/Data-Mining-1

Primary LanguageJupyter Notebook

Marketing Data Analysis

This code performs analysis and visualization on a marketing dataset using Python and various libraries, including scikit-learn, pandas, matplotlib, and seaborn.

Overview

The code performs the following tasks:

  • Data preprocessing, including cleaning and transformation.
  • Exploration of customer demographics and spending behavior.
  • Dimensionality reduction using Principal Component Analysis (PCA).
  • Cluster analysis using KMeans and Agglomerative Clustering.
  • Visualization of data using 3D scatter plots.

Usage

  1. Ensure you have the necessary Python libraries installed, such as scikit-learn, pandas, matplotlib, seaborn, and numpy.

  2. Modify the 'Location' variable to specify the path to your marketing dataset (named 'marketing_campaign.csv').

  3. Run the code in a Python environment.

  4. The code will generate various plots and visualizations to help you understand customer behavior and clustering results.