXVLqEqfdNpfbp//inEBHxhK7czUfOOd5e/Dbdp3TnQPoB+l3Xj+6XdadIIf0zi0jBoYqUT37f8TuJ4xOZvmo6V1W9ihE3jeDc8ud6HUtE5B9U+E9QekY6b81/i3/N/BeFrTBDmw8l8ZJETaomIgWWCv8JWL51OQkpCXy7/luant2U4S2GU6VsFa9jiYjkSIX/OBxMP0i/r/vRe05vShcrzQe3fsC9F92L/wwmEZECTYU/j1L/SCUhJYElm5dw14V38UbTNzi15KlexxIRCZgKf4D2HdrHc7Oeo/+8/pxe6nQ+v+tzbj7/Zq9jiYjkmQp/AGavnk3bcW1ZuX0l7eq245XrX6FciXJexxIROS469SQHuw7souP4jlzz7jVkuAxmtJlB0k1JKvpZhdFNb0REPf5jmvDLBDpM6MAfu/+ge4PuvNjoRUoWK+l1rIInjG56IyI+6vFnsS1tG/f99z5afNSCMsXL8M3D39D/xv4q+scSBje9EZG/U4/fzznHmGVjeGTSI/y1/y+eu/o5elzRQ5Oq5aaA3/RGRP5JhR/YsGsDnSZ2IuXnFOqdUY9RLUdx0WkXeR0rPMTG+oZ3smsXkQIpqod6nHOMWDiCGkNqMO23abx2/WvMS5inop8XBfCmNyKSs6jt8f+2/TfajWvHl6u/5Jq4axhx0wjOPuVsr2OFnyMHcHv29A3vxMb6ir4O7IoUWFFX+NMz0hn03SB6zexF0cJFGd5iOG3rttWkaifC45veiEjeRFXhX7plKQkpCczfMJ8W57ZgaPOhVC5T2etYIiIhFbHd3MzXFFU96yCt3nyBusPrsmrHKj687UNS7k6JuKKv66hEJBAR2eP/2zVFZ85nbZME1m5fSsNS9/J5h4FULFnR64j5TtdRiUigzDnndYZcxcfHu9TU1IDXj4vzn2F4VW+45jnYUwnGD6PqgRasXh2slN46us9ZVK1KxO6ziOTMzBY65+Kztkdkj//otUPbq8OidjCtHxwoy9oIni5f11GJSKAicoz/6LVDS++B8cPgQNm/t0egY+1bJO+ziByfiCz80XhNUTTus4gcn4gs/K1bQ1KSb3zbzPeYlBTZBzmjcZ9F5PhE5MFdERE59sHdiOzxi4jIsanwi4hEGRV+EZEoo8IvIhJlVPhFRKJMWJzVY2ZbgWwmJAhIBWBbPsYJB9rn6KB9jg4nss9VnXP/mJwsLAr/iTCz1OxOZ4pk2ufooH2ODsHYZw31iIhEGRV+EZEoEw2FP8nrAB7QPkcH7XN0yPd9jvgxfhER+bto6PGLiEgmKvwiIlEmogq/mRU2s+/NbHw2rxU3szFmttLMvjOzuNAnzH+57HN3M1tuZkvMbIaZVfUiY37KaX8zrdPKzJyZRcRpf7nts5nd6f+el5nZh6HOFwy5/Hcda2Zf+l9fYmbNvMiYn8xstZn9aGaLzewfUxGbzxv++rXEzOqeyPYiqvADjwIrjvFaArDDOXc2MADoF7JUwZXTPn8PxDvnagGfAK+ELFXw5LS/mFlp/zrfhSxR8B1zn83sHKAHcLlz7kKgWyiDBVFO33Mv4GPn3MXA3cCQkKUKrkbOuTrHOGe/KXCO/ycRGHoiG4qYwm9mlYHmwMhjrHIz8K7/+SdAYzML67vw5rbPzrkvnXNp/sVvgcqhyhYMAXzHAC/h+6W+PyShgiyAfW4HDHbO7QBwzm0JVbZgCWCfHVDG/7ws8EcocnnsZuA95/MtUM7MKh3vh0VM4QcGAk8BGcd4/UxgHYBz7jCwEygfmmhBk9s+Z5YATApunKDLcX/9f/5Wcc5NCGmq4MrtOz4XONfM5prZt2bWJHTRgia3fX4euM/M1gMTgUdClCuYHDDVzBaaWWI2rx+tX37r/W3HJSIKv5m1ALY45xZ6nSVU8rLPZnYfEA+8GvRgQZLb/ppZIeB14PGQBguiAL/jIvj+/L8GuAcYYWblQhAvKALc53uAd5xzlYFmwPv+7z+cXeGcq4tvSKezmV0VzI2F+z/WEZcDLc1sNfAf4Foz+yDLOhuAKgBmVgTfn4h/hjJkPgtknzGz64CeQEvn3IHQRsxXue1vaaAmMMu/TgMgJcwP8AbyHa8HUpxzh5xzvwO/4PtFEK4C2ecE4GMA59w8oAS+iczClnNug/9xC/AZUD/LKkfrl19lf9txbzCifvD1fMZn094ZGOZ/fje+g0Oe5w3yPl8M/Aac43XGUOxvlnVm4Tuw7XneIH/HTYB3/c8r4BsOKO913iDv8yTgQf/zC/CN8ZvXeU9gP0sCpTM9/wZokmWd5v79Nnydmvknss1I6fFny8xeNLOW/sVRQHkzWwl0B57xLlnwZNnnV4FSwFj/aWIpHkYLiiz7GxWy7PMU4E8zWw58CTzpnAvnv2SzlWWfHwfamdkPwEf4fgmE8xQEpwFf+/dnPjDBOTfZzDqYWQf/OhOBVcBKYATQ6UQ2qCkbRESiTET3+EVE5J9U+EVEoowKv4hIlFHhFxGJMir8IiJRRoVfIo6Z7cmy/KCZvZXLe1qaWY6n+JrZNTnMkNnNzGKO8dpIM6uRW+5M6xc3s+n+U3DvMrN/BfpekUAU8TqASEHgnEsBTuQ6h27AB0Ba1hecc23z+FkX+99XB47+Iut7AtlE/kY9fokqZlbRzD41swX+n8v97Uf/KjCz6v4Jz340s95Z/oIoZWafmNlPZpbsnye9K3AG8KWZfZnNNmcdmTrCzPaYWR8z+8G/jdOyrHsqvl8g9fw9/rHASf7nycH5V5Foo8IvkehIoVxsZouBFzO9NggY4JyrB7Qi+6l/BwGDnHMX4ZsLJ7OL8fXuawBn4ZsH/w180wY0cs41yiVbSeBb51xt4Ct80yof5XxztbQF5jjf3Ox3APv8z1vnvusiudNQj0SifUeGScDXm8c3OynAdUCNTLdiKGNmpbK8/zLgFv/zD4HXMr023zm33v+5i4E44Os8ZDsIHDlOsBC4Pg/vFckXKvwSbQoBDZxzf7tRSx7uyZN5htN08v7/0KFM88ocz/tFTpiGeiTaTCXTjTvMrE4263yLbxgIfDO5BmI3vqmhg+GQmRUN0mdLFFLhl2jTFYj337B6OdAhm3W6Ad3NbAlwNr67teUmCZic3cHdfJAELNHBXckvmp1TJAv/+fj7nHPOzO4G7nHO3ex1LpH8ovFFkX+6BHjLfAP/fwEPe5xHJF+pxy8iEmU0xi8iEmVU+EVEoowKv4hIlFHhFxGJMir8IiJR5v8Bt+PTfTTOIR4AAAAASUVORK5CYII=\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": "XHBvZCVSt6Fd",
        "outputId": "4cfcc02f-acd5-4ce1-d661-a5b630e3bfa7"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_test = np.array(X_test).reshape(-1, 1)\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Lasso\r\n",
        "from sklearn.linear_model import Ridge\r\n",
        "from sklearn.linear_model import ElasticNet\r\n",
        "from sklearn.linear_model import ElasticNetCV\r\n",
        "\r\n",
        "model_ridge = Ridge(alpha=1)\r\n",
        "model_ridge.fit(X_train,y_train)\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",
        "model_en = ElasticNet(alpha = 0.5, l1_ratio = 0.5, random_state = 0)\r\n",
        "model_en.fit(X_train,y_train)\r\n",
        "model_encv = ElasticNetCV(cv = 2, random_state = 0)\r\n",
        "model_encv.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(4,5.1,50)\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",
        "y_rid = model_ridge.predict(x_gen)\r\n",
        "y_en = model_en.predict(x_gen)\r\n",
        "y_encv = model_encv.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='black')\r\n",
        "plt.plot(x_gen,y_las,color='red')\r\n",
        "plt.plot(x_gen,y_rid,color='blue')\r\n",
        "plt.plot(x_gen,y_en,color='orange')\r\n",
        "plt.plot(x_gen,y_encv,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tWwkICGBH9A5yvZmLGLcYelfvzRcNvyC/R/4sP79VJCWZtUeCguD3383GCY0awdix8OKL4Opq6wizjSRwIezd8OE398ABcuc2x+/TjRs3+PTTTxk9YTRuL7pBGShduDTBLYKpVbKWBYK2gsuXzRokEyfCoUNQsCD072/+NuXK2To6m5AELoS9S7lQaaFRKGvWrCGgewCH8x7Grb8bSa5JDKs3jIG1B+LmnL2r6d2T1rBunam258wx47jr1DE73LRpAx4ONJTRCiSBC+EIOnXK8oiTiIgIBg0axIRfJuDxsgc8BE/7Ps3EFybyaKFHLRSohVy7BjNmmGp7927Il89sktCzp1W2JnNUDjAmSAj7ExJiBoc4OZmvISG2jujulixZQsXKFZmwdwLO/Zzx8PVgcovJrOqyyr6S95Yt0L27Ge73xhumwp40Cc6cgW++sUzydrQ372601tl2e+KJJ7QQjm7mTK1z59ba/Pve3HLnNsftzcWLF3Xnzp01JdHub7trPkG/MucVfTbyrK1DSxUZqfWkSVo/8UTqH7N7d603b7b8a9nozTt1SuvY2Pv/fWCLTienyjhwITIpm4ZlZ4nWmjlz5tBnQB+uVL2Crq4pma8kE5pPoPkjzW0dnrFrl+ltz5gBkZGmuu7d27SK8ltpBEw2vXmRkWbXteXLzSTQffvM9w3vc+66jAMXwkKsPCw7y86cOUOfPn2Yf2A+rq+6ojwUbz35FsMaDCOPWx7bBhcTYy5GBgWZi5Pu7mZnm549oVYt6y8mZaU3Lz4eNm1KTdgbN5rRjblyQb16ZhLoI49k6SXSJQlciEyy4rDsLNFaM3XqVN7++G2u178O7aF8kfJMbjmZGsVr2Da4gwfNBckffzR7S5YrZ8Ztd+lihgNmFwu9eVrD/v0mWS9fbqrtyEjz