Titanic Survival Prediction 🚢

Machine Learning project that predicts whether a passenger survived the Titanic disaster.

---

Project Overview

This project performs Exploratory Data Analysis (EDA) and builds a Machine Learning model to predict passenger survival using the Titanic dataset.

The project demonstrates the complete data science workflow from data cleaning to model evaluation.

---

Dataset

The dataset contains information about Titanic passengers including:

• Passenger class
• Gender
• Age
• Fare
• Family members
• Port of embarkation

---

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

---

Project Workflow

1. Data Loading
2. Data Cleaning
3. Exploratory Data Analysis
4. Data Visualization
5. Feature Engineering
6. Machine Learning Model Training
7. Model Evaluation

---

Visualizations

Survival Count

![Survival Count]

Age Distribution

![Age Distribution]

Survival by Passenger Class

![Passenger Class]

Machine Learning Model

Logistic Regression was used to classify passengers as survived or not survived.

Model Accuracy:

~79%

---

Key Insights

• Female passengers had a higher survival rate
• First class passengers survived more often
• Passenger age influenced survival probability

---

How to Run the Project

Clone the repository

git clone https://github.com/PankajHarabhare09/TitanicSurvivalPredictionUsingML

Run the project

python src/TitanicInsights.py

---

Future Improvements

• Use Random Forest model
• Add more feature engineering
• Build an interactive dashboard

---

Author

Machine Learning portfolio project created for learning data science.