MLDS_Aula_7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z1O2Ql5ey0kU"
      },
      "outputs": [],
      "source": [
        "#\n",
        "# 0 - homem\n",
        "# 1 - mulher\n",
        "#\n",
        "# REGRESSÃO LOGÍSTICA É PARA CLASSIFICAÇÃO\n",
        "#\n",
        "# 0 ou 1\n",
        "# y >= 0.5 y = 1\n",
        "# y <  0.5 y = 0\n",
        "#\n",
        "# 0.52 => 0 ou 1\n",
        "#\n",
        "# MATRIZ DE CONFUSÃO\n",
        "#\n",
        "# amostra de treino\n",
        "# amostra de teste\n",
        "#\n",
        "#   NEGATIVO ou POSITIVO\n",
        "#\n",
        "# MATRIZ DE CONFUSÃO\n",
        "#\n",
        "# POSITIVO        FALSO-POSITIVO\n",
        "# FALSO-NEGATIVO  NEGATIVO\n",
        "#\n",
        "# RELAÇÃO NEGATIVO = NEGATIVO / ( FALSO-NEGATIVO + NEGATIVO )\n",
        "# NEGATIVO: 50\n",
        "# FALSO-NEGATIVO: 10\n",
        "# 50 / 50 + 10 => 50 / 60 => 0,83 => 83%\n",
        "#\n",
        "# POSITIVO: 100\n",
        "# FALSO-POSITIVO: 25\n",
        "# RELAÇÃO: 100 / 100 + 25 => 100 / 125 => 0,8 => 80%\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import seaborn as sns\n",
        "import statsmodels.api as sm\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/MLDS/Aula 6/Social_Network_Ads.csv')\n",
        "df['male'] = df.apply(lambda row: 1 if row['Gender'] == 'Male' else 0, axis=1)\n",
        "\n",
        "df_train = df.sample(200)\n",
        "df_test = df.drop(df_train.index)\n"
      ],
      "metadata": {
        "id": "7-uO_pMF4Hv_"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train[['male','Age','EstimatedSalary']].to_numpy()\n",
        "Y_train = df_train['Purchased'].to_numpy()\n",
        "\n",
        "X_test = df_test[['male','Age','EstimatedSalary']].to_numpy()\n",
        "Y_test = df_test['Purchased'].to_numpy()\n",
        "\n",
        "classificador = LogisticRegression()\n",
        "classificador.fit(X_train, Y_train)\n",
        "\n",
        "Y_predicted = classificador.predict(X_test)\n",
        "\n",
        "matriz_confusao = confusion_matrix(Y_test, Y_predicted)\n",
        "\n",
        "sns.heatmap(pd.DataFrame(matriz_confusao), annot=True, fmt='g')\n",
        "plt.title(\"Matriz de Confusão\", y=1.1)\n",
        "plt.ylabel(\"Y verdadeiro\")\n",
        "plt.xlabel(\"Y previsto\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "3pmroeGm5_Bc",
