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elbow-method

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NLP-PROJECT-BOOK-INSIGHTS-WITH-PLOTLY

Plotly-Dash NLP project. Document similarity measure using Latent Dirichlet Allocation, principal component analysis and finally follow with KMeans clustering. Project is completed with dynamic visual interaction.

  • Updated Sep 8, 2022
  • Python

Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic …

  • Updated May 25, 2020
  • Jupyter Notebook

Segment airline customers, analyze the characteristics of different customer categories, compare the value of customers from different customer categories, provide personalized services for categories of customers with different values, and formulate the right marketing strategy.

  • Updated Apr 25, 2022
  • Jupyter Notebook

Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.

  • Updated Dec 28, 2022
  • Jupyter Notebook

OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.

  • Updated Nov 19, 2021
  • Python

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