Project Title

Titanic Survival Prediction Using Machine Learning in Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Data Science | Predictive Analytics | Classification

Project Level

Beginner to Intermediate

Project Objective

The Titanic disaster is one of the most famous maritime tragedies in history. The objective of this project is to build an intelligent Machine Learning model capable of analyzing passenger information and predicting whether a passenger would have survived or not survived the Titanic disaster.

By analyzing factors such as age, gender, ticket class, fare, and family size, students will learn how Machine Learning can be used to make predictions based on historical data.

This project provides practical exposure to Artificial Intelligence, Classification Algorithms, and Predictive Analytics concepts commonly used in real-world decision-making systems.

Session Access Link

Project Training Session

Session Link:

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand classification problems in Machine Learning.
• Learn how survival prediction systems work.
• Analyze historical datasets.
• Perform data cleaning and preprocessing.
• Handle missing values effectively.
• Conduct exploratory data analysis (EDA).
• Train and evaluate classification models.
• Interpret feature importance in prediction systems.
• Build a real-world survival prediction system.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn

• NumPy

• Pandas

Data Visualization Libraries

• Matplotlib

• Seaborn

Development Platforms

• Google Colab

• Jupyter Notebook

• VS Code (Optional)

Data Storage

• CSV Files

• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Titanic Dataset containing:

• Passenger ID
• Passenger Name
• Gender
• Age
• Ticket Class (Pclass)
• Fare
• Embarked Port
• SibSp (Number of Siblings/Spouses)
• Parch (Number of Parents/Children)
• Survival Status (Survived / Not Survived)

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Survival Distribution Analysis
• Gender-wise Survival Analysis
• Passenger Class-wise Survival Analysis
• Age Distribution Analysis
• Fare Distribution Analysis
• Correlation Matrix
• Heatmaps
• Count Plots

Benefits:

• Understand factors influencing passenger survival.
• Identify important features affecting predictions.
• Discover patterns from historical data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Handling Missing Age Values
• Label Encoding
• One-Hot Encoding
• Feature Scaling
• Family Size Feature Creation (Optional)

Benefits:

• Improves model performance.
• Enhances prediction accuracy.
• Converts categorical data into numerical form.

Phase 5: Model Building

Students will train Machine Learning models such as:

Logistic Regression

• Most commonly used classification model.
• Provides interpretable results.

Decision Tree Classifier

• Easy to understand and visualize.
• Identifies important survival factors.

Random Forest Classifier

• Ensemble learning method.
• Provides higher prediction accuracy.

Support Vector Machine (SVM)

• Effective for binary classification problems.
• Handles complex decision boundaries.

K-Nearest Neighbors (KNN)

• Useful for comparison and benchmarking.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Accuracy Score
• Confusion Matrix
• Precision
• Recall
• F1-Score
• ROC-AUC Score

Expected Accuracy:

75% – 90% (depending on preprocessing and model selection)

Phase 7: Survival Prediction System

Students will create a system where users can:

• Enter passenger information.
• Click Predict.
• Receive output:
  • Survived
  • Not Survived

Example:

Input:

• Gender: Female
• Age: 28
• Passenger Class: First Class
• Fare: ₹8,000

Output:

• Survived

or

• Not Survived

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)

• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import
• Data Cleaning
• Missing Value Handling
• Exploratory Data Analysis
• Model Training
• Accuracy Results
• Survival Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)
• Prediction Results
• Survival Analysis Report

Industry Applications

• Risk Analysis Systems
• Customer Behavior Prediction
• Insurance Risk Assessment
• Classification-Based Decision Systems
• Historical Data Analytics

Interesting Insights Students Can Discover

• Women had a higher survival rate than men.
• First-class passengers had better survival chances.
• Children were more likely to survive than adults.
• Passenger class significantly influenced survival probability.

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

TitanicSurvivalPrediction_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.