Why_Linear_Regression_is_not_suitable_for_Binary_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Why Linear Regression is not suitable for Binary Classification",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "BD4yBpjEXrNC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin_regression = LinearRegression()\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "log_regression = LogisticRegression(solver='lbfgs')\n",
        "\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWxky-k4c5uu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.array([i for i in range(10,30)])\n",
        "y = np.concatenate([np.ones(10), np.zeros(10)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-BSll46c700",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "a1282007-4848-4dfe-8fa8-c15ca7eca56d"
      },
      "source": [
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "_ = plt.plot(x, y, 'xb')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ho4XRwmhRj12WAAAADWGX5Q5MT0+3PYTOoIXRwmhR0MFoYbQwWtRrdEIWEbdExIWIuBgR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\nY0IGAADQMiZkAAAALWNCBgAA0LL/HxjkQN5+6LvxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TskNBkklAvXJ",
        "colab_type": "code",
        "outputId": "21ec3ace-2a50-478c-ecfc-0a269d26f075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "_ = lin_regression.fit(pd.DataFrame(x), y)\n",
        "\n",
        "lin_y_pred_1 = lin_regression.predict(pd.DataFrame(x))\n",
        "\n",
        "line_point_5 = x * 0 + .5\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "plt.plot(x, y, 'xb')\n",
        "plt.plot(x, lin_y_pred_1, '-r')\n",
        "_ = plt.plot(x, line_point_5,'-g')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cc0c5544CHgAe7eSyhwGzzGy2mX0N3AGMXOo5Y4CrzexjADN7r5PL7DJtbW1x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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrwZCOUEgOyL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "af80bc31-a8b5-469f-c570-a744cc6193b1"
      },
      "source": [
        "test_x = np.array([i for i in range(10,35,5)])\n",
        "test_y_pred = lin_regression.predict(pd.DataFrame(test_x))\n",
        "test_y_pred"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.21428571,  0.83834586,  0.46240602,  0.08646617, -0.28947368])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFxPzPGdic5W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "020c5d72-3fd6-48e9-c549-b59f33fa0c5e"
