Logistic Regression Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass',data=train,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\desatapa\\Anaconda3\\envs\\prophet\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSb29vaqoqEgqFAAgeVHLu62tTbdv39Zvf/tbbdmyRVu2bFF9fb2OHz+ujRs3KhQKqaamJlNZAQBfibrbpKmpSU1NTXfM7+zsTFsgAEBsHKANAAaivAHAQJQ3ABiI8gYAA1HeAGAgyhsADBTzDEsA6RPp6oOLcnImHuPqg5gJ5Q3MkmhXH5x8OQGuPoiZsNsEAAzEN29gDgkMDmo0EIg5jl0x5qO8gTlkNBBQd21tzHHsijEfu00AwEBz7pt3Ku8dGOlX0MlHAiSyPiBZ8XyupcTuswmzzanyTvW9AyP9Cjr9xgL8Cop0ivdzLcV/n02Yj90mAGAgyhsADER5A4CBKG8AMBDlDQAGorwBwECUNwAYKK7y/vDDD7VlyxZJ0pUrV7Rp0yZt3rxZLS0tCofDaQ0IALhTzPJ+7bXX1NTUpGAwKEk6dOiQ6uvr1dXVJdu21dPTk/aQAICpYp5h+cADD+j48eN6+eWXJUn9/f2qrKyUJFVVVencuXOqrq6Ouo5gMCiPx5NwuOHhYXk8Hi3KyZHf7485PhwOxz3uv598EnNcdlbWjOsLj41NmT8cCGggjucX7/MYz5jMc56e7W7Xl6pxkXIlur5UZwyPjc36axMp1/hj6Xht4v3MzrjsV/8vneZeyxWzvGtqajQwMDAxbdu2LMuSJBUUFMjn88XcSG5urtxud8LhPB6P3G63fF7vlNPRI3G5XHGN0+io3nvxxZjDvnfs2Izrm356fF5+vtxlZTHXF+/zkOJ/LtPHTc92t+tL1bhIuRJdX6oz+v3+WX9tIuUafywdr028n9mZjP+/dJq5mCta6Sf8B0uX63+LDA0NqaioKKlQAIDkJXxhqmXLlqmvr08rV65Ub2+vVq1alY5cABxgpitrTr+qpsSVNWdDwuXd0NCg5uZmHT16VKWlpaqpqUlHLgAOMNOVNWfaBcaVNTMvrvIuLi7Wm2++KUkqKSlRZ2dnWkMBAKKbU9fzBjB7UnkjFMRGeQO4a6m+EQpi4/R4ADAQ37yBe1Q8uzm4J6ZzUd7APSje3RzcE9O52G0CAAaivAHAQJQ3ABiI8gYAA/EHSwDGmnztlZmuuTLZXDtBiPIGYKzJ116JddnhuXaCEOUNIKM4jT41KG8AGcNp9KnDHywBwEB8804RTjUGkEmUdwpwqjGATGO3CQAYiPIGAANR3gBgIMobAAyU1B8sw+Gw9u3bp3/961+aN2+eDhw4oMWLF6c6GwCkVCpPEJp8an40X58/P65siUqqvE+fPq2RkRH9+c9/1qVLl3T48GH97ne/S3U2AEiZVJ8gNPnU/GhW/9//xbW+RCW12+Qf//iHVq9eLUlasWKF/vnPf6Y0FAAgOsu2bTvRhV555RWtW7dOa9askSStXbtWp0+fVnb2zF/kL126pNzc3LtLCgD3mGAwqBUrVsz4WFK7TQoLCzU0NDQxHQ6HIxa3pIgbBwAkJ6ndJt/+9rfV29sr6ctv1UuXLk1pKABAdEntNhk/2uTf//63bNvWwYMHtWTJknTkAwDMIKnyBgDMLk7SAQADUd4AYCDKGwAM5MjreTvx9PsPP/xQv/zlL9XR0aErV66osbFRlmWpvLxcLS0tcrky+3MwFAppz5498nq9GhkZ0Y4dO1RWVjbruSRpbGxMTU1Nunz5srKysnTo0CHZtu2IbJL0+eef66mnntIf/vAHZWdnOyLXE088ofvuu0+SVFxcrNraWkfkam9v17vvvqtQKKRNmzapsrLSEblOnTqlt99+W9KXx0J7PB51dXXp4MGDs/7/srGxUV6vVy6XS/v370/fZ8x2oL/97W92Q0ODbdu2/cEHH9i1tbWzmufEiRP2D37wA3vDhg22bdv2c889Z1+4cMG2bdtubm62//73v2c808mTJ+0DBw7Ytm3bg4OD9po1axyRy7Ztu7u7225sbLRt27YvXLhg19bWOibbyMiI/bOf/cxet26d/cknnzgi1/DwsP3DH/5wyjwn5Lpw4YL93HPP2WNjY7bf77ePHTvmiFzT7du3z37jjTccka27u9veuXOnbdu2ffbsWfv5559PWy5H7jZx2un3DzzwgI4fPz4x3d/fr8rKSklSVVWVzp8/n/FMjz32mF544YWJ6aysLEfkkqRHH31U+/fvlyRdvXpVixYtcky21tZWPfPMM/rGN74hyRnv5ccff6xAIKBt27Zp69atunTpkiNynT17VkuXLlVdXZ1qa2u1du1aR+Sa7KOPPtInn3yijRs3OiJbSUmJxsbGFA6H5ff7lZ2dnbZcjtxt4vf7VVhYODGdlZWl0dHRqGdxplNNTY0GBgYmpm3blmVZkqSCggL5fL6MZyooKJD05Wu1c+dO1dfXq7W1ddZzjcvOzlZDQ4O6u7t17NgxnTlzZtaznTp1SgsXLtTq1at14sQJSc54L/Py8vTss89qw4YN+uyzz7R9+3ZH5Lp165auXr2qtrY2DQwMaMeOHY7INVl7e7vq6uokOeO9nD9/vrxer77//e/r1q1bamtr08WLF9OSy5Hlnejp95k2eX/V0NCQioqKZiXHtWvXVFdXp82bN+vxxx/XkSNHHJFrXGtrq3bt2qWnn35awWBwYv5sZXvrrbdkWZbef/99eTweNTQ0aHBwcNZzlZSUaPHixbIsSyUlJVqwYIH6+/tnPdeCBQtUWlqqefPmqbS0VLm5ubp+/fqs5xp3+/Ztffrpp1q1apUkZ/y//OMf/6jvfOc7+sUvfqFr167pxz/+sUKhUFpyOXK3idNPv1+2bJn6+vokSb29vaqoqMh4hps3b2rbtm166aWXtH79esfkkqR33nlH7e3tkqT8/HxZlqXly5fPerbXX39dnZ2d6ujokNvtVmtrq6qqqmY918mTJ3X48GFJ0o0bN+T3+/XII4/Meq4HH3xQ7733nmzb1o0bNxQIBPTQQw/Neq5xFy9e1MMPPzwx7YTPf1FR0cQfnr/2ta9pdHQ0bbkceYalE0+/HxgY0M9//nO9+eabunz5spqbmxUKhVRaWqoDBw4oKysro3kOHDigv/71ryotLZ2Y98orr+jAgQOzmkuSvvjiC+3evVs3b97U6Oiotm/friVLlsz6azbZli1btG/fPrlcrlnPNTIyot27d+vq1auyLEu7du3S/fffP+u5JOnVV19VX1+fbNvWiy++qOLiYkfkkqTf//73ys7O1k9+8hNJcsT/y6GhIe3Zs0f//e9/FQqFtHXrVi1fvjwtuRxZ3gCA6By52wQAEB3lDQAGorwBwECUNwAYiPIGAANR3gBgIMobAAz0/3oO/Mb1nEIkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].hist(bins=30,color='darkred',alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>S</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "      <td>5</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
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     },
     "execution_count": 58,
     "metadata": {},
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   ],
   "source": [
    "sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      S\n",
       "1      C\n",
       "2      S\n",
       "3      S\n",
       "4      S\n",
       "      ..\n",
       "886    S\n",
       "887    S\n",
       "888    S\n",
       "889    C\n",
       "890    Q\n",
       "Name: Embarked, Length: 889, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  male  Q  S  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  \n",
       "4      0            373450   8.0500        S     1  0  1  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('Survived',axis=1), \n",
    "                                                    train['Survived'], test_size=0.30, \n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\desatapa\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8164794007490637\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.91      0.85       163\n",
      "           1       0.81      0.62      0.71       104\n",
      "\n",
      "    accuracy                           0.80       267\n",
      "   macro avg       0.80      0.77      0.78       267\n",
      "weighted avg       0.80      0.80      0.79       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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