Analysis of Mercedes-Benz's Greener Manfacturing Test Bench

DESCRIPTION :

•Reduce the time a Mercedes-Benz spends on the test bench.

Problem Statement Scenario:

•Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations.
•These include the passenger safety cell with a crumple zone, the airbag, and intelligent assistance systems.
•Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium car makers.
•With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.
•To ensure the safety and reliability of every unique car configuration before they hit the road, the company’s engineers have developed a robust testing system.
•As one of the world’s biggest manufacturers of premium cars, safety and efficiency are paramount on Mercedes-Benz’s production lines.
•However, optimizing the speed of their testing system for many possible feature combinations is complex and time-consuming without a powerful algorithmic approach.
•I have to reduce the time that cars spend on the test bench. •Others will work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing.
•Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

Following actions I performed during analysis task:

•Checking if any column(s),the variance is equal to zero and then removing those variable(s).
•Check for null and unique values for test and train sets.
•Apply label encoder.
•Perform dimensionality reduction was used to convert the 376 variables into 12 using n_components.
•XGBoost was used for prediction model.

To Know More about the code:

Please click here: Code