🌟 ML-Capsule: Hands-on ML from Basic to Advance 🌟

Welcome to ML-Capsule!!! This repository is a comprehensive collection of machine learning projects and resources, ranging from beginner to advanced levels. It covers a variety of topics, from basic machine learning concepts to deep learning, natural language processing, and much more.

## 📑 Table of Contents

• 📈 Why Machine Learning?
• 📚 Pre-requisites
• 🗂️ Topics Covered
• 📦 Dataset Resources
• 🗂️ Project Directory
• 📂 Project Descriptions
• 📜 Summary
• 🔗 Useful URLs
• 🚀 Get Started
• ⚙️ Contribution Guidelines
• 📖 Code of Conduct
• 📝 License
• ✨ Contributors
• ❤️ Stargazers
• ❤️ Forkers

📈 Why Machine Learning?

Machine learning is a technique to analyze data that automates the process of building analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Importance of Machine Learning

Machine learning is crucial because it provides enterprises with insights into customer behavior and business operational patterns, and supports the development of new products. Leading companies like Facebook, Google, and Uber integrate machine learning into their operations, making it a significant competitive differentiator.

📚 Pre-requisites

• Python IDE: Install from python.org
• Learn Python: If you're new to Python, start learning from W3Schools

🎨 Getting Started with R Language & RStudio

What is R?

R is an open-source programming language and free software environment designed specifically for statistical computing, data analytics, and scientific research. It is widely used by data scientists and statisticians for data manipulation, calculation, and graphical display.

How to Install R and RStudio IDE

To start setting up your environment for R-based data science models, follow these steps:

1. Install R Environment:

   • Go to the official CRAN repository: Download R from CRAN
   • Choose your respective operating system (Windows, macOS, or Linux) and download the latest binary installer.
   • Run the installer setup and follow the default configuration prompts.

2. Install RStudio Desktop IDE:

   • Go to the official Posit website: Download RStudio Desktop
   • Scroll down to the installer section and download the free version for your OS.
   • Install the executable/package file. RStudio will automatically detect your local R installation.

📚 Recommended Video Tutorials for Beginners

To master R programming from basic to advanced levels, check out these comprehensive video resources:

• 🎥 R Programming Full Course for Beginners (freeCodeCamp) - A complete, hands-on masterclass covering vectors, data frames, and statistical operations.
• 🎥 R Programming Tutorial - Learn the Basics (Edureka) - Excellent fundamental guide focusing on syntax, variables, and IDE setup.
• 🎥 Data Science with R Full Course (Simplilearn) - Comprehensive track covering basic programming up to advanced data analysis and visualization.
• 🎥 Introduction to R for Data Science (DataCamp) - Quick interactive overview perfect for complete beginners looking to dive into analytics.

🗂️ Topics Covered

1. Extracting Data

Extraction refers to methods of constructing combinations of variables to accurately describe the data.

• Web Scraping: Library used - Beautiful Soup, to extract data from web pages.

2. Visualization

Data visualization places data in a visual context to expose patterns, trends, and correlations.

• Libraries Used: Seaborn, pandas, matplotlib

3. Feature Selection

The process of selecting relevant features for use in a model to increase accuracy and performance.

• Library Used: scikit-learn
• Learn More: Feature Selection

4. Basic Concepts of Statistics

• Analytics Types: Descriptive, Diagnostic, Predictive, Prescriptive

• Probability: Conditional, Independent Events, Bayes’ Theorem

• Central Tendency: Mean, Mode, Variance, Skewness, Kurtosis, Standard Deviation

• Variability: Range, Percentiles, Quantiles, IQR, Variance

• Relationships: Causality, Covariance, Correlation

• Probability Distribution: PMF, PDF, CDF

• Hypothesis Testing: Null and Alternative Hypothesis, Z-Test, T-Test, ANOVA, Chi-Square Test

• Regression: Linear Regression, Multiple Linear Regression

  

5. Data Science

• Data science is a dynamic and multidisciplinary field dedicated to extracting insights and solving complex problems through data.

• Multidisciplinary investigations leverage knowledge from various domains, such as economics, biology, and engineering, to create comprehensive solutions by integrating diverse perspectives.

• Models and methods for data are at the heart of data science, employing statistical techniques and advanced machine learning algorithms to uncover patterns, make predictions, and inform decisions.

