{"id":25167342,"url":"https://github.com/nick-peter-marcus/marketing-data-analysis","last_synced_at":"2025-09-09T11:13:30.337Z","repository":{"id":207246194,"uuid":"718780227","full_name":"nick-peter-marcus/marketing-data-analysis","owner":"nick-peter-marcus","description":"Analyzing Marketing Analytics Data on Purchase Behavior and Campaign Responses - Customer Segmentation, Data Visualization, Regression Analysis, Random Forest","archived":false,"fork":false,"pushed_at":"2024-04-04T16:43:24.000Z","size":6662,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-03T18:18:52.082Z","etag":null,"topics":["data-visualization","k-means-clustering","linear-regression","logistic-regression","pca","random-forest","segmentation"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# marketing-analytics-data\nIn 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.\n\nMore precisely:\n- Customers' expenditures on different product categories will be predicted by applying linear regression analyses. \n- Acceptance of the most recent marketing campaign will be regressed on customer data using logistic regression.\n- Importance of individual features in the model predicting response is assessed by implementing a Random Forest Classifier.\n- Finally, customer segmentation will be performed by using the k-means 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