The problem Phoenix solves

The government established the FAME initiative as part of the National Electricity Mobility Mission Plan. Its goal is to provide a push toward the development of a market for hybrid and electric vehicles. However, one of the issues that India's EV business faces today is a lack of proper infrastructure. To accommodate the rising demand for EVs in India, comprehensive charging infrastructure is required.
Our solution provides a blueprint for the proper placement of EV stations that should be installed in a particular region by analyzing the current and future need for EV vehicles. People can easily access the website and can look for the nearest EV stations near them.
Project Phoenix basically considers three things in mind while providing the solution:-
1)The minimal distance user has to travel to the EV charging port.

2. Cost of installing EV charging machines.
3. Better prediction of future demands for EV stations over a region.
   These things make our solution more feasible and also take into account all the requirements from the perspective of the user and supplier.

Challenges we ran into

The challenges we face can be divided into three categories:-

Type-1 Data Based/Data Preprocessing

The Major problem in Machine Learning is to find accurate and structured data so we have to search over the web for different possible data sources as EV is emerging and new technology so enough data is not available.
For example:- We also have to look for the geo JSON file of India.

Type-2 Machine learning based

Another problem was that we have to learn time series analysis and how to embed machine learning models into the web. For this problem, we used Flask and streamlit.

Type 3 Web Development-based Problem

We are a bit new to flask and streamlit but Stackoverflow turns out to be of great help.