+VO9Orz3HjRubHZfs+ZAGUngQmSSFYZlZ9nRo0fpHtidVZGrcO7mjKu7K5/W/5QBTw/A1dlGE1vi4mD+fFNtr1wJLi5mManeveHZZ22zdGsW3ryzZ2HFCpOwly+H06fN8TJloGNHk7CffRYKFLBS7OlJrzFurZtcxBQ5xcyZWvv6aq2U+WqrC5gJCQn6yy+/1O4l3bVTDyfNJ+iG0xrqw5cP2yYgrbU+dkzrDz7QumhRc5HQ11fr4cO1PmsnF04z+OZFRmq9eLHW/ftrXalS6jXPggW1fvllrYODtT56NHtCRi5iCpGz7Nmzh9cDX2ezx2ZUXYVXLi++avoVnSt3zv6tzRISzCJSQUFmUSml4IUXzGJSzz3nEItJJSSYUYwpbZH1601v290d6tY1FXajRmabzOxeiVYuYgqRQ8TFxTFixAg+m/4Z+gUN3tC5cmfGPjeWwp7ZvDnB6dNmMalJk+DUKShWDD76yIzlLlkye2PJJK3NjPyUC4+rVpn5Q0pBtWowYIBJ2LVrm4uR9kgSuBAOZPPmzXTt1ZW9JfbCq+Cbz5dJLSfRuEzj7AsiKclkvaAgWLDALOX63HPw9dem6rbjxaQuXLi5j50y+MTPzwyGSeljFypk0zAzTBK4EA4gOjqaj4d+zJd/folqpnDK7cS7td5laP2h5HbNnT1BXLxotiSbOBGOHjVZbsAAc1GwbNnsiSGToqPN1pcpVfaOHea4tzc0aACDB5squ0wZ28Z5vySBC2HnVq9eTde3unKi0gloC/5F/Zn60lT8H/K3/otrDWvWmKQ9d65pCterB59/Dq1bmwaxHUlMhG3bUvvY//5rBsO4uZlh5sOHmyq7WjWHaMvfkyRwIezUtWvXeO/995gUOgnVQuHh7sH/Gv+PN2q+gbOTlbPP1aswfbppk+zfD15e0KePqbYrVLDua2eC1nDkSGpLZOVKEzpAlSrw5pumwq5b14wWzGkkgQthhxYtWkTA4AAuPHUBmkLjUo0JbhmMr5cVd5nR2kwnDAqCn382syaffNK0Tdq1s5sMeOmSSdQpVXbKDPiSJaFVK5OwGzaEIkVsGma2kAQuhB25ePEiffv3Zc6FOdAKvD28mdBiAu0qtrPe0MDISJg1yyTu0FDw9DQzJHv2hKpVrfOamRATA2vXpibs7dvNZ03+/OaC43vvmaRdrpxt5gbZkiRwIeyA1pqff/6ZXqN6EfFMBDwCr1d5nTFNxlAgl5Wm9u3YYZL2zJkQFQWVK8OECWZaYb581nnNDEhKMp8jKRce1641SdzV1fSxP/vMJOzq1c3kTrsUHwXhuyA8FK4m32pMgALVLPoy9nr6QjwwTp06RcAbASxVS+El8M3jy49tfqS+X33Lv9iNG/DLLyZxb9hgFupo395U208+abMS9vjx1Ap7xQqzexrA44+bmfeNG5s+dh4br8V1G60h5lxqkk65RR4CkidJunmDtz8kxVn85SWBC2EjSUlJBAcH8/YPbxNTPwan3E68X/t9Pq7/MR4uFl4Bad8+M5Jk2jQID4fHHoPx4+G118yYumx25YqZOJOStI8cMccfftgMJW/c2PSxH3oo20O7s6QEiDyYnKR3mK/hoRBzIfUxnn7gXRX8Opmk7e0PuUta7YNRErgQNnD48GE6v9GZjYU3QjOoWqgq01+eTqUiFtwuLC4O5s0z1fbq1aYH0aaNqbafeSZbq+3YWLN6bErC3rLFFK9580L9+ma0SOPG5nPFLvrY8VEQvvPmqvraLkiMMT93coP8FeHh5qmJ2qsyuHlla5iSwIXIRgkJCXw5/kuGLBpCQp0EPNw8GP38aHpX7225oYFHj0JwMEydaibflCoFI0bA669n29CMpCTYuTO1j/3PP6Z74+wMTz1l9iRu1Ahq1rTxxE2t4cbZ1Gr6vxbIYW5ugVSFcn2SE3UVyPcY2MEG0JLAhcgmu3btov2A9uwtsxeehSa+TZjcejIl8pXI+pMnJMCiRaba/usvs9pSixapDeRsWH0pLCw1Ya9YYT47wAwbDww0CfuZZ2x4ffSmFkiaW+zF1MfkKW2StN+ryZV1Fau2QLJKErgQVhYbG8snX3zCyE0j0bU03m7eTG49mdblW2f9yU+ehMmTze3MGSheHD75BAICoIQFPhjuIjzc9LFTJtEcPGiOP/QQNGmS2scuXtyqYaQvPjJNC2RHOi0Qd/CqBCVamorauyp4VwZX242+uR+SwIWwoo0bN9JuSDvCHg+Dp+D1Sq8zrvk48ntkYc/HxERYutRU24sWmTbA88+bIYDNmlltbF1cnBm4smyZuW3ebFolnp6mj927t6myK1bMxoJVa7hx5vaqOupw6mPcC4KXf3ILpKqprPM9Ck72u+hWRmXonVZKeQGTgUqYxlA3oAkQCKT8+2Ow1nqJNYIUwtFcv36dd4a+w8QTE6EulPQoyU8dfqK2T+37f9Lz501fOzjYjLsrUgTef9/0J0qVsljsACEhZqGnsDAzSMXHBw4fhuvXTR+7Zk0YMsQk7KeeMmuNWF1SAkQcuL1fHXsp9TF5ypi2R+kuqRcXcxW32xZIVmX0o/or4E+tdVullBuQG5PAx2mtx1gtOiEc0IoVK2g/qj2Xql7CqaITg54exMcNPsbd5T4WftLajCAJCjIjSuLjzTJ6I0ea7cksnDlPnzYTZaZMMYU+mLVFwsNNsu7Tx8x+tNam8f+Jj4CrO29O1uG7ISnW/NzJHbwehxIvplbVXo87XAskq+6ZwJVS+YF6QFcArXUcEJftO34IYefCw8Pp8UEP5tyYA7Wgcv7KzO48m8cKPZb5J7tyxYzZDgoyzWVvb+jXzwwBfPRRi8UcEQF//506vG/fvvQfp7UJ46WXLPbSqU9843Q6LZAjqY9xL2iS9KNvmFbIfy0Q6QBn5C9QCtMm+UEpVQXYCryV/LN+SqnXgC3AO1rrq7f+slKqB9ADwMfW23YLYSVz583l9cmvE1UtCndnd8Y2GUvvJ3vjpDIx+kNrs4/XxIkwe7YZPF2rFnz4IbRta5FtYeLjYePG1NEiGzeaSjtXLrNKbEAAvPtu+r+bsvnBfUuKT22BpNzCQyH2cupj8pQ1ybr062laIA/n2BZIVt1zT0ylVHVgA1Bba71RKfUVEAF8C1zC9MSHAcW01t3u9lyyJ6bIaS5cuECHgR1YmXslFIVGxRoxvcN0iuUtlvEniYgw65EEBcGuXWZ2y6uvmmq7cuUsxae1qapTEvbq1WbZEycns5ZIo0bmVqtW6tLefn5w4sTtz+Xrm7ry3z3FXUtnIsye1BaIswfkfzw1SXtXMRNhXPNm6XxzqqzsiXkKOKW13ph8fy4wSGt9Ps2TTwIWWSRSIRyA1prJMybzxu9vEFs5lnwqHz+0/YHWFTMxNHDbNpO0Z80yVwerVjXVd8eOWVr049y51KF9y5ebvjaYTXM6dzYJu0GDO8+gHz7cLPsdHZ16LHduc/w2WkP0qZsr6quhEHU09THuhZJbIG+aIXsFqkLeR6QFYgH3/Atqrc8ppU4qpR7VWh8AGgJ7lVLFtNZnkx/WCthtzUCFsBdhYWG0+qAV2x7aBpWhc7nOfN/2e/K6Z6B6vH7dtEeCgsw4vFy5oEMHs3t79er31SqIijKb5qT0sXcn/59YsKAZh52ym7qfX8aer1Mn83XIENM28fExybtTh3gI3397vzruSvJvKshbFgo8AaW7pV5czFVMWiBWcs8WCoBSyh8zjNANOAq8DnwN+GNaKMeBnmkSerqkhSIcWVJSEiO/H8lH6z8i8ZFEijkXY+5rc6nlU+vev7xnj6mup083W59XqGAGTnfubHa7yYSEBJP7Uyrs9etNb9vDw6zYl9IW8ffPwgTMuGsQviN1EszVULi2O3VFvf9aIFVuGQUiLRBryEoLBa11KHDrL79qicCEcAT7D+ynxSctOOx3GKcyTrxX9T2GNx+Oq/NdJoPExsKvv5pq+59/zJC/l1821Xbt2hmuSlNGgKT0sVetMm1zpUzXZcAAU2XXrm2SeKZoDdEnb6+qrx9LfYx7YZOgH+2f2rPOW05aIHZA3gEh7iIhIYH3x7zPuMPj0I9pyruXZ37gfMoVLHfnXzp0yEy2+eEHs7B12bIwejR07Wp2cs+ACxfMeiIpbZGTJ83xUqXM8t0pfeyCBTNxMknxcG3f7f3quJTBY8ok5oI1oGxgarL2eEhaIHZKErgQd7B522ZajmnJubLncC3myuj6o3mz3pvpb20WHw8LFphqe/lyM13xpZfMSJKGDe/Zy4iONn3slCp7505z3NvbJOqUWY9lymQw+P9aILeOAknTAvGqDD7t0iyH+ji4eGb8DyRsThK4ELeIjY2lx+c9mB4+HR6FuvnrMjdwLkU801mK9cQJmDTJTF08d87srDtsGHTrZnYnuIPERNi6NTVhr1tn1hpxc4M6deCLL0xbpGpV81lwR1pDdFg6LZDjqY/xKGImwPzXAqkio0ByCHkHhUjjr7//ov2U9oSXCSdP/jz80PIH2lZte/ODEhPhjz9Mtb0kefmfZs3MRcnnn08342ptdp1JSdgrV5rp6WAuNqZsaFCnzl02f0+Mg4h9tyfr+OQnQkG+R6Dgk1C2R+qQvVyZGJMuHIokcCGAyMhIXvn0Ff7gDygFrR9qzfTXp+PplqalcPasqbQnTTLj6x56yPQ2unc3s1xucemSSdQpfeyUSTA+PtC6tUnYDRrcYY+FuPCbt+36rwUSb37unMu0QHxfSR0JIi2QB44kcPHAC1kUQuC8QG743KBwQmF+ffVX6pata36YlGSycFAQzJ9vxvA1agRffgktW960ncyNG/Dvv6kJe/t2U3nny2cS9XvvmV8tVy7NNUGtIerELRcWd9zSAnnIJOliz6f2q/OUBUvt4CMcliRw8cC6dPkSL3z2Ahs9N6IeVvQt15fx7cfj4uRiyucffzRjtw8fNsM93nrLXJQsZ0agJCXB9jR97LVrzchBV1d4+mmzql+jRmZ+josLyS2QvXAsbQtkx+0tkEJPQbleyQs3VYFc9rSzr7AnksDFA2ncrHG8v/Z94ovGUyqpFEt6L+Gxoo+aLDxxIsyZY64q1qljdrhp0wY8PDh2DJYFm6S9YoVZNBCgUiWz1GqjRmZRqDyuV1JHgWxOTtQRe9O0QHInt0DapxkFUklaICJTJIGLB8qxU8doOqIpBwocwMXLhc+rfs7gugdZcw8AACAASURBVH1QM2dCUBvYu9f0PHr2hJ49uVKsIitXwvL+pso+mrzER/HiZsvJxo01z9U6TmGXNFX18lAzMiRFrmKmmn64WZoWSBlpgYgskwQuHghaawYFD2LM/jEkFU7iCVWNxQ2HU3T6HGhf3DSwa9QgJuhH1pVox7K1uVje1Qz109osENioQSyfv7OX+lVCecg9FJXSr15/zbyIcoK8j0Lh2uDdN3UH81xFbXruIueSBC5yvNADoTT/ujlnipwhl6s702jPy/N3wdCmJOXyZGfTQSwv2Y1l+0rwz9smlxfOf4X2TXbweedQqvqFUtglFBWxF3QCnMC0Orwqg1/H1ETt9Ti43GkMoBCWJwlc5FiJiYkEjA9g+oXpUFDT/VhJvlpwlUtXVzK1eBeWPfETK06UwfOfMPx9N/Ni9UmMaRlKmQKh5NZpWiBJD4OnPxRvbsZVe1WRFoiwC5LARY60YusK2v3YhiuFrlHxmhuB82pzNLwVnco9gnfl0/j7hvJm2UCm+ISS2zXC/JJygnyPgVdt8O6XOmvRI72B2kLYniRwkaPciL1Bp4+bssj1H5zyO9HmeD1a5iqO/9u76VN8AK7OCQAkOnniVKAyyrtz6oXF/JXAJevblgmRXSSBC8emk9CRxwjbtoU/l/3IWOc1HModTUtP+LZwEiUrrCEivhjxeavi5NcCCvmDlz/OecuYilsIByYJXFhNSEg6u7p0ysITJsaY6eRXQ4k6Gcr1U6HkS9xBknMk31yGcW5QEGeGxVbkJb+2FKhdG0pUIZ+0QEQOJQlcWEVIyM37Kp44Ye5DBpN47OWbFmxKvByKityHE4kA6Bt5OBRWhV8vPMUMr/Vc9ojiidOlmTt0KX7FM7rmqhCOLUNbqlmKbKn24MjwzuY6yWyAm3ZqeXio2Sg32fnIEmw+7M/24/7sO10Rj5MJ+Fxdy7pmc1lW8TK5wl34vM5IBrQZYO3TEsImsrSlmhCZFRZ2+zEP1xsUdt4Dh29ZuCkhCgCNM5cTyrPrZH2Wb63ChoNV2XWyMqWKudFIL6Px4Qm8lzSS4JceZuBjYcS6amreqMmSoUso6JWZrWmEyBkkgQur8C9/kYJOofj7mlsVnx089vB+XJwTYRPgkpc4zyocjevCv3urMme5P6tDKxIb70HZstCw1g36+P9Jg6jn8D4QCoULs+vNpjzinJ+TeU+Q62IuQlpOpuNzHW19qkLYjCRwkTU6CSKPpK5ZnXzbNuTMfw85ebkEO8KqsCi0FcUq+nMyyp/Zi0qxe48ZBVKwoNl17NtATaO8G/Bb8DX8NNdsU/bMM8T/bwZdLy9k1vnpkADPJz7Pr6N/JXcumfUoHmySwEXGJdyAa7tv3g0mfOd/LRCUM+QrD0UbgLc/S7f48/anVdh7pBDu7mYp7cSfzc7pdevCq6+Z1fv8fa/iNHM6fBkE+/eDl5fZ3aZXLxbHHaXjTx2JyBWB1wUvZnedzXNPP2fbv4MQdkISuEhfzMVbEnUoROw3FTeAS14z+aX06//NWNT5KnLwiMd/GxqsWgUREWbzgooVzQ40jRtD7drg4a5h0yb4Jgh+/hliYuDJJ81O7u3aEeGUQOvvWrMicgXEw6v5XmXqd1NxcZH/ZIVIIf83POju0ALhRmoLhNwlTZIu2SZ14aY8pUA5cf68WRc7ZVODU8mDR0qVgvbtTYXdoIFpkwAQGQk/hpg1t0NDIU8e6NrVLN/q7w/A5H8m0++PfsS6xVLsVDEWvb2IahWrZeufRQhHIAn8QZIQDeG70yTrHWbTgYTr5ufKBfKXh6INU9cB8fYH99QRHtHR8M8/JlkvWwY7d5rj3t4mUX/4oamyS5e+5bVDQ822ZCEhEBUFVaqY+x07mrVagVPhp2jxfQtC40NxinDi3XLvMvLjkTg5yYxJIdIjCTynirlw++7lkQdSWyCu+UwlXbpb6qa4+SuAs8dNT5OYCFs3pVbY69aZjWrc3MxmNV98YRJ21arpbMYeHQ2//GIS9caNpvndvj306gU1a/63MWSSTmLY4mEM2zCMRJXII2cfYcmHSyjjJxNyhLgbSeCOLikRog6n7mCe0q++cTb1Mbl9TCXt09Ykam9/8PRLs7NuKq3hyJHUhL1yJYQnb9no7w9vvmnaInXrQu47DQLZt8+0SKZNM79cvjyMHw+vvWZK9TR2nd1Fi+AWnOAELudcGF1rNO989g4qndiEEDeTBO5IEqIhfNfto0ASk+erp7RAHmqcZp/FKuBe4K5Pe/GiSdQpSTtlBmXJktC6tamwGzSAIndbUiQ2Fn77zSTuv/82O/u2bWuq7bp1b/uwiE2I5a05bxG8Lxgdp6lxpQYLhy+kaFHZvUaIjJIEbq9unE8zWzGlBXIwTQskv0nQZQNTE3X+CuDsfu+nvmH27k0ZLbJ9uzmeP79J1AMHmqRdtmy6RfrNjhyB4GCYOtXs5F66NIwcaS5M3iHjrzi0gldmvsJlp8t4HPNgQssJdH25a4b/NEIIQxK4rSUlQuShm6eWXw2FmHOpj/H0TW6BvJJaWXv6ZiC7GomJ5hpiSsJeu9YUzK6uUKsWDBtmEvYTT0CGRuklJMDChaa3vXSpaX63bGmq7UaN4A4XHcNjwukyowsLziyACGiS0ISfv/kZLy+vDJ2HEOJmGUrgSikvYDJQCdBAN+AAMBvwA44D7bTWV60SZU6RcP0OLZAb5ufKBfJXhGJNUhO1dxVw877786bj2LHUhL1iBVy5Yo4//jj07WvybL164OmZiSc9eRImTza3M2egRAn49FMICDDbtN+B1pqZ22bSa34volU0+fblI6R7CC8890Kmz0sIkSqjFfhXwJ9a67ZKKTcgNzAYWKG1HqGUGgQMAt63UpyO58a5dCbCHMR8/gGuXsktkJ4mSWeiBZKeK1du7mMfPWqOFy9uiuNGjcx09YceyuQTJyaaKjsoCBYtMlc5n3/e3G/a9J4l+6mIU7T9oS0bwzfCBeiUtxMTf5iIZ6Y+OYQQ6blnAldK5QfqAV0BtNZxQJxS6kWgfvLDpgGreRAT+G0tkORbzPnUx3iWMknat4NJ1AWqmpEhWRhpERNjhvSlJOytW01uzZsX6teH/v1NW+TRR+/zZc6fN33t4GCz/muRIjBoEAQGmrVi7yExKZHRq0fz0d8fkZCYQJG9Rfjtvd+o/XTt+whGCJGejFTgpYCLwA9KqSrAVuAtoKjWOmWs2jkg3eEDSqkeQA8AHx+fLAdsU/FRpgWStledtgXi5Gr2VXy4aepwPa/K4Jb1Hm9Skpk0k9IW+ecfczHSxQWeegqGDjUJu0YN09u+L1qb+e9BQTBvnul1N2gAo0bBiy+awd8ZsPPcTtpOa8uhmEOoY4o3S7/JqJ9G4e5+f/+6EEKk754bOiilqgMbgNpa641Kqa+ACOANrbVXmsdd1VrftVnrMBs6aG0uIt42EeYQ/7VA3LxNNZ2SqL39zY7mzhlLchkRFnZzH/viRXO8QgXTEmncGJ555r+JjPfvyhUzZjsoCA4ehAIFzCiSHj1MCZ9BMQkxDFw8kG+3f4uO1pQ6WIrfP/udypUrZzFAIR5sWdnQ4RRwSmu9Mfn+XEy/+7xSqpjW+qxSqhhwwXLhZqOkBDM879aJMDFpTsfTzyRqv06pyTp3ySy1QNITHm4K4JSkfeiQOV6smGk7N2pkbg8/bIEX0xrWrzdJ+5dfzLCU2rXho4/M+G0Pj3s/Rxorj66kw6wOXEi8gPMuZz5++mMGfzFYFp8Sworu+X+X1vqcUuqkUupRrfUBoCGwN/nWBRiR/HW+VSO1hPgo0/JIW1Vf22U2y4U0LZDmaSbCWKYFkp7YWNiwITVhb95sWiWenqaP3bevufBYsaIFPysiImDmTJO4d+0y5XtAgBkC+PjjmX66Kzeu0OvXXsw5MgeuQOWwyvw6+lfKli1roYCFEHeS0fLoDSAkeQTKUeB1wAn4RSkVAJwA2lknxPtwWwtke3IL5DA3tUC8/aFsb6u1QNILa/fu1IT9999muRBnZ7M0yIcfmgr7yScz3G7OuG3bTNKeNQuuX4dq1WDSJLM2SZ4893Eumlk7Z9Frfi+iEqNw2+LG2JZj6TOujyw+JUQ2yVAC11qHArf1XzDVuG391wK5pV8dezH1MXlKmwTt1zl14SYrtEDSc+qUSdYpt/PJg1MefRS6dTMJu359MwvS4q5fN2ttBwXBli2QK5dZ/a9XL6ie3tuZMcfDj/Pqz6+y9vxaOA31wusRMjGEEiVKWDB4IcS9OFaDMj7y9okwN7VA3EwLpHiL1ETtVRncrJEd0xcRAatXpw7v27/fHC9SJHUsduPGZp0Rq9m926xJMn26CahiRfjmG+jc2ex2c58SkhIYt24cQ1YMIT4uHs8NnkwMmEjHDh1l8SkhbMAxEvi+MXA4+JYWSIHUFkiBqqktEKf7HUN3f+LjzUqpKW2RjRvN3JdcucwIke7dTcKuVOmOM8wtIyYGfv3VVNtr15oeTLt2ZqOE2rWz/K+N7We30/HnjuyP2A8H4UWXF5n08yQKFy5soRMQQmSWYyRw51ymki71Wmq/OlfxbGmB3Eprs1pqSsJevdrsT+DkZLoSgwaZSvvppyFbhj0fOmSq7R9/hMuXzQpUY8ZAly5QqFCWnz46Ppohy4bw1aav0Nc13hu9+fHdH2nZsmXWYxdCZIljJPBH+pqbjZw9e3Mf+0zybmNly8Krr5qE/eyzty11bT3x8TB/vqm2V6wws3leesn0tp991mKl/tIjS+k6tytnY87CNuhavCvjF44nv1Ua9kKIzHKMBJ7NoqLMCJGUPvaePeZ4wYKpY7EbNcrQjHLLOnHCjByZMgXOnQMfH/j8c3M1tFgxi73MxesX6beoH7/s/wUuwcNbH2bm8Jk8++yzFnsNIUTWSQLHzBjfvDk1Ya9fb455eJi9CF57zfSxq1Sxch87PYmJ8McfptpessQca97c9LabNk1nH7P7p7Vmxs4Z9FvYj8i4SPgX+lfrz/Dlw8l9x+13hBC28kAmcK3NjPGUPvaqVWawhlJmePQ775iEXbt2pickWs6ZM6bSnjTJLONarBgMGWKuivr6WvzlDl85TLd53fjn1D8QBmX3lyVkXAg1a9a0+GsJISzjgUng58+bdnFKH/vkSXO8VCkzlyWlj22B6373LynJBBkUZHrciYkmsPHjoUWLLKxSdWfxifGMWTeGoauGkhCTgNNKJz5q+hGDJwzGzeKziawsJMR8yIWFmfbS8OHQqZOtoxLCanJsAr9+3azYl1Jl79xpjnt7mwX2hgwxVXbp0raNEzBbkf3wgxlNcuSIabYPGGAWk7LilPRNpzfR9beu7LuyD/ZC1XNVmTFlBhUrVrTaa1pNSIj5e0Un7w964oS5D5LERY51z9UILcmaqxEmJpo1sVMS9rp1EBdnhkPXqWOSdaNGULWqRdvG909rM1574kSYM8cEW7eu6W23bWvVMYiRsZF8uPJDvtn0DUSC6zJXRnQdwZtvvomzXfxx7oOfX+puzGn5+pr1zIVwYFlZjdAuaQ2HD6deeFy1yqzmB+DvD2+9ZRJ2nTpgV9ffwsNhxgzTJtm718yh79XLJO4KFaz+8osOLqLH/B6cvX4WNkO9+HpM/W0qZcqUsfprW1VYWOaOC5EDOFQCv3jRbBuWUmWnFFw+PtCmjamyGzQAu5scqLVZiyQoCH76yezEULOm2fHmlVey5RPmXNQ53ljyBnP3zUVdVORenpuv3v2KgICAnDEN3scn/Qrc0TcREeIuHCKBBweb3Ld9u7mfP79J1O+/b6rssmVtMinz3qKiTMIOCjKrAXp6mn5sr15mC/hskKSTmLJtCu/89Q5RMVHwNzT3bk7QiiCK32UjYoczfPjNPXAwH4zDh9suJiGszCES+JUrkC+fmbPSqJHJfXa9T8DOnaa3PWMGREaadba//94k73