# 🌱MLCrop Recommender - A Crop Recommendation System

This project implements a **Crop Recommendation System** that suggests suitable crops based on environmental conditions (e.g., **nitrogen**, **phosphorus**, **potassium**, **temperature**, **humidity**, **pH**, and **rainfall**).  
It compares the performance of multiple machine learning models to identify the best-performing approach.

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## 🚀 Features

✅ **Crop recommendation based on soil and climate data**  
✅ **Comparison of multiple ML algorithms:**  
- 🌳 **Random Forest**  
- 🌲 **Decision Tree**  
- 📌 **K-Nearest Neighbors (KNN)**  
- 📈 **Support Vector Machine (SVM)**  
- 🔑 **Logistic Regression**  
- 🕊 **Naive Bayes**  
- ⚡ **XGBoost**  
- 🤖 **Multi-Layer Perceptron (MLPClassifier)**  

✅ **Model evaluation metrics:**  
- 🎯 **Accuracy**  
- 📊 **Precision**  
- 🔄 **Recall**  
- 🧠 **F1 Score**  
- 🚀 **AUC-ROC**  
- 📉 **Log Loss**  

✅ **Flask web interface** for user-friendly crop prediction  

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## 💻 Tech Stack

- **Python**
- **Flask**
- **Scikit-learn**
- **XGBoost**
- **Pandas**
- **NumPy**

### Dissertation work submitted as a part of MCA degree by **Abhiram Swarnatt 23PCOMP001**
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