Aim

This Project is aimed at building a predictive model for predicting used-car prices in Nigeria.

Properties Considered

The Poperties Being considered are: 1. Make 2. Year 3. Model 4. Mileage 5. Transmission 6. Color 7. Location 8. History 9. Car-Rating

Files

data_scrap_c45.ipynb

This file scraps data from the cars45 website. copy the webpage of the car, you want to scrap into cell 3. Keep copying the link and run only cell 3 and 4. Be careful not to run cell 2, this will re-initialize the dataframe.

data_scrap_cheki.ipynb

This file scraps data from the cheki website. copy the webpage of the car, you want to scrap into cell 3. Keep copying the link and run only cell 3 and 4. Be careful not to run cell 2, this will re-initialize the dataframe. Happy Scraping.

data_cars45.ipynb

This file scraps data from car from autochek which is also redirecting the datas from Cheki.com.ng. In this file We scraped 14,195 rows of cars and saved into a CSV file afterwards.

model_2.ipynb

For model development - Here I compared the performance of different models. (Linear Regression, k-Nearest Neigbours, Random Forest and Gradient Boosted Trees) with missing rating set to 2.0

model_2.ipynb-gridsearchcv

For model development - Here I used GridSearchCV to find the best parameters for the Gradient Boosted Trees and K-Nearest Neigbhours. (The 2 best performing models from model_2.ipynb)

model_3.ipynb

For model development - Here I compared the performance of different models. (Linear Regression, k-Nearest Neigbours, Random Forest and Gradient Boosted Trees) with missing rating set to 3.0