Predictive Modeling with R For Beginners
Predict who survives the Titanic. We take you through the process of developing a predictive model. Very beginner friendly.
Updated on Sep, 2023
Language - English
Course Description
This course will take you through the process of predictive analytics/predictive modeling. A statistical technique or machine learning algorithm is utilized to help predict an outcome. We will be utilizing Logistic Regression. In the course, we will take you through the process of how to accurately predict who survives the Titanic.
The goal of this course is to start you on your journey to becoming a top data analyst. To do that, you need to understand the methodology or methods at your disposal in solving these problems. By using a famous example (the Titanic Disaster), we will show you how to understand the problem in-front of you, how to explore your data, pre-process your data, how to create your first model, how to improve model accuracy, and look at some evaluation metrics.
We are lucky to have a top Kaggler as one of the instructors for this course. Aditya is an active Kaggler ranked in the top 5% (2018) of all the data scientists in the world, and is very knowledgeable about the process of solving data science problems. Ermin is the lead instructor and has been teaching data analysis for many years.
Goals
What will you learn in this course:
- Explain the problem you are trying to solve.
- Examine the data and test your hypotheses
- Identify and treat missing values, find and treat outliers, and remove duplicates and/or highly correlated variables (data pre-processing)
- Create, employ and predict with the logistic regression algorithm
- Utilize feature engineering
- Practice other methods to improve model accuracy
Prerequisites
What are the prerequisites for this course?
- A course on R, as we do not cover R in-depth. Instead, the focus is on Predictive Modeling itself.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
-
Outline for Course
02:01
02:01
-
Installing R (Windows, Linux, MacOS)
Understanding The Problem
4 Lectures
Data Exploration
7 Lectures
Data Preprocessing
4 Lectures
Logistic Regression
5 Lectures
Our First Model
3 Lectures
Improving Model Accuracy
7 Lectures
Relevant Concepts
3 Lectures
Instructor Details
Ermin Dedic
All Things Data.I have a passion for anything data, whether it is applying statistical methods to data more generally, or utilizing a data-driven approach in the Healthcare or Finance/Banking industries.
I studied Psychology for 6-years, including 2 years of Graduate school, where I was training to be a Child/School Psychologist. I had an opportunity to experience a blend of course work and clinical work but also recognize some of the problems facing the mental health system and graduate school system.
While I did ultimately decide to voluntarily leave the Grad program, it is via academics that I fell in love with statistics and statistical software like SPSS/SAS.
It was my Graduate school experience that solidified my interest in teaching, and where I received a lot of great feedback on my teaching and teaching style.
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