Complete Machine Learning Project for Absolute Beginners (Tutorial)

Upgini: https://upgini.com

Dataset: https://github.com/upgini/upgini/raw/main/notebooks/train.csv.zip

Machine learning projects are a crucial aspect of learning ML, and most importantly they are a huge part of becoming a machine learning engineer. Doing projects helps you to build your knowledge of ML and also helps to showcase what you have learned as well.

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.

By completing this project, you will be able to learn:
1. How to effectively use popular python libraries like pandas
2. How to use catboost
3. How to enrich data with Upgini
4. Importance of data enrichment
5. What are SHAP values
6. What are SMAPE values
7. How to split time-series datasets into training and testing sets
8. How to train and test models

The dataset we will look at is

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