      },
      "source": [
        "_ = log_regression.fit(pd.DataFrame(x), y)\n",
        "\n",
        "y_pred = log_regression.predict_proba(pd.DataFrame(x))\n",
        "log_y_pred_1 = [item[1] for item in y_pred]\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "plt.plot(x, y, 'xb')\n",
        "plt.plot(x, log_y_pred_1, '-r')\n",
        "_ = plt.plot(x, line_point_5,'-g')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w3HHwu9/BG28M6KlKukOBqYVTC6cW8cX9lGVrCGHLEMK2wBLglyGES5IdWnpm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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mtl71g4TkKtb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "042d1800-73ec-482d-bd58-14e163c96ab8"
      },
      "source": [
        "print(\"Linear regression R2: \", r2_score(y, lin_y_pred_1))\n",
        "print(\"Logistic regression R2: \", r2_score(y, log_y_pred_1))\n",
        "print(\"Linear regression RMSE: \", mean_squared_error(y, lin_y_pred_1))\n",
        "print(\"Logistic regression RMSE: \", mean_squared_error(y, log_y_pred_1))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear regression R2:  0.7518796992481203\n",
            "Logistic regression R2:  0.9404089597242656\n",
            "Linear regression RMSE:  0.062030075187969935\n",
            "Logistic regression RMSE:  0.014897760068933596\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zWmCFxJmlMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.append(x, np.array([i for i in range(60,70)]))\n",
        "y = np.append(y, np.zeros(10))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJgNwuDxIvr-",
        "colab_type": "code",
        "outputId": "ab901083-d2c6-47d0-f8cc-75351e297e90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "_ = lin_regression.fit(pd.DataFrame(x), y)\n",
        "\n",
        "lin_y_pred_2 = lin_regression.predict(pd.DataFrame(x))\n",
        "\n",
        "line_point_5 = x * 0 + .5\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "plt.plot(x, y, 'xb')\n",
        "plt.plot(x, lin_y_pred_2, '-r')\n",
        "_ = plt.plot(x, line_point_5,'-g')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wK9AfOLFYJm0hBLJ2YeJioK6rYP78uP7p9OlxPdQ+fWDffeME7ogjYJ11ltxV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\ngg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwSIDTN5o46i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "46be54c1-c20b-4864-91f8-76d9d45f1b94"
