Titanuc.py

#!/usr/bin/env python
# coding: utf-8

# # Importing Modules

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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
get_ipython().run_line_magic('matplotlib', 'inline')


# # Loading Dataset

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train = pd.read_csv(r'C:\Users\Aditya Singh\Downloads\titanic.csv')
df = train
train.head()


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## Statistics of dataset
train.describe()


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## datatype info
train.info()


# # Exploratory Data Analysis

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## categorical attributes
sns.countplot(train['survived'])


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sns.countplot(train['pclass'])


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sns.countplot(train['sex'])


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sns.countplot(train['sibsp'])


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sns.countplot(train['parch'])


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sns.countplot(train['embarked'])


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## numerical attributes 
sns.distplot(train['age'])


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sns.distplot(train['fare'])


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class_fare = train.pivot_table(index='pclass',values='fare')
class_fare.plot(kind='bar')
plt.xlabel('Pclass')
plt.xlabel('AvgFare')


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class_fare = train.pivot_table(index='pclass',values='fare',aggfunc=np.sum)
class_fare.plot(kind='bar')
plt.xlabel('Pclass')
plt.xlabel('TotalFare')


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sns.barplot(data=train,x='pclass',y='fare',hue='survived')


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#finding null
train.isnull().sum()-1#subtracted one to adjust the index


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train[train['pclass'].isnull()]


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#removing column
df = df.drop(columns=['cabin','boat','body','home.dest'],axis=1)


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df.head()


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#filling null for numerical
df['age'] = df['age'].fillna(df['age'].mean())
df['fare'] = df['fare'].fillna(df['fare'].mean())


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df['embarked'] = df['embarked'].fillna(df['embarked'].mode()[0])


# # Log transform of uniformity

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sns.distplot(train['fare'])


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df['fare']=np.log(df['fare']+1)
sns.distplot(df['fare'])


# # Correlation

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corr=df.corr()


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plt.figure(figsize=(15,9))
sns.heatmap(corr,annot=True,cmap='coolwarm')


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df.head()


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#dropping more
df = df.drop(columns = ['name','ticket'],axis =1)
df.head()


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