• Pedagogy in data science is concerned with the development and implementation of effective teaching practices and educational tools to ensure that learners acquire the necessary skills and knowledge.

• Computing with data involves the use of computational tools and technologies for managing, processing, and analyzing large datasets, including skills in programming and database management.

• The theory behind data science provides the mathematical and statistical foundations necessary for developing and applying various methods. Finally, tool evaluation focuses on assessing and selecting the best software, programming languages, and platforms based on performance and usability to ensure effective data analysis.

• Together, these areas contribute to the robust and evolving nature of data science, driving innovation and informed decision-making across multiple sectors.

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📦 Dataset Resources

🗄️ Click to explore all datasets — Classification, Regression, NLP, Computer Vision & more

🟢 Classification

#	Dataset Name	Short Description	Difficulty	Download Link
1	Titanic - Machine Learning from Disaster	Predict passenger survival based on age, sex, class, and other features.	⭐ Beginner	Kaggle
2	Iris Flower Dataset	Classify iris flowers into 3 species using 4 simple features.	⭐ Beginner	UCI ML Repo
3	Breast Cancer Wisconsin	Binary classification to detect malignant vs. benign tumors.	⭐ Beginner	UCI ML Repo
4	Heart Disease Dataset	Predict heart disease from clinical features like cholesterol, blood pressure, etc.	⭐⭐ Intermediate	UCI ML Repo
5	Pima Indians Diabetes	Predict diabetes onset in Pima Indian women using health metrics.	⭐ Beginner	Kaggle
6	Adult Income (Census Income)	Predict whether a person earns >$50K/year based on census demographic data.	⭐⭐ Intermediate	UCI ML Repo
7	Bank Marketing Dataset	Predict if a client will subscribe to a term deposit.	⭐⭐ Intermediate	UCI ML Repo
8	Wine Quality Dataset	Classify wines as good or bad based on physicochemical properties.	⭐ Beginner	UCI ML Repo

🔵 Regression

#	Dataset Name	Short Description	Difficulty	Download Link
1	Boston Housing Dataset	Predict housing prices based on crime rate, rooms, accessibility, etc.	⭐ Beginner	Kaggle
2	California Housing Prices	Predict median house values for California districts using census data.	⭐ Beginner	Kaggle
3	House Prices: Advanced Regression	Predict final sale price of homes with 79 explanatory variables.	⭐⭐ Intermediate	Kaggle
4	Auto MPG Dataset	Predict fuel efficiency of cars from engine and design attributes.	⭐ Beginner	UCI ML Repo
5	Student Performance Dataset	Predict student exam scores based on study time and social factors.	⭐ Beginner	UCI ML Repo
6	Medical Cost Personal Dataset	Predict medical insurance charges based on age, BMI, smoking status.	⭐ Beginner	Kaggle
7	Bike Sharing Demand	Predict hourly bike rental counts based on weather and seasonal settings.	⭐⭐ Intermediate	Kaggle

🟡 Natural Language Processing (NLP)

#	Dataset Name	Short Description	Difficulty	Download Link
1	SMS Spam Collection	Classify SMS messages as spam or ham.	⭐ Beginner	UCI ML Repo
2	IMDB Movie Reviews	Sentiment analysis on 50,000 movie reviews. Classic NLP benchmark.	⭐ Beginner	Kaggle
3	Twitter US Airline Sentiment	Classify tweets about US airlines into positive, negative, or neutral.	⭐⭐ Intermediate	Kaggle
4	Amazon Product Reviews	Multi-class sentiment analysis on product reviews across different categories.	⭐⭐ Intermediate	Kaggle
5	Fake News Dataset	Detect whether a news article is real or fake using NLP techniques.	⭐⭐ Intermediate	Kaggle
6	AG News Topic Classification	Categorize news articles into 4 topics: World, Sports, Business, Sci/Tech.	⭐⭐ Intermediate	Hugging Face
7	Quora Question Pairs	Identify whether pairs of questions are semantically equivalent.	⭐⭐⭐ Advanced	Kaggle

