https://github.com/nick-peter-marcus/marketing-data-analysis

Analyzing Marketing Analytics Data on Purchase Behavior and Campaign Responses - Customer Segmentation, Data Visualization, Regression Analysis, Random Forest
https://github.com/nick-peter-marcus/marketing-data-analysis

data-visualization k-means-clustering linear-regression logistic-regression pca random-forest segmentation

Last synced: 4 days ago
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Analyzing Marketing Analytics Data on Purchase Behavior and Campaign Responses - Customer Segmentation, Data Visualization, Regression Analysis, Random Forest

• Host: GitHub
• URL: https://github.com/nick-peter-marcus/marketing-data-analysis
• Owner: nick-peter-marcus
• License: mit
• Created: 2023-11-14T19:35:19.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-04T16:43:24.000Z (10 months ago)
• Last Synced: 2025-02-09T06:33:26.430Z (4 days ago)
• Topics: data-visualization, k-means-clustering, linear-regression, logistic-regression, pca, random-forest, segmentation
• Language: Jupyter Notebook
• Homepage:
• Size: 6.35 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# marketing-analytics-data

In this project, I will perform exploratory data analyses, data visualization and statistical analysis utilizing machine learning algorithms on a [kaggle dataset](https://www.kaggle.com/datasets/jackdaoud/marketing-data/data) containing Marketing Data.

More precisely:

- Customers' expenditures on different product categories will be predicted by applying linear regression analyses. 

- Acceptance of the most recent marketing campaign will be regressed on customer data using logistic regression.

- Importance of individual features in the model predicting response is assessed by implementing a Random Forest Classifier.

- Finally, customer segmentation will be performed by using the k-means approach.