      },
      "source": [
        "test_x = np.array([i for i in range(18,26)])\n",
        "test_y_pred = lin_regression.predict(pd.DataFrame(test_x))\n",
        "test_y_pred"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.56495292, 0.55091537, 0.53687781, 0.52284026, 0.50880271,\n",
              "       0.49476516, 0.48072761, 0.46669006])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pau8vT80Napq",
        "colab_type": "code",
        "outputId": "1103b227-4d5b-48cb-c19f-34ba34b1215e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "_ = log_regression.fit(pd.DataFrame(x), y)\n",
        "\n",
        "y_pred = log_regression.predict_proba(pd.DataFrame(x))\n",
        "log_y_pred_2 = [item[1] for item in y_pred]\n",
        "\n",
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "plt.plot(x, y, 'xb')\n",
        "plt.plot(x, log_y_pred_2, '-r')\n",
        "_ = plt.plot(x, line_point_5,'-g')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GSLJrvhMy836b0tx9E/l/QrPsNDU1pT2EyjBlCjz1FGzcCKhrFHWNoa4x1DWG\nusZIsmu+E7JHzezfzWxM9uvjwKORA0vTrFmz0h5CZej9pOXatYC6RlHXGOoaQ11jqGuMJLvmOyH7\nMHAYsB5YB7wTqNjpeE1NTdpDqAybnfpCXWOoawx1jaGuMdQ1RpJdh9ztaGajgLPd/awExlMSenp6\n0h5CZZgyJfNndkKmrjHUNYa6xlDXGOoaI8muQ24hc/fXgbkJjKVktLa2pj2EyrDHHlBV1XfqC3WN\noa4x1DWGusZQ1xhJds13l+WvzewKMzvSzGb0foWOLEU6rUaRjBkDNTV9W8jUNYa6xlDXGOoaQ11j\nJNk13/OQLc9xt7v7u4o/pMHpPGRl5vDDYexYWJ5rFRIREalsRT0Pmbsfk+Mr8clYUvRplSKaOrVv\nl6W6xlDXGOoaQ11jqGuMJLvmdS4xM/tcrvvd/ZLiDqc0NDc3pz2EyjF1KtxyC2zapK5B1DWGusZQ\n1xjqGiPJrvme3PWlft9vQ+ZalquKP5zSUFtbm/YQKsfUqfDqq/DEE+oaRF1jqGsMdY2hrjGS7Jrv\nLsuWfl+XArOAvUJHlqKZM4fc1Sv56nfqC3WNoa4x1DWGusZQ1xhJds33U5ab2w7Yo5gDKSXd3d1p\nD6Fy9J4c9vHH1TWIusZQ1xjqGkNdYyTZNa8JmZk9aGYPZL9WAquBy/P4e7PNbLWZrTGzTw+y3Glm\n5mZWElP8lpaWtIdQOfptIVPXGOoaQ11jqGsMdY2RZNd8jyFr6Pf9a8BT7v7aYH8he4b/bwLHkrnc\n0gozW+LuD2+2XDXwceC+vEcdTP/TKKLqathxR3jsMbq33Tbt0VQkra8x1DWGusZQ1xhJdh30PGRm\ntg2Z61juDTwIfHuoiVi/v3so0Ozu783evhjA3b+w2XKXA3cAFwKfdPdBTzKm85CVoYMOgt13h2XL\n0h6JiIhIoop1HrJrgJlkJmPHA1uz7W53YG2/2+uy9/Uf5Azgze7+06143HD19fVpD6GyTJ0Kjz2m\nrkHUNYa6xlDXGOoaI8muQ03I6tz9HHdfDJwOHFmsJzazKuCrwMI8lm0ysw4z61izZk3feUFqa2vp\n6uqis7OzL9rChQv79vnW1NTQ3d1Ne3t