🔴 Computer Vision / Image

#	Dataset Name	Short Description	Difficulty	Download Link
1	MNIST Handwritten Digits	Classify handwritten digits (0–9). The "Hello World" of computer vision.	⭐ Beginner	Yann LeCun
2	Fashion-MNIST	Classify 10 categories of clothing items. A harder alternative to MNIST.	⭐ Beginner	GitHub
3	CIFAR-10	Classify 60,000 images into 10 categories.	⭐⭐ Intermediate	Official Site
4	Dogs vs. Cats	Binary classification of images as dogs or cats.	⭐⭐ Intermediate	Kaggle
5	Flowers Recognition	Classify images of flowers into 5 categories.	⭐ Beginner	Kaggle
6	Intel Image Classification	Classify natural scenes into 6 categories: buildings, forest, glacier, mountain, sea, street.	⭐⭐ Intermediate	Kaggle
7	Chest X-Ray Images (Pneumonia)	Detect pneumonia from chest X-ray images.	⭐⭐ Intermediate	Kaggle

🟣 Clustering / Unsupervised Learning

#	Dataset Name	Short Description	Difficulty	Download Link
1	Mall Customers Dataset	Segment customers based on age, income, and spending score.	⭐ Beginner	Kaggle
2	Credit Card Customer Segmentation	Cluster credit card users based on spending behavior.	⭐⭐ Intermediate	Kaggle
3	Wholesale Customers Dataset	Annual spending data of wholesale distributor clients.	⭐ Beginner	UCI ML Repo
4	World Happiness Report	Happiness scores and rankings across countries.	⭐ Beginner	Kaggle
5	Online Retail Dataset	Transactional data for market basket analysis.	⭐⭐ Intermediate	UCI ML Repo

🟠 Time Series / Forecasting

#	Dataset Name	Short Description	Difficulty	Download Link
1	Air Passengers Dataset	Monthly airline passenger counts (1949–1960). Intro to seasonality.	⭐ Beginner	GitHub
2	Stock Market Data	Historical daily stock prices for major companies.	⭐⭐ Intermediate	Kaggle
3	COVID-19 Dataset (Johns Hopkins)	Global COVID-19 confirmed cases, deaths, and recoveries.	⭐ Beginner	GitHub
4	Energy Consumption Dataset	Hourly power consumption data. Great for LSTM/Prophet forecasting.	⭐⭐ Intermediate	Kaggle
5	Jena Climate Dataset	~420,000 hourly weather readings. Perfect for LSTM sequence modeling.	⭐⭐⭐ Advanced	TF Tutorials

🔵 Recommendation Systems

#	Dataset Name	Short Description	Difficulty	Download Link
1	MovieLens Dataset (ml-100k)	100,000 movie ratings. Standard collaborative filtering benchmark.	⭐ Beginner	GroupLens
2	Book-Crossing Dataset	User ratings for 270,000+ books.	⭐⭐ Intermediate	Kaggle
3	Amazon Product Ratings	Explicit ratings for products across various Amazon categories.	⭐⭐ Intermediate	UCSD
4	Jester Jokes Dataset	Continuous ratings of 100 jokes by over 73,000 users. Good for collaborative filtering.	⭐⭐ Intermediate	UC Berkeley

🌐 Useful Dataset Platforms

Platform	Description	Link
Kaggle	Largest community of datasets + competitions	kaggle.com/datasets
UCI ML Repository	Classic academic datasets used in research	archive.ics.uci.edu
Google Dataset Search	Search engine for public datasets	datasetsearch.research.google.com
Hugging Face Datasets	NLP-focused datasets hub	huggingface.co/datasets
Papers With Code	Datasets tied to research papers	paperswithcode.com/datasets
data.gov	U.S. government open datasets	data.gov
OpenML	Automated machine learning datasets	openml.org

💡 Tip for Beginners: Start with Iris, Titanic, or MNIST — each teaches you a complete ML workflow in under 100 lines of code!

