🌾 Crop Recommendation System

A machine learning project that recommends the best crop to grow based on soil and environmental conditions.

📋 About

This project uses machine learning to help farmers choose the right crop based on:

• Soil nutrients (NPK values)
• Temperature & Humidity
• pH level
• Rainfall

Dataset: 2,200 samples covering 22 different crops

🎯 Results

Model	Accuracy
Gaussian Naive Bayes	99.55% 🥇
Support Vector Machine	99.55% 🥇
Random Forest	99.32%
XGBoost	99.09%

🚀 Quick Start

Install Dependencies

pip install -r requirements.txt

Run the Notebook

jupyter notebook crop_recommendation.ipynb

📊 Features Used

• N: Nitrogen content
• P: Phosphorus content
• K: Potassium content
• Temperature: °C
• Humidity: %
• pH: Soil pH level
• Rainfall: mm

🌱 Crops Supported (22 types)

Rice, Wheat, Maize, Cotton, Jute, Coffee, Apple, Banana, Mango, Grapes, Watermelon, Orange, Papaya, Coconut, Chickpea, Lentil, Blackgram, Mungbean, Mothbeans, Pigeonpeas, Kidneybeans, Pomegranate

🛠️ Technologies

• Python 3.8+
• scikit-learn
• pandas, numpy
• matplotlib, seaborn
• XGBoost, LightGBM, CatBoost

📁 Files

• crop_recommendation.ipynb - Main notebook with all models
• EM(1).ipynb - Ensemble methods experiments
• Crop_recommendation.csv - Dataset

⭐ Star this repo if you find it helpful!

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Machine Learning for Agriculture - Making farming smarter 🌾