Team Name: VIT/OW/55 "The future is green energy, sustainability, renewable energy. Once you got a solar panel on a roof, energy is free. Once we convert our entire electricity grid to green and renewable energy, the cost of living goes down." Welcome to our Sustainable Energy Management Hackathon kit. Check out our video Report!! https://drive.google.com/file/d/1me1sje1o5Lrm8SkKQpQy9Sx5j_2Itowi/view?usp=sharing

Exploratory Data Analysis in Python

These libraries are used for statistical, Univariate, descriptive, and exploratory analysis.

Machine Learning Libraries for Prescriptive Analysis

These libraries are used for developing the machine learning pipeline model .

Annotation & Labelling libraries

These are used for data visualization, data Labelling, data annotation.

Kit Solution Source

My Github Repository Link: https://github.com/Sarthak1807/Sustainable-Energy-Hackathon Short Video Explanation : https://drive.google.com/file/d/1me1sje1o5Lrm8SkKQpQy9Sx5j_2Itowi/view?usp=sharing

Kit Deployment Instructions

This section has the instructions to install the kit. For theSarthak & Ashutosh kit source, the deployment instructions are: 1. Clone our github repository from the source: https://github.com/Sarthak1807/Sustainable-Energy-Hackathon 2. Download and save all your files. 3. For the Prescriptive and Exploratory data analysis, Navigate to the 'energy hack.ipynb' and open and run each cell. 4. For the Statistical and Descriptive, time Series analysis , Navigate to the 'energy hack.ipynb' and open and run each cell. 5. For the Data Labelling and Annotation, Navigate to the 'tortus_labelling.ipynb' and open and run each cell. 6. Install the required libraries by 'pip install -r requirements.txt' 7. Read out report.