Aerofit Treadmill Customer Profile Analysis

This repository contains a comprehensive analysis of Aerofit treadmill customer data. The project aims to identify customer profiles for each treadmill model and uncover relationships between customer characteristics and purchasing decisions. This analysis will help Aerofit develop more targeted product recommendations and marketing strategies.

Business Case

Aerofit, a fitness equipment company, wants to understand the characteristics of customers who purchase different treadmill models (KP281, KP481, KP781) to provide better recommendations to new customers.

Dataset

• aerofit.csv: Contains customer data, including:
  • Product Purchased (KP281, KP481, KP781)
  • Age
  • Gender
  • Education
  • Marital Status
  • Usage (average weekly treadmill use)
  • Income
  • Fitness (self-rated)
  • Miles (average weekly distance)

Analysis Performed

1. Data Preprocessing

• Data import and cleaning
• Outlier detection and handling
• Exploration of data types and structure

2. Descriptive Analytics

• Customer profile creation for each treadmill model using tables and visualizations (histograms, boxplots, countplots).

3. Insights and Inferences

• Examination of relationships between customer demographics, lifestyle factors, and treadmill model preferences.
• Calculation of marginal and conditional probabilities.

4. Recommendations

• Development of data-driven product recommendation strategies.
• Suggestions for targeted marketing campaigns based on customer profiles.

Repository Structure

• Aerofit.ipynb: Jupyter Notebook containing the complete analysis code.
• Aerofit_treadmill.csv: Dataset used in the analysis.
• aerofit.pdf: A PDF outlining the business problem statement.

How to Run the Analysis

1. Dependencies: Ensure you have the following libraries installed:

   • pandas
   • numpy
   • matplotlib
   • seaborn

2. Running the Notebook: Open Aerofit.ipynb in a Jupyter Notebook environment and execute the code.