swarajkumarsingh/linear-regression-basic

Basic Linear regression model using Pytorch, to predict number of crops growth with rainfall, temperature, humidity as model's parameter

Crop Prediction Using Simple Linear Regression with PyTorch

This repository contains a simple linear regression model built with PyTorch to predict crop yields based on rainfall, temperature, and humidity.

Table of Contents

• Introduction
• Dataset
• Model Architecture
• Training
• Installation
• Usage
• Results
• Contributing
• License

Introduction

Predicting crop yields is crucial for efficient agricultural planning and management. This project uses a simple linear regression model implemented in PyTorch to predict crop yields based on key environmental parameters: rainfall, temperature, and humidity.

Dataset

The dataset used in this project includes historical data on crop yields and corresponding values for rainfall, temperature, and humidity. Ensure your dataset is in a CSV format and follows this structure: [ ] Rainfall [ ] Temperature [ ] Humidity [ ] Crop_yield

Model Architecture

The model is a simple linear regression model with the following structure:

• Input layer: 3 neurons (rainfall, temperature, humidity)
• Output layer: 1 neuron (predicted crop yield)

Training

The training loop includes:

• Data loading and preprocessing
• Forward pass
• Loss calculation using Mean Squared Error (MSE)
• Backward pass and optimization using Stochastic Gradient Descent (SGD)

Installation

1. Clone the repository:

   git clone https://github.com/swarajkumarsingh/linear-regression-basic.git ll_model
   cd ll_model

Usage

1. Prepare your dataset and place it in the data/ directory.
2. Train the model by running:

python train.py

Results

After training, the model will output the predicted crop yields. You can evaluate the model's performance using metrics like Mean Squared Error (MSE).

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or enhancements.

License

This project is licensed under the MIT License. See the LICENSE file for details.