https://github.com/ovuiproduction/crop-price-prediction-using-random-forest
Machine learning model using the Random Forest algorithm to predict crop prices and analyze future price trends.
https://github.com/ovuiproduction/crop-price-prediction-using-random-forest
crop-price-prediction flask future-trends machine-learning python random-forest-regression web-application
Last synced: 3 months ago
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Machine learning model using the Random Forest algorithm to predict crop prices and analyze future price trends.
- Host: GitHub
- URL: https://github.com/ovuiproduction/crop-price-prediction-using-random-forest
- Owner: ovuiproduction
- Created: 2023-11-25T08:04:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-27T06:35:03.000Z (9 months ago)
- Last Synced: 2024-08-27T07:47:02.587Z (9 months ago)
- Topics: crop-price-prediction, flask, future-trends, machine-learning, python, random-forest-regression, web-application
- Language: Jupyter Notebook
- Homepage: https://github.com/ovuiproduction/Crop-Price-Prediction-Using-Random-Forest
- Size: 12.7 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- 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)
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 : csv files for ML Model , MongoDB.
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
cd src
python app.py
```
### After running command successfully project is active on this link
```
http://127.0.0.1:5000
```
## Run Locally
Clone the project
```
git clone https://github.com/ovuiproduction/Crop-Price-Prediction-Using-Random-Forest.git
```
Go to the project directory
```
cd src
```
Install dependencies
```
pip install numpy,pandas,matplotlib,scikit-learn,scipy,flask;
```
Start the server
```
python app.py
```
## Contributors
1. Onkar Waghmode
2. Shripad Wattamwar
3. Atharva Wagh
4. Aditya Zite