Welcome to my Used Car Price Prediction project! In this project, I utilized data analysis and machine learning techniques to predict the selling prices of second hand cars. The goal would be to help potential buyers and sellers make informed decisions based on various car features.
In this project, I:
Analyzed a Diverse Dataset: I used a dataset containing information about mileage, torque, kilometers driven, and more for different second hand cars.
Built a Predictive Model: Using machine learning, I developed a model that predicts the selling price of a car based on its features.
Shared Insights: Through visualizations and analysis, I extracted meaningful insights from the data to help understand the factors affecting the selling price of used cars.
Transmission Matters: Cars with automatic transmission sell for more than cars with manual transmission.
Ownership History Affects Prices: The number of previous owners significantly influences the selling price. Cars sold by the first owner command the highest prices, followed by those sold by the Test Drive Cars, and so on.
Brand Influence: Certain car brands are associated with higher resale values.
Dataset: The dataset used for this project is available as Car details v3.csv. You can explore the dataset to understand the features and the structure of the data.
Jupyter Notebook: The core analysis, data preprocessing, and machine learning model development can be found in the Jupyter Notebook named car_price_predicions.ipynb. Open this notebook to dive into the project in more detail.
I am passionate about leveraging data to derive meaningful insights and create solutions. Feel free to connect with me on LinkedIn to learn more about my work and other projects.