Toyota and Honda are one of the most popular car brands in the USA. As a lot of people buy and sell these cars, there is a need for an application that will predict used car prices for the brand's Honda and Toyota. In our project, we first performed data visualization and found some insight that helped us to compare the two brands. Our findings were consistent with findings from other websites. Our dataset consisted of 6 label features and 5 numeric features. We had to preprocess these features before passing them to our models. We then used the models to predict the price of used Toyota and Honda cars.
The data was collected from the https://www.carmax.com/ website. On the website, people post pictures of the car they want to sell. The dataset consists of more than 400 entries and a total of 11 features. Our dataset consists of 2 brands and from each brand, we choose 10 different models. One of the features is mileage (k miles), which implies how many distances the car had traveled. It also gives engine information of the car, the model the car was built, exterior color, etc. Finally, it consists of the price (in $dollars) of the car which is our target column.
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- Language : Python
- IDE : Google Colab
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- Scikit-learn
- Matplotlib
- Numpy
- Seaborn
- Pandas
- Build Web Scraper to automatically Collect Data
- Collect more features data to improve the model price prediction
According to various websites, Toyota Cars are more reliable and lasts longer than Honda Cars. Toyota Cars requires less fixing cost and hence the prices decreases less over time and people get to use the Toyota Cars more. The following graphs proves these statements.
From the above graph, we can clearly see that Price of Toyota Car's price is higher than Honda Cars
From the above graph, its clear that Toyota car gets used more, because it is less prone to breakdowns.