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

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    "colab": {
      "name": "ridge_regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Shuaib11-Github/shuaib/blob/master/Linear%2C%20Ridge%2C%20Lasso%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 without outliers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouXwumKqt6CV",
        "outputId": "1281d1c9-275e-44f5-c90c-e49a187944b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "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]]\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 = [50, 55, 60, 65, 70, 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": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHBvZCVSt6Fd",
        "outputId": "3dba03fe-a0d9-454d-af86-a25edb7960f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "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",
        "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",
        "\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",
        "\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.show()"
      ],
      "execution_count": 35,
      "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": "HqFEimdhxrAw"
      },
      "source": [
        "### Accuracy with respect to Test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gghy3Tr0xedu",
        "outputId": "b66dd9ae-f6bf-4f2a-e3c6-4901f0d55e7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.47219710669077686\n",
            "0.5433722632761219\n",
            "0.6515527549175185\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgBJ0ulgxRmO"
      },
      "source": [
        "### Accuracy with respect to Training data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjPXyetMuZ5W",
        "outputId": "6e5ad3ed-9428-4cc4-c65a-386fedc769ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "0.6539792387543252\n",
            "0.8824489795918363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2i6YBhTwvyg"
      },
      "source": [
        "### Predicting for a new data point"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxNWHxOavYoj",
        "outputId": "690334d5-2747-4230-d69b-9902fdbfd2e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
        "\r\n",
        "print(lnr1)\r\n",
        "print(rid1)\r\n",
        "print(las1)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[80.]\n",
            "[69.70588235]\n",
            "[74.]\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 with outliers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuPGu_Avx2kn",
        "outputId": "741dee58-0006-435a-e06b-530c0df54345",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "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": 73,
      "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": "Lv14pp4Fx2n3",
        "outputId": "366b8028-8cdd-4f3c-85f8-33aa477fa2a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "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",
        "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",
        "\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",
        "\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.show()"
      ],
      "execution_count": 74,
      "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": {
        "id": "p1Hz8_JUx2sQ",
        "outputId": "2345736d-c45c-461f-c86e-8ca8a23201f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6511514996393614\n",
            "0.5555311454203375\n",
            "0.6332601997781482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7sOJUzMy5YG"
      },
      "source": [
        "### Accuracy with respect to Training data with outlier:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOzBCVkdx2wv",
        "outputId": "4a799cae-0e58-4d69-da0d-6f955007dd8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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",
        "\r\n",
        "print(lnr_score)\r\n",
        "print(rid_score)\r\n",
        "print(lasso_score)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9789759725400458\n",
            "0.9046225987188817\n",
            "0.9618063501144165\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8ufdW73zr4w"
      },
      "source": [
        "### Predicting for a new data point:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4-1FfMDzEvI",
        "outputId": "3482d98f-424e-4e87-e95a-0a1d3bf91538",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lnr1 = model_lnr.predict([[5.4]])\r\n",
        "rid1 = model_ridge.predict([[5.4]])\r\n",
        "las1 = model_lasso.predict([[5.4]])\r\n",
        "\r\n",
        "print(lnr1)\r\n",
        "print(rid1)\r\n",
        "print(las1)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[80.2173913]\n",
            "[76.41732283]\n",
            "[78.39130435]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvpHd3vIzqaA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lfxDi10zqeZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yp1f4l75zqhc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYUFUU9izqlW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        ""
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      "outputs": []
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}