Titanic Survival Prediction - Data Preprocessing

This project focuses on data cleaning, handling null values, encoding categorical features, and feature scaling using the Titanic dataset.
The preprocessing ensures the dataset is ready for applying Machine Learning models.

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📂 Dataset Overview

• Source: Titanic dataset (Kaggle)
• Rows: 891
• Columns: 12
  (PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked)

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🔧 Preprocessing Steps

1. Handling Missing Values

• Age: Missing values filled with the median.
• Cabin: Dropped (too many missing entries).
• Embarked: Missing values filled with the mode.

2. Encoding Categorical Features

• Sex: Encoded (0 = female, 1 = male).
• Embarked: Encoded using label encoding.

3. Dropping Irrelevant Columns

• Removed PassengerId, Name, Ticket (not useful for prediction).

4. Feature Scaling

• Applied StandardScaler to numerical features to normalize data.

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📊 Final Dataset

• Columns: Pclass, Sex, Age, SibSp, Parch, Fare, Embarked, Survived
• Cleaned and ready for ML model training.

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▶️ Example Usage

from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd

# Load dataset
df = pd.read_csv('Titanic-Dataset.csv')

# Handle nulls
df['Age'].fillna(df['Age'].median(), inplace=True)
df.drop(columns=['Cabin'], inplace=True)
df['Embarked'].fillna(df['Embarked'].mode()[0], inplace=True)

# Encode
le = LabelEncoder()
df['Sex'] = le.fit_transform(df['Sex'])
df['Embarked'] = le.fit_transform(df['Embarked'])

# Drop irrelevant columns
df.drop(columns=['Name', 'Ticket', 'PassengerId'], inplace=True)

# Scale
scaler = StandardScaler()
scaled_features = scaler.fit_transform(df.drop(columns=['Survived']))