A Python-based project analyzing sales data to extract meaningful insights, such as identifying the best month for sales, top-performing cities, optimal advertisement timing, and products often sold together.
- What was the best month for sales?
Answer: December was the best month for sales, with total earnings of over $4,6 mil.
- What city had the highest number of sales?
Answer: San Francisco had the highest number of sales, making it the top-performing city.
- What time should we display advertisements to maximize the likelihood of customers buying products?
Answer: The optimal advertisement times are around 11 AM and 7 PM, when customer purchase activity peaks. Unusually high average sales in the early hours, possibly due to online shoppers in different time zones or late-night impulse buying. After work hours (~7pm), there's another surge in purchases when customers are more relaxed and have free time.
- What products are most often sold together?
Answer: The most common product combination sold together is iPhone and Lightning Charging Cable, followed by Google Phone and USB-C Cable.
- The dataset contains sales records including order ID, product, quantity ordered, price, order date, purchase address, etc.
- Data preprocessing includes:
- Handling missing values
- Parsing dates
- Extracting useful features (e.g., month, city, purchase hour)
- Identifying product combinations
- Dataset and Questions: Provided by Keith Galli
- Solutions: Implemented by me