ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
Jupyter Notebook
684
2,154 commits
updated Sep 16, 2026
π Code of Conduct β’ π€ Contributing Guidelines β’ πΊοΈ Learning Roadmap β’ π Project Template β’ π PR Template
ML-CaPsule is a comprehensive, open-source learning hub for aspiring and experienced data science enthusiasts. Whether you lack a mentor or want structured, project-based learning β this repository helps you master Machine Learning, Deep Learning, NLP, Computer Vision, and more through 500+ real-world projects.
π‘ No mentor? No problem. Learn by building, become interview-ready, and grow with a thriving open-source community.
|
ML Fundamentals |
Statistics & Analytics |
Data Science |
| π | Why Machine Learning? | ποΈ |
| π | Pre-requisites | π¦ |
| π€ | ML Algorithm Recommendation Guide | β |
| π | All Projects | π |
| βοΈ | Contribution Guidelines | π |
| β¨ | Contributors | β€οΈ |
| π | Notebook Health Check | π |
Machine learning automates analytical model building β enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention. It sits at the heart of modern AI.
| Benefit | Impact |
|---|---|
| π Pattern Discovery | Uncover hidden trends in massive datasets |
| β‘ Automation | Reduce manual analysis and repetitive tasks |
| π― Personalization | Power recommendations at Netflix, Amazon & Spotify |
| π₯ Healthcare | Early disease detection & medical imaging |
| πΌ Business Edge | Used by Google, Meta, Uber as a competitive differentiator |
| Step | Resource | Link |
|---|---|---|
| 1οΈβ£ | Install Python | python.org |
| 2οΈβ£ | Learn Python Basics | W3Schools Python ML |
| 3οΈβ£ | ML with Python Course | freeCodeCamp ML |
R is an open-source language built for statistical computing, data analytics, and scientific research.
1. π₯ Data ExtractionMethods for constructing variable combinations to describe data accurately.
2. π VisualizationExpose patterns, trends & correlations visually.
3. π― Feature SelectionSelect relevant features to boost model accuracy.
|
4. π Statistics
5. π¬ Data ScienceMultidisciplinary field combining statistics, ML, computing, and domain expertise to extract insights and drive decisions across industries. |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Titanic | Predict passenger survival | β | Kaggle |
| 2 | Iris Flower | Classify 3 iris species | β | UCI |
| 3 | Breast Cancer Wisconsin | Malignant vs benign tumors | β | UCI |
| 4 | Heart Disease | Predict from clinical features | ββ | UCI |
| 5 | Pima Indians Diabetes | Diabetes onset prediction | β | Kaggle |
| 6 | Adult Income | Income >$50K prediction | ββ | UCI |
| 7 | Bank Marketing | Term deposit subscription | ββ | UCI |
| 8 | Wine Quality | Good vs bad wine classification | β | UCI |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Boston Housing | Predict housing prices | β | Kaggle |
| 2 | California Housing | Median house values | β | Kaggle |
| 3 | House Prices | 79-variable price prediction | ββ | Kaggle |
| 4 | Auto MPG | Fuel efficiency prediction | β | UCI |
| 5 | Student Performance | Exam score prediction | β | UCI |
| 6 | Medical Cost | Insurance charge prediction | β | Kaggle |
| 7 | Bike Sharing Demand | Hourly rental counts | ββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | SMS Spam Collection | Spam vs ham classification | β | UCI |
| 2 | IMDB Reviews | 50K movie sentiment analysis | β | Kaggle |
| 3 | Twitter Airline Sentiment | Airline tweet classification | ββ | Kaggle |
| 4 | Amazon Reviews | Multi-class sentiment | ββ | Kaggle |
| 5 | Fake News | Real vs fake articles | ββ | Kaggle |
| 6 | AG News | 4-topic news classification | ββ | Hugging Face |
