rakteem007 / crop_prediction_system Goto Github PK

Project Title: Crop Yield Prediction Using Machine Learning

Overview: This project aims to develop a machine learning-based system for predicting crop yields. By leveraging historical crop data, weather patterns, soil information, and other relevant factors, the system will provide accurate predictions of crop yields for different regions. This README provides an overview of the project, instructions for setup, usage guidelines, and additional resources for further exploration.

Setup Instructions:

Requirements:

Python Required Python libraries: scikit-learn, pandas, numpy, matplotlib, seaborn, etc. Dataset: Obtain the historical crop data, weather data, and soil information relevant to your region or use publicly available datasets. Installation:

Clone the project repository from GitHub: git clone [https://github.com/Rakteem007/Crop_Prediction_System.git] Navigate to the project directory: cd Crop_Prediction_System Install the required Python libraries: pip install -r requirements.txt

Data Preprocessing:

Prepare the dataset by cleaning and preprocessing the data. Merge crop data, weather data, and soil information into a single dataset. Handle missing values, encode categorical variables, and perform feature scaling if necessary. Model Training:

Split the dataset into training and testing sets. Choose appropriate machine learning algorithms such as linear regression, decision trees, random forests, or neural networks for training. Train the models using the training data. Model Evaluation:

Evaluate the trained models using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or R-squared. Fine-tune the models by adjusting hyperparameters for better performance. Prediction:

Use the trained models to make predictions on new data. Visualize the predicted crop yields using graphs or charts.

Contributing: Contributions to the project are welcome! If you have any ideas for improvement, bug fixes, or new features, feel free to submit a pull request.