738ndmpqa+q5NVV1dTU9PD0uXLmXOnDkANDY2cuqpp/Y+\nLwBtbW00NjYCMGfOHJYuXUpPTw/V1dVA5lpXTU1NQOYkcu3t7XR3d/ddJb6lpYWFCzMvs76+ns7O\nTrq6uvo+Ttvc3Bz+mtra2lJ7TdfcfTev//nPfOKCCyrmNZXS+zQ1+8GJSnpNpfA+tba2VtxrKoX3\n6dlnn62411QK71Nra2vFvaYUZNdCAAAaOUlEQVRSeJ8ez34grdDXlI+hdln+zt1nDHR70AceYpel\nmU0AHgFezP6V3YDngJMG222ZxC7L/m+cFEFLC3zyk6xZsYK99dHsotP6GkNdY6hrDHWNUYyuxdpl\neYCZbch+9QBv7/3ezDYM8XdXANPNbE8zGwucBSzp/aG7v+DuO7n7NHefBtzLEJOxpDQ0NAy9kOQv\nuwVnwWmnpTyQyqT1NYa6xlDXGOoaI8mug37K0t1HDfeB3f01M/sYcDswCviOu680s0uADndfMvgj\npKerqyvtIVSW7IRsyTe+kfJAKpPW1xjqGkNdY6hrjCS7DvfEsHlx99vcvdbd35I9wz/u/rlckzF3\nn1UKW8dA1wQruuy5yG771rdSHkhl0voaQ11jqGsMdY2RZNfQCZkIALvsAttsw4QXXkh7JCIiIiVp\n0IP6S5HOQ1am3vpWOOAAuOmmtEciIiKSmGId1D8i6ZMqAaZO5Q9LSvawwbKm9TWGusZQ1xjqGiPJ\nrpqQ5bBMZ5QvvilT2G/77dMeRUXS+hpDXWOoawx1jZFkV03Icujp6Ul7CJVn6lRGP/MMbNyY9kgq\njtbXGOoaQ11jqGuMJLtqQpZD7xmApYiyp77g8cfTHUcF0voaQ11jqGsMdY2RZFcd1C/JuPdeOPRQ\nWLIEspeXEBERqXQ6qL8AvdfAkiLad9/Mnw8/nO44KpDW1xjqGkNdY6hrjCS7akKWQ+8FSqWIJkyg\nZ8IETcgCaH2Noa4x1DWGusZIsqt2WUpyjjsOnn8eVqxIeyQiIiKJ0C7LAuh/GjH+69e/hlWrYNOm\ntIdSUbS+xlDXGOoaQ11jJNlVE7IctAUuxhmLFsFLL8HatWkPpaJofY2hrjHUNYa6xkiyqyZkOSR5\ndfeR5M/bbpv5RseRFZXW1xjqGkNdY6hrjCS7akKWQ5JXdx9JFt1wQ+YbTciKSutrDHWNoa4x1DVG\nkl11UL8ka7fd4MQT4dvfTnskIiIi4XRQfwF0xuMYTU1NUFenLWRFpvU1hrrGUNcY6hojya6akOUw\nc+aQE1kZhpkzZ/5zQlZmW2ZLmdbXGOoaQ11jqGuMJLtql6Uk68or4fzzYd062H33tEcjIiISSrss\nC1BdXZ32ECpSdXV1ZgsZaLdlEWl9jaGuMdQ1hrrGSLKrJmQ5dHd3pz2EitTd3a0JWQCtrzHUNYa6\nxlDXGEl21YQsh/b29rSHUJHa29th551h0iRNyIpI62sMdY2hrjHUNUaSXTUhy6G1tTXtIVSk1tZW\nMNMnLYtM62sMdY2hrjHUNUaSXXVQvyTvwx+Gm26Cv/41M0ETERGpUDqovwCNjY1pD6Ei9XWtq4Pn\nn4enn053QBVC62sMdY2hrjHUNUaSXTUhy6GhoSHtIVSkvq46sL+otL7GUNcY6hpDXWMk2VW7LCV5\n3d2Zc5BdcUXmnGQiIiIVSrssC2A6rilEX9fJk2HCBG0hKxKtrzHUNYa6xlDXGEl21YQsh3Lbalgu\n+rrqk5ZFpfU1hrrGUNcY6hojya6akOXQ1taW9hAq0hu6akJWNFpfY6hrDHWNoa4xkuyqCVkOy5Yt\nS3sIFekNXevqMp+yfPbZ9AZUIbS+xlDXGOoaQ11jJNlVB/VLOn7+czj+eLjnHjjyyLRHIyIiEkIH\n9Rdgzpw5aQ+hIr2hq059UTRaX2Ooawx1jaGuMZLsqglZDk1NTWkPoSK9oeub3wzjx2tCVgRaX2Oo\nawx1jaGuMZLsql2WOfT09FBdXR36HCPRFl0PPjhz+os77khvUBVA62sMdY2hrjHUNUYxumqXZQFq\namrSHkJF2qKrPmlZFFpfY6hrDHWNoa4xkuyqCVkOPT09aQ+hIm3Rta4uc9b+v/0tnQFVCK2vMdQ1\nhrrGUNcYSXbVhCyH1tbWtIdQkbbo2ntg/6pVyQ+mgmh9jaGuMdQ1hrrGSLKrJmQ56LQaMbboqk9a\nFoXW1xjqGkNdY6hrjCS76qB+Sc/rr0N1NXzkI9DSkvZoREREik4H9Rdg1qxZaQ+hIm3RddQo2Gcf\nbSErkNbXGOoaQ11jqGuMJLtqQpZDc3Nz2kOoSDm76