        "outputId": "8bbf7b6b-c378-40d8-d1d4-45803dccb4fb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 23.52222222222222, 'Y previsto')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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AAACjSDYAAIBRJBsAAMAokg0AAGAUyQYAADCKZAMAABhFsgEAAIzy8XQAJmSePODpEIAbkn94S0+HANxwsjKOGX+GXX8v+Va82Zb7FDQqGwAAwKgiWdkAAOCGkpPt6Qg8imQDAADTrBxPR+BRJBsAAJiWU7yTDeZsAAAAo6hsAABgmMUwCgAAMIphFAAAAHOobAAAYBrDKAAAwKhivs8GwygAAMAoKhsAAJjGMAoAADCK1SgAAADmUNkAAMAwNvUCAABmFfNhFJINAABMK+aVDeZsAAAAo6hsAABgWjHf1ItkAwAA0xhGAQAAMIfKBgAAprEaBQAAGMUwCgAAKIrWr1+vjh07Kjw8XA6HQ5988onzXGZmpp599lnVr19fAQEBCg8PV+/evXX8+HGXe5w6dUq9evVSYGCgypYtq/79++vs2bNuxUGyAQCAaTk59hxuSk1NVcOGDfXmm2/mOpeWlqb4+Hi98MILio+P18cff6y9e/eqU6dOLv169eqln376SV999ZWWL1+u9evXa+DAgW7F4bAsy3I7+htc5skDng4BuCH5h7f0dAjADScr45jxZ5zf8bkt9ynZsMM1X+twOLR06VJ16dLlsn22bNmiu+66S4cPH1bVqlW1e/du1alTR1u2bFGTJk0kSStWrFCHDh109OhRhYeH5+vZVDYAAIAkKSUlRQ6HQ2XLlpUkxcXFqWzZss5EQ5IiIyPl5eWlTZs25fu+TBAFAMA0myaIpqenKz093aXNz89Pfn5+133v8+fP69lnn9Wjjz6qwMBASVJCQoKCg4Nd+vn4+Kh8+fJKSEjI972pbAAAYJpNczaio6MVFBTkckRHR193eJmZmerevbssy9Ls2bNt+MKuqGwAAGCaTZWN0aNHKyoqyqXteqsaFxONw4cPa82aNc6qhiSFhoYqKSnJpX9WVpZOnTql0NDQfD+DZAMAgELCriGTiy4mGvv27dPatWtVoUIFl/MRERFKTk7Wtm3b1LhxY0nSmjVrlJOTo6ZNm+b7OSQbAACY5qEXsZ09e1a//PKL8/PBgwe1fft2lS9fXmFhYXrkkUcUHx+v5cuXKzs72zkPo3z58ipRooRq166t+++/XwMGDNCcOXOUmZmpIUOGqEePHvleiSKx9BUoVlj6CuRWIEtfNy+25T4l7+rmVv+vv/5arVu3ztXep08fjRs3TjVq1MjzurVr16pVq1aSLmzqNWTIEH322Wfy8vJS165dNX36dJUuXTrfcZBsAMUIyQaQW1FONm4UDKMAAGAaL2IDAABG8SI2AAAAc6hsAABgGsMoAADAqGKebDCMAgAAjKKyAQCAYZblmU29bhQkGwAAmFbMh1FINgAAMI2lrwAAAOZQ2QAAwDSGUQAAgFEMowAAAJhDZQMAANMYRgEAAEYxjAIAAGAOlQ0AAExjGAUAABhVzJMNhlEAAIBRVDYAADCtmE8QJdkAAMC0Yj6MQrIBAIBpxbyywZwNAABgFJUNAABMYxgFAAAYxTAKAACAOVQ2AAAwjWEUAABgVDFPNhhGAQAARlHZAADANMvydAQeRbIBAIBpDKMAAACYQ2UDAADTinllg2QDAADTivmmXiQbAACYVswrG8zZAAAARlHZAADANJa+AgAAoxhGAQAAMIfKBgAAphXzygbJBgAAphXzpa8MowAAAKOobAAAYJiVw2oUAABgUjGfs8EwCgAAMIrKBgAApjFBFAAAGJVj2XO4af369erYsaPCw8PlcDj0ySefuJy3LEtjxoxRWFiY/P39FRkZqX379rn0OXXqlHr16qXAwECVLVtW/fv319mzZ92Kg2QDAADTcnLsOdyUmpqqhg0b6s0338zz/KRJkzR9+nTNmTNHmzZtUkBAgNq1a6fz5887+/Tq1Us//fSTvvrqKy1fvlzr16/XwIED3YrDYVlFb8P2zJMHPB0CcEPyD2/p6RCAG05WxjHjz0ibMciW+5QaOuuar3U4HFq6dKm6dOki6UJVIzw8XP/85z81cuRISVJKSopCQkI0f/589ejRQ7t371adOnW0ZcsWNWnSRJK0YsUKdejQQUePHlV4eHi+nk1lAwAA02yqbKSnp+vMmTMuR3p6+jWFdPDgQSUkJCgyMtLZFhQUpKZNmyouLk6SFBcXp7JlyzoTDUmKjIyUl5eXNm3alO9nkWwAAGCaZdlyREdHKygoyOWIjo6+ppASEhIkSSEhIS7tISEhznMJCQkKDg52Oe/j46Py5cs7++QHq1EAACgkRo8eraioKJc2Pz8/D0WTf1Q24Lat23dq8Kixat2pl+o1b6/V679zOf/mO++p46MDdGebLmp2fzc9/vRo/fDTHpc+KWf+0LPjXlXTtg8rot0jeiF6qtLSzhXk1wA84qkn++iXnzfq7Jn9+m7DZ7qzSSNPh4SCYNMwip+fnwIDA12Oa002QkNDJUmJiYku7YmJic5zoaGhSkpKcjmflZWlU6dOOfvkB8kG3Hbu3HnddsvN+r9/5j3hqXqVm/SvqEH6+N3ZenfWawoPDdHAEf+nU6eTnX2efXGSfjl4RPOmTdSbk8Zp2/YfNW7S9AL6BoBndOvWSa9NHquXJkzRnU3v144fdunz/y5UpUoVPB0aTPPQ0tcrqVGjhkJDQ7V69Wpn25kzZ7Rp0yZFRERIkiIiIpScnKxt27Y5+6xZs0Y5OTlq2rRpvp9FsgG3tYy4U8MG9lHkvc3zPP/Afa0VceftqnJTmG65uZpGDRugs6lp+nn/QUnS/kNHtGHjVr343NNqULeW7mhYT/8a8ZS+WLVOSSd+L8ivAhSoEU8P0NvvxGrBux9q9+59GjT4OaWlnVO/vj08HRqKqLNnz2r79u3avn27pAuTQrdv364jR47I4XBo+PDhmjBhgpYtW6adO3eqd+/eCg8Pd65YqV27tu6//34NGDBAmzdv1rfffqshQ4aoR48e+V6JInl4zsbJkyf173//W3Fxcc6JJqGhoWrWrJn69u2rSpUqeTI82CAzM1OLP/1CZUoH6LZbbpYk7fhxtwLLlFa92rc6+93d5HZ5eTn0w649l01igMLM19dXd9zRQK9MmulssyxLq9ds0N13N/ZgZCgQHtpBdOvWrWrdurXz88X5Hn369NH8+fM1atQopaamauDAgUpOTlaLFi20YsUKlSxZ0nnNwoULNWTIELVp00ZeXl7q2rWrpk93rxLtsWRjy5YtateunUqVKqXIyEjdeuuFv3gSExM1ffp0vfLKK/ryyy9dltug8Pj62016ZuwrOn8+XZUqlNfcaS+rXNkgSdLJ30+r/P//+SIfH28FlSmjk6dOeyJcwLiKFcvLx8dHSYknXdqTkk6o1m01PRQVCoyH3vraqlUrXWk7LYfDofHjx2v8+PGX7VO+fHnFxsZeVxweSzaGDh2qbt26ac6cOXI4HC7nLMvSk08+qaFDhzrX+l5Oenp6rjXGXunphWJ2blF21x0NtWT+mzqdnKKPPluhkS9EK3beNFUoV9bToQEACpjH5mzs2LFDI0aMyJVoSBcyrREjRjjHmK4krzXHr74xx0DEcEcp/5KqWjlcDevV1kujR8jb21sff/alJKlihXI6lZzi0j8rK1spf/yhiuXLeSJcwLiTJ08pKytLwSEVXdqDgyspIfGEh6JCQbFycmw5CiuPJRuhoaHavHnzZc9v3rw510YjeRk9erRSUlJcjmefftLOUGGDnJwcZWRmSpIa1qutM3+c1U97/veyn03btisnx1KDOrU8FSJgVGZmpuLjf9BfW7dwtjkcDv21dQtt3LjtCleiSLgBV6MUJI8No4wcOVIDBw7Utm3b1KZNG2dikZiYqNWrV2vevHl67bXXrnofPz+/XEMmmRknL9MbdkhLO6cjR487Px87nqg9P+9XUGAZBQUFau6CD9S6RVNVqlhep5PP6P2PP1PSyd/VrvWF93LUrF5VLe5uonGvvqExzwxVZlaWJk6drfaR9yqYJYAowqa+MU8x70zVtvgftGXL9xo2dIACAvw1f8EiT4cG04r5K+Y9lmwMHjxYFStW1NSpUzVr1ixlZ2dLkry9vdW4cWPNnz9f3bt391R4uIIf9+zTY0OfdX6eNGOuJKlz+0iNeWaoDh7+Vcu+WKXTKSkqGxioerVv1YJZk3XLzdWc17w6dpRenjJL/YeNlpeXQ5Gtmutfw58q8O8CFKTFi5epUsXyGjdmpEJDK2nHjp/0wIN/V1IS/0BC0XZDvPU1MzNTJ09e+MNWsWJF+fr6Xt/9eOsrkCfe+grkVhBvfU0d38uW+wSMWWjLfQraDfFuFF9fX4WFhXk6DAAAzCjEkzvtwA6iAADAqBuisgEAQJFWiFeS2IFkAwAA04r5ahSGUQAAgFFUNgAAMI1hFAAAYFJh3mrcDgyjAAAAo6hsAABgGsMoAADAKJINAABgFEtfAQAAzKGyAQCAaQyjAAAAk6xinmwwjAIAAIyisgEAgGnFvLJBsgEAgGnsIAoAAGAOlQ0AAExjGAUAABhVzJMNhlEAAIBRVDYAADDMsop3ZYNkAwAA04r5MArJBgAAphXzZIM5GwAAwCgqGwAAGFbc341CsgEAgGnFPNlgGAUAABhFZQMAANOK96tRSDYAADCtuM/ZYBgFAAAYRWUDAADTinllg2QDAADTivmcDYZRAACAUVQ2AAAwrLhPECXZAADAtGI+jEKyAQCAYcW9ssGcDQAAYBSVDQAATGMYBQAAmGQV82SDYRQAAIqg7OxsvfDCC6pRo4b8/f1Vs2ZNvfTSS7Ks/80fsSxLY8aMUVhYmPz9/RUZGal9+/bZHgvJBgAApuXYdLjh1Vdf1ezZszVz5kzt3r1br776qiZNmqQZM2Y4+0yaNEnTp0/XnDlztGnTJgUEBKhdu3Y6f/789X3fP2EYBQAAwzwxjPLdd9+pc+fOeuCBByRJ1atX1/vvv6/NmzdfiMmyNG3aND3//PPq3LmzJOndd99VSEiIPvnkE/Xo0cO2WKhsAABQSKSnp+vMmTMuR3p6ep59mzVrptWrV+vnn3+WJO3YsUMbNmxQ+/btJUkHDx5UQkKCIiMjndcEBQWpadOmiouLszVukg0AAEyzaRglOjpaQUFBLkd0dHSej3zuuefUo0cP1apVS76+vrr99ts1fPhw9erVS5KUkJAgSQoJCXG5LiQkxHnOLtc0jLJu3Tq99tpr2r17tySpTp06euaZZ9SyZUtbgwMAoCiwaxhl9OjRioqKcmnz8/PLs++HH36ohQsXKjY2VnXr1tX27ds1fPhwhYeHq0+fPvYElE9uVzbee+89RUZGqlSpUho2bJiGDRsmf39/tWnTRrGxsSZiBACgULNy7Dn8/PwUGBjoclwu2XjmmWec1Y369evrH//4h0aMGOGshISGhkqSEhMTXa5LTEx0nrOL28nGyy+/rEmTJmnRokXOZGPRokV65ZVX9NJLL9kaHAAAuDZpaWny8nL9a97b21s5ORfKLDVq1FBoaKhWr17tPH/mzBlt2rRJERERtsbidrJx4MABdezYMVd7p06ddPDgQVuCAgCgKLGrsuGOjh076uWXX9Z///tfHTp0SEuXLtWUKVP00EMPSZIcDoeGDx+uCRMmaNmyZdq5c6d69+6t8PBwdenSxdbv7/acjSpVqmj16tW65ZZbXNpXrVqlKlWq2BYYAABFhuUo8EfOmDFDL7zwggYNGqSkpCSFh4friSee0JgxY5x9Ro0apdTUVA0cOFDJyclq0aKFVqxYoZIlS9oai8O6dCuxfJg9e7aGDx+uxx57TM2aNZMkffvtt5o/f77eeOMNPfHEE7YGeC0yTx7wdAjADck/nEncwJ9lZRwz/ozEVq1suU/I11/bcp+C5nZl46mnnlJoaKhef/11ffjhh5Kk2rVra9GiRc5NQQAAwP8U93ejuJVsZGVlaeLEiXrssce0YcMGUzEBAFCkWDkFP4xyI3FrgqiPj48mTZqkrKwsU/EAAIAixu3VKG3atNG6detMxAIAQJHkidUoNxK352y0b99ezz33nHbu3KnGjRsrICDA5XynTp1sCw4AgKLA8sBqlBuJ26tR/rxBiMvNHA5lZ2dfd1DXi9UoQN5YjQLkVhCrUY5F/NWW+9wUt8aW+xQ0tysbF3ceAwAA+VOYh0DscE0vYgMAAPlX3Fej5CvZmD59ugYOHKiSJUtq+vTpV+w7bNgwWwIDAKCocG/CQtGTrzkbNWrU0NatW1WhQgXVqFHj8jdzOHTggOfnSzBnA8gbczaA3ApizsaRJm1suU/Vrauv3ukGlK/KxqUvWONlawAAuKe4D6O4vc/GRRkZGdq7dy8bfAEAcBVWjsOWo7ByO9lIS0tT//79VapUKdWtW1dHjhyRJA0dOlSvvPKK7QECAIDCze1kY/To0dqxY4e+/vprl1fQRkZGatGiRbYGBwBAUWBZ9hyFldtLXz/55BMtWrRId999txyO/5V06tatq/3799saHAAARUFhHgKxg9uVjRMnTig4ODhXe2pqqkvyAQAAIF1DstGkSRP997//dX6+mGC8/fbbioiIsC8yAACKCMty2HIUVm4Po0ycOFHt27fXrl27lJWVpTfeeEO7du3Sd999x9tgAQDIQ3HfrtztykaLFi20fft2ZWVlqX79+lq5cqWCg4MVFxenxo0bm4gRAAAUYtf0bpSaNWtq3rx5dscCAECRlFOIh0DskK9k48yZM/m+YWBg4DUHAwBAUVSY51vYIV/JRtmyZfO90iQ7O/u6AgIAoKgp7ktf85VsrF271vnzoUOH9Nxzz6lv377O1SdxcXFasGCBoqOjzUQJAAAKrXy99fVSbdq00eOPP65HH33UpT02NlZz587V119/bWd814S3vgJ5462vQG4F8dbX3X/pYMt9au/73Jb7FDS3V6PExcWpSZMmudqbNGmizZs32xIUAABFCS9ic1OVKlXyXIny9ttvq0qVKrYEBQAAig63l75OnTpVXbt21RdffKGmTZtKkjZv3qx9+/ZpyZIltgcIAEBhV9yXvrpd2ejQoYP27dunTp066dSpUzp16pQ6duyon3/+WR062DMmBQBAUcJ25degcuXKevnll+2OBQAAFEHXlGxIUlpamo4cOaKMjAyX9gYNGlx3UAAAFCXurfssetxONk6cOKF+/frpiy++yPM8m3oBAOCKORtuGj58uJKTk7Vp0yb5+/trxYoVWrBggf7yl79o2bJlJmIEAACFmNuVjTVr1ujTTz9VkyZN5OXlpWrVqqlt27YKDAxUdHS0HnjgARNxAgBQaBXmyZ12cLuykZqaquDgYElSuXLldOLECUlS/fr1FR8fb290AAAUAZZlz1FYuZ1s3Hbbbdq7d68kqWHDhnrrrbd07NgxzZkzR2FhYbYHCABAYZdjOWw5Ciu3h1Gefvpp/fbbb5KksWPH6v7779fChQtVokQJzZ8/3+74AABAIef