contextinnovationlab / mercedes-benz-greener-manufacturing

You are required to reduce the time that cars spend on the test bench. You 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 Daimler’s standards.

Mercedes-Benz-Greener-Manufacturing

You are required to reduce the time that cars spend on the test bench. You 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 Daimler’s standards.

Step1: Import the required libraries

Step1.1: linear algebra

Step1.2: data processing

Step1.3: for dimensionality reduction

Step2: Read the data from train.csv

Step2.1: let us understand the data

Step2.2: print few rows and see how the data looks like

Step3: Collect the Y values into an array

Step3.1: seperate the y from the data as we will use this to learn as the prediction output

Step4: Understand the data types we have

Step4.1:iterate through all the columns which has X in the name of the column

Step5: Count the data in each of the columns

Step6: Read the test.csv data

Step6.1: remove columns ID and Y from the data as they are not used for learning

Step7: Check for null and unique values for test and train sets

Step8: If for any column(s), the variance is equal to zero, then you need to remove those variable(s).

Step8.1: Apply label encoder

Step9: Make sure the data is now changed into numericals

Step10: Perform dimensionality reduction

Step10.1: Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space.

Step11: Training using xgboost

Step12: Predict your test_df values using xgboost