Exploratory Data Analysis of a Sales dataset

Table of Contents

• About the Project
  • The Dataset
  • Tasks
  • Questions
  • Built With
• Fork the Repo and Contribute
• Contact

About the Project

In this Sales Analysis Jupyter notebook, we perform Exploratory Data Analysis of the huge Sales data by following the tasks mentioned below. We try to answer the following set of real-world business questions to draw insights from this huge Sales dataset.

The Dataset

The dataset contains 12 CSV files containing sales details for the 12 months of the year 2019. The naming convention is as follows: Sales_[MONTH_NAME]_2019
Each file contains anywhere from around 9000 to 26000 rows and 6 columns. The columns are as follows:
Order ID, Product, Quantity Ordered, Price Each, Order Date, Purchase Address

Tasks

• Task 1: Merge 12 months of sales data into a single CSV file
• Task 2: Create new 'Month' column from 'Order Date' column
• Task 3: Add a Sales column
• Task 4: Add a City column

Questions

• Question 1: What was the best month for sales? How much was earned that month?
• Question 2: What city has the highest sales?
• Question 3: What time should we display advertisements to maximize likehood of customers buying products?
• Question 4: What products are most often sold together?
• Question 5: What product sold the most? Why do you think it sold the most?

Built With

• Jupyter Notebook
• Pandas
• Matplotlib

Fork the Repo and Contribute

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project (click on Fork in the top-left corner)
2. Create your Feature Branch (git checkout -b feature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature)
5. Open a Pull Request

Contact

Sinjoy Saha

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