https://github.com/ovuiproduction/kisan-dhan
Crop Price Prediction Using Random Forest (Supervised Machine learning Algorithm)
https://github.com/ovuiproduction/kisan-dhan
crop croppriceprediction flask machine-learning mongodb-atlas random-forest
Last synced: about 1 month ago
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Crop Price Prediction Using Random Forest (Supervised Machine learning Algorithm)
- Host: GitHub
- URL: https://github.com/ovuiproduction/kisan-dhan
- Owner: ovuiproduction
- Created: 2024-08-06T15:43:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-17T14:44:01.000Z (over 1 year ago)
- Last Synced: 2025-03-01T03:49:11.996Z (over 1 year ago)
- Topics: crop, croppriceprediction, flask, machine-learning, mongodb-atlas, random-forest
- Language: Jupyter Notebook
- Homepage: https://github.com/ovuiproduction/Kisan-Dhan
- Size: 11.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Crop Price Prediction Using Random Forest (Supervised Machine learning Algorithm)
#### [Research Paper](https://internationalpubls.com/index.php/cana/article/view/762)
#### [Project Demo](https://www.youtube.com/watch?v=AkiO8RtKaps)
#### [Updated Version Demo](https://youtu.be/NM0VEcjNxeE)
This proposed system aims to enhance agricultural price prediction by analyzing a comprehensive dataset encompassing five years of historical price data. The primary focus is on evaluating the efficacy of machine learning algorithms, specifically Decision Trees and Random Forest, to accurately forecast agricultural commodity prices. Recognizing the significance of factors such as market demand, geopolitical events, government policies, and meteorological conditions like rainfall and temperature, the system aims to contribute to global food production, economic stability, and food security. By providing precise price forecasts, the system benefits farmers, insurance companies, and businesses involved in supply chain management. The approach involves a deep analysis of existing challenges and proposes a sophisticated solution to address them, ultimately contributing to the advancement of the agricultural sector.The system also offers a platform where farmers can view the crop sowing trends in different regions and decide which crop will give them maximum benefits. We provide a basic overview of the current crop sowing data, which shows which crops are planted by other farmers in which regions. This helps the farmers avoid low prices due to excessive crop production and serves as a crop sowing guide.
## Tech Stack
Frontend : Html5 , CSS , javascript , Flask Module.
DataBase : Mysql,csv files.
Machine learning : Jupyter , python.
Python Library :
1. numpy
2. pandas
3. matplotlib
4. scikit-learn
5. sciPy
Machine learning Algoritms :
1. linear Reggression
2. Decision tree
3. Random Forest
## Installation
Installation Required
prerequisite -
Python and pip must installed
check if python is download or not run this on commond prompt
python --version
``` for pip install run this two commond on cmd
curl https://bootst/rap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py
```
```for Installation of Libraries run this commond on cmd
pip install numpy,pandas,matplotlib,scikit-learn,scipy
```
```for Flask module intallation run this commond
pip install Flask
```
## Deployment
To deploy this project
```run on bash or terminal
python app.py
```
````After running command successfully project is active on this link
http://127.0.0.1:5000
```
## Documentation
[Documentation](https://drive.google.com/drive/folders/14cs2C_AFZbujImscB6uY-aX1RmSmF_tb?usp=sharing)
## Run Locally
Clone the project
```bash
git clone https://github.com/ovuiproduction/Crop_Price_Predictor_Using-Random-Forest-Supervised-Machine-Learning-.git
```
Go to the project directory
```bash
cd Crop_Price_Prediction
```
Install dependencies
```bash
pip install numpy,pandas,matplotlib,scikit-learn,scipy,flask;
```
Start the server
```bash
python app.py
```
## Contributors
-- Onkar Waghmode
-- Shripad Wattamwar
-- Atharva Wagh
-- Aditya Zite