z5si2M/Zf2E7ggkLUn18IJ8F7rzYRhE2jXrl3OqLrTSrlQKaNQxAMkx1zEtLkbN8zFyKAgMxPI3d20R3r1MptWZmPCjEuMY8TaEXz+9+ckxiSS9GcSnSp2Yvy48RSy6ThJIcT9yHEXMe3GgQOpi0ldvQqPPAJjx5o9JQsUyPZw/g37l+4LurP/8n7YBcV2FmPy+Mk0a9Ys22MRQliXJPD7ERdndm2fONEMf3FxgdatTbVdv75NGvLXYq7xwYoPmLBlAs5RzjAfejfqzYgtI8iXjW0bIUT2kQSeGceOmSuqU6fChQtm7PEXX5jFpIoWtVlY8/bNo+/ivpyLOgcbwC/Mj6mTp1KvXj2bxSSEsD5J4PeSkACLF5ve9l9/meq6RQtTbT/3nA1Wt0p1OuI0/f7ox+/7f8flkgvqd8XATgMZOm8ouXLlsllcQojsIQn8Tk6fhsmTze3UKSheHIYONTu423jvxySdRNCWIN5f9j43Ym/AcqgQXYGpv0/liWwaniiEsD1J4GklJcHSpaa3vXChud+kidlP8oUX7GLs4p4LewhcGMj6U+txCXPBaZETn775KQMHDsTVCotdCSHsl+0zkj24cMH0tYODTZ+7cGF47z0IDLST1a4gJiGG4WuGM/LfkRALLITqntWZunoq5cuXt3V4QggbeHATuNZm252gIPjtN7NN2TPPwP/+Z7Yny5YNLTPm7+N/02NhDw5eOYjLHhdcV7oy5uMx9O3b13EXnxJCZNmDl8CvXoVp00ziPnAAvLygb18zDdvOKtmrN67y3rL3mLJ9Ch7RHjAXni3zLMFbgvHL9v3chBD25sFI4FrDxo0mac+eDTExZtv4H3+Edu3AzkZsaK35Zc8vvPnnm1y6fgnn9c64b3VnwugJdOnSJedNgxdC3JecncAjI81C/0FBsGMH5MkDr79ulm6tUsXW0aXrRPgJ+izpw5JDS8h9NTdJs5No9XQrvtv5HcUsuHGxEMLx5cwEvn27GUkSEmJWBPT3N/c7dIC8eW0dXboSkxL5ZtM3fLjyQ+Li4nBa5kSeo3mY9s002rZta+vwhBB2KOck8Oho+OUXU21v3Gh2I+7QwUy4qVHDTtebNULPhRK4MJAtZ7bgecaT+NnxdHmpC18u/JICNlhPRQjhGBw/ge/da6rr6dPNbjfly8NXX8Grr5oNMO1YdHw0n67+lLHrx+KW6Aa/QYGIAvw6+1eaNGli6/CEEHbOMRN4bKwZ+hcUBGvWmI0v27Qx1XbdunZdbadYdmQZvRb34ujVo+Q5kIeo36PoF9CPL774grx22uYRQtgXx0rgR46kLiZ16RKUKQMjR5oLk3a3j1r6LkVfYsBfA5ixcwb54vNBCBR3L87kpZOpU6eOrcMTQjiQDCVwpdRxIBJIBBK01tWVUp8AgcDF5IcN1lovsXiEISEwYICZLQlm8aiXXjLVdsOGNl1MKjO01szcOZO3/3qbazHX8NziSdTSKD545wM+/vhjPDw8bB2iEMLBZKYCf1ZrfemWY+O01mMsGdBNQkJu3+fQzc2svd24sdVe1tKOXDlCr8W9WH50OQWuFyDhxwQqFa/E1A1TqVq1qq3DE0I4KPsuX4cMuTl5g5mEM2SIbeLJpPjEeEb9O4rHJzzO2mNrybUqF1FfR/HF21+wadMmSd5CiCzJaAWugaVKKQ1M1FoHJx/vp5R6DdgCvKO1vnrrLyqlegA9AHx8fDIXXVhY5o7bkc2nNxO4MJAd53dQ+HJhLk67SO3HazM5dDKPPfaYrcMTQuQAGa3A62itqwFNgb5KqXrABKAM4A+cBcam94ta62CtdXWtdfXCmb3QeKeEn9kPgmwUFRfF23++zVNTnuLYhWO4/+7OjR9u8M3wb1izZo0kbyGExWQogWutTyd/vQDMA2pqrc9rrRO11knAJKCmxaMbPhxy5775WO7c5rgdWnJoCRW/r8j4jeMpHFaYiC8iqF+0Prt376Zfv344OcgFVyGEY7hnRlFKeSql8qZ8DzwH7FZKpV2YoxWw2+LRdepkhg36+pqx3b6+5n6nThZ/qaw4F3WO9nPb03xWc25cu4HLdBfifotjWvA0/vjjD3x9fW0dohAiB8pID7woMC95BTwXYJbW+k+l1AyllD+mP34c6GmVCDt1sruEnUJrzdTtU3l32btcj7vOQ/se4tzcc7zc+mW+/vprHnroIVuHKITIwe6ZwLXWR4Hblu7TWr9qlYgcxMHLB+m5qCerj6+mRFIJIoIiwAl+m/MbrVq1snV4QogHgGPNxLQDcYlxjPp3FJ+v+RxX5UqRjUU49ecpur3ejTFjxuBt5+uvCCFyDkngmbD+5HoCFway5+IeysaU5fC3hylUqBDLli6jUaNGtg5PCPGAkWERGRARG0HfxX2pPbU2F65doPCywhwZeYQ3u73Jrl27JHkLIWxCKvB7+H3/7/Rb0o8zkWd47Npj7PtuH+XLlGf+v/N5+umnbR2eEOIBJhX4HZyJPEPr2a1pNbsVLnEueM314tA3h/hw4Ids375dkrcQwuakAr9Fkk5i4paJDFoxiLiEOCqeqcieyXt4ouoTTNkyhSp2upemEOLBIxV4Gnsu7KHuD3Xps6QPxSmO62RXDk87zMj/jWTDhg2On7xDQsDPzyzB6+dn7gshHJZU4EBMQgxf/PMFI9aOwNPFkwoHK7B31l7q1avHpKWTeOSRR2wdYtbdujTviRPmPtjtRCkhxN098BX4mhNr8A/yZ9iaYVR2rkzs2FhOLjzJhAkTWLVqVc5I3pD+0rzR0Q6zNK8Q4nYPbAK/euMqgQsCeebHZ4i6EUX5LeXZOmQrzz75LHv27KFXr145a/EpB16aVwiRvgeuhaK1Zu7eubzxxxtcir5EHVWHjZ9uJMYjhpkzZ9KxY0eUA2yKnGk+PqZtkt5xIYRDykEl5r2dvHaSF39+kXZz21HApQCll5dm7dC1tGnRhr1799KpU6ecmbzB4ZbmFULc2wNRgScmJfLd5u8YsnIISTqJ+rH1+fvdvylWtBjz58+nZcuWtg7R+lIuVA4ZYtomPj4mecsFTCEcltJaZ9uLVa9eXW/ZsiXbXg9g5/mdBC4MZNPpTdQsUJPzU85zYscJAgMDGTVqFF5eXtkajxBCZJZSaqvWuvqtx+2+hXK/Q5dvxN/gg+Uf8ETwExy7eoxG4Y3Y9OYmnCOdWbFiBcHBwXaRvGVothDiftl1C+V+hy6vOLqCnot6cuTqERoXasyecXtYeWwlAwYM4LPPPsPT09P6wWeADM0WQmSFXbdQ/PzSHzjh6wvHj99+/HL0Zd5Z+g7TdkyjVP5SlN5TmhWTV1CxYkWmTJnCk08+ed+xW0Nmz08I8WByyBZKRocua60J2RnCY989RsiuEF4q+BKRIyNZM20NQ4cOZdu2bXaXvEGGZgshssauWygZGbp89OpRei/uzdIjS6lapCqVtlTi919+p0aNGkxZOoXHH388+wLOJBmaLYTICruuwO82dDkhKYEx68ZQ6ftKrDu5jg75O3Bk8BE2LtzImDFjWL9+vV0nb5Ch2UKIrLHrCvxOQ5cfe3YrNSZ1J/RcKA1LNOTGrzf46Y+fqF+/PpMmTaJs2bK2DTyDZGi2ECIr7Poi5q2ux13n41UfM37jeIp4FqFJYhNmfzIbN1c3Ro8eTffu3XPW+iVCCMGdL2LadQWe1h+H/qD34t6cuHaCl0u9zJGJR5i2bhovvPACEyZMoESJErYOUQghspVDlKvvLn2XZrOa4eHiweu8zu/dfyfsYBizZs1iwYIFkryFEA8kh6jAG5RqQMTFCP4d9S8/7PyBjh07Mn78eAoXLmzr0IQQwmYcIoFvm72NKUOnUKxYMRYuXMgLL7xg65CEEMLmHKKFUqZMGQIDA9mzZ48kbyGESOZQo1CEEOJB5JBT6YUQQtyZJHAhhHBQGbqIqZQ6DkQCiUCC1rq6UqoAMBvwA44D7bTWV60TphBCiFtlpgJ/Vmvtn6YPMwhYobUuB6xIvi+EECKbZKWF8iIwLfn7acBLWQ9HCCFERmU0gWtgqVJqq1Iqec8YimqtzyZ/fw4omt4vKqV6KKW2KKW2XLx4MYvhCiGESJHRiTx1tNanlVJFgGVKqf1pf6i11kqpdMcjaq2DgWAwwwizFK0QQoj/ZKgC11qfTv56AZgH1ATOK6WKASR/vWCtIIUQQtzunhN5lFKegJPWOjL5+2XAZ0BD4LLWeoRSahBQQGs98B7PdRFIZw+aDCkEXLrP33UEOfn85NwcV04+P0c6N1+t9W2LP2UkgZfGVN1gWi6ztNbDlVIFgV8AH0xSbqe1vmLZmG+KY0t6M5Fyipx8fnJujisnn19OOLd79sC11keBKukcv4ypwoUQQtiAzMQUQggH5UgJPNjWAVhZTj4/OTfHlZPPz+HPLVtXIxRCCGE5jlSBCyGESEMSuBBCOCi7TOBKKWel1Hal1KJ0fuaulJqtlDqslNqolPLL/gjv3z3ObYBSaq9SaqdSaoVSytcWMd6vu51bmse0UUpppZTDDd+61/kppdolv397lFKzsju+rLjHf5c+SqlVyT/fqZRqZosY75dS6rhSapdSKlQpdduOMsr4Ojmn7FRKVbNFnPfDLhM48Baw7w4/CwCuaq3LAuOAkdkWlWXc7dy2A9W11pWBucCobIvKMu52biil8iY/ZmO2RWRZdzw/pVQ54AOgtta6ItA/OwOzgLu9dx8Cv2itqwLtge+zLSrLuXU11bSaAuWSbz2ACdkaWRbYXQJXSpUAmgOT7/CQtKsgzgUaKqVUdsSWVfc6N631Kq11dPLdDUCJ7IotqzLwvgEMw3zgxmRLUBaUgfMLBL5LWRM/edkJh5CBc9NAvuTv8wNnsiOubPQiMF0bGwCvlGVC7J3dJXBgPDAQSLrDz4sDJwG01gnANaBg9oSWZfc6t7QCgD+sG45F3fXckv9ZWlJrvThbo7Kce713jwCPKKX+VUptUEo9n32hZdm9zu0ToLNS6hSwVqFxxQAAAflJREFUBHgjm+KylPRWU03rv5yS7FTyMbtnVwlcKfUCcEFrvdXWsVhaZs5NKdUZqA6MtnpgFnCvc1NKOQFfAu9ka2AWksH3zgXzT/D6QAdgklLKKxvCy5IMnlsH4EetdQmgGTAj+T11FHW01tUwrZK+Sql6tg7IUuztTagNtEzewu1noIFSauYtjzkNlARQSrlg/kl3OTuDvE8ZOTeUUo2AIUBLrXVs9oZ43+51bnmBSsDq5Mc8BSxwoAuZGXnvTgELtNbxWutjwEFMQrd3GTm3AMy6R2it1wMemIWgHMIdVlNN67+ckqxE8jH7p7W2yxumklmUzvG+QFDy9+0xF1dsHq+Fzq0qcAQoZ+sYLX1utzxmNeZirc3jteB79zwwLfn7Qph/khe0dbwWOrc/gK7J35fH9MCVrePN4Dl5AnnTfL8OeP6WxzRPPkeFKS422TrujN7srQJPl1LqM6VUy+S7U4CCSqnDwAAcfC/OW85tNJAHmJM85GmBDUPLslvOLce55fz+Ai4rpfYCq4D3tFnwzSHdcm7vAIFKqR3AT5hk7ihTuIsCa5Nj3wQs1lr/qZTqpZTqlfyYJcBR4DAwCehjm1AzT6bSCyGEg3KIClwIIcTtJIELIYSDkgQuhBAOShK4EEI4KEngQgjhoCSBCyGEg5IELoQQDur/K//9Gg5ny5wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqFEimdhxrAw"
