Titanic Survival Prediction – Machine Learning Project

A complete end-to-end Machine Learning project based on the legendary Kaggle Titanic Dataset.
This project explores passenger survival patterns with visualizations, builds an ML model, and generates predictions for Kaggle submission.

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Project Overview

The goal is to predict whether a passenger survived the Titanic disaster using:

• Exploratory Data Analysis (EDA)
• Data Cleaning & Preprocessing
• Feature Engineering
• Machine Learning (Random Forest Classifier)
• Model Evaluation
• Final Kaggle Submission File

This project is structured following industry-grade ML pipeline standards.

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Folder Structure

Titanic-Survival-Prediction/
│
├── data/                     # Raw dataset files
│     ├── gender_submission.csv
│     ├── test.csv
│     ├── train.csv
│
├── images/                   # Saved visualization outputs
│     ├── age_distribution.png
│     ├── confusion_matrix.png
│     ├── fare_boxplot.png
│     ├── feature_importance.png
│     ├── gender_survival.png
│     ├── survival_by_class.png
│     ├── survival_by_gender.png
│
├── Titanic_Survival_Prediction.ipynb   # Jupyter Notebook (Main project)
├── titanic_excel_dashboard.xlsx        # Excel dashboard (Optional)
├── submission.csv                      # Final Kaggle submission file
└── README.md                           # Project documentation

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Data Preprocessing

Key transformation steps used:

• Filling missing values:
  • Age → median
  • Fare → median
  • Embarked → mode
• Label Encoding:
  • Sex
  • Embarked
• Dropping irrelevant columns:
  • Name, Ticket, Cabin
• Feature Engineering:
  • FamilySize = SibSp + Parch + 1

Preprocessing function is reusable and scalable.

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Exploratory Data Analysis (EDA)

Visual insights include:

Survival by Gender

Survival by Class

Age Distribution

Fare Boxplot

Confusion Matrix

✔ Feature Importance

These visualizations uncover survival patterns and help build feature intuition.

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Machine Learning Model

Model used:

RandomForestClassifier

n_estimators = 300
max_depth    = 10
random_state = 42

After training the model:

Accuracy: ~85% (approx)

Confusion Matrix is included in /images.

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Kaggle Submission

Final predictions on test.csv were exported as:

submission.csv

Format:

PassengerId, Survived
892, 0
893, 1
...

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Technologies Used

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Seaborn
• Matplotlib
• Scikit-Learn
• Excel (optional dashboard)

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How to Run

1. Clone the repository:

   git clone https://github.com/<your-username>/Titanic-Survival-Prediction.git
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Open Jupyter Notebook:

   jupyter notebook
   

4. Run:

   Titanic_Survival_Prediction.ipynb
   

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Final Notes

This project demonstrates:

• Data cleaning
• EDA visualization skills
• Feature engineering
• ML model building
• Real-world problem solving
• Kaggle-ready workflow

Perfect for portfolio, job applications, and LinkedIn showcase.

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Contact

If you want to connect or collaborate — feel free to reach out!