ML-Logistic_Regression-Dec01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x213810aad88>"
      ]
     },
     "execution_count": 7,
     "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(df.isnull(), yticklabels=False, cbar =False, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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 = df, palette = 'deep');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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= 'Survived', hue = 'Sex', data = df, palette = 'deep');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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= 'Survived', hue = 'Pclass', data = df, palette = 'deep');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'].dropna(), kde=False, color='darkred', bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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 = df, palette = 'deep');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'].hist(color='blue', bins=40, figsize=(10,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Fare'].dropna(), kde=False, color='darkred', bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.heatmap(df.corr(), annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nP/vZz/KBD3wg48aN2/94a2trfvGLXxzwOQYGBtLb21uLeNRYf39/knj9oM6ce1AO515jq0lZnjhxYl7zmtekUqlk4sSJOfbYY/PMM8/sf3z37t0vKs8vpbm5OW1tbbWIR421tLQkidcP6sy5B+Vw7h35DvSLTk2WYdx111359Kc/nSR58skn8/zzz6elpSWPPfZYqtVqHnjggUyePLkWQwMAwLCpyczy7Nmzc+211+Z973tfKpVKPvWpT6WpqSlXXXVV9u3bl6lTp+bMM8+sxdAHdcstt6Svr6+UsUeKF/59X9gdTG1MmjQp3d3dZccAgIZWk7I8duzYfOYzn/mNj9955521GO5l6evry3880ps9o15ddpSG1TR0dJLk4f96tuQkjWvsvi1lRwCAEWFE3pRkz6hX5+njPlR2DDhkJz57a9kRAGBEqMmaZQAAaATKMgAAFFCWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACijLAABQQFkGAIACyjIAABRQlgEAoICyDAAABZRlAAAooCwDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWUZQAAKKAsAwBAAWUZAAAKKMsAAFBAWQYAgALKMgAAFFCWAQCggLIMAAAFlGUAACjwW5Xln/3sZ/n2t7+dJ554ItVqtdaZAADgsDD6YJ+wYsWKrF27Ns8++2wuvPDCPPbYY5k/f349sgEAQKkOOrO8evXqfPWrX82xxx6byy67LBs3bqxHLgAAKN1By/ILyy4qlUqSZOzYsbVNBAAAh4mDLsNob2/PnDlzsmXLlnzgAx/IueeeW49cAABQuoOW5Y6OjrztbW/Lo48+mtNOOy2vf/3r65ELAABKd9CyfO211+7/7/Xr12fMmDE5+eSTM2fOnBx33HGFX7dt27a8+93vzle+8pWMHj0611xzTSqVSiZNmpQFCxakqclV6wAAOLwdtLEODAzkpJNOyjve8Y783u/9Xp588sns2bMnc+fOLfyawcHBzJ8/P0cddVSSZOHChenu7s4dd9yRarWadevWDd93AAAANXLQmeXt27dn8eLFSZJzzjknl19+ebq7uzNnzpzCr7npppty8cUX50tf+lKSZNOmTZkyZUqSZNq0aXnwwQdz3nnnHXDcgYGB9Pb2/tbfyG+rv79/2J8TytDf31+Tc4Qj1wvvb34uoL6ce43toGV5165d+clPfpLTTz89P/nJT9Lf358dO3YUls677747J5xwQs4555z9Zblare6/mkZra2t27tx50GDNzc1pa2t7Od/Lb6WlpSXJs8P+vFBvLS0tNTlHOHL9+v0tfi6gzpx7R74D/aJz0LI8f/78XH311Xnqqady1FFH5V3vele+9a1v5UMf+tBLfv43vvGNVCqVPPzww+nt7c3cuXOzffv2/Y/v3r0748aNO4RvAwAA6uuga5b/6I/+KB//+Mfztre9Lc8//3y2bduWOXPmZPr06S/5+bfffntWrFiR5cuXp62tLTfddFOmTZuWDRs2JPn1JsHJkycP73cBAAA1UDizvGfPnqxevTq33357xo4dm127dmXdunX