      },
      "source": [
        "### Accuracy with respect to Test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gghy3Tr0xedu",
        "outputId": "f3d469ad-11fd-4b73-cae9-87936289ea55"
      },
      "source": [
        "lnr_score = model_lnr.score(X_test, y_test)\r\n",
        "rid_score = model_ridge.score(X_test, y_test)\r\n",
        "lasso_score = model_lasso.score(X_test, y_test)\r\n",
        "en_score = model_en.score(X_test, y_test)\r\n",
        "encv_score = model_encv.score(X_test, y_test)\r\n",
        "\r\n",
        "print('Alpha from Cross Validation:')\r\n",
        "print(model_encv.alpha_)\r\n",
        "print('\\nModel predictions:')\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Alpha from Cross Validation:\n",
            "0.005333333333333334\n",
            "\n",
            "Model predictions:\n",
            "0.566844218097124\n",
            "0.5252660508234068\n",
            "0.6498792404243197\n",
            "0.3962304078098091\n",
            "0.5832453279342337\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgBJ0ulgxRmO"
      },
      "source": [
        "### Accuracy with respect to Training data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KjPXyetMuZ5W",
        "outputId": "698b6012-022c-482f-cff7-525d1d5cec50"
      },
      "source": [
        "lnr_score = model_lnr.score(X_train, y_train)\r\n",
        "rid_score = model_ridge.score(X_train, y_train)\r\n",
        "lasso_score = model_lasso.score(X_train, y_train)\r\n",
        "en_score = model_en.score(X_train, y_train)\r\n",
        "encv_score = model_encv.score(X_train, y_train)\r\n",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8847926267281104\n",
            "0.578636008483089\n",
            "0.76036866359447\n",
            "0.4366960477206072\n",
            "0.8843112906749513\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2i6YBhTwvyg"
      },
      "source": [
        "### Predicting for a new data point"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxNWHxOavYoj",
        "outputId": "888fb12c-9e07-4f97-b877-f49c7d970b93"
      },
      "source": [
        "lnr1 = model_lnr.predict([[5.2]])\r\n",
        "rid1 = model_ridge.predict([[5.2]])\r\n",
        "las1 = model_lasso.predict([[5.2]])\r\n",
        "en1 = model_en.predict([[5.2]])\r\n",
        "encv1 = model_encv.predict([[5.2]])\r\n",
        "\r\n",
        "print(lnr1)\r\n",
        "print(rid1)\r\n",
        "print(las1)\r\n",
        "print(en1)\r\n",
        "print(encv1)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[78.33333333]\n",
            "[68.92156863]\n",
            "[72.33333333]\n",
            "[66.9469697]\n",
            "[77.96014898]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FlShWVgx2ha"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9UZW1V1x4WK"
      },
      "source": [
        "### Analysis of Linear, Ridge, Lasso, ElasticNet with outliers being ***Homoscedasticity***:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "DuPGu_Avx2kn",
        "outputId": "c2e37f83-4d93-4cfd-f517-b56e3807bf1f"
      },
      "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],[4.2,55],[4.4,60],[4.6,65], [4.8,70], [5,75], [6, 90]]\r\n",
        "test = [[5,68], [4.3,53], [4.7,69], [4,55], [4.5,58]]\r\n",
        "X_train = [4.0, 4.2, 4.4, 4.6, 4.8, 5, 6]\r\n",
        "y_train = [50, 55, 60, 65, 70, 75, 90]\r\n",
        "X_test = [4, 4.3, 4.5, 4.7, 5]\r\n",
        "y_test = [55, 53, 58, 69, 68]\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='green') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 93,
      "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": "Lv14pp4Fx2n3",
        "outputId": "9cb56488-5281-431d-97c0-203430be9275"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_test = np.array(X_test).reshape(-1, 1)\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Lasso\r\n",
        "from sklearn.linear_model import Ridge\r\n",
        "from sklearn.linear_model import ElasticNet\r\n",
        "from sklearn.linear_model import ElasticNetCV\r\n",
        "\r\n",
        "model_ridge = Ridge(alpha=1)\r\n",
        "model_ridge.fit(X_train,y_train)\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",
        "model_en = ElasticNet(alpha = 0.5, l1_ratio = 0.5, random_state = 0)\r\n",
        "model_en.fit(X_train,y_train)\r\n",
        "model_encv = ElasticNetCV(cv = 2, random_state = 0)\r\n",
        "model_encv.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(4,6.1,50)\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",
        "y_rid = model_ridge.predict(x_gen)\r\n",
        "y_en = model_en.predict(x_gen)\r\n",
        "y_encv = model_encv.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='black')\r\n",
        "plt.plot(x_gen,y_las,color='red')\r\n",
        "plt.plot(x_gen,y_rid,color='blue')\r\n",
        "plt.plot(x_gen,y_en,color='orange')\r\n",
        "plt.plot(x_gen,y_encv,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 94,
      "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": "markdown",
      "metadata": {
        "id": "OwQVL_0qyoTc"
      },
      "source": [
        "### Accuracy with respect to Test data with outlier:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1Hz8_JUx2sQ",
        "outputId": "8944ea62-eb98-4356-b472-9ddf2490d0ec"
      },
      "source": [
