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ML Mini Projects

Welcome to my ML-Mini-Projects repository! This repository is a collection of machine learning projects that I have developed, designed, and implemented to solve various real-world problems using data and machine learning algorithms. From predicting diseases and detecting fraud, to identifying handwritten digits and predicting rainfall, this repository demonstrates the versatility of machine learning techniques across a range of domains.

Projects Overview

1. Heart Disease Predictor

  • A predictive model using machine learning to detect the likelihood of heart disease based on various health features.
  • Includes a Flask web application for real-time prediction.

2. Disease Predictor

  • An ML model designed to predict various diseases based on input symptoms and parameters.

3. Credit Card Fraud Detector

  • An algorithm to detect fraudulent credit card transactions using data science techniques.

4. Fake News Detector

  • A machine learning model for detecting fake news articles based on textual analysis.

5. Rainfall Predictor

  • A model to forecast rainfall based on historical weather data.

6. Housing Price Predictor

  • A regression model to predict house prices based on various features like location, size, etc.

7. Calories Burnt Predictor

  • A model predicting the number of calories burnt during a workout based on various parameters.

8. Human Activity Recognizer

  • A machine learning model to recognize human activities from sensor data.

9. Crop Yield Predictor

  • Predicts crop yields based on environmental and farming parameters.

10. Solar Irradiance Predictor

  • Predicts solar irradiance based on weather patterns and historical data.

11. Titanic Survivor Classifier

  • A model to predict the survival of passengers aboard the Titanic.

12. PUBG Game Predictor

  • Predicts the outcome of PUBG matches based on various in-game parameters.

13. MNIST Handwritten Digit Recognizer

  • A model to recognize handwritten digits from the famous MNIST dataset.

Getting Started

  1. Clone the repository to your local machine:
    git clone https://github.com/tiwariar7/ML-Mini-Projects.git
    

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