---

Available Projects

S.No	Projects	S.No	Projects	S.No	Projects	S.No	Projects
1.	Advanced Visualizations	2.	Alzheimer's Disease Predictor	3.	Analysis & Predict Black Friday Sale	4.	Anime Data Analysis and Prediction
5.	Artificial Neural Network from Scratch	6.	Association Rule Implementation	7.	Audio Classification	8.	Autism Identification System
9.	Automatic Summarization of Scientific Papers	10.	Basics of ML and DL	11.	Basics of Power BI	12.	Basics of Python
13.	Bidirectional LSTM	14.	Bird Species Classification Web App	15.	Bitcoin Price Prediction Web App	16.	Bitcoin Price Predictor
17.	Brain Tumor Detection	18.	Breast Cancer Detection using DL with Webapp	19.	CBT ChatBot	20.	COVID-19 Data Analysis
21.	Chatbot Using RASA	22.	Cheat Sheets	23.	Chi-Square Test	24.	Chicken Disease Classification
25.	Chronic Kidney Disease Prediction	26.	Class Imbalance Problem	27.	Classification Algorithms	28.	Cloud Details
29.	Clustering Algorithms	30.	Company Bankruptcy Using Unsupervised Learning	31.	Covid-19 Forecasting with Prophet	32.	Covid Third Wave Forecasting
33.	CrowdAI Plant Disease	34.	Crude Oil Forecasting	35.	Customer Segmentation USvAlgorithm	36.	Customer Segmentation using Machine Learning
37.	Dark Pattern Detection	38.	Data Cleaning Techniques	39.	Data Filling and Cleaning Techniques	40.	Deepfake Image Analyzer
41.	Defective Captcha Image Recognition	42.	Diabetes Prediction	43.	Different Types of Clustering	44.	Different Types of Feature Selection Techniques
45.	Different Types of Scaling Methods	46.	Diseases Prediction	47.	Driver Drowsiness Detection	48.	Duplicate Question Pair
49.	EDA and Perform Modelling on Ionosphere Dataset	50.	Email Classifier	51.	Emotion Recognition Based on NLP	52.	Eye Gaze Tracking & Attention Estimation
53.	Portuguese Bank Marketing

& many more.......

You can find All the Projects

Live Project -- https://github.com/Niketkumardheeryan/ML-CaPsule

📂 Project Descriptions

Here are some of the exciting projects featured in this repository:

1. Alzheimer's Disease Predictor
   A machine learning model to predict the likelihood of Alzheimer's disease based on patient data, using classification algorithms and feature selection techniques.

2. Chatbot Using RASA
   A conversational AI chatbot built with RASA, capable of handling various user queries and providing intelligent responses.

3. COVID-19 Forecasting with Prophet
   Utilize the Prophet library to forecast COVID-19 case trends and predict future outbreaks based on historical data.

4. Fake News Detection
   A project that uses NLP techniques to detect and classify fake news articles, employing various text processing and classification methods.

5. Handwritten Digit Recognition
   A deep learning model that recognizes handwritten digits using a Convolutional Neural Network (CNN) trained on the MNIST dataset.

6. Movie Genre Classification
   A machine learning model that predicts movie genres based on descriptions using text classification techniques and feature extraction.

7. Employee Attrition Prediction
   A predictive model that identifies employees at risk of leaving a company, using historical HR data and various classification algorithms.

8. Heart Disease Prediction
   A predictive model for diagnosing heart disease based on patient attributes, utilizing statistical and machine learning techniques to improve diagnosis accuracy.

9. Eye Gaze Tracking & Attention Estimation
   A real-time attention tracking system using the MediaPipe Face Mesh Tasks API, robust iris vector projections, and 3D Perspective-n-Point head pose estimation with a glowing telemetry HUD console.

10. Portuguese Bank Marketing
    A Supervised Machine Learning – Binary Classification project to predict if a client will subscribe to a term deposit based on demographic and campaign features.

📜 Summary

This repository offers a rich collection of machine learning and data science projects. It includes well-documented examples, practical projects, and extensive resources to help you understand and implement various machine learning techniques.

🔗 Useful URLs

• 8 Basic Statistics Concepts
• Coursera: Machine Learning with Python
• W3Schools: Python ML Getting Started
• freeCodeCamp: Machine Learning with Python
• Great Learning: Data Science

🚀 Get Started

This repository showcases a diverse collection of machine learning projects and data science algorithms, ranging from basic to advanced levels. It includes topics on machine learning, deep learning, SQL, NLP, object detection, classification, recommendation systems, chatbots, and much more.

🌟 Have a Look!

Give this project a ⭐ if you love it!

⚙️ Contribution Guidelines

• Check the Contribution Guidelines
• Take a look at the Existing Issues
• Create your Pull Request

Submitting a Pull Request

To submit your contributions, follow these steps:

1. Fork the Repository: Click the "Fork" button at the top right corner of the repository to create your own copy.

2. Clone Your Fork: Clone your forked repository to your local machine and navigate into the directory:

   git clone https://github.com/Niketkumardheeryan/ML-CaPsule
   cd ML-CaPsule
   git checkout -b my-feature

   4. Make Changes: Make your desired changes to the codebase.

3. Commit Changes: Commit your changes with a descriptive commit message:

   git commit -m "Add new feature"
   git push origin my-feature
   
   7. **Submit a Pull Request**: Go to your forked repository on GitHub and submit a pull request. Be sure to provide a detailed description of your changes and why they are necessary.
   

