SALES DATA ANALYSIS
Tags: datascience, hnginternshi, python, dataanalytics
Published: 2024-06-29 13:30:39

## INTRODUCTION
Dear readers, in this article, I am going to share my findings of a data analysis project that I recently undertook during my HNG internship program. HNG is a fast paced program that helps developers and people in the tech field to practice their skills according to their domain. In this task, we were given sample datasets from Kaggle and we were asked to perform analysis on the project. The main aim of this project was to come up with a detailed data analysis report. Without wasting more time, let's dive into the details. 

## PREREQUISITES AND PREPARATION
**Install necessary tools**
You can use either Excel, SQL, Python or your preferred tool. In my case, I used Python. I installed the Python libraries for data analysis such as Pandas for data manipulation and Matplotlib for visualization. 
**Extract your data**
I extracted my data from kaggle using Pandas. The following is the link
https://www.kaggle.com/datasets/kyanyoga/sample-sales-data

**Perform your data cleaning and analysis**
After extracting your data, study it then start working on it

## KEY VARIABLES AND DATATYPES

- Numeric(Integers and Floats):
I. Sales
II. Price Each
III. Quantity Ordered
IV. Order Number

- Categorical:
I. Country
II. City
III. Customer Name
IV. Product Line
V. Deal Size
VI. Status

- Date:
I. Order Date

## INITIAL INSIGHTS

1. The dataset has 2823 rows and 25 columns
This is achieved by using .shape method on your Dataframe eg if your Dataframe is named df, below is how you can implement this.
`print(df.shape)`
2. There is a total of 7 products in the dataset that is unique names in the PRODUCTLINE column
3. The dataset has null values and these are found in 3 columns namely 
 State, Address line 2 and Postal code

**SALES PERFORMANCE BY PRODUCT**
Among the 7 products in the dataset, classic cars have the highest total sales of **3.27 million** while Trains have the lowest of **201 thousand**. Also, classic cars have the highest orders of **28,547** while trains have the least number of orders of **2,395**.
Below is a Pie chart showing sales of each product in percentage:
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6p4j5df4adacgxucpu9x.png)
**SALES PERFORMANCE BY TERRITORY**
EMEA has the highest number of sales while Japan has the lowest number of sales
**SALES PERFORMANCE BY MONTH**
November has the highest number of sales of **2.1 million** while June has the lowest number of sales of **454,756.78** thousand. 
Below is a figure that shows this data:
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/watxjhm2gamxkm9ovhku.png)
**PERFORMANCE BY STATUS**
**2617** products were successfully shipped
**60 **products that were initially ordered got cancelled

## CONCLUSION
Initial observation of the sales dataset reveals sales distribution over key metrics such as year, month, territory and even products. More insights to be found include:
a. Customers with highest number of sales
b. The countries with least number of sales and the ones with highest number of sales
c. Customers that have the highest number of orders cancelled

To view a detailed analysis of this project, check my repository on github below:
https://github.com/Doreen970/HNG_data

To learn about HNG internship, follow the following links:
https://hng.tech/internship
https://hng.tech/hire