Crop_recommendation added

Recommendation Systems/Crop_recommendation System/README.md

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+# **Crop Recommendation System 🌾**
+
+This project is a machine learning-powered tool that suggests the most suitable crop based on environmental parameters such as **temperature, humidity, pH, and rainfall**. It aims to help farmers make data-driven decisions, boosting productivity and sustainability.  
+
+## **Features**  
+- **Multiple ML Models**: Utilizes Logistic Regression, Naive Bayes, SVM, Random Forest, and more.  
+- **Flask-based Web App**: User-friendly interface for real-time crop recommendations.  
+- **Scalable Solution**: Adaptable for different regions and climates.  
+
+## **Tech Stack**  
+- **Python**, **Scikit-Learn** for machine learning  
+- **Flask** for web application  
+- **Pickle** for model persistence  
+- **Jupyter Notebook** for development  
+
+## **How to Run the Application**  
+1. **Clone the repository**:  
+   ```bash
+   git clone <repo_url> && cd crop-recommendation-system
+   ```
+
+2. **Install dependencies**:  
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Train the Model (Optional)**:  
+   Use `crop_main.ipynb` to train the model or load the existing one.  
+   ```python
+   import pickle
+   model = pickle.load(open('model.pkl', 'rb'))
+   ```
+
+4. **Run the Flask App**:  
+   ```bash
+   python app.py
+   ```
+
+
+5. **Sample Input Example**:
+   ```
+   N = 10, P = 10, K = 10  
+   Temperature = 15.0°C  
+   Humidity = 80%  
+   pH = 4.5  
+   Rainfall = 10 mm
+   ```
+
+## **Troubleshooting**  
+- **Pickle Load Error**: Ensure the **same scikit-learn version** used for training is installed.  
+- **Alternative with Joblib**:  
+   ```python
+   import joblib
+   model = joblib.load('model.pkl')
+   ```
+