Project Directory Structure

The project directory is organized as follows:

• Projects: Contains subdirectories for individual projects, each with its own README.md file detailing project-specific information and instructions.
• Contributing.md: Provides guidelines for contributing to the repository.
• Code_of_Conduct.md: Outlines our community code of conduct and expectations for contributors.
• LICENSE: Specifies the license under which the repository is distributed.

📖 Code of Conduct

Please read our Code of Conduct.

📝 License

This project is licensed under the MIT License.

Feel free to create new issues, fix bugs, and contribute to our projects. Join our community and help us build amazing machine learning solutions!

Happy Coding! 👩‍💻👨‍💻

Some awesome Contributors ✨

Niket kumar Dheeryan (Author) 💻
Abhishek Sharma 💻	Sakalya100 💻	Kaustav Roy 💻	Soumayan Pal 💻	Komal Gupta 💻	Manu Varghese 💻
Abhishek Panigrahi 💻	Padmini Rai 💻	psyduck1203 💻	Rutik Bhoyar 💻	Ayushi Shrivastava 💻	Anshul Srivastava 💻
RISHAV KUMAR 💻	Megha0606 💻	Jagannath8 💻	Harshita Nayak 💻	ayushgoyal9991 💻	SurajPawarstar 💻
Sumit11081996 💻	Tanvi Bugdani 💻	Suyash Singh 💻	Abhinav Dubey 💻	Nisha Yadav 💻	Neeraj Ap 💻
Nishi 💻	shivani rana 💻

Stargazers ❤️

Forkers ❤️

🗂️ Project Directory

Explore the machine learning and data science projects available in this repository based on your domain interest and skill level.

🟢 Beginner Projects & Data Analysis

Project Name	Category	Difficulty	Description	Link
Anime Data Analysis	Data Analysis	Beginner	Analyze anime datasets and user trends through Exploratory Data Analysis (EDA).	View Project
Medical Cost Prediction	Machine Learning	Beginner	Predict medical insurance costs using regression models like Linear Regression and Random Forest.	View Project
Heart Disease Detection	Machine Learning	Beginner	Predict the presence of heart disease in patients using classification algorithms.	View Project
Water Potability	Machine Learning	Intermediate	Determine if water is safe for human consumption based on water quality metrics.	View Project

🔵 Deep Learning & Computer Vision

Project Name	Category	Difficulty	Description	Link
Alzheimer's Disease Predictor	Deep Learning	Intermediate	Predict Alzheimer's disease using structural MRI and clinical data models.	View Project
Yoga Pose Detection	Computer Vision	Intermediate	Real-time tracking and classification of different yoga poses using computer vision.	View Project
Speech-to-Image Generator	Deep Learning	Advanced	Generate visual images directly from speech audio inputs using deep neural networks.	View Project
Weapon Detection System	Computer Vision	Advanced	Detect weapons in images and video streams for security applications.	View Project

🟡 Natural Language Processing & Advanced ML

Project Name	Category	Difficulty	Description	Link
Toxic Comment Classifier	NLP	Intermediate	Classify and filter toxic, obscene, or threatening text data using NLP modules.	View Project
Currency Arbitrage with RL	Reinforcement Learning	Advanced	Detect currency arbitrage opportunities in financial markets using Reinforcement Learning.	View Project
Sales Prediction (Research)	Machine Learning	Advanced	Advanced sales prediction implementing complex models from research papers.	View Project

License

🔍 Notebook Health Check Bot

This repository uses an automated Notebook Health Check Bot to maintain code quality.

What it does:

• ✅ Automatically tests all Jupyter notebooks on every Pull Request
• 🔍 Detects missing imports and dependencies
• ⏱️ Identifies notebooks with timeout issues
• 🚨 Flags deprecated code patterns

Results:

• 📊 Scans all notebooks in the repository
• 📈 Provides detailed logs of notebook health status
• 🎯 Helps maintainers identify broken notebooks quickly

Recent Run:

• Found 16 notebooks
• ✅ 1 notebooks passed the health check
• All issues were successfully detected and reported