        "lnr_score = model_lnr.score(X_test, y_test)\r\n",
        "rid_score = model_ridge.score(X_test, y_test)\r\n",
        "lasso_score = model_lasso.score(X_test, y_test)\r\n",
        "en_score = model_en.score(X_test, y_test)\r\n",
        "encv_score = model_encv.score(X_test, y_test)\r\n",
        "\r\n",
        "print('Alpha from Cross Validation:')\r\n",
        "print(model_encv.alpha_)\r\n",
        "print('\\nModel predictions:')\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Alpha from Cross Validation:\n",
            "0.01736367707335356\n",
            "\n",
            "Model predictions:\n",
            "0.6511514996393614\n",
            "0.5555311454203375\n",
            "0.6332601997781482\n",
            "0.4161962616100134\n",
            "0.6517632736386324\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7sOJUzMy5YG"
      },
      "source": [
        "### Accuracy with respect to Training data with outlier:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOzBCVkdx2wv",
        "outputId": "5611843a-2f39-40e1-e821-0c54d504b76c"
      },
      "source": [
        "lnr_score = model_lnr.score(X_train, y_train)\r\n",
        "rid_score = model_ridge.score(X_train, y_train)\r\n",
        "lasso_score = model_lasso.score(X_train, y_train)\r\n",
        "en_score = model_en.score(X_train, y_train)\r\n",
        "encv_score = model_encv.score(X_train, y_train)\r\n",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9789759725400458\n",
            "0.9046225987188817\n",
            "0.9618063501144165\n",
            "0.8066549564114908\n",
            "0.9784250967258091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8ufdW73zr4w"
      },
      "source": [
        "### Predicting for a new data point:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4-1FfMDzEvI",
        "outputId": "281feb82-3f40-4303-9b47-f93b6810f583"
      },
      "source": [
        "lnr1 = model_lnr.predict([[6.2]])\r\n",
        "rid1 = model_ridge.predict([[6.2]])\r\n",
        "las1 = model_lasso.predict([[6.2]])\r\n",
        "en1 = model_en.predict([[6.2]])\r\n",
        "encv1 = model_encv.predict([[6.2]])\r\n",
        "\r\n",
        "print(lnr1)\r\n",
        "print(rid1)\r\n",
        "print(las1)\r\n",
        "print(en1)\r\n",
        "print(encv1)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[96.30434783]\n",
            "[88.07086614]\n",
            "[92.34782609]\n",
            "[83.76998369]\n",
            "[95.59565174]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvpHd3vIzqaA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hy2im0dw6oRW"
      },
      "source": [
        "### Analysis of Linear, Ridge, Lasso, ElasticNet with outliers being ***Heteroscedasticity***:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "_lfxDi10zqeZ",
        "outputId": "1e8c5178-1bb2-4768-e3f2-c16bd4facd9e"
      },
      "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],[4.2,55],[4.4,60],[4.6,65], [4.8,70], [5,75], [4.2, 90], [4.3, 100]]\r\n",
        "test = [[5,68], [4.3,53], [4.7,69], [4,55], [4.5,58]]\r\n",
        "X_train = [4.0, 4.2, 4.4, 4.6, 4.8, 5, 4.2, 4.3]\r\n",
        "y_train = [50, 55, 60, 65, 70, 75, 90, 100]\r\n",
        "X_test = [4, 4.3, 4.5, 4.7, 5]\r\n",
        "y_test = [55, 53, 58, 69, 68]\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='green') # predicted\r\n",
        "plt.xlabel('Height in ft')\r\n",
        "plt.ylabel('Weight in kgs')\r\n",
        "plt.show()"
      ],
      "execution_count": 81,
      "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": "yp1f4l75zqhc",
        "outputId": "969ed6ea-fe42-45eb-b275-155bab2dafde"
      },
      "source": [
        "X_train = np.array(X_train).reshape(-1, 1)\r\n",
        "X_test = np.array(X_test).reshape(-1, 1)\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "from sklearn.linear_model import Lasso\r\n",
        "from sklearn.linear_model import Ridge\r\n",
        "from sklearn.linear_model import ElasticNet\r\n",
        "from sklearn.linear_model import ElasticNetCV\r\n",
        "\r\n",
        "model_ridge = Ridge(alpha=1)\r\n",
        "model_ridge.fit(X_train,y_train)\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",
        "model_en = ElasticNet(alpha = 0.5, l1_ratio = 0.5, random_state = 0)\r\n",
        "model_en.fit(X_train,y_train)\r\n",
        "model_encv = ElasticNetCV(cv = 2, random_state = 0)\r\n",
        "model_encv.fit(X_train,y_train)\r\n",
        "\r\n",
        "x_gen = np.linspace(4,5.1,50)\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",
        "y_rid = model_ridge.predict(x_gen)\r\n",
        "y_en = model_en.predict(x_gen)\r\n",
        "y_encv = model_encv.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='black')\r\n",
        "plt.plot(x_gen,y_las,color='red')\r\n",
        "plt.plot(x_gen,y_rid,color='blue')\r\n",
        "plt.plot(x_gen,y_en,color='orange')\r\n",
