Incorporating the correction of inaccurately spelled corporate designations is a fundamental objective. Additionally, the integration of scalability attributes remains a pertinent consideration.

Flyheap / Car-Valuation-Prediction

This project seeks to create a precise car price forecasting system for both new and used vehicles, employing data analysis and machine learning. It aids consumers and industry experts in informed automotive transactions.

MIT License

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Libraries to use

NumPy (imported as np): NumPy is used for numerical and array operations. It's commonly used in data manipulation and scientific computing.

Pandas (imported as pd): Pandas is used for data manipulation and analysis. It provides data structures like DataFrames that are useful for working with structured data.

Matplotlib and Seaborn: These libraries are used for data visualization. Matplotlib is a widely-used plotting library, while Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive and informative statistical graphics.

scikit-learn (imported as sklearn): Scikit-learn is used for machine learning tasks. In your code, it's specifically used for linear regression.

Linear Regression Model: The code builds a linear regression model, which is a part of the scikit-learn library.

Polynomial Features: The code also uses PolynomialFeatures from scikit-learn to create polynomial features for the linear regression model.

Hello, @Flyheap I'd like to take ownership of this issue.

Incorporating the correction of inaccurately spelled corporate designations is indeed a crucial task. I also recognize the importance of integrating scalability attributes into our project.

I'll be utilizing the following libraries to work on this:

NumPy (imported as np): For numerical and array operations, crucial in data manipulation and scientific computing.

Pandas (imported as pd): To handle data manipulation and analysis, especially with DataFrames for structured data.

Matplotlib and Seaborn: For data visualization, using Matplotlib for plotting and Seaborn for enhanced statistical graphics.

scikit-learn (imported as sklearn): Primarily for linear regression tasks within the code.

Additionally, I'll be constructing a Linear Regression Model, leveraging the scikit-learn library. Polynomial features will be created for this linear regression model using PolynomialFeatures.

I'm excited to work on this issue and contribute to its resolution. Please assign it to me, and I'll get started promptly. If there are specific requirements or expectations for this task, please feel free to share them, and I'll make sure to align my work accordingly.

Flyheap / Car-Valuation-Prediction

Incorporating the correction of inaccurately spelled corporate designations is a fundamental objective. Additionally, the integration of scalability attributes remains a pertinent consideration. #1