Titanic Survival Prediction

Objective: Predicted passenger survival on the Titanic using machine learning techniques.

Approach: Performed data cleaning & preprocessing (handled missing values, encoded categorical variables).

Conducted EDA with histograms and correlation analysis to understand patterns in survival.

Addressed class imbalance using SMOTE.

Trained a Logistic Regression model to classify survival outcomes.

Evaluated performance using Accuracy Matrix.

Tools: Pandas, NumPy, Scikit-learn,Matplotlib, Seaborn.

Achieved a strong baseline accuracy and built a reproducible pipeline for binary classification.