        "plt.plot(x_gen,y_encv,color='green')\r\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaTUlEQVR4nO3df2wk533f8feXv+74437yeBRJiUfJvthyrMiS6N+SIeWM4GwFltAWio0Lek0FE0GF1q6DNhdcYRctDraburHitCoYO43k0I5VwYaExHFtHOT+iCOlvFiVZKmJVVs8HXl3pO6XTuQduSS//WOGvOVydpfkDHe5Dz8vYLA7z8zsPs/R/mj2mWeeMXdHRETCUlftCoiISPYU7iIiAVK4i4gESOEuIhIghbuISIAaql0BgD179nhfX1+1qyEiUlNOnDjxurt3JG3bEOHe19fH8PBwtashIlJTzGyk2DZ1y4iIBEjhLiISIIW7iEiAFO4iIgFSuIuIBKhsuJvZH5nZuJm9mFe228x+YGY/jV93xeVmZr9vZq+Y2fNmdvt6Vl6yMTQEfX1QVxe9Dg1Vu0YiktZKztz/GDhYUHYEOO7u+4Hj8TrAR4D98TIAPJJNNWW9DA3BwACMjIB79DowoIAXqXVlw93d/wdwvqD4PuDR+P2jwP155Y955Blgp5l1ZVVZyd7RozA1tbRsaioqF5HatdY+9053Px2/PwN0xu97gNfy9jsVly1jZgNmNmxmwxMTE2ushqR18uTqykWkNqS+oOrR0z5W/cQPdx9093537+/oSLx7Viqgt3d15SJSG9Ya7mcXulvi1/G4fBS4IW+/6+My2aCOHYOWlqVlLS1RuYjUrrWG+1PA4fj9YeDJvPJ/GI+aeR9wKa/7RjagQ4dgcBD27QOz6HVwMCoXkdpVduIwM/smcDewx8xOAZ8DvgA8bmYPAiPAA/Hu3wU+CrwCTAG/sQ51lowdOqQwFwlN2XB3908U2XQgYV8HHkpbKRERSUd3qIqIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAUoW7mX3KzF40s5+Y2afjst1m9gMz+2n8uiubqm4uQ0PQ1wd1ddHr0FC1ayQitWTN4W5m7wQ+CbwHuBX4VTN7K3AEOO7u+4Hj8bqswtAQDAzAyAi4R68DAwp4EVm5NGfuNwPPuvuUu88C/x34e8B9wKPxPo8C96er4uZz9ChMTS0tm5qKykVEViJNuL8I3GVm7WbWAnwUuAHodPfT8T5ngM6kg81swMyGzWx4YmIiRTXCc/Lk6spFRAqtOdzd/WXgi8D3ge8BzwFzBfs44EWOH3T3fnfv7+joWGs1gtTbu7pyEZFCqS6ouvvX3P0Od/8QcAH4O+CsmXUBxK/j6au5uRw7Bi0tS8taWqJyEZGVSDtaZm/82kvU3/4N4CngcLzLYeDJNN+xGR06BIODsG8fmEWvg4NRuYjISljUc7LGg83+J9AO5IDPuPtxM2sHHgd6gRHgAXc/X+pz+vv7fXh4eM31EBHZjMzshLv3J21rSPPB7n5XQtk54ECazxURkXR0h6qISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAUoV7mb2z83sJ2b2opl908y2mtmNZvasmb1iZt8ys6asKisiIiuz5nA3sx7gnwH97v5OoB74OPBF4Pfc/a3ABeDBLCoqIiIrl7ZbpgFoNrMGoAU4Dfwy8ES8/VHg/pTfISIiq7TmcHf3UeDfAyeJQv0ScAK46O6z8W6ngJ6k481swMyGzWx4YmJirdUQEZEEabpldgH3ATcC3UArcHClx7v7oLv3u3t/R0fHWqshIiIJ0nTLfBj4ubtPuHsO+DbwQWBn3E0DcD0wmrKOIiKySmnC/STwPjNrMTMDDgAvAU8D/yDe5zDwZLoqiojIaqXpc3+W6MLp3wAvxJ81CPw28BkzewVoB76WQT1FRGQVGsrvUpy7fw74XEHxz4D3pPlcERFJR3eoiogESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiwNAQ9PVBXV30OjSU/Xfkcjlee+01nnnmGb797W/zla98hRdeeCH7LyLlxGEiIiEYGoKBAZiaitZHRqJ1gEOHyh/v7ly4cIHR0dHFZWxsbNn78fFx3H3JsQ8//DC33HJLxi0CK/yiaujv7/fh4eFqV0NENqm+vijQC+3bBy+/fGUxnJMCe2xsjLGxMa5evbrs+D179tDT00N3dzc9PT2J7/fs2UNd3do6UczshLv3J23TmbuIbEpzc3OMj48zNjbGyMgo0UPjxuLXaBkZGaOl5cKyY5ubmxcD+r3vfW9icHd1dbFly5bKNiqPwl1EguLuvPHGG8vOrgvPvM+cOcPc3FzB0XXAdUAPsJ9t2+7myJHuJcHd3d3Njh07iB5At3Ep3EWkZszMzHD69OmiXSQL7ycnJ5cdu3PnzsVwfsc73rEkrJ9/vofPf76Hq1c7gXoAWlrgkUdW1ue+ESncRaTq5ufnOXfuXMl+7dHRUSYmJpYd29TUtBjSt912G/fee++ybpLu7m5aWlqKfv/998Nb3gJHj8LJk9DbC8eO1W6wgy6oisg6m5ycTOwWKQzvXC637Ni9e/cmBnX+a3t7+4bvIlkvuqAqIpmbnZ3lzJkzJfu1x8bGuHTp0rJj29raFgP6rrvuSgzwrq4uGhsbq9CyMCjcRWQJd+fixYtFA3th/ezZs8zPzy85tqGhga6uLrq7u7n55ps5cODAkuBeCO/t27dXqXWbh8JdZBO5evXq4rjsYv3aY2NjXLlyZdmxu3fvXgzqW2+9NXH4X0dHx5rHbEu2FO4iAZifn2diYqLkKJLR0VHOnz+/7NitW7cuhvS73/3uov3bW7durULLNqb5+ehu1jffvLZMTi5dL1yStk9Owmc/C7/2a9nXUeEussFdvny56Fn2wvvTp08zOzu75Dgz47rrrqO7u5sbb7yRO++8M/FOyZ07dwZ7QdIdpqdXFrbl1vOXhWkKVsIMWluhrW3p0t4ejcrZtWt92q5wF6mSXC7H6dOnS97aPjo6yptvvrns2B07diz2Yd9zzz2JFyQ7Oztr6oJkLrc0UEuFa7Gz4KR9Cy4LlNTcHAVva2u0bNsWre/duzSg87eVK29ujgK+0hTuIhlzd86fP1/2RpukSaQaGxsXQ/uWW27h4MGDifORtLa2Vql1yV0SheFarosiaZ+ZmZXXoaFh+ZlwWxt0d18L2MKz5WLr+fvX16/fv1ulKdxFVmFqampFFySnp6eXHdvR0bEYznfccUfiBcn29vbMLkjmd0ms5ky33P4JN38WZZYcpLt3R10SxYK38Ey4cHtTUyb/REFTuItwbRKpUtO1jo6OcvHixWXHtrS0LIbz+9///sX3+cHd1dVFU4lEmp2NgnNsbPX9v6X2XzZ1SglbtlzrUshfOjqSQze/CyJpe0W6JHwe5nPR4rlr74uVrWl9NsPPKlifm4E7vgxv+ceZ/9Mo3CVo7s6lS5fK3miTNIlUfX091113HT09Pezfv5+7776brq4e9uzpZdeuG9i2rYu2tuswa2Ny0paF6/PPw49+tLKwTjjRLyq/SyL/zLarawXh2+q0ts6xrTXHttYcrS052ppztDTnqLeMwnEyB5fL7T+bTTj6KjrUUzGoa4wWa7z2vuT6FmhoW7698Jjtb1uXGivcpWZNT08vTiJVGNynTo0yOvo6Y2NvcOVKHdAKtC0ura172b79VrZtO8jevXu54YY9NDbuor5+B2ZtzM+3MDPTxOSk8frrztipHH95JcfMdI7G+nhpyNFYf5bG+lMFZdeWba05trflaG/Nsa81R1tHjtbmHM1bZ2lpztGyNUfzlmjZ2pRjS1P82hgtTXmf19CQo7EuRx1rDMO5HFzMwfIfH+vDGlYYgnnrDW3J2/M/q1xYFl1vWGU456/XXmd8TYf7p3/nNp678vNqV0OSLPwUN89b98RyB+Yx5qmLX405h3lfKL+2jwMev2JEv/kXfvcb0AC2D+iDJpwbzTFz6uwyZtEnROu+ZN1tnpw5c3nlDebsNGfXGroV5oHpeHm93M5zwFS8eLwAePzFietWvLzoMXXgW8p8VpnPXm3dFs3Gy/Kboza7dzXfyJc//+PMP3fN4W5mbwO+lVd0E/BZ4LG4vA94FXjA3ZfPdi/LlQjA5etp98svL1gvcUz0/2FbLHcWsjU+xhxb3H/lza5nYaLV1XEHdwMMd8OXhMu1dcsLr/ymGb5kW3RsfcH6wvaUQbfSQN3AZnJw9Ur0H946g63N0FQ7oy03lUxmhTSzeqJHl7wXeAg47+5fMLMjwC53/+1Sx695VsglF1NmV/7zdL0ujqRany3f3ozM08C8NzIXL7Pz8TLXSG6ukZnZeMk1Mh0vV2cauTodbV9cZgvWC8rmMebmITc3z8zsHNO5GaZzOXKzs+TmcosL5mzZ2sTW1ibatrewY2cbO3dvZ2/nLjq72um+vpMb9nWyY1cLTVsasfq8n9mmW90rpfA5oxDNeT44WNtT49ayUrNCZhXuvwJ8zt0/aGZ/C9zt7qfNrAv4obuXvGKw5nB/6Yvw3JE11Xl1yl1MKdWX11Syf3CehSBtYGY2CtH8ML0SL1NXGpm62sjklUYmpxp5M14uT8bLm428ES+XLkfHJAXw7FwDhafVW7aUGobmNDRM4/4mc3MXmZm5wJUrrzM5eZbLl09z6dIpzp07yfnzJ4E3gcvAJHCFhob6xUmk8keQFN5ss23btgr8DSWtUs8ZffXVStdGoDLh/kfA37j7H5jZRXffGZcbcGFhveCYAWAAoLe3946RpP/VlDPxVzD+dN5ZXJkLIykuprgn37hRapjaSm7qWM0oifr6ayMjVnKHXNL44vyyhoarXLo0xtmzxcdrj46OJj74t729veg82wvvNYlUWOrqICkuzFZ3F6hkZ13D3cyaiJ4q+4vufjY/3OPtF9y95OwJWT6sY+HGjZWME15JWf6NG6v5pyp1R1ypu+SKbV+4cWMlY4bn5uaWTSKVNG47aRKphQf/5k/PmhTi1Xzwr+QZGqrY44N05r7xrPfDOj5CdNZ+Nl4/a2Zded0y4xl8R6Kvfx0efnh5QK/2xo2kcN2zZ3W3Ledvb26OznLWQ+GDf4tNIlU4Zruuro7Ozk56enq46aabuOuuuxJn/gt5EqngFHaCj4xE67AuAX/sWHKf+7FjmX+VZCCLcP8E8M289aeAw8AX4tcnM/iORM3N0NkZPfuw1BlvsRs7WlujG0I2gpmZGc6cOVP2bLvYJFIL4fz2t789sX+7s7OTho3SWMnG0aPLpyecmorK1yHcFz4ypOeMhixVt4yZtQIngZvc/VJc1g48DvQCI0RDIZf//s8T8jNU3X3xwb+l+rXHx5f/wGlqair63Mj812pOIiVVpE7wTW/dumXcfRJoLyg7BxxI87m1YmESqXLdJDMJ093t3bt3MaD7+/uLTiKlLhIpqrc3uRO8t7fydQlBBa9fVIJ+pyeYm5vj7NmzJSeRGhsbS5xEqrW1dTGc8x+OkH+BstwkUiIrok7w7FT4+kUlZDIUMq1KdcssTCJVqntkdHSUM2fOLHvwb3398jHbSUMA9eBfqajAzjarpkaHAq37OPe0sgj36enpJfNsFzvbnkp4PtbCg3+LBXd3dzd79+6lPqSZ/EXkmhq9frHeQyGr5rHHHuNLX/oSo6OjnDt3btn2LVu2LIZ1f39/Ynh3dXXR3NxchdqLyIYR4PWLmg731tZW9u3bxwc+8IHEbpJdu3bpgqSIlBfg9YtgumVERFKpwesXwXbLiIhk5tChDR/mq6FZnUREAqRwFxEJkMJdRCRACncRkQAp3EUyNDQU3exYVxe9Dg1Vu0ayWWm0jEhGApyeRGqYztxFMlJqenWRSlO4i2Tk5MnVlYusJ4W7SEaKTUNSw9OTSA1TuItk5NixaDqSfDU+PYnUMIW7SEYOHYLBwWgKcLPodXBQF1OlOjRaRiRDgU1PIjVMZ+4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISoFThbmY7zewJM/u/Zvaymb3fzHab2Q/M7Kfx666sKith0KPoRNZf2jP3h4HvufvbgVuBl4EjwHF33w8cj9dFgGuPohsZAfdrj6JTwItky9x9bQea7QCeA27yvA8xs78F7nb302bWBfzQ3d9