We brainstormed the project idea on electricity saving based on theme as we know that the electricity is most important and daily used component and without it even a minute feels like a day. In today's world there are many models that provide us with the information that predicts that there should be X amount of electricity that should be produced but there is lack of model, data and platforms which shows or calculate the overflow of electricity sent to that particular route. So we decided to create and train our model so that it can find the precise amout of electricity needed for that particular location so that there is minimum energy lost.
It predicts the average electricity usage/consumption based on model training data and analysis.
So for this project we used the famous rule "Divide and Conquer" where we divided the tasks into four parts, Front-end, backend, front-end for backend and modeling the data.
It was difficult to identify and find the suitable kind of data for the machine-learning model. Our first pass at integrating the model into the web application was quite bulky as well as took some time to infer from. Figuring out the bugs and fixing them was challenging as well.
We were able to learn some new things about html, flask and python and got our first project done in ML/AI. Understanding the workflow of a ML model in flask.
Flask, Python, Machine Learning, Designing and training model, Git/GitHub.
For future we are planning to implement the graphical content that helps us understand the data more precisely and different curves and area of the graph can help us to predict many other things. We are also planning to train our model further with more recent an continous data so that the future prediction that the model will provide will be more accurate.