| 7 | Quora Question Pairs | Semantic equivalence detection | βββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MNIST | Handwritten digit classification | β | Yann LeCun |
| 2 | Fashion-MNIST | Clothing category classification | β | GitHub |
| 3 | CIFAR-10 | 10-class image classification | ββ | Official |
| 4 | Dogs vs Cats | Binary image classification | ββ | Kaggle |
| 5 | Flowers Recognition | 5 flower categories | β | Kaggle |
| 6 | Intel Image Classification | 6 natural scene categories | ββ | Kaggle |
| 7 | Chest X-Ray Pneumonia | Pneumonia detection | ββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Mall Customers | Customer segmentation | β | Kaggle |
| 2 | Credit Card Segmentation | Spending behavior clusters | ββ | Kaggle |
| 3 | Wholesale Customers | Distributor spending data | β | UCI |
| 4 | World Happiness Report | Country happiness scores | β | Kaggle |
| 5 | Online Retail | Market basket analysis | ββ | UCI |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Air Passengers | Monthly airline counts (1949β1960) | β | GitHub |
| 2 | Stock Market Data | Historical stock prices | ββ | Kaggle |
| 3 | COVID-19 (JHU) | Global case tracking | β | GitHub |
| 4 | Energy Consumption | Hourly power data (LSTM-ready) | ββ | Kaggle |
| 5 | Jena Climate | 420K hourly weather readings | βββ | TensorFlow |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MovieLens (ml-100k) | 100K movie ratings | β | GroupLens |
| 2 | Book-Crossing | 270K+ book ratings | ββ | Kaggle |
| 3 | Amazon Product Ratings | Cross-category ratings | ββ | UCSD |
| 4 | Jester Jokes | 73K user joke ratings | ββ | UC Berkeley |
| Platform | Description | Link |
|---|---|---|
| Kaggle | Largest dataset community + competitions | kaggle.com/datasets |
| UCI ML Repository | Classic academic datasets | archive.ics.uci.edu |
| Google Dataset Search | Public dataset search engine | datasetsearch.research.google.com |
| Hugging Face | NLP datasets hub | huggingface.co/datasets |
| Papers With Code | Research-linked datasets | paperswithcode.com/datasets |
| data.gov | U.S. government open data | data.gov |
| OpenML | AutoML datasets | openml.org |
π‘ Beginner tip: Start with Iris, Titanic, or MNIST β each teaches a complete ML workflow in under 100 lines of code!
| ποΈ Computer Vision | π€ Deep Learning | π¦ Finance & Business |
| Eye Gaze Tracking | ANN from Scratch | Portuguese Bank Marketing |
| Deepfake Image Analyzer | Bidirectional LSTM | Bitcoin Price Predictor |
| Chicken Disease Classification | Weapon Detection System | Customer Segmentation |
| # | Project | Description |
|---|---|---|
| 1 | Alzheimer's Disease Predictor | ML model predicting Alzheimer's likelihood using classification & feature selection |
| 2 | Chatbot Using RASA | Conversational AI handling diverse user queries |
| 3 | COVID-19 Forecasting with Prophet | Time-series forecasting of case trends |
| 4 | Fake News Detection | NLP-based fake news classification |
| 5 | Eye Gaze Tracking & Attention Estimation | Real-time attention tracking with MediaPipe & 3D head pose estimation |
| 6 | Portuguese Bank Marketing | Binary classification for term deposit subscription |
π & many more... Explore the full repository for 500+ additional projects!
| Project | Category | Level | Link |
|---|---|---|---|
| Alzheimer's Predictor | Deep Learning | Intermediate | View β |
| Yoga Pose Detection | Computer Vision | Intermediate | View β |
| Speech-to-Image Generator | Deep Learning | Advanced | View β |
| Weapon Detection System | Computer Vision | Advanced | View β |
| Fashion Recommendation System | Deep Learning & Recommenders | Intermediate | View β |
