data analysis

This project focuses on analyzing the famous Titanic dataset and building an interactive web application using Python and Streamlit.

The main goal is to explore the data, identify key patterns related to passenger survival, and create a machine learning model to predict the survival chances of new passengers.

️ Implementation Details:

Used Pandas and NumPy for data cleaning, preprocessing, and analysis.

Visualized insights with Matplotlib and Seaborn (age distribution, class, gender, survival rate, etc.).

Built predictive models using Logistic Regression and Random Forest for survival prediction.

Developed an interactive Streamlit dashboard where users can:

Input passenger details and get real-time survival predictions.

View analytical charts and visual insights.

Explore data relationships through interactive visualizations.

Project Outcomes:

A clean and interactive data analysis dashboard.

A user-friendly web app powered by Streamlit.

A machine learning model with strong accuracy.

Clear data insights and survival probability visualization.