Crop Recommendation System:

This project focuses on analyzing agricultural data to recommend suitable crops for various regions based on soil, climate, and other environmental factors. The goal is to help farmers make informed decisions about which crops to plant to optimize yield and resource use.

Overview

The Colab notebook Crop_analysis_and_prediction.ipynb provides a detailed analysis of crop recommendations. It includes data exploration, preprocessing, feature selection, model training, and prediction. The system aims to suggest the best crops based on input parameters.

Getting Started

1. Open the Colab notebook:

   Open the notebook here.

2. Install necessary libraries:

   Run the following commands in a Colab cell to install required libraries:

   pip install numpy pandas scikit-learn matplotlib seaborn

3. Upload the dataset:

   Upload the dataset file(s) needed for the analysis to the Colab environment. Ensure the dataset contains relevant features like soil quality, climate data, and crop yield information.

4. Run the notebook:

   Execute the cells in the notebook sequentially to perform the analysis and view the recommendations.

Usage

The notebook covers the following key areas:

• Data Exploration: Analyzing the dataset to understand its structure and key features.
• Data Preprocessing: Cleaning and preparing the data, including handling missing values and feature engineering.
• Feature Selection: Identifying important features that influence crop suitability.
• Model Training: Training machine learning models to predict suitable crops based on input features.
• Prediction: Using the trained model to make crop recommendations.
• Visualization: Creating plots to visualize data distributions, feature importance, and prediction results.

Project Structure

• Crop_analysis_and_prediction.ipynb: The main Colab notebook containing the analysis, code, and predictions.
• requirements.txt: (Optional) List of required Python libraries for the project.
• data/: Directory where the dataset file(s) should be uploaded.

Acknowledgements

• Dataset Source: [kaggle]
• Libraries used: NumPy, Pandas, scikit-learn, Matplotlib, Seaborn

Contact

For questions or feedback, please contact [[email protected]].