| Project | Category | Level | Link |
|---|---|---|---|
| Toxic Comment Classifier | NLP | Intermediate | View β |
| Currency Arbitrage with RL | Reinforcement Learning | Advanced | View β |
| Sales Prediction (Research) | ML | Advanced | View β |
| TweetMiner AI | NLP | Beginner | View β |
| Intelligent Network Intrusion Detection System | ML | Advanced | View β |
| Mental Health Risk Scorer | NLP & Transformers | Advanced | View β |
| Code Vulnerability Classifier | ML & Transformers | Advanced | View β |
| Resource | Link |
|---|---|
| 8 Basic Statistics Concepts | KDnuggets |
| Machine Learning with Python (Coursera) | Coursera |
| Python ML Getting Started (W3Schools) | W3Schools |
| ML with Python (freeCodeCamp) | freeCodeCamp |
# 1. Fork & clone the repository
git clone https://github.com/Niketkumardheeryan/ML-CaPsule.git
cd ML-CaPsule
# 2. Create a feature branch
git checkout -b my-feature
# 3. Pick a project folder and start learning!
We welcome contributions from everyone! Here's how to get involved:
| Step | Action | Link |
|---|---|---|
| 1 | Read contribution guidelines | CONTRIBUTING.md |
| 2 | Browse existing issues | Issues |
| 3 | Submit a pull request | Pull Requests |
# Fork β Clone β Branch β Commit β Push β PR
git clone https://github.com/YOUR_USERNAME/ML-CaPsule.git
cd ML-CaPsule
git checkout -b my-feature
# Make your changes
git add .
git commit -m "Add: descriptive message about your change"
git push origin my-feature
Then open a Pull Request on GitHub with a clear description of your changes.
ML-CaPsule/
βββ π .github/ # GitHub templates & workflows
β βββ ISSUE_TEMPLATE/ # Standardized issue templates
β βββ readme_template.md # Template for project READMEs
β βββ pullrequest_template.md # Template for submitting Pull Requests
βββ π Project Folders/ # 500+ self-contained ML/DL/NLP/CV projects
β βββ π <Project_Name>/ # Individual project folder (descriptive name)
β β βββ π README.md # Project specific description, results & instructions
β β βββ π *.ipynb / *.py # Implementation notebooks/scripts
β β βββ π requirements.txt # (Optional) Folder-specific dependencies
β β βββ π assets / images # Screenshots & visual demonstrations
βββ π build_readme.py # Utility script for automated README index updating
βββ π CODE_OF_CONDUCT.md # Community behavioral standards
βββ π CONTRIBUTING.md # Detailed contribution workflow & coding guidelines
βββ π LICENSE # MIT License
βββ π README.md # Main repository overview & directory hub
βββ π ROADMAP.md # Structured 3-tier learning path (Beginner/Intermediate/Advanced)
build_readme.py acts as a repository maintenance tool that dynamically updates project listings.ROADMAP.md organizes projects into clear skill tiers (Beginner, Intermediate, Advanced) guiding new learners step by step.For the full troubleshooting guide covering installation, environment setup, runtime errors, and git workflow issues, see TROUBLESHOOTING.md.
Refer to ROADMAP.md which classifies projects into Beginner (foundational ML & EDA), Intermediate (Neural Networks, NLP, Web Apps), and Advanced (Computer Vision, Reinforcement Learning, Transformers).
Ensure your virtual environment is active and install common data science packages:
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
If the project folder contains a local requirements.txt, install it directly using:
pip install -r requirements.txt
Jupyter Notebooks (.ipynb) are preferred for project submissions as they combine code, visualization outputs, and explanations. Ensure all notebook cells are executed before committing.
Please read our Code of Conduct before contributing.
Thanks to all these amazing people who made ML-CaPsule possible! π
Niket Kumar Dheeryan Author π» | Abhishek Sharma π» | Sakalya100 π» | Kaustav Roy π» | Soumayan Pal π» | Komal Gupta π» |
Manu Varghese π» | Abhishek Panigrahi π» | Padmini Rai π» | psyduck1203 π» | Rutik Bhoyar π» | Ayushi Shrivastava π» |


This project is licensed under the MIT License.
Feel free to create issues, fix bugs, and contribute. Join our community!
Built with β€οΈ by Niketkumardheeryan and contributors
Start with beginner-friendly projects, then move to intermediate and advanced ones as your confidence grows. The table below adds quick difficulty and time guidance for each project.
ML-capsule is a Project for beginners and experienced data science Enthusiasts who don't have a mentor or guidance and wish to learn Machine learning. Using our repo they can learn ML, DL, and many related technologies with different real-world projects and become Interview ready.
