The group aproject analyzes electronic data sales to gain insights, follow trends, uncover patterns, see how well a product performs, and its sales metrics. The different analyses covers multiple aspects of the raw data, including the customer demographic, sales performance, product types, and even the payment methods.

The language used is Python, and the libraries imported are:

• Matplotlib

• NumPy

• Pandas

• Seaborn

• Altair

• WordCloud

• mpl_toolkits

  Meanwhile, the raw data 'electronic_sales.csv' contains info about:

  • customer demographics
  • product details
  • payment methods
  • purchase information
  • shipping types
  • ratings
  • and; add on purchases

FINDINGS Product Preferences

• Smartphones were the highest-selling product category
• Product appeal spans multiple generations, with a slight skew towards younger customers

Payment Methods Credit Card (29.3%) and PayPal (29.0%) are the most popular payment methods Cash is the least used method (12.5%)

Sales Trends

• Sales peaks observed in December 2023 and May-July 2024
• Sales lows occurred in October 2023, March 2024, and August 2024

Customer Satisfaction

• Product ratings typically fall between 3.0 and 3.5, indicating neutral satisfaction

Add-ons Analysis

• Detailed breakdown of various add-on purchases and their popularity

GRAPH VISUALIZATIONS Product type distribution Rating distribution histogram Age distribution by product type Payment methods Products sold over time Gender-based product preferences Add-on purchase analysis Shipping type analysis

FILES

• Electronic_sales.csv
• Data Visualizations
• Python Codes