pOWBdP6GkNdY6hrDHWNkWTX0AmZmc02s9Vm\ntsbMPp3j5wvM7GEze8DM7jSzqZHjyVdtbW3aQ6hIObvW1cHjj4M+ITRsWl9jqGsMdY2hrjGS7Bo2\nITOzUcA3geOBOmCumdVtttjvgZnu/nbgFuDLUePZGjNnDrmrV4YhZ9feA/v/+MdkB1NBtL7GUNcY\n6hpDXWMk2TVyC9nBwBp3f9Td/wHcAJzcfwF3X+7uL2dv3gvsETievHV3d6c9hIqUs6s+aVkwra8x\n1DWGusZQ1xhJdo2ckO0OrO13e132voF8CPhZrh+YWZOZdZhZx5o1a/r26dbW1tLV1UVnZyf19fUA\nLFy4kJbsJ/Zqamro7u6mvb2978C8pqamvvOKVFdX09PTw9KlS/suINrY2MjZZ5/d+7wAtLW10djY\nCGQuNLp06dI3XE6htbW173pXs2bNor29ne7u7r4z/La0tLBw4UIA6uvr6ezspKurq29TaHNzc/hr\namtrS/01feITn9jyNe21F68APb/9bVm+plJ4nw455JCKe02l8D61tLRU3Gsqhfdp5513rrjXVArv\nU0tLS8W9plJ4nyZMmFCU15QXdw/5Ak4Hru53ex5wxQDLnkNmC9m4oR63vr7eoy1YsCD8OUaiAbvu\nv797Q0Oyg6kgWl9jqGsMdY2hrjGK0RXo8DzmTWHnITOzQ4Fmd39v9vbF2QngFzZb7j3AN4Cj3f3p\noR5X5yGrQGedBStWwCOPpD0SERGRoiqF85CtAKab2Z5mNhY4C1jSfwEzOwhYDJyUz2QsKb2bLaW4\nBuxaVwd//jO8/HLun8ugtL7GUNcY6hpDXWMk2TVsQuburwEfA24HVgE3uftKM7vEzE7KLvYVYDxw\ns5ndb2ZLBni4ROmaYDEG7LrvvuAOq1cnO6AKofU1hrrGUNcY6hojya6jIx/c3W8Dbtvsvs/1+/49\nkc8/XL0HDkpxDdi1/yctDzoouQFVCK2vMdQ1hrrGUNcYSXbVmfpzaGhoSHsIFWnArtOnZy6jpFNf\nDIvW1xjqGkNdY6hrjCS76uLiUhr23TdzXcsf/zjtkYiIiBRNKRzUX7Z0TbAYg3bVNS2HTetrDHWN\noa4x1DVGxVzLUiRvdXWwZg288kraIxEREUmcdllKabj+emhshAcegP33T3s0IiIiRaFdlgVI8uru\nI8mgXXVNy2HT+hpDXWOoawx1jZFkV03Icli2bFnaQ6hIg3atrYWqKli1KrkBVQitrzHUNYa6xlDX\nGEl21YQsh56enrSHUJEG7brttrDXXrByZXIDqhBaX2Ooawx1jaGuMZLsqglZDr1XkZfiGrLrwQfD\n8uXwj38kM6AKofU1hrrGUNcY6hojya46qF9Kx89+BiecALfeCiefnPZoRERECqaD+guwcOHCtIdQ\nkYbseuyxsOuu8P3vJzOgCqH1NYa6xlDXGOoaI8mumpDlUFNTk/YQKtKQXUePhrPPhqVL4bnnkhlU\nBdD6GkNdY6hrDHWNkWRX7bKU0nL//ZkLjF95JXzkI2mPRkREpCDaZVkA/U8jRl5dDzggc2JY7bbM\nm9bXGOoaQ11jqGuMJLtqQpaDtsDFyKurGZx7Ltx7L3R1xQ+qAmh9jaGuMdQ1hrrGSLKrJmQ5dGki\nECLvro2NmZPEXntt7IAqhNbXGOoaQ11jqGuMJLtqQpZDkld3H0ny7lpTk/nE5bXXwqZNoWOqBFpf\nY6hrDHWNoa4xkuyqg/qlNLW1ZT5x2d4ORx+d9mhERESGRQf1F0BnPI6xVV1POQXGj9fB/XnQ+hpD\nXWOoawx1jZFkV03Icpg5c8iJrAzDVnXdbjs44wy4+WZ4+eW4QVUAra8x1DWGusZQ1xhJdtUuSyld\n7e1wzDGZ3Zdz56Y9GhERka2mXZYFqK6uTnsIFWmrux51FEyZot2WQ9D6GkNdY6hrDHWNkWRXTchy\n6O7uTnsIFWmru1ZVwbx58ItfwBNPxAyqAmh9jaGuMdQ1hrrGSLKrJmQ5tLe3pz2EijSsrvPmZU59\n0dZW9PFUCq2vMdQ1hrrGUNcYSXbVhCyH1tbWtIdQkYbV9a1vhXe+U7stB6H1NYa6xlDXGOoaI8mu\nOqhfSt+VV8L552cuPH7AAWmPRkREJG86qL8AjY2NaQ+hIg2767/8C4wZo61kA9D6GkNdY6hrDHWN\nkWRXTchyaGhoSHsIFWnYXSdNgoYGuO46eO214g6qAmh9jaGuMdQ1hrrGSLKrdllKebj1Vnjf++C2\n2+D449MejYiISF60y7IAZpb2ECpSQV1POAEmTtRuyxy0vsZQ1xjqGkNdYyTZVROyHMptq2G5KKjr\n2LGZs/Xfeiu88ELxBlUBtL7GUNcY6hpDXWMk2VUTshzadM6rEAV3ff/7YeNG+PjH4fXXizOoCqD1\nNYa6xlDXGOoaI8mumpDlsGzZsrSHUJEK7vqOd8CiRXDNNZmtZf/4R3EGVua0vsZQ1xjqGkNdYyTZ\nVQf1S/lpaYFPfhJOPBFuvhm23TbtEYmIiOSkg/oLMGfOnLSHUJGK1nXhQli8OPOJyxNPhJ6e4jxu\nmdL6GkNdY6hrDHWNkWRXTchyaGpqSnsIFamoXZua4Npr4Z574Nhj4fnni/fYZUbrawx1jaGuMdQ1\nRpJdtcsyh56eHqqrq0OfYyQK6XrrrZkz+e+zD/ziF7DrrsV9/DKg9TWGusZQ1xjqGqMYXbXLsgA1\nNTVpD6EihXQ95RRYtgzWrIGjjoK1a4v/HCVO62sMdY2hrjHUNUaSXTUhy6FnhB+TFCWs67HHwu23\nw5NPwpFHwiOPxDxPidL6GkNdY6hrDHWNkWRXTchyaG1tTXsIFSm06xFHwF13wYsvwgEHwGmnwfe+\nB888E/ecJULrawx1jaGuMdQ1RpJdQydkZjbbzFab2Roz+3SOn48zsxuzP7/PzKZFjidfOq1G8Xz5\ny7B8eeb73q7Ll2fuz7VMr61d5stfhuUb6uE3v4FzzoH77oMPfhDfdVc47DD4whf4zoKHWH6XD/4Y\nBY4j77EW4Xl6aX2Noa4x1LV4Ev39WgKPkeTz9Ep0fXX3kC9gFPAIsBcwFvgDULfZMh8Frsp+fxZw\n41CPW19f71I+7rrLfaedMn/mul2sZba4fecmf9cOnf7oBz7vPnOmO7iD/6Vqmq895WPuV1/tf/i/\ny/xdO3T4r29c6/7KK+mNdZjPIyIjWzn9ziqnsRYb0OF5zJvCPmVpZocCze7+3uzti7MTwC/0W+b2\n7DL/Y2ajgSeBnX2QQSXxKctZs2bR3t4e+hwjyfLlcOaZsN121/Dyy+/nppvgmGNyL/ORj8C3vsWw\nlhn0593d8NOf8ux3l7Ld//yS7fj7lgOdNImXxu9KZ/du7Dh9Zx56ZFuOOm4cu+85Dsb982vN2nF8\n57pxHHrUGJb/ahRN/6uKfeqqoKoKRo2CqipWrqri8v9XxXuONe64Ay74hPG2/Q2yF6p98CHjshZj\n9mz42c+NCy+E/fd/43AefBC+8hWYfbzxs5/BRRdtuQzAZz7zGS699NKte1NkSOoaQ12L68EHM1t2\nxo27i42vvJuLLoK3v/2NyzzwQGaZ44+n73fJ1i5TKo9R7OeZeW4dl964d85/c6A484F8P2UZuYXs\ndODqfrfnAVdstsxDwB79bj8C7JTjsZqADqBjwoQJvmjRInd3nz59uq9evdo7Ojp8xowZ7u6+YMEC\nv+yyy9zdffLkyb5+/Xpfvny5H3300e7uPn/+fF+8eLG7u48fP943bNjgS5Ys8YaGBnd3nzt3rn/m\nM5/pndW6u/t1113nc+fOdXf3hoYGX7JkiW/YsMHHjx/v7u6LFy/2+fPnu7v70Ucf7cuXL/f169f7\n5MmT3d39sssu8wULFri7+4wZM7yjo8NXr17t06dPd3f3RYsWhb+m6667LtXX9NGPPuvgvtturQO+\npnnz/uzg/tnPDvyapk+/3sF9v/1+mPM17bffDx3cp0+/fsDX1HzxRp/Kn33R7B/5X7/zHf/khAnu\nn/+8//6ww/wPe+/tf3nz4f5Hav3Z8bv7qzvt5H+rqnLfZhvflN3Kpi996Utf+qqMrwv5kn/0o88O\n+G/upEmTCv43lxLYQnY6MNvdz8venge8090/1m+Zh7LLrMvefiS7zLMDPW4SW8i6u7v1EeIi6t1y\ndc45PfzgB9XpbCErxmO4w2uvcc8drzB/3kY+9P7X+P73NtF61SYOO2RT5oLnmzbBpk3c+5tNLLzg\ndc46C268wflqi3PwO7zvcX77W+eiC+HMM5ybb878T+0d73jjWFesgE9d5Jx+OtxyC3zpS1suA/D0\nM8+wy8475/1+SH7UNYa6FteKFfCpT8GJJ7zIbbe9iS9+Mffvkk9/mr7fJcNZplQeo9jP894P1vCV\na3cbcAtZMeYDpbCF7FDg9n63LwYu3myZ24FDs9+PBp4le7Lagb6SOIasdyuQFK7/vvnJkyeX9HED\n5XqMg9bXGOoaQ12LR79fy+P3K3luIYv8lOUKYLqZ7WlmY8kctL9ks2WWAO/Pfn86cFd28Knq7u5O\newgVY8WKf25l6u7u5phjMrdXrMi9DDCsZUrlMZJ8nl5aX2Ooawx1LR79fq2w