Jupyter Notebook
684
2,154 commits
updated Sep 16, 2026
π Code of Conduct β’ π€ Contributing Guidelines β’ πΊοΈ Learning Roadmap β’ π Project Template β’ π PR Template
ML-CaPsule is a comprehensive, open-source learning hub for aspiring and experienced data science enthusiasts. Whether you lack a mentor or want structured, project-based learning β this repository helps you master Machine Learning, Deep Learning, NLP, Computer Vision, and more through 500+ real-world projects.
π‘ No mentor? No problem. Learn by building, become interview-ready, and grow with a thriving open-source community.
|
ML Fundamentals |
Statistics & Analytics |
Data Science |
| π | Why Machine Learning? | ποΈ |
| π | Pre-requisites | π¦ |
| π€ | ML Algorithm Recommendation Guide | β |
| π | All Projects | π |
| βοΈ | Contribution Guidelines | π |
| β¨ | Contributors | β€οΈ |
| π | Notebook Health Check | π |
Machine learning automates analytical model building β enabling systems to learn from data, identify patterns, and make decisions with minimal human intervention. It sits at the heart of modern AI.
| Benefit | Impact |
|---|---|
| π Pattern Discovery | Uncover hidden trends in massive datasets |
| β‘ Automation | Reduce manual analysis and repetitive tasks |
| π― Personalization | Power recommendations at Netflix, Amazon & Spotify |
| π₯ Healthcare | Early disease detection & medical imaging |
| πΌ Business Edge | Used by Google, Meta, Uber as a competitive differentiator |
| Step | Resource | Link |
|---|---|---|
| 1οΈβ£ | Install Python | python.org |
| 2οΈβ£ | Learn Python Basics | W3Schools Python ML |
| 3οΈβ£ | ML with Python Course | freeCodeCamp ML |
R is an open-source language built for statistical computing, data analytics, and scientific research.
1. π₯ Data ExtractionMethods for constructing variable combinations to describe data accurately.
2. π VisualizationExpose patterns, trends & correlations visually.
3. π― Feature SelectionSelect relevant features to boost model accuracy.
|
4. π Statistics
5. π¬ Data ScienceMultidisciplinary field combining statistics, ML, computing, and domain expertise to extract insights and drive decisions across industries. |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Titanic | Predict passenger survival | β | Kaggle |
| 2 | Iris Flower | Classify 3 iris species | β | UCI |
| 3 | Breast Cancer Wisconsin | Malignant vs benign tumors | β | UCI |
| 4 | Heart Disease | Predict from clinical features | ββ | UCI |
| 5 | Pima Indians Diabetes | Diabetes onset prediction | β | Kaggle |
| 6 | Adult Income | Income >$50K prediction | ββ | UCI |
| 7 | Bank Marketing | Term deposit subscription | ββ | UCI |
| 8 | Wine Quality | Good vs bad wine classification | β | UCI |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Boston Housing | Predict housing prices | β | Kaggle |
| 2 | California Housing | Median house values | β | Kaggle |
| 3 | House Prices | 79-variable price prediction | ββ | Kaggle |
| 4 | Auto MPG | Fuel efficiency prediction | β | UCI |
| 5 | Student Performance | Exam score prediction | β | UCI |
| 6 | Medical Cost | Insurance charge prediction | β | Kaggle |
| 7 | Bike Sharing Demand | Hourly rental counts | ββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | SMS Spam Collection | Spam vs ham classification | β | UCI |
| 2 | IMDB Reviews | 50K movie sentiment analysis | β | Kaggle |
| 3 | Twitter Airline Sentiment | Airline tweet classification | ββ | Kaggle |
