🚢Titanic Survival Prediction

📌 Project Overview

This project analyzes the Titanic dataset to predict passenger survival using various machine learning models. The dataset is preprocessed, explored, and evaluated through multiple classification algorithms.

📂 Dataset

The titanic dataset used is this project is fetched from Seaborn:

• titanic.csv
• Features include age, fare, pclass, sex, embarked, and others.

⚙️ Tech Stack

• Languages & Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Plotly
• ML Models: Logistic Regression, KNN, Random Forest, SVM, Decision Tree, Naive Bayes
• Other Tools: Streamlit for visualization, GridSearchCV for hyperparameter tuning

🔍 Data Preprocessing

• Handling missing values (mean/mode imputation)
• Dropping columns having high missing values
• Encoding categorical variables using OneHotEncoding
• Normalization (MinMaxScaler, StandardScaler)

📊 Exploratory Data Analysis (EDA)

• Count plots for survival distribution
• Scatter plots for fare vs. age
• Histograms of fares for different passenger classes
• Boxplots for age and fare distribution
• FacetGrid visualizations for survival analysis

🚀 Model Training & Evaluation

Models were trained using a pipeline approach:

1. Preprocessing (Scaling + Encoding)
2. Splitting Data (80% train, 20% test)
3. Model Training (Logistic Regression, KNN, etc.)
4. Hyperparameter Tuning (GridSearchCV for Logistic Regression)
5. Evaluation Metrics:
   • Accuracy
   • Precision, Recall, F1 Score
   • Confusion Matrix
   • Cross-validation scores

🔥 Best Model Selection

• The best model was Logistic Regression, achieving the highest accuracy of 83.7079%.
• Logistic Regression with hyperparameter tuning performed well with an accuracy of 84.26966%.

📌 Deployment

• The trained model is saved using joblib (titanic_trained_model.pkl).
• The trained model is tested using sample data.
• Streamlit is used for interactive visualizations.
• Confusion matrices, survival rate visualizations, and EDA graphs are included.

📂 File Structure

|Titanic-Insights/
   |dashboard/
      |-- dataset/
         |-- titanic.csv
         |-- cleaned_data.csv
      |-- notebook/
         |-- titanic.ipynb
         |-- titanic_trained_model.pkl
   |-- app.py  # Streamlit app
   |-- requirements.txt
|-- README.md

🔧 Installation & Usage

1. Clone the repository:

   git clone https://github.com/UFAQUE123/Titanic-Insights.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

✨ Conclusion

• Feature engineering and proper preprocessing significantly improve model performance.
• Logistic Regression is the best-performing model for this dataset.
• The project demonstrates an end-to-end machine learning pipeline, from data preprocessing to deployment.

🚀 Future Work:  Feature selection for better interpretability, and deploying a web-based ML model interface.

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📌 Author: UFAQUE SHADAB
📧 Contact: [email protected]