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Build machine learning models to predict the price variable with CatBoost Regressor, Linear Regression, and Random Forest Regressor.

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Car Price Prediction

Project developed using data from the Automobile Dataset whose objective is to perform an exploratory analysis of the data and build a Machine Learning model to predict the price variable.

The project was developed in the order of these three files below:

  • data_cleaning.ipynb
  • data_analysis.ipynb
  • data_processing.ipynb

Below are the main tasks performed in each file:

  • data_cleaning.ipynb: Handle missing data, Fix data with incorrect types
  • data_analysis.ipynb: Exploratory Data Analysis (EDA), Feature Engineering, and Selection
  • data_processing.ipynb: Transformations in data for model training, model training, performance evaluation, and interpretation of results

The data is available on the UC Irvine Machine Learning Repository: UCI

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Build machine learning models to predict the price variable with CatBoost Regressor, Linear Regression, and Random Forest Regressor.

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