| 4 | Amazon Reviews | Multi-class sentiment | ββ | Kaggle |
| 5 | Fake News | Real vs fake articles | ββ | Kaggle |
| 6 | AG News | 4-topic news classification | ββ | Hugging Face |
| 7 | Quora Question Pairs | Semantic equivalence detection | βββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MNIST | Handwritten digit classification | β | Yann LeCun |
| 2 | Fashion-MNIST | Clothing category classification | β | GitHub |
| 3 | CIFAR-10 | 10-class image classification | ββ | Official |
| 4 | Dogs vs Cats | Binary image classification | ββ | Kaggle |
| 5 | Flowers Recognition | 5 flower categories | β | Kaggle |
| 6 | Intel Image Classification | 6 natural scene categories | ββ | Kaggle |
| 7 | Chest X-Ray Pneumonia | Pneumonia detection | ββ | Kaggle |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Mall Customers | Customer segmentation | β | Kaggle |
| 2 | Credit Card Segmentation | Spending behavior clusters | ββ | Kaggle |
| 3 | Wholesale Customers | Distributor spending data | β | UCI |
| 4 | World Happiness Report | Country happiness scores | β | Kaggle |
| 5 | Online Retail | Market basket analysis | ββ | UCI |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | Air Passengers | Monthly airline counts (1949β1960) | β | GitHub |
| 2 | Stock Market Data | Historical stock prices | ββ | Kaggle |
| 3 | COVID-19 (JHU) | Global case tracking | β | GitHub |
| 4 | Energy Consumption | Hourly power data (LSTM-ready) | ββ | Kaggle |
| 5 | Jena Climate | 420K hourly weather readings | βββ | TensorFlow |
| # | Dataset | Description | Level | Download |
|---|---|---|---|---|
| 1 | MovieLens (ml-100k) | 100K movie ratings | β | GroupLens |
| 2 | Book-Crossing | 270K+ book ratings | ββ | Kaggle |
| 3 | Amazon Product Ratings | Cross-category ratings | ββ | UCSD |
| 4 | Jester Jokes | 73K user joke ratings | ββ | UC Berkeley |
| Platform | Description | Link |
|---|---|---|
| Kaggle | Largest dataset community + competitions | kaggle.com/datasets |
| UCI ML Repository | Classic academic datasets | archive.ics.uci.edu |
| Google Dataset Search | Public dataset search engine | datasetsearch.research.google.com |
| Hugging Face | NLP datasets hub | huggingface.co/datasets |
| Papers With Code | Research-linked datasets | paperswithcode.com/datasets |
| data.gov | U.S. government open data | data.gov |
| OpenML | AutoML datasets | openml.org |
π‘ Beginner tip: Start with Iris, Titanic, or MNIST β each teaches a complete ML workflow in under 100 lines of code!
| ποΈ Computer Vision | π€ Deep Learning | π¦ Finance & Business |
| Eye Gaze Tracking | ANN from Scratch | Portuguese Bank Marketing |
| Deepfake Image Analyzer | Bidirectional LSTM | Bitcoin Price Predictor |
| Chicken Disease Classification | Weapon Detection System | Customer Segmentation |
| # | Project | Description |
|---|---|---|
| 1 | Alzheimer's Disease Predictor | ML model predicting Alzheimer's likelihood using classification & feature selection |
| 2 | Chatbot Using RASA | Conversational AI handling diverse user queries |
| 3 | COVID-19 Forecasting with Prophet | Time-series forecasting of case trends |
| 4 | Fake News Detection | NLP-based fake news classification |
| 5 | Eye Gaze Tracking & Attention Estimation | Real-time attention tracking with MediaPipe & 3D head pose estimation |
| 6 | Portuguese Bank Marketing | Binary classification for term deposit subscription |
π & many more... Explore the full repository for 500+ additional projects!
