This repository contains the code for a sales prediction model developed by analyzing and cleaning data from IronHack. Several algorithms were compared in order to build the best possible model for predicting sales.

To get started with this project, clone the repository to your local machine and open the solution.ipynb file using Jupyter Notebook or another compatible IDE. The sales.csv file contains the raw data used for the analysis, while the validation_for_students.csv file contains data for which to predict sales.

This project requires Python 3 and several Python libraries including pandas, numpy, matplotlib, seaborn, xgboost, lightgbm, pickle, and scikit-learn. You can install these libraries using pip.

The model is contained within the solution.ipynb file. Simply run the cells in order to load and analyze the data, train the models, and predict sales for the validation data. The final predictions will be output to a csv file.

This project was created by Fabio SARMENTO PEDRO.

This project is licensed under the MIT License.