Create TitanicSurvival.py

Author: aman-coder03

TitanicSurvival.py

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+import numpy as np 
+import pandas as pd 
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))import pandas as pd
+
+titanic_df = pd.read_csv('/kaggle/input/titanic-datasets/titanic.csv')
+
+titanic_df.head(10)
+titanic_df.info()
+titanic_df.describe()
+titanic_df.columns
+print(titanic_df.isnull().sum())
+titanic_df['Age'].isnull().sum()
+print(titanic_df['Sex'].unique())
+print(titanic_df.tail())
+titanic_df.boxplot()
+titanic_df.hist()
+survival_rate = titanic_df['Survived'].count()
+print(survival_rate)
+
+import matplotlib.pyplot as plt
+
+survival_by_class = titanic_df.groupby('Pclass')['Survived'].count()
+survival_by_class.plot(kind='bar',color='green',edgecolor='black')
+plt.title('Survival Rate by Passenger Class')
+plt.xlabel('Passenger Class')
+plt.ylabel('Survival Rate')
+plt.show()
+print(survival_by_class)
+
+plt.hist(titanic_df['Age'],bins=20,color='green',edgecolor='black')
+plt.title('Age Distribution')
+plt.xlabel('Age')
+plt.ylabel('Frequency')
+plt.show()
+
+import seaborn as sns
+
+sns.histplot(titanic_df['Age'].dropna(),bins=20,kde=True)
+plt.title('Age Distribution')
+plt.show()
+
+sns.histplot(x='Age',hue='Survived',data=titanic_df,bins=20,kde=True)
+plt.title('Age Distribution by survival status')
+plt.show()
+
+numeric_columns = titanic_df.select_dtypes(include=['float64','int64']).columns
+
+correlation_matrix = titanic_df[numeric_columns].corr()
+
+sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm',linewidths=0.5)
+plt.title('Correlation Matrix')
+plt.show()
+
+sns.countplot(x='Sex',data=titanic_df)
+plt.title('Gender Distribution')
+plt.show()
+
+sns.barplot(x='Sex',y='Survived',data=titanic_df)
+plt.title('Survival Rate by Gender')
+plt.show()
+
+