| Project | Category | Level | Link |
|---|---|---|---|
| Alzheimer's Predictor | Deep Learning | Intermediate | View β |
| Yoga Pose Detection | Computer Vision | Intermediate | View β |
| Speech-to-Image Generator | Deep Learning | Advanced | View β |
| Weapon Detection System | Computer Vision | Advanced | View β |
| Fashion Recommendation System | Deep Learning & Recommenders | Intermediate | View β |
| Project | Category | Level | Link |
|---|---|---|---|
| Toxic Comment Classifier | NLP | Intermediate | View β |
| Currency Arbitrage with RL | Reinforcement Learning | Advanced | View β |
| Sales Prediction (Research) | ML | Advanced | View β |
| TweetMiner AI | NLP | Beginner | View β |
| Intelligent Network Intrusion Detection System | ML | Advanced | View β |
| Mental Health Risk Scorer | NLP & Transformers | Advanced | View β |
| Code Vulnerability Classifier | ML & Transformers | Advanced | View β |
| Resource | Link |
|---|---|
| 8 Basic Statistics Concepts | KDnuggets |
| Machine Learning with Python (Coursera) | Coursera |
| Python ML Getting Started (W3Schools) | W3Schools |
| ML with Python (freeCodeCamp) | freeCodeCamp |
# 1. Fork & clone the repository
git clone https://github.com/Niketkumardheeryan/ML-CaPsule.git
cd ML-CaPsule
# 2. Create a feature branch
git checkout -b my-feature
# 3. Pick a project folder and start learning!
We welcome contributions from everyone! Here's how to get involved:
| Step | Action | Link |
|---|---|---|
| 1 | Read contribution guidelines | CONTRIBUTING.md |
| 2 | Browse existing issues | Issues |
| 3 | Submit a pull request | Pull Requests |
# Fork β Clone β Branch β Commit β Push β PR
git clone https://github.com/YOUR_USERNAME/ML-CaPsule.git
cd ML-CaPsule
git checkout -b my-feature
# Make your changes
git add .
git commit -m "Add: descriptive message about your change"
git push origin my-feature
Then open a Pull Request on GitHub with a clear description of your changes.
ML-CaPsule/
βββ π .github/ # GitHub templates & workflows
β βββ ISSUE_TEMPLATE/ # Standardized issue templates
β βββ readme_template.md # Template for project READMEs
β βββ pullrequest_template.md # Template for submitting Pull Requests
βββ π Project Folders/ # 500+ self-contained ML/DL/NLP/CV projects
β βββ π <Project_Name>/ # Individual project folder (descriptive name)
β β βββ π README.md # Project specific description, results & instructions
β β βββ π *.ipynb / *.py # Implementation notebooks/scripts
β β βββ π requirements.txt # (Optional) Folder-specific dependencies
β β βββ π assets / images # Screenshots & visual demonstrations
βββ π build_readme.py # Utility script for automated README index updating
βββ π CODE_OF_CONDUCT.md # Community behavioral standards
βββ π CONTRIBUTING.md # Detailed contribution workflow & coding guidelines
βββ π LICENSE # MIT License
βββ π README.md # Main repository overview & directory hub
βββ π ROADMAP.md # Structured 3-tier learning path (Beginner/Intermediate/Advanced)
build_readme.py acts as a repository maintenance tool that dynamically updates project listings.ROADMAP.md organizes projects into clear skill tiers (Beginner, Intermediate, Advanced) guiding new learners step by step.For the full troubleshooting guide covering installation, environment setup, runtime errors, and git workflow issues, see TROUBLESHOOTING.md.
Refer to ROADMAP.md which classifies projects into Beginner (foundational ML & EDA), Intermediate (Neural Networks, NLP, Web Apps), and Advanced (Computer Vision, Reinforcement Learning, Transformers).
Ensure your virtual environment is active and install common data science packages:
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
If the project folder contains a local requirements.txt, install it directly using:
pip install -r requirements.txt
Jupyter Notebooks (.ipynb) are preferred for project submissions as they combine code, visualization outputs, and explanations. Ensure all notebook cells are executed before committing.
Please read our Code of Conduct before contributing.
Thanks to all these amazing people who made ML-CaPsule possible! π
Niket Kumar Dheeryan Author π» | Abhishek Sharma π» | Sakalya100 π» | Kaustav Roy π» | Soumayan Pal π» | Komal Gupta π» |
Manu Varghese π» | Abhishek Panigrahi π» | Padmini Rai π» | psyduck1203 π» | Rutik Bhoyar π» | Ayushi Shrivastava π» |


This project is licensed under the MIT License.
Feel free to create issues, fix bugs, and contribute. Join our community!
Built with β€οΈ by Niketkumardheeryan and contributors
Start with beginner-friendly projects, then move to intermediate and advanced ones as your confidence grows. The table below adds quick difficulty and time guidance for each project.