HackCoVIT - Team Greenify

#kisan badhega to desh bhi badhega

Introduction

Team Greenify is on a mission to address a critical real-life problem statement: helping farmers quickly determine the nutrient content of their soil, compared to traditional lab methods. This not only saves valuable time but also assists in analyzing soil properties for future agricultural planning.

Collaborative Code

You can access our collaborative code on Google Colab: HackCoVIT - Team Greenify Colab

Dataset

Our project relies on the soil nutrient dataset, which can be found here: Soil Nutrient Dataset

Project Presentation

Watch our project presentation on YouTube: HackCoVIT - Team Greenify Presentation

Project Website

Visit our project website: Team Greenify Website

Project Presentation (PPT)

Access our project presentation (PPT): Team Greenify PPT

Modules and Libraries Used

• Numpy: For numerical operations in Python.
• Matplotlib: Used for creating static, animated, and interactive visualizations, including graphs and charts.
• Pandas: For data manipulation and analysis.
• Math: Provides mathematical functions for various operations.
• Statsmodel: Offers classes and functions for statistical modeling, tests, and data exploration.
• Tensorflow: Provides a workflow for developing and training machine learning models.
• Scikit-learn (sklearn): A powerful library for machine learning and statistical modeling, including classification, clustering, and dimension reduction.

How to Run the Code

You have two options to run our code:

1. Google Colab (Recommended): You can directly run the code on Google Colab by clicking the following link: HackCoVIT - Team Greenify Colab.

2. Local Setup: If you prefer to run the code locally, please follow these steps:

   • Download the code and dataset.
   • Update the dataset path in the code to the local path where you have stored the dataset.