Data Dimensionality reduction- High correlation filter

Hi. I am performing a data analytics task on 'used_cars' data set. I am applying dimensionality reduction using a high correlation filter. I am not sure which parameters to consider for a reduction. I read online that we can remove one variable so the inter-correlation between variables will be minimum. I am considering 0.6 as the threshold for dropping out highly correlated variables. Please suggest which variables should be considered for dimensionality reduction in this case.

Correlation plot:


highlyCorrM <- findCorrelation(corrM, cutoff=0.6)

I a getting output:

# [1] "city_fuel_economy"              "highway_fuel_economy"           "horsepower"                    
# [4] "fuel_tank_volume**"               "width"                          "wheel_system_Front_Wheel_Drive"
# [7] "wheelbase**"                      "height**"                         "transmission_Automatic"        
# [10] "body_type_SUV_Crossover"        "fuel_type_Gasoline"             "mileage"                       
# [13] "salvage_False" 

Thank you!

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