2i9j+LC0tTXv27FHVqlVVsWJFu+K6LntuZXMxIC/1Du3wdAjADacgXsS25aaHbLnPnceW2nKfgnbN+2xcVKpUKd1xxx12xAIAQJFUmIdA7JCvZCMqKirfN5wyZco1BwMAAIqefCUb33//vcvn+Ph4ZWVl6bbbbpMk/fzzz/L29lbjxo3tjxAAgEKuEC8ksUW+ko21a9c6f54yZYrKlCmjBQsWqFy5cpKk06dPq1+/fmrZsqWZKAEAKMSK+zCK2xNEb7rpJq1cuVJ169Z1af/xxx9133336fjx47YGeC2YIArkjQmiQG4FMUH0u7Cuttyn2W9LbLlPQXN7guiZM2ecG3ld6sSJE/rjjz9sCQoAgKKEHUTd9NBDD6lfv376+OOPdfToUR09elRLlixR//799fDDD5uIEQCAQi3HpqOwcruyMWfOHI0cOVI9e/ZUZmbmhZv4+Kh///6aPHmy7QECAIDCza1kIzs7W1u3btXLL7+syZMna//+/ZKkmjVrKiAgwEiAAAAUdpYYRsk3b29v3XfffUpOTlZAQIAaNGigBg0akGgAAHAFOZY9h7uOHTumv//976pQoYL8/f1Vv359bd261XnesiyNGTNGYWFh8vf3V2RkpPbt22fjN7/A7Tkb9erV04EDB2wPBACAoipHDlsOd5w+fVrNmzeXr6+vvvjiC+3atUuvv/66c9sKSZo0aZKmT5+uOXPmaNOmTQoICFC7du10/vx5W7+/23M2JkyYoJEjR+qll15S48aNc1U1AgMDbQsOAABcm1dffVVVqlRRTEyMs61GjRrOny3L0rRp0/T888+rc+fOkqR3331XISEh+uSTT9SjRw/bYnG7stGhQwft2LFDnTp1UuXKlVWuXDmVK1dOZcuWdcmWAADABZYcthzp6ek6c+aMy5Genp7nM5ctW6YmTZqoW7duCg4O1u2336558+Y5zx88eFAJCQmKjIx0tgUFBalp06aKi4uz9fu7Xdm4dDdRAABwdXYtW42OjtaLL77o0jZ27FiNGzcuV98DBw5o9uzZioqK0r/+9S9t2bJFw4YNU4kSJdSnTx8lJCRIkkJCQlyuCwkJcZ6zi9vJxr333mtrAAAAIH9Gjx6d6+Wofn5+efbNyclRkyZNNHHiREnS7bffrh9//FFz5sxRnz59jMd6KbeHUSTpm2++0d///nc1a9ZMx45d2Ob1P//5jzZs2GBrcAAAFAV2DaP4+fkpMDDQ5bhcshEWFqY6deq4tNWuXVtHjhyRJIWGhkqSEhMTXfokJiY6z9nF7WRjyZIlateunfz9/RUfH+8cK0pJSXFmTwAA4H88sYNo8+bNtXfvXpe2n3/+WdWqVZN0YbJoaGioVq9e7Tx/5swZbdq0SREREW4+7crcTjYmTJigOXPmaN68efL19XW2N2/eXPHx8bYGBwAArs2IESO0ceNGTZw4Ub/88otiY2M1d+5cDR48WJLkcDg0fPhwTZgwQcuWLdPOnTvVu3dvhYeHq0uXLrbG4vacjb179+qee+7J1R4UFKTk5GQ7YgIAoEjxxHtN7rzzTi1dulSjR4/W+PHjVaNGDU2bNk29evVy9hk1apRSU1M1cOBAJScnq0WLFlqxYoVKlixpayxuJxuhoaH65ZdfVL16dZf2DRs26Oabb7YrLgAAigxPbVf+4IMP6sEHH7zseYfDofHjx2v8+PFG43B7GGXAgAF6+umntWnTJjkcDh0/flwLFy7UyJEj9dRTT5mIEQAAFGJuVzaee+455eTkqE2bNkpLS9M999wjPz8/jRw5UkOHDjURIwAAhVpO8X4PmxyWZV3Dq12kjIwM/fLLLzp79qzq1Kmj0qVL2x3bNdtzawdPhwDckOod2uHpEIAbTlbGMePP+DS0py336ZwQa8t9Cprbwyjvvfee0tLSVKJECdWpU0d33XXXDZVoAABwo7FsOgort5ONESNGKDg4WD179tTnn3+u7OxsE3EBAIAiwu1k47ffftMHH3wgh8Oh7t27KywsTIMHD9Z3331nIj4AAAo9T2zqdSNxO9nw8fHRgw8+qIULFyopKUlTp07VoUOH1Lp1a9WsWdNEjAAAFGo5DoctR2Hl9mqUS5UqVUrt2rXT6dOndfjwYe3evduuuAAAQBFxTS9iS0tL08KFC9WhQwfddNNNmjZtmh566CH99NNPdscHAEChV9wniLpd2ejRo4eWL1+uUqVKqXv37nrhhRdsf2ELAABFSWGeb2EHt5MNb29vffjhh2rXrp28vb1NxAQAAIoQt5ONhQsXmogDAIAiq7jvIHpdE0QBAMDV5XjoRWw3imuaIAoAAJBf+U42jh8/bjIOAACKrOK+GiXfyUbdunUVG1s4XwADAIAn5TjsOQqrfCcbL7/8sp544gl169ZNp06dMhkTAABFCtuV59OgQYP0ww8/6Pfff1edOnX02WefmYwLAAAUEW6tRqlRo4bWrFmjmTNn6uGHH1bt2rXl4+N6i/j4eFsDBACgsCvM8y3s4PbS18OHD+vjjz9WuXLl