🚢 Titanic Survival Prediction – A Data Science & Machine Learning Project

📌 Overview This project applies Data Science & Machine Learning techniques to predict whether a passenger would survive the Titanic disaster based on key factors such as class, age, gender, fare, and embarkation point. The model is integrated into a PyQt5 desktop application, allowing users to input values and receive predictions interactively.

🎯 Key Data Science & ML Skills Demonstrated ✅ Data Preprocessing – Handling missing values, encoding categorical variables, and feature scaling ✅ Exploratory Data Analysis (EDA) – Understanding patterns in survival rates ✅ Machine Learning Model Development – Training a Logistic Regression model ✅ Model Evaluation – Using accuracy and probability scores to assess predictions ✅ Deployment in GUI – Integrating the ML model into a PyQt5 application for real-world usability

📂 Dataset Used 📌 https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv

🛠 Technologies Used 🔹 Python (for Data Science & GUI) 🔹 Pandas, NumPy (Data Handling) 🔹 Scikit-Learn (Machine Learning Model) 🔹 Matplotlib (Data Visualization) 🔹 PyQt5 (Interactive Desktop Application)

🔮 How It Works 1️⃣ User Inputs values (Passenger Class, Age, Gender, etc.) 2️⃣ Data Preprocessing scales and encodes input features 3️⃣ ML Model Predicts survival probability 4️⃣ Results Displayed with probability & visualization

🚀 Future Enhancements ✅ Improve model with advanced ML algorithms (Random Forest, Neural Networks) ✅ Add interactive survival probability charts ✅ Deploy as a standalone EXE for wider use

💡 Connect & Contribute Want to improve this project? Feel free to fork & contribute! Let me know if you want any modifications or additional sections! 🚀