akashnaskar / Test_Bench_Time_Reduction_for_MercedesBenz

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 Daimler’s standards.

Test Bench Time Reduction for MercedesBenz

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