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Machine Learning for Marketing Department Case Studies

Project Highlights:

  1. Conducted Exploratory Data Analysis using distplot, histogram, and KDE
  2. Solved missing data issue
  3. Established how to find the optimal number of clusters using elbow method
  4. Applied K Means in scikit learn to perform market segmentation
  5. Built and trained autoencoder models in keras
  6. Applied PCA to perform dimensionality reduction using real world dataset

About

The project is aimed to help New York City Bank marketing team to launch a targeted ad marketing campaign by dividing their customers into at least 3 distinctive groups. The bank has extensive data on their customers for the past 6 months.

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machine-learning k-means k-means-clustering

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