Machine Learning Projects Repository

Welcome to the Machine Learning Projects Repository! This repository contains a collection of machine learning projects covering various domains and techniques. Each project is designed to showcase different aspects of machine learning, from natural language processing to image classification and financial prediction.

Projects

1. AI ChatBot

• Description: An AI-powered chatbot that uses natural language processing techniques to understand and respond to user queries.
• Repository: AI_ChatBot

2. Credit Card Fraud Detection

• Description: A machine learning model that detects fraudulent credit card transactions to prevent financial fraud.
• Repository: Credit_Card_Fraud_Detection

3. Genetic Algorithm

• Description: Implementation of genetic algorithm techniques for optimization problems, inspired by natural selection and genetics.
• Repository: GeneticAlgorithm

4. Image Classifier

• Description: A neural network-based image classifier trained on a dataset to recognize and classify different objects or categories in images.
• Repository: Image_Classifier

5. Neural Network

• Description: A basic neural network implementation for various tasks such as classification, regression, or prediction.
• Repository: Neural_Network

6. Reading Text from Images using TesseractOCR

• Description: Utilizes TesseractOCR library to extract text from images, useful for applications like optical character recognition (OCR).
• Repository: Reading_Text_from_Images_using_TesseractOCR

7. Stock Price Predictor

• Description: Predicts stock prices using machine learning models, enabling investors to make informed decisions in financial markets.
• Repository: Stock_Price_Predictor

Usage

1. Clone or download the individual repositories for the projects you're interested in.
2. Follow the instructions provided in each project's README.md file to set up and run the project.
3. Explore the code, experiment with parameters, and learn from the implementation details.
4. Contribute to the projects by providing feedback, reporting issues, or submitting pull requests.

Note

• Each project may have specific dependencies, datasets, or setup instructions. Please refer to the respective project's README.md for detailed information.
• Feel free to reach out for any questions or collaboration opportunities.
• Have fun exploring and learning from these machine learning projects!