1Llz51zJBgAAcFWY51vYwa1MYd68efrnP/+pyMhI/fTTT6pUqZKpuAAAQBGR72Tj/vvv1+bNmzVz5kz17t3bZEwAABQphXlypx3ynWxkZ2frhx9+UOXKlU3GAwBAkUOykU9fffWVyTgAAEARxexOAAAMs5ggCgAATCruwyi8iA0AABhFZQMAAMOKe2WDZAMAAMPYQRQAABhV3HcQZc4GAAAwisoGAACGMWcDAAAYVdyTDYZRAACAUVQ2AAAwjNUoAADAKFajAAAAGERlAwAAw4r7BFGSDQAADCvuczYYRgEAAEZR2QAAwLCcYl7bINkAAMAw5mwAAACjinddgzkbAADAMCobAAAYxjAKAAAwih1EAQBAkffKK6/I4XBo+PDhzrbz589r8ODBqlChgkqXLq2uXbsqMTHR9meTbAAAYFiOLFuOa7Vlyxa99dZbatCggUv7iBEj9Nlnn2nx4sVat26djh8/rocffvh6v24uJBsAABhm2XRci7Nnz6pXr16aN2+eypUr52xPSUnRO++8oylTpuivf/2rGjdurJiYGH333XfauHHjNT4tbyQbAAAUEunp6Tpz5ozLkZ6efsVrBg8erAceeECRkZEu7du2bVNmZqZLe61atVS1alXFxcXZGjfJBgAAhuXYdERHRysoKMjliI6OvuxzP/jgA8XHx+fZJyEhQSVKlFDZsmVd2kNCQpSQkHB9X/hPWI0CAIBhdm1XPnr0aEVFRbm0+fn55dn3119/1dNPP62vvvpKJUuWtOX514pkAwCAQsLPz++yycWfbdu2TUlJSbrjjjucbdnZ2Vq/fr1mzpypL7/8UhkZGUpOTnapbiQmJio0NNTWuEk2AAAwzBPblbdp00Y7d+50aevXr59q1aqlZ599VlWqVJGvr69Wr16trl27SpL27t2rI0eOKCIiwtZYSDYAADDMEzuIlilTRvXq1XNpCwgIUIUKFZzt/fv3V1RUlMqXL6/AwEANHTpUERERuvvuu22NhWQDAADDbtRXzE+dOlVeXl7q2rWr0tPT1a5dO82aNcv25zgsy7oxfwPXYc+tHTwdAnBDqndoh6dDAG44WRnHjD8jqnoPW+4z5dAHttynoFHZAADAsCL3r3o3kWwAAGBYcX/rK5t6AQAAo6hsAABgmFXMB1JINgAAMIxhFAAAAIOobAAAYNiNus9GQSHZAADAsOKdapBswAY118TIt3JIrvbTC5cr8cVZcpTwVfDoAQrscI8cJXyVuiFeCePeVPbvyQUfLOBhTz3ZR/+MekqhoZX0ww+79PTwF7Rl63ZPhwUYRbKB63ao69OSt7fzs9+t1VR1/kT98cU3kqTgfw1U6VZ36tjT0cr+I1WhY57STTOf15FHR3oqZMAjunXrpNcmj9Wgwc9p85bvNWzo4/r8vwtVp949OnHid0+HB4OK+zAKE0Rx3bJPn1H2ydPOo3Sru5Rx+LjSNu+UV+lSKvvIfUqKnqe0jTuU/tMv+m30VJVqXEclG97m6dCBAjXi6QF6+51YLXj3Q+3evU+DBj+ntLRz6tfXnq2scePKsekorEg2YC9fHwV2bq2UJSslSSXr/eXC0Ml3251dMg4cVeaxJPnfXttDQQIFz9fXV3fc0UCr13zjbLMsS6vXbNDddzf2YGQoCJZN/1dYkWzAVmUiI+RdprRSPl4lSfKpWE45GZnK+SPVpV/W76flU7GcJ0IEPKJixfLy8fFRUuJJl/akpBMKDankoaiAgnFDJxu//vqrHnvssSv2SU9P15kzZ1yOjJzsAooQfxb0yH1KXb9VWUmnPB0KANwwGEa5gZ06dUoLFiy4Yp/o6GgFBQW5HHNPHyigCHEpn/BgBTRrpOTFXzrbsk6ellcJX3mVCXDtW6Gcsk6eLugQAY85efKUsrKyFBxS0aU9OLiSEhJPeCgqFJTiPozi0dUoy5Ytu+L5AweunjSMHj1aUVFRLm2H7uh2XXHh2pTt2lbZv6fo7NebnW3nf9wnKyNTARGN9MfKbyVJJWrcJN+bgnXu+92eChUocJmZmYqP/0F/bd1Cy5ZdSMgdDof+2rqFZs2O8XB0gFkeTTa6dOkih8Mhy7p8tuZwOK54Dz8/P/n5+bm0lfDyvkxvGONwKOjhtkr5ZJWU/b9iX87ZNCV/tFLBowcoO+UPZZ9NU8gLTyotfpfO79jrwYCBgjf1jXmKeWeqtsX/oC1bvtewoQMUEOCv+QsWeTo0GFaYh0Ds4NFkIywsTLNmzVLnzp3zPL99+3Y1bsws7cKgVLNG8r0pWMkffZXrXNLEuZJl6aYZ//f/N/XapoRxszwQJeBZixcvU6WK5TVuzEiFhlbSjh0/6YEH/66kpJNXvxiFWs4V/lFdHDisK5UVDOvUqZMaNWqk8ePH53l+x44duv3225WT415OuOfWDnaEBxQ59Q7t8HQIwA0nK+OY8Wf8o9rDttznP4c/tuU+Bc2jlY1nnnlGqamplz1/yy23aO3atQUYEQAA9ivedQ0PJxstW7a84vmAgADde++9BRQNAABmsF05AACAQbyIDQAAwwrzHhl2INkAAMAwlr4CAACjmLMBAABgEJUNAAAMY84GAAAwqrjP2WAYBQAAGEVlAwAAwzz4ZpAbAskGAACGsRoFAADAICobAAAYVtwniJJsAABgWHFf+sowCgAAMIrKBgAAhhX3CaIkGwAAGMbSVwAAYFRxnyDKnA0AAGAUlQ0AAAwr7qtRSDYAADCsuE8QZRgFAAAYRWUDAADDWI0CAACMYhgFAADAICobAAAYVtxXo1DZAADAsBzLsuVwR3R0tO68806VKVNGwcHB6tKli/bu3evS5/z58xo8eLAqVKig0qVLq2vXrkpMTLTzq0si2QAAoEhat26dBg8erI0bN+qrr75SZmam7rvvPqWmpjr7jBgxQp999pkWL16sdevW6fjx43r44Ydtj8VhFcEpsntu7eDpEIAbUr1DOzwdAnDDyco4ZvwZLW9qY8t9vjm2+pqvPXHihIKDg7Vu3Trdc889SklJUaVKlRQbG6tHHnlEkrRnzx7Vrl1bcXFxuvvuu22JWWLOBgAAxtm1GiU9PV3p6ekubX5+fvLz87vqtSkpKZKk8uXLS5K2bdumzMxMRUZGOvvUqlVLVatWtT3ZYBgFAADDcmTZckRHRysoKMjliI6Ovvrzc3I0fPhwNW/eXPXq1ZMkJSQkqESJEipbtqxL35CQECUkJNj6/alsAABQSIwePVpRUVEubfmpagwePFg//vijNmzYYCq0KyLZAADAMLumR+Z3yORSQ4YM0fLly7V+/XpVrlzZ2R4aGqqMjAwlJye7VDcSExMVGhpqS7wXMYwCAIBhdg2juMOyLA0ZMkRLly7VmjVrVKNGDZfzjRs3lq+vr1av/t+k07179+rIkSOKiIiw5XtfRGUDAIAiaPDgwYqNjdWnn36qMmXKOOdhBAUFyd/fX0FBQerfv7+ioqJUvnx5BQYGaujQoYqIiLB1cqhEsgEAgHGe2EF09uzZkqRWrVq5tMfExKhv376SpKlTp8rLy0tdu3ZVenq62rVrp1mzZtkeC/tsAMUI+2wAuRXEPhtNwlracp+tv31jy30KGnM2AACAUQyjAABgWHF/xTzJBgAAhhXBGQtuYRgFAAAYRWUDAADDGEYBAABGeWLp642EZAMAAMNymLMBAABgDpUNAAAMYxgFAAAYxTAKAACAQVQ2AAAwjGEUAABgFMMoAAAABlHZAADAMIZRAACAUQyjAAAAGERlAwAAwxhGAQAARllWjqdD8CiSDQAADCvur5hnzgYAADCKygYAAIZZxXw1CskGAACGMYwCAABgEJUNAAAMYxgFAAAYxQ6iAAAABlHZAADAMHYQBQAARhX3ORsMowAAAKOobAAAYFhx32eDZAMAAMOK+zAKyQYAAIax9BUAAMAgKhsAABjGMAoAADCquE8QZRgFAAAYRWUDAADDGEYBAABGsRoFAADAICobAAAYxovYAACAUQyjAAAAGERlAwAAw1iNAgAAjCruczYYRgEAwDDLsmw5rsWbb76p6tWrq2TJkmratKk2b95s87e7OpINAACKqEWLFikqKkpjx45VfHy8GjZsqHbt2ikpKalA4yDZAADAME9VNqZMmaIBAwaoX79+qlOnjubMmaNSpUrp3//+t4FveXkkGwAAGGbZdLgjIyND27ZtU2RkpLPNy8tLkZGRiouLu67v4y4miAIAUEikp6crPT3dpc3Pz09+fn65+p48eVLZ2dkKCQlxaQ8JCdGePXuMxvlnRTLZqPXz554OAbrwhyI6OlqjR4/O8w8CCl6WpwOAJP5sFEdZGcdsuc+4ceP04osvurSNHTtW48aNs+X+pjis4r74F8acOXNGQUFBSklJUWBgoKfDAW4Y/NnAtXKnspGRkaFSpUrpo48+UpcuXZztffr0UXJysj799FPT4ToxZwMAgELCz89PgYGBLsflqmMlSpRQ48aNtXr1amdbTk6OVq9erYiIiIIKWVIRHUYBAABSVFSU+vTpoyZNmuiuu+7StGnTlJqaqn79+hVoHCQbAAAUUX/729904sQJjRkzRgkJCWrUqJFWrFiRa9KoaSQbMMbPz09jx45lAhzwJ/zZQEEaMmSIhgwZ4tEYmCAKAACMYoIoAAAwimQDAAAYRbIBAACMItkAAABGkWzAmDfffFPVq1dXyZIl1bRpU23evNnTIQEetX79enXs2FHh4eFyOBz65JNPPB0SUCBINmDEokWLFBUVpbFjxyo+Pl4NGzZUu3btlJSU5OnQAI9JTU1Vw4YN9eabb3o6FKBAsfQVRjRt2lR33nmnZs6cKenCFrlVqlTR0KFD9dxzz3k4OsDzHA6Hli5d6vLOCqCoorIB22VkZGjbtm2KjIx0tnl5eSkyMlJxcXEejAwA4AkkG7DdyZMnlZ2dnWs73JCQECUkJHgoKgCAp5BsAAAAo0g2YLuKFSvK29tbiYmJLu2JiYkKDQ31UFQAAE8h2YDtSpQoocaNG2v16tXOtpycHK1evVoREREejAwA4Am89RVGREVFqU+