7N+29HHPnzs28efOyePHinHbaaYVFGwAADieFZfntb397Zs6cmZtvvjmvfe1r8/73v/9lF+Xly5fv/+8VK1YcekoAAChBYVm+9NJLs2rVqjz++OOZPXv2/tteAwDASFG4ZvmDH/xg7rnnnnR0dGTVqlX5wQ9+kEWLFuXRRx+tZz4AACjNQTf4TZkyJYsWLcratWtz8skn52/+5m/qkQsAAEr3W99zety4ceno6MjKlStrmQcAAA4bv3VZBgCAkUZZBgCAAge9g1+j2bZtW8bufSonPntr2VHgkI3duyXbtu0tOwYANDwzywAAUGDEzSyPHz8+j24ZnaeP+1DZUeCQnfjsrRk//riyYwBAwzOzDAAABZRlAAAoMOKWYQDluOWWW9LX11d2jIb2wr9vV1dXyUka26RJk9Ld3V12DKBOlGWgLvr6+vK9/7spe445sewoDWvUvjFJkod++lTJSRrX2F1Plx0BqDNlGaibPcecmCfO+j9lx4BDdvIj3yg7AlBn1iwDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWUZQAAKKAsAwBAAWUZAAAKKMsAAFBAWQYAgALKMgAAFFCWAQCgwOiyAwAAtXPLLbekr6+v7BgN7YV/366urpKTNL5Jkyalu7u7rmMqywDQwPr6+vK9TT/InhOPLztKwxo1ppIkeeipX5acpLGNffqZUsZVlgGgwe058fg8MfvPyo4Br8jJd91fyrjWLAMAQAFlGQAACijLAABQQFkGAIACNdngt2/fvlx//fXZvHlzRo0alYULF6Zareaaa65JpVLJpEmTsmDBgjQ16eoAABy+alKW77//17sVv/71r2fDhg37y3J3d3fOPvvszJ8/P+vWrct5551Xi+EBAGBY1GRq99xzz82NN96YJNmyZUsmTJiQTZs2ZcqUKUmSadOm5aGHHqrF0AAAMGxqdp3l0aNHZ+7cuVm7dm0+//nP5/7770+l8uuLdre2tmbnzp0H/PqBgYH09vYOe67+/v5hf04oQ39/f03OkVpx7tEonHtQnjLOv5relOSmm27KVVddlfe+970ZGBjY//Hdu3dn3LhxB/za5ubmtLW1DXumlpaWJM8O+/NCvbW0tNTkHKmVX597u8qOAa/YEXnu7dpedgwYFrU6/w5UwGuyDGPlypW57bbbkiRHH310KpVK3vSmN2XDhg1JkvXr12fy5Mm1GBoAAIZNTWaWzz///Fx77bWZM2dO9u7dm+uuuy6nn3565s2bl8WLF+e0007L9OnTazE0cJjatm1bxu56Oic/8o2yo8AhG7vr6WzbNqrsGEAd1aQst7S05HOf+9xvfHzFihW1GA4AAGqipmuWAV4wfvz4/OjZfXnirP9TdhQ4ZCc/8o2MHz++7BhAHbkrCAAAFFCWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACozIS8eN3bclJz57a9kxGlbT0M4kyVDTsSUnaVxj921JclzZMQCg4Y24sjxp0qSyIzS8vr6nkiSTJp1ScpJGdpyfZQCogxFXlru7u8uO0PC6urqSJEuXLi05CQDAK2PNMgAAFFCWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACijLAABQQFkGAIACyjIAABRQlgEAoMCIu901AIwk27Zty9inn8nJd91fdhR4RcY+/Uy2jTq67uOaWQYAgAJmlgGggY0fPz4/2vd8npj9Z2VHgVfk5Lvuz/jx4+s+rpllAAAoYGYZqJuxu57OyY98o+wYDWvUnv4kyb6xLSUnaVxjdz2d5KSyYwB1pCwDdTFp0qSyIzS8vr6+JMmk05S52jnJzzKMMMoyUBfd3d1lR2h4XV1dSZKlS5eWnASgcVizDAAABZRlAAAooCwDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWG/Q5+g4ODue666/L4449nz549+fCHP5zXve51ueaaa1KpVDJp0qQsWLAgTU16OgAAh7dhL8v33HNPjj/++CxatCg7duzIu971rvzBH/xBuru7c/bZZ2f+/PlZt25dzjvvvOEeGgAAhtWwl+ULLrgg06dP3388atSobNq0KVOmTEmSTJs2LQ8++OBBy/LAwEB6e3uHOx510N/fnyReP6gz5x4v5YWfC2gE/f39dX+PG/ay3NramiTZtWtXPvrRj6a7uzs33XRTKpXK/sd37tx50Odpbm5OW1vbcMejDlpaWpLE6wd15tzjpbS0tCS7tpcdA4ZFS0tLTd7jDlTAa7Jw+Fe/+lUuvfTSzJo1K+985ztftD559+7dGTduXC2GBQCAYTXsZXnr1q25/PLLc/XVV2f27NlJkje84Q3ZsGFDkmT9+vWZPHnycA8LAADDbtjL8q233prnnnsuy5YtS0dHRzo6OtLd3Z0lS5bkoosuyuDg4IvWNAMAwOFq2NcsX3/99bn++ut/4+MrVqwY7qEAAKCmXOwYAAAKKMsAAFBg2JdhAACHl7FPP5OT77q/7BgNa1T//yRJ9rUcVXKSxjb26WeSk06p+7jKMgA0sEmTJpUdoeH19fUlSSaVUORGlJNOKeXnWVkGgAbW3d1ddoSG19XVlSRZunRpyUmoBWuWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACijLAABQQFkGAIACyjIAABRQlgEAoICyDAAABZRlAAAooCwDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWUZQAAKKAsAwBAAWUZAAAKKMsAAFBAWQYAgALKMgAAFFCWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACijLAABQoGZleePGjeno6EiS/PznP8/73ve+XHLJJVmwYEGGhoZqNSwAAAybmpTlL3/5y7n++uszMDCQJFm4cGG6u7tzxx13pFqtZt26dbUYFgAAhlVNyvKpp56aJUuW7D/etGlTpkyZkiSZNm1aHnrooVoMCwAAw2p0LZ50+vTp+eUvf7n/uFqtplKpJElaW1uzc+fOgz7HwMBAent7axGPGuvv708Srx/UmXMPyuHca2w1Kcv/W1PT/z+BvXv37owbN+6gX9Pc3Jy2trZaxqJGWlpaksTrB3Xm3INyOPeOfAf6RacuV8N4wxvekA0bNiRJ1q9fn8mTJ9djWAAAeEXqUpbnzp2bJUuW5KKLLsrg4GCmT59ej2EBAOAVqdkyjFNOOSV33nlnkmTixIlZsWJFrYYCAICacFMSAAAooCwDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWUZQAAKKAsAwBAAWUZAAAKKMsAAFBAWQYAgALKMgAAFFCWAQCggLIMAAAFlGUAACigLAMAQAFlGQAACijLAABQYHTZAQAOR2vWrMmqVavKjvGy9PX1JUm6urpKTvLyzJw5MzNmzCg7BsBLMrMM0CCOPvro7Nq1K88991zZUQAahpllgJcwY8aMI262c/r06UmSp556KsuXLy85DUBjMLMM0AD+/d//PTt37kyS7Ny5M9/73vdKTgTQGJRlgAYwb968Fx3/7d/+bUlJABqLsgzQAF6YVS46BuDQKMsADeDYY4894DEAh0ZZBmgAN95444uOP/nJT5aUBKCxKMsADeD4448/4DEAh0ZZBmgAN9xww4uOFyxYUFISgMaiLAM0gM2bNx/wGIBDoywDNICJEyce8BiAQ6MsAzSAyy677EXHV1xxRTlBABqM210f5tasWZNVq1aVHeNl6evrS5J0dXWVnOTlmTlz5hF3e2N4wbJly150/IUvfCFvf/vbS0oD0DiUZYbd+PHjy44AI86TTz75ouMnnniipCQAjUVZPszNmDHDbCcAQEnqVpaHhoby8Y9/PD/60Y8yduzYfOITn8hrXvOaeg0P0NBaWlrS39+//7i1tbXENPDKHGlLEC0/bGx12+D3r//6r9mzZ0/+8R//MR/72Mfy6U9/ul5DAzS8T33qUy86XrhwYUlJYOQZP368JYgNrG4zy//xH/+Rc845J0ly1lln5Qc/+MEBP39gYCC9vb31iAZwxDv22GPT3NycgYGBNDc3p7W11XsoR6zXvva1+chHPlJ2jBHB+8TB1a0s79q1K8ccc8z+41GjRmXv3r0ZPfqlIzQ3N6etra1e8QCOeDfddFP++q//OosWLfL+CfAyHOiXhrqV5WOOOSa7d+/efzw0NFRYlAF4+aZMmZIHHnig7BgADaVua5bf/OY3Z/369UmSRx55JGeccUa9hgYAgENSt6nd8847Lw8++GAuvvjiVKvV39iMAgAAh5u6leWmpqb83d/9Xb2GAwCAV6xuyzAAAOBIoywDAEABZRkAAAooywAAUEBZBgCAAsoyAAAUUJYBAKCAsgwAAAWUZQAAKKAsAwBAgbrd7vrlGhgYSG9vb9kxAABocAMDA4WPVarVarWOWQAA4IhhGQYAABRQlgEAoICyDAAABZRlAAAooCwDAEABZRkAAAooywy7jRs3pqOjo+wYMKIMDg7m6quvziWXXJLZs2dn3bp1ZUeCEbGQwrcAAAQLSURBVGHfvn259tprc/HFF2fOnDl57LHHyo7EMDtsb0rCkenLX/5y7rnnnhx99NFlR4ER5Z577snxxx+fRYsWZceOHXnXu96VP//zPy87FjS8+++/P0ny9a9/PRs2bMjChQvzxS9+seRUDCczywyrU089NUuWLCk7Bow4F1xwQf7yL/9y//GoUaNKTAMjx7nnnpsbb7wxSbJly5ZMmDCh5EQMNzPLDKvp06fnl7/8ZdkxYMRpbW1NkuzatSsf/ehH093dXXIiGDlGjx6duXPnZu3atfn85z9fdhyGmZllgAbxq1/9KpdeemlmzZqVd77znWXHgRHlpptuyr333pt58+alv7+/7DgMI2UZoAFs3bo1l19+ea6++urMnj277DgwYqxcuTK33XZbkuToo49OpVKxDKrBKMsADeDWW2/Nc889l2XLlqWjoyMdHR35n//5n7JjQcM7//zz89///d+ZM2dOrrjiilx33XVpbm4uOxbDqFKtVqtlhwAAgMORmWUAACigLAMAQAFlGQAACijLAABQQFkGAIACyjLAYWjDhg1561vfuv8ycO9973uzfPnyl/zcjo6O/OQnP6lzQoCRwe2uAQ5Tb3nLW/LZz342SbJnz55ccMEFmTVrVsaNG1dyMoCRQ1kGOALs2rUrTU1N+eEPf5ibb7451Wo1r3rVq3LzzTfv/5wnnngiH//4xzMwMJBnnnkmXV1dOffcc/PZz3423/nOdzI0NJT29vZcdtlluf3227Ny5co0NTXlzW9+c+bOnVvidwdw+FKWAQ5T3/nOd9LR0ZFKpZIxY8Zk3rx5+cQnPpHPfvazOf3003P77be/aPnFT3/603R2dubss8/Of/7nf2bJkiU599xzs3LlyqxYsSKvetWrcvfddydJ7r777sybNy9nnXVW7rjjjuzduzejR/tfAsD/5p0R4DD1/y7DeMF1112X008/PUkyZ86cFz124okn5otf/GLuuuuuVCqV7N27N0myePHiLF68OFu3bs0555yTJFm4cGG+8pWv5Oabb85ZZ50VN3MFeGk2+AEcQU466aT87Gc/S5J86Utfytq1a/c/9rnPfS6zZs3KokWLcvbZZ6darWbPnj35l3/5lyxevDh///d/n3/+53/O448/njvvvDM33HBDVqxYkd7e3nz/+98v6TsCOLyZWQY4gtxwww257rrr0tTUlBNPPDGXXXZZ/uEf/iFJcsEFF+STn/xkbrvttvzu7/5uduzYkbFjx+a4447LrFmzctxxx+VP/uRP8upXvzqvf/3rM3v27PzO7/xOXvWqV+XMM88s+TsDODxVqv72BgAAL8kyDAAAKKAsAwBAAWUZAAAKKMsAAFBAWQYAgALKMgAAFFCWAQCgwP8HzBfOAzDcgaQAAAAASUVORK5CYII=\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 = df, palette='winter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    if pd.isnull(Age):\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df[['Age', 'Pclass']].apply(impute_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Cabin', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889, 11)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(df['Sex'], drop_first=True)\n",
    "embark = pd.get_dummies(df['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Sex', 'Embarked', 'Name', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, sex, embark], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Survived',axis=1),\n",
    "                                                    df['Survived'], test_size=0.25,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7837837837837838"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7982062780269058"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Accuracy Scores 0.7839920948616601\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(model, X_test, y_test, cv=10)\n",
    "print('Cross-Validation Accuracy Scores', scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86956522 0.7826087  0.86956522 0.68181818 0.68181818 0.95454545\n",
      " 0.77272727 0.63636364 0.77272727 0.81818182]\n"
     ]
    }
   ],
   "source": [
    "print(scores) # ustte CV score olarak bize mean degeri veriyor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.86      0.84       141\n",
      "           1       0.74      0.70      0.72        82\n",
      "\n",
      "    accuracy                           0.80       223\n",
      "   macro avg       0.78      0.78      0.78       223\n",
      "weighted avg       0.80      0.80      0.80       223\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21382a0cd48>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.datasets import load_occupancy\n",
    "visualizer = ClassificationReport(model, support=True)\n",
    "visualizer.fit(X_train, y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_test, y_test)        # Evaluate the model on the test data\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[121,  25],\n",
       "       [ 20,  57]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(52.0, 0.5, 'Predicted Label')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\", fmt='g')\n",
    "plt.xlabel('Actual Label')\n",
    "plt.ylabel('Predicted Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}