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https://github.com/rahuldkjain/crop_prediction

ApnaAnaaj aims to solve crop value prediction problem in an efficient way to ensure the guaranteed benefits to the poor farmers. The team decided to use Machine Learning techniques on various data to came out with better solution. This solution uses Decision Tree Regression technique to predict the crop value using the data trained from authentic datasets of Annual Rainfall, WPI Index for about the previous 10 years. This implementation proved to be promising with 93-95% accuracy.
https://github.com/rahuldkjain/crop_prediction

crop crop-price-prediction dataset decision-tree-regression flask machine-learning multivariable price-prediction python rainfall wpi

Last synced: about 2 months ago
JSON representation

Host: GitHub
URL: https://github.com/rahuldkjain/crop_prediction
Owner: rahuldkjain
Created: 2019-03-02T05:50:40.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2019-04-23T08:16:09.000Z (over 5 years ago)
Last Synced: 2024-05-02T01:09:53.558Z (8 months ago)
Topics: crop, crop-price-prediction, dataset, decision-tree-regression, flask, machine-learning, multivariable, price-prediction, python, rainfall, wpi
Language: HTML
Homepage:
Size: 6 MB
Stars: 55
Watchers: 5
Forks: 27
Open Issues: 3
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

        # ApnaAnaaj (Crop_Prediction)

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/ApnaAnaajLogo.png)](https://github.com/rahuldkjain/Crop_Prediction)

## Features

  - Around 23 commodities(including all kind of crops) crop value forecasting

  - Crop detailed forecast upto next 12 months

  - Top Gainers and Losers of current time

  - Crop price prediction with 93-95% accuracy

  - Model trained on authenticated datasets provided by [data.gov.in](https://data.gov.in)

  - Detailed analysis of crop prices using tables and charts

  - Prediction done by using Decision Tree Regression techniques.

  - Annual Rainfall, WPI(Wholesale Price Index) datasets are used for training the model

  - User friendly UI made by using materializecss

 

### Tech

* [Python(3.0 or above)](https://www.python.org/)

* [Flask](http://flask.pocoo.org/)

* [Scikit-Learn](https://scikit-learn.org/)

* [MaterializeCSS](https://materializecss.com/)

* [Chart.js](https://www.chartjs.org/)

## Installation Guide

To install and run this webapp, you will need [Python(3.0 or above)](https://www.python.org/), and [pip](https://pypi.org/project/pip/) installed on your system

```sh

$ git clone https://github.com/rahuldkjain/Crop_Prediction.git

$ cd Crop_Prediction

$ pip install -r requirements.txt

$ python app.py

```

## Screenshots

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/Screenshot%20(23).png)](https://github.com/rahuldkjain/Crop_Prediction)

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/Screenshot%20(24).png)](https://github.com/rahuldkjain/Crop_Prediction)

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/Screenshot%20(25).png)](https://github.com/rahuldkjain/Crop_Prediction)

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/Screenshot%20(26).png)](https://github.com/rahuldkjain/Crop_Prediction)

[![ApnaAnaaj](https://github.com/rahuldkjain/Crop_Prediction/blob/master/static/Screenshot%20(27).png)](https://github.com/rahuldkjain/Crop_Prediction)

## Video Demonstration

[ApnaAnaaj](https://youtu.be/UahZf4VaCTE)

## Designed & Developed with :sparkling_heart: by

* [Rahul Jain](https://rahuldkjain.github.io)

* [Pratyush Garg](https://github.com/Pratyush2710)

* [Somya Jain](https://github.com/jainsomya972)

* [Abhay Gupta](https://github.com/abhaygupta5)