Machine Learning Project – How to Analyze and Clean Data, Create an ML Model, and Set Up an API

This is a series on Data Science and Machine Learning applied to a House Prices dataset from the Kaggle competition House Prices: Advanced Regression Techniques.

You can download a PDF version of this Data Science and Machine Learning Project with the full source code repository linked in the book.

In this series we begin with the EDA (Exploratory Data Analysis) of the data, we create a script to clean the data, then we use the cleaned data to create a Machine Learning Model, and finally we use the Machine Learning model to implement a prediction API:

• Exploratory Data Analysis – House Prices – Part 1
• Exploratory Data Analysis – House Prices – Part 2
• Data Science Project: Data Cleaning Script – House Prices DataSet
• Data Science Project: Machine Learning Model – House Prices Dataset
• Data Science Project: House Prices Dataset – API

You can download the complete code in the Github Repository with clear instructions to execute this end-to-end project.

>>>You can also watch how to run this project on Youtube<<<

原文链接：Machine Learning Project – How to Analyze and Clean Data, Create an ML Model, and Set Up an API