The automotive industry, particularly the used car market, is a dynamic and competitive domain. In India, for instance, the used automobile market is predicted to increase at a compound annual growth rate (CAGR) of 15.12% from 2020 to 2025. With approximately 40 million used vehicles sold annually, effective pricing strategies are crucial for both companies and individuals to efficiently sell their products in a competitive market and make a profit. The Used Car Price Prediction Model is a machine learning project aimed at accurately predicting the price of used cars based on various factors such as mileage, make, model, year, and location. This project is motivated by the need for both sellers and buyers to make informed decisions about the price of used cars, given the many factors that influence a car's worth on the market.
This project requires Python 3.6+ and the following Python libraries installed:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
To run this project, download the project files and run the Jupyter Notebook.
After installing the necessary libraries, open the Jupyter Notebook. You can view the code and output for each cell and run each cell individually. To run all cells at once, click Cell -> Run All in the menu.
The project consists of several components:
- Data Preprocessing: Cleaning the dataset to ensure it’s suitable for training a model. This includes removing outliers, handling missing values, and handling duplicates.
- Feature Engineering: Transforming the raw data into a format that is more suitable for modelling. This includes encoding categorical variables, scaling numerical features, and handling highly correlated features.
- Model Training: Training the machine learning model on the preprocessed dataset. This includes splitting the dataset, feature scaling, and hyperparameter tuning.
- Model Evaluation: Assessing the performance of the trained model. This includes using metrics and achieving a high accuracy rate.
- Prediction: Using the trained and evaluated model to predict new data.
- User Interface: Developing a simple web interface where users can input car features and receive predicted prices.
We are a group of dedicated students working collaboratively on the Used Car Price Prediction Model project. Our diverse backgrounds and shared interest in machine learning have brought us together to develop this model. Each member has contributed significantly to various aspects of the project, from data preprocessing and feature engineering to model training and user interface design. We are committed to providing a valuable tool for sellers and buyers in the used car market, helping them make informed decisions about the price of used cars. Below is a list of our group members:
| S/N | Full Name | Student ID |
|---|---|---|
| 1 | Aljesh Basnet | C0919827 |
| 2 | Bharat Dhungana | C0916253 |
| 3 | Bijay Adhikari | C0883819 |
| 4 | Lateef Taofeek | C0916723 |
| 5 | Oluwatimileyin Oyedele | C0902571 |
| 6 | Satish Kandel | C0916210 |
| 7 | Shishir Dhakal | C0913605 |
We appreciate your interest in our project and welcome any feedback or questions.