🌟 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.

📈 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

🗂️ 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.

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

& 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.

📜 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 using the following command:

   git clone https://github.com/Niketkumardheeryan/ML-CaPsule

3. Create a Branch: Create a new branch for your changes:

   git checkout -b my-feature

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

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

   git commit -m "Add new feature"

6. Push Changes: Push your changes to your forked repository:

   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 💻

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