"This is a complete tutorial for a sales forecasting project using machine learning for beginners. The dataset we will make use of contains 5-years worth of product sales data. Our goal is to effectively forecast the future sales of those products for the next 3-months. To achieve this goal we will be making use of a state-of-the-art gradient boosting algorithm as well as a python library called Upgini, for data enrichment"
"We will use the Intuit dataset available on OpenML website with CCO license. The task is email response prediction so the dataset contains some features and a binary target variable"
"We will do an in-depth review of the 5 of the most popular libraries that provide access to different data types:
📚holidays — holidays in different countries
📚yfinance — stock data from Yahoo Finance
📚meteostat — weather data from weather stations around the world
📚pandas-datareader — stock data and economic statistics from many sources around the world
📚upgini — ready-made features based on many sources"