Overview / Usage

Car price prediction using Intel oneAPI primarily involves using the statistical modeling capabilities provided by libraries like StatsModels in combination with Intel oneAPI's parallel processing capabilities for efficient computation

Intel oneAPI can provide performance optimizations when working with large datasets or when performing computations in parallel. You can explore parallelization options and take advantage of Intel's hardware acceleration features for faster data processing and model training.

Remember that this is a general overview, and the specifics of your project may require additional steps or considerations. Be sure to consult the documentation for StatsModels and Intel oneAPI for detailed implementation guidance and optimization strategies.

Methodology / Approach

Intel oneAPI is a comprehensive cross-architecture programming model and development environment that can be used for a wide range of tasks, including machine learning and predictive modeling. Here's a high-level methodology for building a car price prediction model using Intel oneAPI:

Data Collection and Preprocessing:

Gather a dataset containing information about cars and their prices. This dataset should include features such as make, model, year, mileage, condition, and any other relevant factors. Clean and preprocess the data to handle missing values, outliers, and categorical variables. You may also need to perform feature scaling or normalization.

Feature Engineering:

Select the relevant features (independent variables) that are likely to have an impact on the car's price. Transform categorical variables into numerical representations using techniques like one-hot encoding or label encoding. Create new features or perform dimensionality reduction if necessary.

Model Evaluation:

Evaluate the model's performance on the validation set using appropriate regression metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or R-squared. Refine the model based on the validation results.

Model Testing:

Finally, assess the model's generalization performance on the test set to ensure it can make accurate predictions on new, unseen data.

Deployment:

Once you are satisfied with the model's performance, you can deploy it in a production environment where it can take new car data as input and provide price predictions.

Intel oneAPI provides a powerful and flexible environment for implementing machine learning models and can be particularly beneficial when dealing with large datasets and complex models that can benefit from parallel processing capabilities. Be sure to consult the oneAPI documentation and resources for specific details on using its libraries and tools for your machine learning tasks.

Technologies Used

The processing and analyzing modes of computation is done using Intel OneDAL ( Data Analytics Library) and the data has been displayed over the graph using the sns plot. The visual presentation for my project has been done by using matplotlib and seaborn libraries and in order to provide an object oriented API, numpy libraries has been installed to work upon the statistical data. Intel scikit learn extension is implemented to feature various classifications, regression and clustering algorithms. Linear regression is used in the project to define the relation between independent variables and make predictions accordingly.

Repository

https://github.com/Rabiya9392/Car_price_prediction