🚢 Titanic Survival Prediction

This project aims to predict whether Titanic passengers survived or not using machine learning techniques.

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📂 Contents

• 🔍 Exploratory Data Analysis & Missing Value Handling
• ⚙️ Feature Engineering (gender, age, ticket class, etc.)
• 🤖 Model Training (Logistic Regression)
• 📊 Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC AUC)
• 📉 ROC Curve & Confusion Matrix Visualization

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🛠️ Tools & Libraries

• Python 3.x
• pandas, numpy
• matplotlib, seaborn
• scikit-learn

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/codelones/titanic-survival-prediction.git
   cd titanic-survival-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the project in PyCharm or any Python IDE.

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📎 Dataset Source

• Kaggle Titanic Dataset

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🙋 Personal Note

I am still in the learning phase and working on projects to apply what I learn in practice.
I truly welcome any feedback, questions, or suggestions you may have — feel free to reach out!

📉 ROC Curve

This plot shows the performance of the logistic regression model in terms of True Positive Rate vs. False Positive Rate.

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📊 Confusion Matrix

A visual representation of the model’s predictions compared to the actual outcomes.

Threshold with Boxplot

Confusion Matrix

If you found this helpful, a ⭐ would be appreciated!