36/5zNqG+wWcAHSR\nOTbsM9n7LgFOyn6/DXAzsAb4LbDXUI+ZxBay3n3HUlzqGkNdY6hrDHWNoa4xitGVPLeQjQ6e7N0G\n3LbZfZ/r9/1G4IzIMQyH/gcXQ11jqGsMdY2hrjHUNUaSXXVxcREREZEgurh4Aerr69MeQkVS1xjq\nGkNdY6hrDHWNkWRXTchy0DXBYqhrDHWNoa4x1DWGusaomGtZlqvq6uq0h1CR1DWGusZQ1xjqGkNd\nYyTZVROyHBoaGtIeQkVS1xjqGkNdY6hrDHWNkWRXHdQvIiIiEkQH9Regubk57SFUJHWNoa4x1DWG\nusZQ1xhJdi27LWRm9gzwWPDT7ETmMk5SXOoaQ11jqGsMdY2hrjGK0XWquw95Adeym5Alwcw68tm8\nKFtHXWOoawx1jaGuMdQ1RpJdtctSREREJGWakImIiIikTBOy3HSGvRjqGkNdY6hrDHWNoa4xEuuq\nY8hEREREUqYtZCIiIiIp04RMREREJGUjfkJmZt8xs6fN7KF+9000szvM7E/ZP3dMc4zlyMzebGbL\nzexhM1tpZh/P3q+2BTCzbczst2b2h2zXz2fv39PM7jOzNWZ2o5mNTXus5cjMRpnZ781sWfa2uhbI\nzP5iZg+a2f1m1pG9T78HCmRmO5jZLWb2RzNbZWaHqmthzOyt2fW092uDmV2QVNcRPyEDvgfM3uy+\nTwN3uvt04M7sbdk6rwEL3b0OOAQ438zqUNtCvQK8y90PAA4EZpvZIcCXgK+5+97A88CHUhxjOfs4\nsKrfbXUtjmPc/cB+53PS74HCfR34ubvvAxxAZr1V1wK4++rsenogUA+8DPyYhLqO+AmZu98DPLfZ\n3ScD12S/vwY4JdFBVQB3f8Ldf5f9vofML4vdUduCeMaL2Ztjsl8OvAu4JXu/ug6Dme0BnAhcnb1t\nqGsU/R4ogJlNAI4Cvg3g7v9w97+hrsX0buARd3+MhLqO+AnZAHZ19yey3z8J7JrmYMqdmU0DDgLu\nQ20Llt2tdj/wNHAH8AjwN3d/LbvIOjKTX9k6lwMXAZuytyehrsXgwC/MrNPMmrL36fdAYfYEngG+\nm93FfrWZvQl1LaazgOuz3yfSVROyIXjmvCA6N8gwmdl44IfABe6+of/P1HZ43P317Cb1PYCDgX1S\nHlLZM7MG4Gl370x7LBXoCHefARxP5tCFo/r/UL8HhmU0MAP4lrsfBLzEZrvR1HX4sseKngTcvPnP\nIrtqQpbbU2Y2GSD759Mpj6csmdkYMpOx69z9R9m71bZIsrsolgOHAjuY2ejsj/YA1qc2sPJ0OHCS\nmf0FuIHMrsqvo64Fc/f12T+fJnM8zsHo90Ch1gHr3P2+7O1byEzQ1LU4jgd+5+5PZW8n0lUTstyW\nAO/Pfv9+4CcpjqUsZY+/+Tawyt2/2u9HalsAM9vZzHbIfr8tcCyZ4/OWA6dnF1PXreTuF7v7Hu4+\njcyuirvc/WzUtSBm9iYzq+79HjgOeAj9HiiIuz8JrDWzt2bvejfwMOpaLHP55+5KSKjriD9Tv5ld\nD8wCdgKeAhYBtwI3AVOAx4Az3X3zA/9lEGZ2BPAr4EH+eUzOf5A5jkxth8nM3k7moNJRZP5DdZO7\nX2Jme5HZsjMR+D1wjru/kt5Iy5eZzQI+6e4N6lqYbL8fZ2+OBtrc/VIzm4R+DxTEzA4k8wGUscCj\nwAfJ/k5AXYct+x+Hx4G93P2F7H2JrK8jfkImIiIikjbtshQRERFJmSZkIiIiIinThExEREQkZZqQ\niYiIiKRMEzIRERGRlGlCJiIjgpmdYmZuZrqygYiUHE3IRGSkmAv8d/ZPEZGSogmZiFS87DVVjwA+\nROZM/JhZlZldaWZ/NLM7zOw2Mzs9+7N6M7s7e0Hs23svmyIiEkUTMhEZCU4Gfu7uXcBfzaweOBWY\nBtQB88hcE7T3GqzfAE5393rgO8ClaQxaREaO0UMvIiJS9uaSuVg4ZC6FNJfM77+b3X0T8KSZLc/+\n/K3A24A7MpdkZRTwRLLDFZGRRhMyEaloZjYReBewv5k5mQmW889rLG7xV4CV7n5oQkMUEdEuSxGp\neKcD17r7VHef5u5vBv4MPAeclj2WbFdgVnb51cDOZta3C9PM9ktj4CIycmhCJiKVbi5bbg37IbAb\nsA54GPgB8DvgBXf/B5lJ3JfM7A/A/cBhyQ1XREYic/e0xyAikgozG+/uL5rZJOC3wOHu/mTa4xKR\nkUfHkInISLbMzHYAxgL/qcmYiKRFW8hEREREUqZjyERERERSpgmZiIiISMo0IRMRERFJmSZkIiIi\nIinThExEREQkZf8f62Wjpvk0riAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKD-z7e7xKRV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "075858e7-aeb7-4827-e61f-a895e4ac502d"
