Predicting Customer Lifetime Value in Python
Learn how to calculate Customer Life Time Value in Python. Italian economist Vilfredo Pareto states that 80% of the effect
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Month: October 2020
Introduction to Customer Segmentation in Python
In this tutorial, you’re going to learn how to implement customer segmentation using RFM(Recency, Frequency, Monetary) analysis from scratch in
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Introduction to Factor Analysis in Python
In this tutorial, you’ll learn the basics of factor analysis and how to implement it in Python. Factor Analysis (FA)
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Dimensionality Reduction using tSNE
tSNE stands for t-distributed Stochastic Neighbor Embedding. It is a dimensionality reduction technique and is extremely useful for visualizing datasets
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Introduction to Apache Spark
In this tutorial, we will focus on Spark, Spark Framework, its Architecture, working, Resilient Distributed Datasets, RDD operations, Spark programming
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Dimensionality Reduction using PCA
Dimensionality refers to the number of input variables (or features) of the dataset. Data with a large number of features
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Evaluating Clustering Methods
Predicting optimal clusters is of utmost importance in Cluster Analysis. For a given data, we need to evaluate which Clustering
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Spectral Clustering
Spectral Clustering is gaining a lot of popularity in recent times, owing to its simple implementation and the fact that
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K-Means Clustering
k-Means Clustering is the Partitioning-based clustering method and is the most popular and widely used method of Cluster Analysis. The
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DBSCAN Clustering
Cluster Analysis comprises of many different methods, of which one is the Density-based Clustering Method. DBSCAN stands for Density-Based Spatial
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