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Logistic regression in R Studio tutorial for beginners. You can do Predictive modeling using R Studio after this course.

Introduction

Welcome to the course!
Course Resources

Basics of Statistics

Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Practice Exercise 1
Measures of Dispersion
Practice Exercise 2

Getting started with R and R studio

Installing R and R studio
Basics of R and R studio
Packages in R
Inputting data part 1: Inbuilt datasets of R
Inputting data part 2: Manual data entry
Inputting data part 3: Importing from CSV or Text files
Creating Barplots in R
Creating Histograms in R

Introduction to Machine Learning

Introduction to Machine Learning
Building a Machine Learning model

Data Preprocessing

Gathering Business Knowledge
Data Exploration
The Data and the Data Dictionary
Importing the dataset into R
Project Exercise 1
Univariate analysis and EDD
EDD in R
Project Exercise 2
Outlier Treatment
Outlier Treatment in R
Project Exercise 3
Missing Value Imputation
Missing Value imputation in R
Project Exercise 4
Seasonality in Data
Variable transformation in R
Project Exercise 5
Dummy variable creation: Handling qualitative data
Dummy variable creation in R
Project Exercise 6

Classification Models

Three Classifiers and the problem statement
Why can't we use Linear Regression?
Logistic Regression
Training a Simple Logistic model in R
Project Exercise 7
Results of Simple Logistic Regression
Logistic with multiple predictors
Training multiple predictor Logistic model in R
Quiz
Project Exercise 8
Confusion Matrix
Evaluating Model performance
Predicting probabilities, assigning classes and making Confusion Matrix
Project Exercise 9
Quiz

Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis
Linear Discriminant Analysis in R
Project Exercise 10

Test-Train Split

Test-Train Split
Test-Train Split in R
Project Exercise 11
Quiz

K-Nearest Neighbors classifier

K-Nearest Neighbors classifier
K-Nearest Neighbors in R
Project Exercise 12

Understanding the Results

Understanding the results of classification models
Summary of the three models
The Final Exercise!

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