Titanic Survival Prediction System

📌 Project Overview

This project is a machine learning-based system that predicts whether a passenger on the Titanic would survive or not based on various features such as age, gender, ticket class, fare, and embarkation point.

The model is built using Python, pandas, scikit-learn, and Streamlit for deployment, providing an interactive web interface for predictions.

🚀 Features

• Data Preprocessing: Handles missing values and categorical variables.
• Exploratory Data Analysis (EDA): Visualizes key patterns and insights.
• Feature Engineering: Transforms data for better model performance.
• Machine Learning Model: Implements Logistic Regression for prediction.
• Web App with Streamlit: Allows users to input passenger details and get survival predictions.

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📂 Project Structure

TitanicSurvivalSystem/
│── data/
│   ├── train.csv
│── app.py
│── README.md

• data/: Contains Titanic dataset.
• app.py: Streamlit app for prediction.
• run_streamlit.py: Script to launch the web app.
• README.md: Project documentation.

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🔧 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/TitanicSurvivalSystem.git
cd TitanicSurvivalSystem

2️⃣ Create a Virtual Environment (Optional but Recommended)

python -m venv venv  # Windows
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate  # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

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📊 Data Preprocessing & EDA

The dataset contains:

• PassengerId (Unique ID)
• Survived (Target: 1 = Survived, 0 = Not Survived)
• Pclass (Ticket class)
• Name, Sex, Age
• SibSp, Parch (Family relations)
• Ticket, Fare, Cabin, Embarked

🔹 Handling Missing Data

• Age: Filled with median age.
• Embarked: Filled with mode (most common value).
• Cabin: Dropped due to excessive missing values.

🔹 Encoding Categorical Variables

• Sex: Converted to numeric (0 = Male, 1 = Female).
• Embarked: One-hot encoding for categorical values (C, Q, S).

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🔍 Model Training

Algorithm Used: Logistic Regression

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

Model Performance

• Accuracy: 81%
• Precision, Recall, F1-Score: Evaluated for class imbalance.

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🖥️ Running the Web App

Run Streamlit App

streamlit run app.py

Alternative: Run via Python Script

python -m streamlit run app.py

Access in Browser

Once running, open in your browser.

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🛠️ Future Improvements

• Implement more models (Random Forest, SVM, XGBoost).
• Add a graphical dashboard for EDA.
• Deploy the app using Heroku or AWS.

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🤝 Contributing

Feel free to contribute by submitting a pull request or reporting issues!

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📩 Contact

📧 Email: ramnrngupta@gmail.com 📌 Linkedin: Ram Narayan Gupta