sales prediction using machine learning python github

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in this tutorial, we'll explore how to predict sales using machine learning techniques in python. we'll cover the following steps:
let's get started!
the goal is to predict sales based on historical data. we'll use a dataset containing features such as date, product attributes, and sales figures.
choose a regression model suitable for sales prediction, such as linear regression, decision trees, random forest, etc.
evaluate the model using metrics like mean squared error (mse), root mean squared error (rmse), r-squared, etc.
once the model is trained, make predictions on new data.
if you want to deploy the model, you can use frameworks like flask or fastapi to create a rest api.
this tutorial provides a basic outline of how to perform sales prediction using machine learning in python. remember to explore different algorithms, tune hyperparameters, and continuously improve your model for better accuracy. happy coding!
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