🧠 ML_PROJECTS

A collection of machine learning and data science projects, each designed to solve real-world problems using modern ML techniques. This repository showcases a variety of applications, including recommendation systems, healthcare assistants, crop and fertilizer prediction, and regression analysis.

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📁 Project Overview

1. 🌱 Crop and Fertilizer Recommendation

• Purpose: Recommends optimal crops and fertilizers based on soil and environmental data.
• Features: Pre-trained models, interactive notebooks, web app interface.
• Main files: app.py, Crop_Prediction.ipynb, Fertilizer_recommendation.ipynb
• Data: data/Crop_recommendation.csv, data/Fertilizer Prediction.csv
• Models: model/crop_model.sav, model/fertilizer_model.sav

2. 🏥 Healthcare Question-Answering App

• Purpose: Answers health-related questions using a curated knowledge base and custom functions.
• Features: Extensible Q&A logic, modular design, ready for CLI or API.
• Main files: app.py, knowledege_base.py, src/functions.py, src/prompt.py

3. 📈 Linear Regression from Scratch

• Purpose: Educational notebook demonstrating linear regression implementation without libraries.
• Main file: main.ipynb

4. 🏠 ML Real Estate Prediction

• Purpose: Predicts real estate prices using machine learning models.
• Features: Data analysis, model training, and evaluation.
• Main files: model.py, data.csv, housing.data, housing.names
• Documentation: README.md, requirements.txt

5. 🎬 Hybrid Movie Recommendation System

• Purpose: Recommends movies using a hybrid of content-based and collaborative filtering.
• Features: Streamlit web app, modular ML code, model persistence.
• Main files: app.py, src/data/load_data.py, src/models/recommender.py
• Documentation: README.md, requirements.txt, pyproject.toml

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🏗️ Directory Structure

ML_PROJECTS/
├── Crop and fertilizer Recomendation/
├── Helthcare-app/
├── LINEAR_REGRESSION_FROM_SCRATCH/
├── ML_real_state_prediction/
├── Recommendation-app/
└── Readme.md

Each subdirectory contains its own code, data, models, and documentation.

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🛠️ Technologies Used

• Python 3.8+
• scikit-learn
• pandas
• numpy
• streamlit
• Jupyter Notebook
• Custom Python modules

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📋 Prerequisites

• Python 3.8 or higher
• pip package manager

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🔧 Installation

Clone the repository:

git clone <repository-url>
cd ML_PROJECTS

For each project, refer to its own README for setup and usage instructions.

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🚀 Usage

• Crop and Fertilizer Recommendation:
  Run app.py or open notebooks for interactive analysis.
• Healthcare App:
  Run app.py for CLI or integrate with an API.
• Linear Regression:
  Open main.ipynb in Jupyter or VS Code.
• Real Estate Prediction:
  Run model.py or follow notebook instructions.
• Movie Recommendation:
  Run streamlit run app.py for the web interface.

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🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Open a Pull Request

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👤 Author

Pawan Parida
Email: 13zero7two005@gmail.com
GitHub: @zer-art

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📚 References

• scikit-learn Documentation
• Streamlit Documentation
• Pandas Documentation
• Jupyter Documentation

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⭐ If you found these projects helpful, please give the repository a star!