https://github.com/gajendrasharma-github/titanic_case_study
Classification Based Learning Project on Survivors of Titanic
https://github.com/gajendrasharma-github/titanic_case_study
classification machine-learning titanic-survival-prediction
Last synced: 3 months ago
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Classification Based Learning Project on Survivors of Titanic
- Host: GitHub
- URL: https://github.com/gajendrasharma-github/titanic_case_study
- Owner: gajendrasharma-github
- Created: 2024-08-16T12:01:57.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-16T12:14:12.000Z (10 months ago)
- Last Synced: 2025-01-16T22:31:52.496Z (5 months ago)
- Topics: classification, machine-learning, titanic-survival-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 142 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Titanic Survival Prediction
This project explores the famous Titanic dataset, applying various data analysis and machine learning techniques to predict passenger survival.
The dataset is provided by Kaggle and contains information about the passengers, such as age, gender, class, and other features.
## Project Overview
The main objectives of this project are:
- **Data Analysis and Visualization:** To explore the dataset, understand the relationships between different features, and visualize the data using plots and graphs.
- **Data Preprocessing:** Handling missing values, feature engineering, and preparing the data for machine learning models.
- **Modeling:** Implementing different machine learning models, such as Logistic Regression, Decision Trees, and Random Forest, to predict the survival of Titanic passengers.
- **Evaluation:** Comparing model performance using accuracy, precision, recall, and other metrics to determine the best model for this task.
## Key Features
- **Jupyter Notebook:** The project is developed in Jupyter Notebook, with clear explanations and visualizations to understand each step of the process.
- **Scikit-learn Models:** Several machine learning models are built and compared to select the best-performing model.
- **Data Visualization:** Various plots (e.g., histograms, bar charts, heatmaps) are used to explore the dataset and feature relationships.
## Conclusion
This project provides a hands-on experience with data analysis and machine learning, allowing you to practice and improve your skills in these areas. The Titanic dataset is a classic starting point for anyone interested in data science, making this project an excellent way to showcase your learning and expertise.