# Smart Crop Recommendation System
The Smart Crop Recommendation System is designed to help farmers choose the best crop to grow based on various factors. This README provides detailed instructions on how to set up, run, and contribute to the project using terminal commands.
## Table of Contents
1. [Clone the Repository](#clone-the-repository)
2. [Create an Environment](#create-an-environment)
3. [Activate the Environment](#activate-the-environment)
4. [Install Dependencies](#install-dependencies)
5. [Generate Model](#generate-model)
6. [Run the Application](#run-the-application)
7. [License](#license)
## Clone the Repository
First, clone the repository to your local machine:
```sh
git clone https://github.com/Rvssm-Sandeep/Udyog-Saarathi-Application.git
cd Udyog-Saarathi-Application
Create a project folder and a virtual environment within it:
mkdir myproject
cd myproject
py -3 -m venv .venv
Before you work on your project, activate the corresponding environment:
On Windows:
.venv\Scripts\activate
On macOS and Linux:
source .venv/bin/activate
Install the necessary dependencies:
pip install flask
pip install pandas
pip install scikit-learn
Run the Jupyter Notebook file to generate the .pkl
file required for the model:
- Open Jupyter Notebook:
jupyter notebook
- Run the cells in your
.ipynb
file to train the model and save it as a.pkl
file.
Finally, run the application:
python app.py
Click on the development server link provided in the terminal to open the application in your browser.
That's it!
This project is licensed under the MIT License - see the LICENSE file for details.
This single README file includes all the necessary steps to clone the repository, create and activate a virtual environment, install dependencies, generate the model, and run the application. It ensures that anyone following the instructions will be able to set up and run your Smart Crop Recommendation System successfully.