Sales analysis of an online electronics store. This project contains basic analysis of the dataset from an online electronic store in US and aims to answer some basic questions that may arise for the store manager/owner giving a much better insight about the store and how to increase the productivity. The dataset can be found in the Data Directory. The dataset is of the year 2019 which is monthly segregated. The combined dataset is in the all_data.csv file. Step by step execution can be found in the 'Sales-Analysis.ipynb' File.
- Getting main info about data. (Top values, unique values, NaN amount, how orders are presented, etc.
- Cleaning data from NaN.
- Adding necessary info to perform analysis.
- Answering questions.
- Plotting graphical results.
1. What was the best month for sales?
Answer
2. What city sold the most product?
Answer
3. What time should we display advertisements to maximize the likelihood of purchases?
Answer
As per the chart, the advertisements should be displayed between 11am to 12pm and between 6pm to 8pm to maximize the likelihood of purchase.
4. What products are most often sold together?
Answer
The 2 most common products sold together are iPhone and Lightning Charging Cable.
5. What product is sold the most?
Answer
The AAA Batteries are the most sold product followed by AA Batteries with USB-C and Lightning Charging Cables coming close. Since these items are more cheaper than many other items there is a possibility of this being the reason for most selling of these items.
This project is licensed under the MIT License - see the LICENSE file for details.