I am working on a regression model, more precisely, multiple regression model for predicting one single value. I have a dataset of cars and some technical data.
For example, I have the following prediction for the following car data:
Make: Volvo Model: XC90 T8 Recharge AWD Doors: 4 Seats: 7 SeatsMin: 7 SeatsMax: 7 Dimensions: 4950x1931x1776 Length: 4950 Width: 1931 Height: 1776 WheelBase: 2984 Weight: 2320 MaxWeight: 2980 TrunkVolMin: 262 TrunkVolMax: 1816 TankVolume: 70 Electric: Electric or hybrid TurboCharged: 1: Turbo charged engine Kompressor: 1: Kompressor charged engine Displacement: 1969 EnginePower: 288(390)/6000 EngineTorque: 640/2200 MaxPower: 288.00 MaxTorque: 640.00 Druve: All wheel drive FrontWheelDrive: Front wheel drive RearWheelDrive: Rear wheel drive Gears: 8 Automatic: Automatic gearbox Acceleration: 5.8 MaxSpeed: 180 FuelConsumption: 2.1 S CO2: 57 FuelType: S Predicted_Acceleration: 5.66731 Residuals: .13269
Acceleration by default is 5.8, the model gives 5.66731, meaning it is off by 0.1 or something. According to the model summary, $R^2$ is roughly 75.9% percent accurate.
EDIT: I do not know for sure, what other bits of data here can be used to form a more accurate model. I haven't made any dummy variables yet. Not decoded into binary.