End-to-End Machine Learning Project

The goal is just to illustrate the main steps of a Machine learning project where we'll predict the house pricing. The main steps involved are:

Big Picture & Problem Formulation

Collect Data

EDA (Exploratory Data Analysis)

ETL (Extract, Transform, and Load)

ETL stands for Extract, Transform, and Load and so any ETL tool should be at least have the following features: Extract This is the process of extracting data from various sources. A good ETL tool supports various types of data sources. This should include most databases (both NoSQL and SQL-based) and file formats like csv, xls, xml, and json. Transform The extracted data is usually kept in a staging area where raw data is cleansed and transformed into a meaningful form for storing it in a data warehouse. A standard ETL tool supports all the basic data transformation features like row operations, joins, sorting, aggregations, etc. Load In the load process, the transformed data is loaded into the target warehouse database. The standard ETL tools support connectors for various databases like Snowflake, MS SQL, and Oracle.

Model Selection and Training

Model Fine-tuning

Solution Presentation

Launch, Monitor, and Maintain