bahsulayman689-hash/gambia-crop-recommendation-ml
Domain:
agricultureRecord type:
softwaremodel
Creator:
bah
Host:
# gambia-crop-recommendation-ml
# 🌾 Smart Crop Recommendation System for The Gambia
A data-driven web application built with Streamlit and Machine Learning to optimize agricultural crop selection in The Gambia. By evaluating core soil nutrition and regional climatic parameters, the system dynamically recommends the most viable crop to cultivate for maximum harvest yield.
## 👨💻 Developer
* **Sulayman Bah** (Machine Learning & Agricultural Technology Engineer)
## 🔗 Live Demo
▶️ **Launch the Live Web Application Here**
*(Note: Replace this link with your actual URL once Streamlit completes your deployment)*
## 📊 Features
* **Wide-Screen UI:** Optimized interactive layouts built for both desktops and mobile browsers.
* **Smart Parameter Controls:** Fine-tuned inputs for Soil Nutrients (NPK, pH) and Weather Conditions (Temperature, Humidity, Rainfall).
# 🌾 Smart Crop Recommendation System for The Gambia
## ⚙️ Project Architecture
```text
├── crop_recommendation.csv # Raw Training Dataset
├── loan.py / train.py # ML Model Training Pipeline Script
├── best_crop_recommendation_model.pkl # Pickled Deployment Package (Model, Scaler, Encoder)
├── app.py # Live Streamlit Web App Script
├── requirements.txt # Cloud Dependency Configurations
└── README.md # Documentation Setup
```
## 🚀 Local Deployment Setup
1. **Clone or download this repository to your machine:**
```bash
git clone github.com
cd gambia-crop-recommendation-ml
```
2. **Install the environment dependencies:**
```bash
pip install -r requirements.txt
```
3. **Train the ML model pipeline to generate the production package:**
```bash
python train.py
```
4. **Launch the live interactive web app locally:**
```bash
streamlit run app.py
```
## 💡 Technologies Used
* **Python 3.13**
* **Scikit-Learn** (LabelEncoding, StandardScaler, RandomForestClassifier)
* **Streamlit Framework** (UI Architecture)
* …