Shopology : Decoding Customer shopping Trends

Project Description

This project focuses on Exploratory Data Analysis (EDA) of a retail dataset to uncover customer behavior, purchasing trends, and seasonal patterns. The dataset contains information about customers’ demographics, purchase history, payment methods, subscriptions, discounts, and reviews.

The main objectives of the analysis are:

To understand customer profiles (age, gender, location).

To analyze purchasing behavior such as most commonly bought items, purchase amounts, and seasonal spending trends.

To explore the impact of discounts and promotions on purchasing decisions.

To identify customer satisfaction patterns through review ratings.

To compare payment methods and subscription status across different customer groups.

The analysis is carried out using Python (Pandas, Seaborn, Matplotlib, Plotly), with a structured pipeline that includes:

Data Cleaning & Preprocessing – handling missing values, formatting data, and preparing it for analysis.

Univariate Analysis – studying individual features such as age distribution, most common items, and payment methods.

Bivariate & Multivariate Analysis – exploring relationships between variables (e.g., age vs. reviews, payment method vs. season).

Correlation Analysis – using heatmaps to identify strong relationships between numerical features.

Visual Storytelling – leveraging bar plots, scatter plots, box plots, and interactive visualizations to present insights.

Key Insights

The dataset reveals preferred items and seasonal trends, which can guide inventory planning.

Discounts and promo codes significantly affect purchasing decisions.

Certain age groups show stronger engagement with subscriptions and promotions.

Review ratings highlight areas for customer experience improvement.

Conclusion

This project demonstrates how EDA can transform raw retail data into actionable business insights, helping organizations improve marketing strategies, customer retention, and sales optimization.