Titanic

Overview

This project analyzes Titanic passenger data to identify factors influencing survival rates. It applies machine learning techniques to predict survival based on features like age, gender, and ticket class.

Key Features:

1. Data Preprocessing: Cleans and structures the dataset.
2. Exploratory Data Analysis (EDA): Visualizes survival trends.
3. Machine Learning Models: Uses Logistic Regression, Random Forest, and SVM for prediction.

Technologies Used:

1. Python (Pandas, NumPy, Scikit-learn)
2. Matplotlib, Seaborn for visualization
3. Jupyter Notebook for analysis

Future Enhancements:

1. Improve model accuracy with feature engineering.
2. Experiment with deep learning models.