fPmrSpInuuusuTZs2TampqerXr5+nQwM85uzZs/rll1+cnw8ePKjt27erfPnyqlq1qgcjA8xi6SuMmTlzpiZPnqyEhAQ1atRI06dPV9OmTT0dFuAxX3/9tVq3bp2rvU+fPpo/f37BBwQUEJINAABgFHM2AACAUSQbAADAKJINAABgFMkGAAAwimQDAAAYRbIBAACMItkAAABGkWwAyKV69eqaNm2ap8MAUESQbACGWZalyMhItWvXLte5WbNmqWzZsjp69KgHIru8LVu2aODAgfnqS2IC4GpINgDDHA6HYmJitGnTJr311lvO9oMHD2rUqFGaMWOGKleubMuzMjMzbblPpUqVVKpUKVvuBQAkG0ABqFKlit544w2NHDlSBw8elGVZ6t+/v+677z794x//yPMah8Oh2bNnq3379vL399fNN9+sjz76yHn+0KFDcjgcWrRoke69916VLFlSCxculCS9/fbbql27tkqWLKlatWpp1qxZzuuaNWumZ5991uVZJ06ckK+vr9avXy/JtVphWZbGjRunqlWrys/PT+Hh4Ro2bJgkqVWrVjp8+LBGjBghh8Mhh8PhvOeSJUtUt25d+fn5qXr16nr99dev/xcJoHCyABSYzp07W61atbKmT59uVapUyUpKSrpsX0lWhQoVrHnz5ll79+61nn/+ecvb29vatWuXZVmWdfDgQUuSVb16dWvJkiXWgQMHrOPHj1vvvfeeFRYW5mxbsmSJVb58eWv+/PmWZVnWzJkzrapVq1o5OTnOZ82YMcOlrVq1atbUqVMty7KsxYsXW4GBgdbnn39uHT582Nq0aZM1d+5cy7Is6/fff7cqV65sjR8/3vrtt9+s3377zbIsy9q6davl5eVljR8/3tq7d68VExNj+fv7WzExMXb/SgEUAiQbQAFKTEy0KlasaHl5eVlLly69Yl9J1pNPPunS1rRpU+upp56yLOt/yca0adNc+tSsWdOKjY11aXvppZesiIgIy7IsKykpyfLx8bHWr1/vPB8REWE9++yzzs+XJhuvv/66deutt1oZGRl5xnlp34t69uxptW3b1qXtmWeeserUqXPF7wygaGIYBShAwcHBeuKJJ1S7dm116dLlqv0jIiJyfd69e7dLW5MmTZw/p6amav/+/erfv79Kly7tPCZMmKD9+/dLujAf47777nMOuRw8eFBxcXHq1atXnjF069ZN586d080336wBAwZo6dKlysrKumLcu3fvVvPmzV3amjdvrn379ik7O/uq3xtA0UKyARQwHx8f+fj42Ha/gIAA589nz56VJM2bN0/bt293Hj/++KM2btzo7NerVy999NFHyszMVGxsrOrXr6/69evnef8qVapo7969mjVrlvz9/TVo0CDdc889tk1GBVD0kWwAN7BLE4SLn2vXrn3Z/iEhIQoPD9eBAwd0yy23uBw1atRw9uvcubPOnz+vFStWKDY29rJVjYv8/f3VsWNHTZ8+XV9//bXi4uK0c+dOSVKJEiVyVStq166tb7/91qXt22+/1a233ipvb+98fXcARYd9/7wCYLvFixerSZMmatGihRYuXKjNmzfrnXfeueI1L774ooYNG6agoCDdf//9Sk9P19atW3X69GlFRUVJulAN6dKli1544QXt3r1bjz766GXvN3/+fGVnZ6tp06YqVaqU3nvvPfn7+6tatWqSLqxcWb9+vXr06CE/Pz9VrFhR//znP3XnnXfqpZde0t/+9jfFxcVp5syZLqtiABQfVDaAG9iLL76oDz74QA0aNNC7776r999/X3Xq1LniNY8//rjefvttxcTEqH79+rr33ns1f/58l8qGdGEoZceOHWrZsqWqVq162fuVLVtW8+bNU/PmzdWgQQOtWrVKn332mSpUqCBJGj9+vA4dOqSaNWuqUqVKkqQ77rhDH374oT744APVq1dPY8aM0fjx49W3b9/r+4UAKJQclmVZng4CQG4Oh0NLly7N10RSALiRUdkAAABGkWwAAACjmCAK3KAY4QRQVFDZAAAARpFsAAAAo0g2AACAUSQbAADAKJINAABgFMkGAAAwimQDAAAYRbIBAACMItkAAABG/T8gkYjT7wR4PgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(Y_test, Y_predicted)\n",
        "sns.regplot(x=df_test['Age'].to_numpy(), y=Y_predicted, data=df_test, logistic=True, ci=None,scatter_kws={'color': 'black'}, line_kws={'color': 'red'})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jRX1TjOv9Y5N",
        "outputId": "b9aca452-bc70-4be9-efdc-bcf1902bec13"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}