I have made this model which will predict estimated price of old car base on thier features. As now a day we know many people are going to buy second hand car instaed of buying new one, so its better investment option where we get almost 30-40% discount. but main question is how will us know actual price of car base on their features so in orer to solve this problem I have used this dataset to build model which will give a estimated price of car at which car should be sold.
This Dataset contains information of 5000+ old cars with different models and features like their Year, Name of the Company, Power, Fuel Type and Location.
This Dataset contains total 12 features
Name
Location
Year
Kilometers_Driven
Fuel_Type
Transmission
Owner_Type
Mileage
Engine
Power
Seats
Price
1-Linear Regression
2-Polynomial regression
3-Polynomial regression with PCA due to high number of features
4-Random Forest regression without PCA
The best accuracy I got is from RandomForestRegressor
Accuracy on Traing set: 0.9825878876014844
Accuracy on Testing set: 0.9088929588684407