      },
      "source": [
        "fig = plt.figure(figsize=(10,5))\n",
        "xlabel = 'Age'\n",
        "ylabel = 'Purchased'\n",
        "plt.xlabel(xlabel)\n",
        "plt.ylabel(ylabel)\n",
        "plt.grid(color='k', linestyle=':', linewidth=1)\n",
        "plt.plot(x, y, 'xb')\n",
        "plt.plot(x, lin_y_pred_2, '-r')\n",
        "plt.plot(x, log_y_pred_2, '-r')\n",
        "_ = plt.plot(x, line_point_5,'-g')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qdiAzMzPsEmJTWhqsWOE/ImC56rBcdViuOixXHZarjiBzDbPRNgPo4Zzb3TnXEBgCTNrm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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTnHOp5Qp5t5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "eb5e5cf6-8c67-4be6-fd91-361d1b014369"
      },
      "source": [
        "test_y_pred = log_regression.predict_proba(pd.DataFrame(test_x))\n",
        "test_y_pred"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.14286332, 0.85713668],\n",
              "       [0.35497559, 0.64502441],\n",
              "       [0.64502249, 0.35497751],\n",
              "       [0.85713565, 0.14286435],\n",
              "       [0.95194543, 0.04805457],\n",
              "       [0.98494151, 0.01505849],\n",
              "       [0.99539093, 0.00460907],\n",
              "       [0.99859958, 0.00140042]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hY-vemTnV_D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0d561d1c-0b5a-425a-fcde-52374f6205be"
      },
      "source": [
        "print(\"Linear regression R2: \", r2_score(y, lin_y_pred_2))\n",
        "print(\"Logistic regression R2: \", r2_score(y, log_y_pred_2))\n",
        "print(\"Linear regression RMSE: \", mean_squared_error(y, lin_y_pred_2))\n",
        "print(\"Logistic regression RMSE: \", mean_squared_error(y, log_y_pred_2))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear regression R2:  0.42112651342340734\n",
            "Logistic regression R2:  0.9553066567250715\n",
            "Linear regression RMSE:  0.12863855257257611\n",
            "Logistic regression RMSE:  0.009931854061095221\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}