Titanic Passenger Survival Prediction

This repository contains a machine learning pipeline developed to predict the survival of passengers aboard the Titanic. It is part of a series of projects undertaken during a Data Science internship at CodSoft.

Project Overview

The objective of this project is to build a supervised machine learning model that predicts whether a passenger survived the Titanic disaster. This problem is a classic classification task using structured data. The solution employs a Random Forest Classifier and incorporates robust preprocessing and feature engineering techniques.

Dataset

• Source: Kaggle - Titanic: Machine Learning from Disaster

• Files Used:

  • train.csv: Labeled training data
  • test.csv: Unlabeled test data for prediction
  • gender_submission.csv: Sample submission format

Project Workflow

Data Preprocessing

• Handling Missing Values:

  • Age: Mean imputation
  • Fare: Median imputation
  • Embarked: Mode imputation

• Encoding:

  • Categorical variables encoded using OneHotEncoder

• Feature Scaling:

  • Standardization of numerical features using StandardScaler

Feature Engineering

New informative features were created to improve model performance:

• FamilySize: Combines SibSp and Parch to reflect total family size
• IsAlone: Binary indicator of whether the passenger was traveling alone
• Title: Extracted from the name to capture social status

Model Building

• Model Used: RandomForestClassifier

• Pipeline: Preprocessing and classification are combined using Pipeline

• Model Evaluation:

  • Accuracy on training data
  • Cross-validation scores to check generalization
  • Feature importance analysis

Output

• Predictions are saved as:

  • titanic_predictions.csv: Basic submission format
  • titanic_predictions_detailed.csv: Includes prediction probabilities and confidence

Technologies Used

• Python 3.x
• Jupyter Notebook
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn

Acknowledgments

• This project is one of the three required tasks for the Data Science Internship at CodSoft.
• I have actively used the Data Science course by Krish Naik and various YouTube tutorials to guide my learning and implementation throughout this project.

Upcoming Tasks

As part of the CodSoft internship, I will also complete two additional data science projects:

1. Movie Rating Prediction
2. Iris Flower Classification (or another task from the provided list)