Logistic Reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(r'C:\\Users\\Laptop\\Desktop\\Refactored_Py_DS_ML_Bootcamp-master\\13-Logistic-Regression\\titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   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",
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "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') #yticklabels is the markings on y axis, cbar is the colorbar on the right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acd641b288>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r') #most people dying were males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acd64bcc48>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Pclass',data=train) #here we can say the no of ppl died were mostly from 3rd class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acd64c0288>"
      ]
     },
     "execution_count": 11,
     "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) #dropna means drop null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acd65ea888>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train) #most of people were single and very less had siblings greter than 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acd669b188>"
      ]
     },
     "execution_count": 14,
     "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['Fare'].dropna(),kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "histfunc": "count",
         "histnorm": "",
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)",
          "line": {
           "color": "#4D5663",
           "width": 1.3
          }
         },
         "name": "Fare",
         "opacity": 0.8,
         "orientation": "v",
         "type": "histogram",
         "x": [
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          71.2833,
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          30.0708,
          16.7,
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       "                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
       "                    ).then(function(){\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].iplot(kind='hist') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acdbfa8ec8>"
      ]
     },
     "execution_count": 22,
     "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.boxplot(x='Pclass',y='Age',data=train) #to find mean age of passengers travelling in each of classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    if(pd.isnull(Age)):\n",
    "        \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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acda5abf88>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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",
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       "      <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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acde29f348>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'],drop_first=True)   #now we convert sex into a boolean value ie 1 is male and 0 is female. At first we had 2 columns male and female which were representations of each other, so we dropped female and used only male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "embark = pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Q  S\n",
       "0    0  1\n",
       "1    0  0\n",
       "2    0  1\n",
       "3    0  1\n",
       "4    0  1\n",
       "..  .. ..\n",
       "886  0  1\n",
       "887  0  1\n",
       "888  0  1\n",
       "889  0  0\n",
       "890  1  0\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <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",
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       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</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>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
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       "      <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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Name','Embarked','Sex','Ticket'],axis=1,inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>13.0000</td>\n",
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       "<p>889 rows × 10 columns</p>\n",
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      ],
      "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\n",
       "..           ...       ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887         0       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888         1       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889         0       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890         1       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[889 rows x 10 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>889 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0           0       3  22.0      1      0   7.2500     1  0  1\n",
       "1           1       1  38.0      1      0  71.2833     0  0  0\n",
       "2           1       3  26.0      0      0   7.9250     0  0  1\n",
       "3           1       1  35.0      1      0  53.1000     0  0  1\n",
       "4           0       3  35.0      0      0   8.0500     1  0  1\n",
       "..        ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886         0       2  27.0      0      0  13.0000     1  0  1\n",
       "887         1       1  19.0      0      0  30.0000     0  0  1\n",
       "888         0       3  24.0      1      2  23.4500     0  0  1\n",
       "889         1       1  26.0      0      0  30.0000     1  0  0\n",
       "890         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[889 rows x 9 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Survived',axis=1)\n",
    "y= train['Survived']  #Always use the single data as y\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laptop\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning:\n",
      "\n",
      "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",
      "\n"
     ]
    },
    {
     "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.90      0.86       163\n",
      "           1       0.82      0.71      0.76       104\n",
      "\n",
      "    accuracy                           0.83       267\n",
      "   macro avg       0.83      0.81      0.81       267\n",
      "weighted avg       0.83      0.83      0.83       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions)) #gives accuracyof the boolean results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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