Titanic Survival Analysis (NumPy + Matplotlib)

📌 Project Overview

This project analyzes the Titanic dataset using NumPy & Matplotlib.
We perform data preprocessing, feature engineering, statistical analysis, and visualization to uncover survival patterns.

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⚙️ Data Processing Steps

1️⃣ Data Loading & Cleaning

• Merged Name columns
• Handled missing values (Age, Fare → mean | Embarked → mode)

2️⃣ Encoding

• Sex encoded (female=0, male=1)
• Embarked encoded (S=0, C=1, Q=2)

3️⃣ Feature Engineering

• Dropped Name, Ticket, Cabin
• Added FamilySize & IsAlone features

4️⃣ Normalization

• Applied Z-score scaling on Age & Fare

5️⃣ Statistical Analysis

• Computed mean, median, std for key features
• Calculated survival rates by gender & class
• Correlation matrix of numerical features

6️⃣ Visualizations

• Survival Rate by Gender (bar chart)
• Fare Distribution (histogram)
• Correlation Heatmap

7️⃣ Train/Test Split

• Random shuffle
• 80% training, 20% testing