W6rP6+/t9eHh4TfWQ2tLXFwV6oX374NVXK10bkdpmZifcvT9pW5oz9xuBCeC/mNmPzeyrZtYKdLr76XifM0BnkUoNmNmwmQ1PTEykqIbUEj2KTqQy0oR7A3A78Ii73wZMUtAFE5/RJ/40cPdBd+939/6Ojo4U1ZBaokfRiVRGmnA/BZxy92fj9SeIwv5s3B1D/DqerooSEj2KTqQy1hzu7n4GeM3MFvrTDwAvAU8Bh+Oyw8CTqWooQdGj6EQqY80XVAHM7F3AV4Em4GfAbxD9B+NxoBcYAR5w9/OlPkcXVEVEVm+9Lqji7s/F/ea/5O73u/sFdz/n7gfcfb+7f7hcsIusOw2sl01ID8iWsC0MrJ+aitYXBtaD+oIkaJp+QMJ29Oi1YF8wNRWViwRM4S5h08B62aQU7hI2DayXTUrhLmHTwHrZpBTuEjYNrJdNSqNlJHyHDinMZdPRmbuISIAU7iIiAVK4i4gESOEuIhKg2g33wOcLCbx5IrLOanO0TODzhQTePBGpgFRT/mZl1VP+Bv4gzsCbJyIZWbcpf6sm8PlCAm+eiFRAbYZ74POFBN48EamA2gz3wOcLCbx5IlIBtRnugc8XEnjzRKQCavOCqoiIBHhBVbKlQfUiwanNce6SHQ2qFwmSztw3Oz1jVCRICvfNToPqRYKkcN/sNKheJEgK981Og+pFgqRw3+w0qF4kSBotI3rGqEiAdOYuIhIghbuISIAU7iIiAVK4i4gEKFW4m9mrZvaCmT1nZsNx2W4z+4GZ/TR+3ZVNVZfSdCgiIsVlceZ+j7u/K29msiPAcXffDxyP1zO1MB3KyAi4X5sORQEvIhJZj26Z+4BH4/ePAvdn/QWaDkVEpLS04e7A983shJnFUwnS6e6n4/dngM6kA81swMyGzWx4YmJiVV+q6VBEREpLG+53uvvtwEeAh8zsQ/kbPXoSSOLTQNx90N373b2/o6NjVV+q6VBEREpLFe7uPhq/jgPfAd4DnDWzLoD4dTxtJQtpOhQRkdLWHO5m1mpm2xbeA78CvAg8BRyOdzsMPJm2koU0HYqISGlp5pbpBL5jZguf8w13/56Z/W/gcTN7EBgBHkhfzeU0HYqISHFrDnd3/xlwa0L5OeBAmkqJiEg6ukNVRCRACncRkQAp3EVEAqRwFxEJkEX3GVW5EmYTRCNr1mIP8HqG1dloQm6f2la7Qm5fLbVtn7sn3gW6IcI9DTMbzpu0LDght09tq10hty+UtqlbRkQkQAp3EZEAhRDug9WuwDoLuX1qW+0KuX1BtK3m+9xFRGS5EM7cRUSkgMJdRCRANRXuZlZvZj82sz9L2LbFzL5lZq+Y2bNm1lf5Gq5dmbZ9xsxeMrPnzey4me2rRh3TKNW+vH3+vpm5mdXUMLRybTOzB+K/30/M7BuVrl8aZf532WtmT8fbnzezj1ajjmtlZq+a2Qtm9pyZDSdsNzP7/ThTnjez26tRz7WqqXAHPgW8XGTbg8AFd38r8HvAFytWq2yUatuPgX53/yXgCeDfVaxW2SnVPuJnA3wKeLZiNcpO0baZ2X7gd4APuvsvAp+uZMUyUOrv9q+Ax939NuDjwH+qWK2yc4+7v6vIuPaPAPvjZQB4pKI1S6lmwt3MrgfuBb5aZJf8B3M/ARyweLL5ja5c29z9aXdfeCT4M8D1lapbFlbwtwP4t0T/Qb5akUplZAVt+yTwH939Aiw+tawmrKBtDmyP3+8AxipRrwq6D3jMI88AOxeeMlcLaibcgS8D/xKYL7K9B3gNwN1ngUtAe2Wqllq5tuV7EPiL9a1O5kq2L/65e4O7/3lFa5WNcn+7XwB+wcz+0syeMbODlataauXa9q+BXzezU8B3gX9aoXplxYHvm9kJMxtI2L6YKbFTcVlNqIlwN7NfBcbd/US165K11bTNzH4d6Ad+d90rlpFy7TOzOuA/AL9V0YplYIV/uwain/V3A58A/tDMdlageqmssG2fAP7Y3a8HPgp8Pf571oo73f12ou6Xh8zsQ9WuUJZq5Q/xQeBjZvYq8KfAL5vZnxTsMwrcAGBmDUQ/E89VspJrtJK2YWYfBo4CH3P36cpWMZVy7dsGvBP4YbzP+4CnauSi6kr+dqeAp9w95+4/B/6OKOw3upW07UHgcQB3/ytgK9GkWzXB3Ufj13HgO8B7CnZZzJTY9XFZbXD3mlqIzoD+LKH8IeA/x+8/TnShp+r1zahttwH/D9hf7TquR/sK9vkh0cXjqtc3o7/dQeDR+P0eop/57dWub0Zt+wvgH8Xvbybqc7dq13eFbWoFtuW9/xFwsGCfe+M2GtFJx19Xu96rWWrlzD2Rmf0bM/tYvPo1oN3MXgE+AxypXs3SK2jb7wJtwH+Nh209VcWqZaKgfUEpaNt/A86Z2UvA08C/8Og5wzWpoG2/BXzSzP4P8E2ioK+VW947gf8V1/2vgT939++Z2W+a2W/G+3wX+BnwCvCHwD+pTlXXRtMPiIgEqKbP3EVEJJnCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEA/X8mFoAV4U3ZWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kdku86cb65Ci"
      },
      "source": [
        "### Analysis with respect to Test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vYUFUU9izqlW",
        "outputId": "9e2721dc-416e-4437-efa2-e8430dafc168"
      },
      "source": [
        "lnr_score = model_lnr.score(X_test, y_test)\r\n",
        "rid_score = model_ridge.score(X_test, y_test)\r\n",
        "lasso_score = model_lasso.score(X_test, y_test)\r\n",
        "en_score = model_en.score(X_test, y_test)\r\n",
        "encv_score = model_encv.score(X_test, y_test)\r\n",
        "\r\n",
        "print('Alpha from Cross Validation:')\r\n",
        "print(model_encv.alpha_)\r\n",
        "print('\\nModel predictions:')\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Alpha from Cross Validation:\n",
            "1.4531249999999982\n",
            "\n",
            "Model predictions:\n",
            "-1.9831478919275443\n",
            "-2.10814140796779\n",
            "-2.2717139466546112\n",
            "-2.1958579891394017\n",
            "-2.2717139466546112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bf244UVy69Yx"
      },
      "source": [
        "### Analysis with respect to Training data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kn0UhJxazqqO",
        "outputId": "c88fe438-db37-4216-92e4-cf4ce2c2a254"
      },
      "source": [
        "lnr_score = model_lnr.score(X_train, y_train)\r\n",
        "rid_score = model_ridge.score(X_train, y_train)\r\n",
        "lasso_score = model_lasso.score(X_train, y_train)\r\n",
        "en_score = model_en.score(X_train, y_train)\r\n",
        "encv_score = model_encv.score(X_train, y_train)\r\n",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)\r\n",
        "print(en_score)\r\n",
        "print(encv_score)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.020415099951139437\n",
            "0.014105382722826909\n",
            "0.0\n",
            "0.006927815501673984\n",
            "0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW-_UUMM7Eb7"
      },
      "source": [
        "### Predicting for a new data point:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4qM8Qpm7EGd",
        "outputId": "656608ac-6afa-41b2-dbfb-79f85ddb2bd0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lnr1 = model_lnr.predict([[7.0]])\r\n",
        "rid1 = model_ridge.predict([[7.0]])\r\n",
        "las1 = model_lasso.predict([[7.0]])\r\n",
        "en1 = model_en.predict([[7.0]])\r\n",
        "encv1 = model_encv.predict([[7.0]])\r\n",
        "\r\n",
        "print(lnr1)\r\n",
        "print(rid1)\r\n",
        "print(las1)\r\n",
        "print(en1)\r\n",
        "print(encv1)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[89.27230047]\n",
            "[78.90548992]\n",
            "[70.625]\n",
            "[74.11567664]\n",
            "[70.625]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxG74-Ja7DzT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}