Inspiration

I was inspired to do this project and further explore it due to one of my professor's transportation lectures. I learned that The Government of India launched the National Electric Mobility Mission Plan in 2013, intending to boost demand for electric vehicles (EVs) in India and create a self-sustaining ecosystem. A slew of schemes and programmes aimed at increasing demand and establishing a supply chain in India was implemented. Even though India's electric vehicle programme has been in place for eight years, electric vehicles still account for only about 1% of total vehicle sales.

What it does

This project uses data from Germany and the United Kingdom, where electric cars have already been established, as a case study to show how customers in India could benefit from adopting the same behaviour.

How we built it

Data Cleaning Data Visualization

  1. Correlation plot using the heatmap
  2. Scatter Plots to find the relation between various parameters
  3. Joint plot to analyse the relationship between two different variables
  4. Bar graph was used to analyse each parameter more carefully.
  5. Random forest was used as the machine learning model to predict the price
  6. The accuracy metrics of the predicted value was done using MAE, MSE, RMSE and R-squared.

Challenges we ran into

I was also looking for real-time India data but couldn't find it.

Accomplishments that we're proud of

The accuracy of the predictions was reasonably good, achieved an R-Squared value of 0.815 which shows a high level of correlation.

What we learned

Price in Germany and Price in the UK have a higher correlation. Top Speed has a higher correlation with Price in Germany and Price in the UK. A car with a higher speed has a higher price. KWH and range have a higher correlation.

What's next for Predicting the price of Electric Transportation

I'll be using India's data to create a more efficient supply chain in order to increase profits and electric vehicle penetration in the Indian market!

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