10-LOGISTIC REGRESSION in ACTION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression in Action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about logistic regression in action.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri=pd.read_csv(\"titanic.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>pclass</th>\n",
       "      <th>survived</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St Louis, MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
       "\n",
       "                         home.dest  \n",
       "0                     St Louis, MO  \n",
       "1  Montreal, PQ / Chesterville, ON  \n",
       "2  Montreal, PQ / Chesterville, ON  \n",
       "3  Montreal, PQ / Chesterville, ON  \n",
       "4  Montreal, PQ / Chesterville, ON  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass       float64\n",
       "survived     float64\n",
       "name          object\n",
       "sex           object\n",
       "age          float64\n",
       "sibsp        float64\n",
       "parch        float64\n",
       "ticket        object\n",
       "fare         float64\n",
       "cabin         object\n",
       "embarked      object\n",
       "boat          object\n",
       "body         float64\n",
       "home.dest     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258176dcb38>"
      ]
     },
     "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.countplot(x=\"survived\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258177adcf8>"
      ]
     },
     "execution_count": 8,
     "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.countplot(x=\"survived\",hue=\"pclass\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25817822b00>"
      ]
     },
     "execution_count": 9,
     "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.countplot(x=\"survived\",hue=\"sex\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x258178ff2e8>"
      ]
     },
     "execution_count": 10,
     "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": [
    "veri[\"age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2581798ce80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "veri[\"fare\"].plot.hist(bins=20,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25817a271d0>"
      ]
     },
     "execution_count": 12,
     "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.countplot(x=\"sibsp\",data=veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass          1\n",
       "survived        1\n",
       "name            1\n",
       "sex             1\n",
       "age           264\n",
       "sibsp           1\n",
       "parch           1\n",
       "ticket          1\n",
       "fare            2\n",
       "cabin        1015\n",
       "embarked        3\n",
       "boat          824\n",
       "body         1189\n",
       "home.dest     565\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25819216c18>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(veri.isnull(),yticklabels=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.drop([\"cabin\",\"boat\",\"body\",\"home.dest\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>pclass</th>\n",
       "      <th>survived</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass        1\n",
       "survived      1\n",
       "name          1\n",
       "sex           1\n",
       "age         264\n",
       "sibsp         1\n",
       "parch         1\n",
       "ticket        1\n",
       "fare          2\n",
       "embarked      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      0\n",
       "survived    0\n",
       "name        0\n",
       "sex         0\n",
       "age         0\n",
       "sibsp       0\n",
       "parch       0\n",
       "ticket      0\n",
       "fare        0\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      float64\n",
       "survived    float64\n",
       "name         object\n",
       "sex          object\n",
       "age         float64\n",
       "sibsp       float64\n",
       "parch       float64\n",
       "ticket       object\n",
       "fare        float64\n",
       "embarked     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(veri[\"sex\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     0\n",
       "1     1\n",
       "2     0\n",
       "3     1\n",
       "4     0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    781\n",
       "C    212\n",
       "Q     50\n",
       "Name: embarked, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked=pd.get_dummies(veri[\"embarked\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pclass=pd.get_dummies(veri[\"pclass\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.drop([\"sex\",\"embarked\",\"pclass\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri=pd.concat([veri,sex,embarked,pclass],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived    float64\n",
       "name         object\n",
       "age         float64\n",
       "sibsp       float64\n",
       "parch       float64\n",
       "ticket       object\n",
       "fare        float64\n",
       "male          uint8\n",
       "Q             uint8\n",
       "S             uint8\n",
       "2.0           uint8\n",
       "3.0           uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "veri.drop([\"name\",\"ticket\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>survived</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",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived      age  sibsp  parch      fare  male  Q  S  2.0  3.0\n",
       "0       1.0  29.0000    0.0    0.0  211.3375     0  0  1    0    0\n",
       "1       1.0   0.9167    1.0    2.0  151.5500     1  0  1    0    0\n",
       "2       0.0   2.0000    1.0    2.0  151.5500     0  0  1    0    0\n",
       "3       0.0  30.0000    1.0    2.0  151.5500     1  0  1    0    0\n",
       "4       0.0  25.0000    1.0    2.0  151.5500     0  0  1    0    0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=veri.drop(\"survived\",axis=1)\n",
    "y=veri[\"survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,\n",
    "                                              random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg_model=LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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=1000,\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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7701149425287356"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8081841432225064"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model=LogisticRegression(C=0.1,max_iter=1000)\n",
    "lg_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7816091954022989\n",
      "0.7953964194373402\n"
     ]
    }
   ],
   "source": [
    "print(lg_model.score(X_test,y_test))\n",
    "print(lg_model.score(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin=lg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[124,  21],\n",
       "       [ 36,  80]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,tahmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>survived</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",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived      age  sibsp  parch      fare  male  Q  S  2.0  3.0\n",
       "0       1.0  29.0000    0.0    0.0  211.3375     0  0  1    0    0\n",
       "1       1.0   0.9167    1.0    2.0  151.5500     1  0  1    0    0\n",
       "2       0.0   2.0000    1.0    2.0  151.5500     0  0  1    0    0\n",
       "3       0.0  30.0000    1.0    2.0  151.5500     1  0  1    0    0\n",
       "4       0.0  25.0000    1.0    2.0  151.5500     0  0  1    0    0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "veri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "yeni_veri=np.array([[29,0,0,211.3375,0,0,1,0,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.predict(yeni_veri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03164997, -0.32005828,  0.06664671,  0.0064837 , -1.80769546,\n",
       "        -0.41992741, -0.37328606, -0.18082597, -1.02543165]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeni_veri2=np.array([[30,1,1,150,0,0,1,0,0]])\n",
    "lg_model.predict(yeni_veri2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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