Introduction to two components of Regression Line - Slope & Intercept
7:26
Introduction to two components of Regression Line - Slope & Intercept
Daisy function to cluster mixed data, Gower coefficient, Some Examples
12:57
Daisy function to cluster mixed data, Gower coefficient, Some Examples
What would be the right number of clusters, Elbow method
13:33
What would be the right number of clusters, Elbow method
cluster ordinal data
8:39
cluster ordinal data
Kmodes Clustering, Simple matching dissimilarity
6:18
Kmodes Clustering, Simple matching dissimilarity
Introduction to Kmodes clustering
3:50
Introduction to Kmodes clustering
Convert single Nominal column to multiple Binary column
7:56
Convert single Nominal column to multiple Binary column
Cluster Binary data, Simple Matching, Jaccard & Dice coefficient
14:34
Cluster Binary data, Simple Matching, Jaccard & Dice coefficient
hclust function in R, Represent clusters using ggplot function
14:08
hclust function in R, Represent clusters using ggplot function
Introduction to Hierarchical Clustering
6:27
Introduction to Hierarchical Clustering
Understand Partitioning Around Medoids Clustering
13:51
Understand Partitioning Around Medoids Clustering
Introduction to manhattan distance, use automobile dataset to calculate manhatta
9:20
Introduction to manhattan distance, use automobile dataset to calculate manhatta
Introduction to K means clustering, k means function in R, Understanding the out
6:28
Introduction to K means clustering, k means function in R, Understanding the out
Make two clusters using randomly chosen cluster centroids
8:12
Make two clusters using randomly chosen cluster centroids
Using small subset to understand euclidean distance measure
4:54
Using small subset to understand euclidean distance measure
Different types of data - ordinal & nominal data, Scaling the data
5:55
Different types of data - ordinal & nominal data, Scaling the data
Different types of data - continuous/interval & binary
5:26
Different types of data - continuous/interval & binary
understand clustering using some examples
4:31
understand clustering using some examples
clustering benefits, meaning of unlabeled data
4:58
clustering benefits, meaning of unlabeled data
Machine Learning, clustering/classification, supervised/unsupervised
5:05
Machine Learning, clustering/classification, supervised/unsupervised
discussing confidence intervals
7:20
discussing confidence intervals
qqplot for identifying data problems, qqnorm function
11:00
qqplot for identifying data problems, qqnorm function
Different kinds of scatterplots, Plotting transformed data
8:29
Different kinds of scatterplots, Plotting transformed data
Basics of log transformation
5:40
Basics of log transformation
Different ways to identify whether data transformation is needed or not
8:47
Different ways to identify whether data transformation is needed or not
access residuals in train/test data, Using raw residuals to identify outliers
4:30
access residuals in train/test data, Using raw residuals to identify outliers
Using some examples to understand vif function, An introduction to outliers
5:20
Using some examples to understand vif function, An introduction to outliers
Discussion on residuals, vif function for checking collinearity
5:35
Discussion on residuals, vif function for checking collinearity
why divide the data? Going through the steps for making the regression model
4:22
why divide the data? Going through the steps for making the regression model
Missmap function in amelia, amelia function for filling empty cells
6:10
Missmap function in amelia, amelia function for filling empty cells