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Machine Learning project to Reduce the time a Mercedes-Benz spends on the test bench

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Mercedes-Benz-Greener-Manufacturing

Machine Learning project to 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 carmakers. Mercedes-Benz is the leader in the premium car industry. 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.

You are required 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 should be performed

• If for any column(s), the variance is equal to zero, then you need to remove those variable(s).

• Check for null and unique values for test and train sets.

• Apply label encoder.

• Perform dimensionality reduction.

• Predict your test_df values using XGBoost.

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Machine Learning project to Reduce the time a Mercedes-Benz spends on the test bench

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