ypedia_cluster_analysis's Introduction

C_cluster_analysis

Problem Statement:

In this project, we will mainly concentrate on clustering customers from the YPedia homepage search data, find out various insights and patterns of customer behavior and solve few business related queries.

Step 1 : Handling Missing Data

Step 2 : Exploratory Data Ananlysis and Feature Engineering

Step 3 :

3.1 Find out Under-performing and Out-performing Marketing Channels. 3.2 Perform A/B Test on the Out-performing Channels. (to be finished)

Step 4 : K-means Clustering:

What is the optimal number of Clusters for this data? Descriptive Analysis of all the Clusters in terms of booking rate.(to be finished) What are the important features that best describes 95% of the variance for each cluster?

Step 5 : What lead to a higher chance of booking for individuals in each Cluster?

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cccchloe / ypedia_cluster_analysis Goto Github PK

ypedia_cluster_analysis's Introduction

C_cluster_analysis

Problem Statement:

Step 1 : Handling Missing Data

Step 2 : Exploratory Data Ananlysis and Feature Engineering

Step 3 :

Step 4 : K-means Clustering:

Step 5 : What lead to a higher chance of booking for individuals in each Cluster?

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