Titanic- Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\sklearn\\datasets\\_openml.py:1002: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "titanic_data= fetch_openml(\"titanic\", version=1, as_frame=True)"
   ]
  },
  {
   "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>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",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass                            name     sex      age  sibsp  parch  \\\n",
       "0     1.0   Allen, Miss. Elisabeth Walton  female  29.0000    0.0    0.0   \n",
       "1     1.0  Allison, Master. Hudson Trevor    male   0.9167    1.0    2.0   \n",
       "\n",
       "   ticket      fare    cabin embarked boat  body  \\\n",
       "0   24160  211.3375       B5        S    2   NaN   \n",
       "1  113781  151.5500  C22 C26        S   11   NaN   \n",
       "\n",
       "                         home.dest survived  \n",
       "0                     St Louis, MO        1  \n",
       "1  Montreal, PQ / Chesterville, ON        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = titanic_data['data'] # to variable \"df\" titanic data is stored\n",
    "df['survived']=titanic_data['target'] #creating new colmn \"survived\" and storing targets of the titanic_dataset\n",
    "df.head(2) # printing the data using \".head()\" passing value 2 to print only two rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"survived\", data=df) # Checking how many people survived, for better understanding of data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"survived\",hue='sex' ,data=df) #Checking which gender had better chance of surviving ,  in \"hue\" arrgument we can pass any colmn name to check the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='survived', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"survived\",hue='pclass' ,data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['age'].plot.hist() #ploting the graph to know wwhich age group were higher present in titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 14 columns):\n",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   pclass     1309 non-null   float64 \n",
      " 1   name       1309 non-null   object  \n",
      " 2   sex        1309 non-null   category\n",
      " 3   age        1046 non-null   float64 \n",
      " 4   sibsp      1309 non-null   float64 \n",
      " 5   parch      1309 non-null   float64 \n",
      " 6   ticket     1309 non-null   object  \n",
      " 7   fare       1308 non-null   float64 \n",
      " 8   cabin      295 non-null    object  \n",
      " 9   embarked   1307 non-null   category\n",
      " 10  boat       486 non-null    object  \n",
      " 11  body       121 non-null    float64 \n",
      " 12  home.dest  745 non-null    object  \n",
      " 13  survived   1309 non-null   category\n",
      "dtypes: category(3), float64(6), object(5)\n",
      "memory usage: 116.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() #information about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets start preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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",
       "      <th>survived</th>\n",
       "      <th>family</th>\n",
       "      <th>travelled_alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</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",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass                           name     sex   age  sibsp  parch ticket  \\\n",
       "0     1.0  Allen, Miss. Elisabeth Walton  female  29.0    0.0    0.0  24160   \n",
       "\n",
       "       fare cabin embarked boat  body     home.dest survived  family  \\\n",
       "0  211.3375    B5        S    2   NaN  St Louis, MO        1     0.0   \n",
       "\n",
       "   travelled_alone  \n",
       "0              0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['family']=df['parch']+df['sibsp'] # as we can see both colums \"parch\" and \"sibsp\" are related to family\n",
    "                                    # we combined both colums to singe colmn name \"family\"\n",
    "df.loc[df['family']>0, 'travelled_alone' ]=1 # here feature engineering done\n",
    "                                             #creating a new column travelled_alone\n",
    "                                             #assign value 1 if a person is travelling with his family\n",
    "df.loc[df['family']==0, 'travelled_alone' ]=0 # here \".loc()\" for comparing data with family column and assign the values in travelled_alone\n",
    "df.head(1) # print and check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['sex']=='female', 'sex_updated' ]=1 # as we can see datatype of sex is catogry\n",
    "                                              #which canot be used while training the model\n",
    "                                              #we have created a new column \"sex_update\" and stored sex in form of numerical value\n",
    "                                              #female=1,male=0\n",
    "df.loc[df['sex']=='male', 'sex_updated' ]=0\n",
    "df.drop(['sex'], axis=1, inplace=True) # as sex column is not necessary drop it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.countplot(x='travelled_alone', data = df) # check no of plp travellin alone\n",
    "plt.title(\"plp alone\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>survived</th>\n",
       "      <th>family</th>\n",
       "      <th>travelled_alone</th>\n",
       "      <th>sex_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass   age  sibsp  parch      fare cabin embarked boat  body survived  \\\n",
       "0     1.0  29.0    0.0    0.0  211.3375    B5        S    2   NaN        1   \n",
       "\n",
       "   family  travelled_alone  sex_updated  \n",
       "0     0.0              0.0          1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['name', 'ticket','home.dest'], axis=1, inplace=True) # droping the unwanted columns which may not be usefull for training model\n",
    "                                                                # \".drop()\" methos is used and list of column names are sent\n",
    "                                                                # axis=1, means droping colums, \n",
    "                                                                # inplace=True update the changes in local dataframe saved in computer(if saved)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['cabin','body','boat'], axis=1, inplace=True) # droping the unwanted columns which may not be usefull for training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass               0\n",
       "age                263\n",
       "sibsp                0\n",
       "parch                0\n",
       "fare                 1\n",
       "embarked             2\n",
       "survived             0\n",
       "family               0\n",
       "travelled_alone      0\n",
       "sex_updated          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #checking the null values present in dataset and suming up them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now, we have to remove null values from data. we can use Imputer method in sklearn to do so\n",
    "from sklearn.impute import SimpleImputer \n",
    "imp = SimpleImputer(strategy='mean') # creating variaple imp and calling simpleimputer by setting statergy of creating new values to mean of neighbour values\n",
    "df['age']=imp.fit_transform(df[['age']]) #using fit_trasform method to impute\n",
    "df['fare']=imp.fit_transform(df[['fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_freq= SimpleImputer(strategy='most_frequent')  # as \"embarked\" column is category we use statergy as \"most frequent\" which update values based on neighbour\n",
    "df['embarked']=imp_freq.fit_transform(df[['embarked']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Q  S\n",
      "0     0  1\n",
      "1     0  1\n",
      "2     0  1\n",
      "3     0  1\n",
      "4     0  1\n",
      "...  .. ..\n",
      "1304  0  0\n",
      "1305  0  0\n",
      "1306  0  0\n",
      "1307  0  0\n",
      "1308  0  1\n",
      "\n",
      "[1309 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "embark= pd.get_dummies(df['embarked'], drop_first=True) #As embarked has 3 values, creating dummies of each value in seperate colums and droping first one\n",
    "                                                        #droping 1 out of 3 still gives the same result\n",
    "print(embark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,embark], axis=1) #concatinating newly created dumies to main dataframe\n",
    "df.drop(['embarked'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass             0\n",
       "age                0\n",
       "sibsp              0\n",
       "parch              0\n",
       "fare               0\n",
       "survived           0\n",
       "family             0\n",
       "travelled_alone    0\n",
       "sex_updated        0\n",
       "Q                  0\n",
       "S                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #checking the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spliting data into input_x and output_y\n",
    "x=df.drop(['survived'], axis=1)\n",
    "y=df['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spliting data into test and train\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test  = train_test_split(x,y,test_size=0.3, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression #importing linearregreession algorithm\n",
    "mod=LogisticRegression() #assign a variable\n",
    "mod.fit(x_train, y_train) #fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mod.predict(x_test) #storing predictions in a variable. using \".predict\" method to do predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.38931297709924"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score #accuracy\n",
    "accuracy_score(y_test,predictions)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[205,  31],\n",
       "       [ 50, 107]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix #matric which says TP,TN,FP,FN cases\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_titanic.csv', index=False) #exporting the preprocessed file without index"
   ]
  }
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