What you’ll learn
Feature Engineering with Excel
Building Machine Learning models with Python
Getting started with their first Kaggle Competition
Learning to build and choose best Machine Learning Model
Machine Learning is getting increasingly famous for new aspirants to learn. I have seen many start this journey of never-ending learning start using Python or R to begin their journey. Due to all coding and no visual cue’s many often miss the joy of creating features and experimenting with data using Excel.
I started my journey of Data Science with Excel, which helped me create a visual memory for creating features. My love for Data Science began with this simple yet powerful tool and a plethora of opportunities to solve new problems.
In this video series, I will extract features using the Titanic Data from Kaggle. Excel will be used to do missing value treatment, engineering new features, creating test and train datasets. Python is our choice of tool for modelling. Hopefully, as a beginner, you will begin to discover Data Science as I have.
Who this course is for:
- Beginner Data Science aspirants want to solve their first Kaggle Problem Statement
- Data Science Students who want to learn how Feature Engineering happens visually
- Dealing with Missing Values
- Feature Engineering : Numerical Features
- Feature Engineering : Categorical Features
- Prepare data for Modelling
- Build Base Model using Python (Logistic Regression)