Feature Engineering with Excel & Python Machine Learning

Visually learn to create Features using Excel and build Machine Learning Models using Python (Submit solution to Kaggle)
Rating: 4.0 out of 5 (67 ratings)
8,326 students
Feature Engineering with Excel & Python Machine Learning
Rating: 4.0 out of 5 (67 ratings)
8,327 students
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

Requirements

  • Excel Functions - IF, Sumifs, Counta, Vlookup
  • Excel Pivot Tables
  • Excel Name Manager
  • Basic of Python for Machine Learning
  • Python Jupyter Notebooks
Description

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
Course content
6 sections • 12 lectures • 1h 48m total length
  • Introduction
    06:02
  • Explore Data and Identify Missing Values
    06:15
  • Treat Embarked Variable
    06:45
  • Treat Age Variable
    08:00
  • Treat Cabin and Recode Sex Variable
    06:57
  • Create Family Size and Is Alone Feature from Sibsp and Parch
    03:37
  • Extract Title from Name Variable
    09:16
  • Extract Feature from Ticket Variable
    11:49
  • Create Dummy Variables
    09:34
  • Create Train and Test Datasets
    18:49
  • Build Logistic Regression
    11:14
  • Evaluate Model and Submit to Kaggle
    10:38

Instructor
Data Science Instructor and Marketing Adviser at Dell
Kunaal Naik
  • 4.0 Instructor Rating
  • 67 Reviews
  • 8,324 Students
  • 2 Courses

Kunaal loves teaching Data Science and is an avid learner. He aspires to inspire aspirants to make it into the Analytical industry. He has over ten years of experience in the field of Data Science across domains such as Marketing, Insurance, HR and Retail. Currently, he works at Dell as a Marketing Advisor.


Apart from his professional background, he has experience in teaching Analytics, Machine Learning, Excel, SAS, Python, SQL Tableau over 50 Corporates/Institutes including CITI Bank, Genpact, Fidelity, Corporate Executive Board, Madras School of Economics.


Apart from his professional side, he is a Scuba Diver, Apprentice Philosopher and Lifeaholic Evangelist. Also, he loves creating content to teach Data Science on YouTube.