Skip to content
#

consumer-behavior

Star

Here are 14 public repositories matching this topic...

Language: All
Filter by language
All 14 Jupyter Notebook 7 Python 3 R 2 HTML 1
Sort: Most stars
Sort options
Most stars Fewest stars Most forks Fewest forks Recently updated Least recently updated

SaurabhSSB / consumer-behavior-analytics

Star 8

A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

exploratory-data-analysis jupyter-notebook pandas data-visualization seaborn matplotlib feature-engineering data-cleaning sales-analysis customer-segmentation python-data-analysis data-storytelling csv-analysis retail-analytics consumer-behavior correlation-heatmap market-insights shopping-trends eda-project retail-dataset
  • Updated Jul 17, 2025
  • Jupyter Notebook

chloedb / InstaCart-Python

Star 0

Career Foundry data analytics project to provide a client recommendations on a marketing strategy. Jupyter notebooks include cleaning and merging data along with creating new columns to offer the best understanding of consumers' interactions with products.

python pandas-dataframe data-visualization data-analytics data-analysis jupyter-notebooks product-analytics numpy-library data-analysis-python consumer-analytics seaborn-python consumer-behavior
  • Updated Aug 18, 2023
  • Jupyter Notebook

Mandar123454 / Predictive-Modeling-for-Smartphone-Purchase-Behavior-ML

Star 0

Smartphone Purchase Prediction Dashboard - An interactive web application for predicting and analyzing smartphone purchase behavior.

python flask data-science machine-learning dashboard deployment random-forest interactive azure numpy web-app scikit-learn chartjs pandas data-visualization classification smartphone predictive-modeling azure-app-service consumer-behavior
  • Updated Nov 7, 2025
  • Jupyter Notebook

ahmadizza / Clustering-MixITup-Customer-Segmentation-Using-Loyalty-Data

Star 0

Customer segmentation analysis for MixITup using demographic, behavioral, and preference data. Includes preprocessing, exploratory data analysis, standardization, categorical encoding, clustering using K-Means++ and K-Medoids, and strategic marketing recommendations based on customer profiles.

data-mining clustering k-means k-medoids customer-segmentation marketing-analytics consumer-behavior mixltup
  • Updated Nov 25, 2025
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the consumer-behavior topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the consumer-behavior topic, visit your repo's landing page and select "manage topics."

Learn more