Our client is one of the top leaders in the automobile industry and constantly strives to improve the design of vehicles through continuous experiments. With this model, the client wanted to decrease fuel consumption through meticulous vehicle design, giving more benefits to users.

Project Summary

Our client wanted a model to identify factors that impact fuel consumption to improve the design of automobiles. The main challenge here was the environmental factors. The lack of data also made the task difficult. We used fuel consumption attributes to cluster vehicles and utilized different regression algorithms. That helped us create a model to predict the appropriate engineering for the automobile. The AI of the model helped our client in predicting the correct weight of the vehicle and reduced the cost it takes to design it.

  • Collect and cluster the data of auto attributes and treat them as individual data sets
  • Use that information to make decisions on the average fuel consumption
  • That would help to improve the design and engineering of the vehicles
  • Accuracy of the model was a challenge considering the environmental factors whose data was not available
  • Data had a huge dependency on several different attributes
  • Identifying highly important attributes were important and challenging
  • Proposed clustering of vehicles to be done based on the fuel consumption attributes
  • Training the regression model using different regression algorithms
  • Curating dimensionally reduced data to build a supervised learning model that can predict the right engineering for the vehicles.
  • Received predictions of right weights using the AI driven model
  • The cost of an experiment to design the right weight of the automobile was highly controlled.

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