This project involves predicting used car prices using linear regression in Jupyter Notebook. Used car price prediction is an important task in the automotive industry, as it helps estimate the value of pre-owned vehicles based on various factors such as mileage, brand, age, etc. Through this project, we aim to explore and understand how linear regression can be used for used car price prediction.
The dataset used for this project contains information about used cars, including features such as mileage, brand, age, fuel type, and more. The dataset should be in a structured format, such as a CSV (Comma Separated Values) file, with each row representing a used car instance and its corresponding price. Make sure to preprocess and clean the dataset before using it for modeling.
To get started with the project, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Car-Price-Prediction-LinearRegression.git- Change into the project directory:
cd Car-Price-Prediction-LinearRegression-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the
Car Price Prediction.ipynbnotebook in Jupyter. -
Follow the instructions in the notebook to load the dataset, preprocess the data, train the linear regression model, and make price predictions.
The notebook provides an overview of the steps involved in used car price prediction using linear regression. The steps include:
- Data Loading: Loading the dataset into a pandas DataFrame.
- Data Preprocessing: Handling missing values, encoding categorical variables, feature scaling, and splitting the dataset into training and testing sets.
- Linear Regression Model: Training the linear regression model on the preprocessed dataset.
- Model Evaluation: Assessing the model performance using evaluation metrics such as mean squared error (MSE) or R-squared.
- Price Prediction: Using the trained model to predict prices for new used car instances.
The notebook includes explanations, code snippets, and visualizations to aid in understanding the used car price prediction process using linear regression.
The project aims to predict used car prices using linear regression. The results and insights gained from this project include:
- Evaluating the performance of the linear regression model in terms of prediction accuracy and other evaluation metrics.
- Understanding the important factors or features that influence used car prices.
- Applying the trained model to predict prices for new, unseen used car instances.
The insights gained from this project can help in estimating the value of pre-owned cars and assist in making informed decisions in the used car market.
You can customize the project by modifying the dataset, experimenting with different preprocessing techniques, trying other regression algorithms, or exploring additional features for used car price prediction. This project serves as a starting point for used car price prediction using linear regression, and you can extend it further to suit your needs.
This project is licensed under the MIT License. See the LICENSE file for more information.
- This project is created for the purpose of exploring used car price prediction using linear regression in Jupyter Notebook.
Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more features, please open an issue or submit a pull request.