🌱 Crop Recommendation System (Machine Learning Project)

A Machine Learning project that predicts the most suitable crop to cultivate based on soil nutrients and climatic conditions.

This project trains and compares multiple classification algorithms to determine the best-performing model for crop prediction.

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📌 Project Objective

To build a Machine Learning model that recommends the optimal crop using the following input parameters:

• Nitrogen (N)
• Phosphorous (P)
• Potassium (K)
• Temperature
• Humidity
• pH
• Rainfall

The goal is to assist in data-driven agricultural decision-making and support farmers in selecting the most suitable crop for given environmental conditions.

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🛠️ Technologies Used

• Programming Language: Python 3
• Libraries:
  • NumPy
  • Pandas
  • Scikit-learn
  • XGBoost
  • Matplotlib
  • Seaborn
  • Pickle (for model serialization)
• Environment: Jupyter Notebook

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📊 Dataset Information

• Dataset File: Crop_recommendation.csv
• Located inside the /data directory.

The dataset contains seven agricultural parameters:

Parameter	Description
N	Nitrogen content in soil
P	Phosphorous content in soil
K	Potassium content in soil
Temperature	Temperature (°C)
Humidity	Relative Humidity (%)
pH	Soil pH value
Rainfall	Rainfall (mm)

Each record corresponds to a specific crop recommendation.

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🧠 Machine Learning Models Implemented

The following classification models were trained and evaluated:

• Logistic Regression
• Decision Tree Classifier
• Gaussian Naive Bayes
• Random Forest Classifier
• XGBoost Classifier

All trained models are saved in the /models directory as .pkl files.

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📈 Model Evaluation

The project includes:

• Data preprocessing
• Train-test split
• Accuracy comparison
• Model performance evaluation

📌 Result:
The Random Forest Classifier achieved the highest accuracy among the tested models on this dataset.

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📂 Project Structure

Crop_Recommendation/
│
├── data/
│   └── Crop_recommendation.csv
│
├── models/
│   ├── DecisionTree.pkl
│   ├── LogisticRegression.pkl
│   ├── NBClassifier.pkl
│   ├── RandomForest.pkl
│   └── XGBoost.pkl
│
├── notebooks/
│   └── code.ipynb
│
├── requirements.txt
├── .gitignore
└── README.md

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/Harshv2608/Crop_Recommendation.git
cd Crop_Recommendation

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2️⃣ Create Virtual Environment

python -m venv .venv

Activate the environment:

Windows

.venv\Scripts\activate

Mac/Linux

source .venv/bin/activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Run Jupyter Notebook

jupyter notebook

Then open:

notebooks/code.ipynb

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🔍 How to Use a Saved Model

Example of loading a trained model:

import pickle

model = pickle.load(open("models/RandomForest.pkl", "rb"))

sample = [[90, 40, 40, 20, 80, 6.5, 200]]
prediction = model.predict(sample)

print("Recommended Crop:", prediction[0])

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🚀 Future Improvements

• Add feature importance visualization
• Add confusion matrix analysis
• Perform hyperparameter tuning
• Build a Flask/Streamlit web interface
• Deploy as a cloud-based application
• Add cross-validation and performance metrics (Precision, Recall, F1-score)

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👨‍💻 Author

Harsh Vardhan

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📜 License

This project is open-source and available under the MIT License.