Sales Data Analysis Project Overview This project involves analyzing a sizable sales dataset to identify patterns, best-selling items, and key revenue indicators. The goal is to provide data-driven insights to support business decision-making and improve sales strategies.
Objectives Total Sales Calculation: Compute the overall revenue generated from sales. Sales Trends Analysis: Analyze sales trends over different periods (e.g., daily, monthly, yearly). Best-Selling Products: Identify the products with the highest sales volumes. Visualizations: Create visual representations of the data to better understand sales performance and trends. Data
The sales dataset includes information such as:
Order ID Product Name Quantity Ordered Price Each Order Date Purchase Address Steps for Analysis Load and Inspect the Data:
Read the dataset into a pandas DataFrame. Inspect the first few rows and data types to understand the structure of the data. Data Cleaning:
Remove unnecessary columns. Handle missing values by either removing or imputing them. Correct data types for date and numeric columns. Eliminate duplicate entries. Sanitize specific columns by trimming whitespace and ensuring consistent formatting. Data Transformation:
Extract additional features from the date column, such as day, month, and year. Calculate total sales for each order by multiplying quantity ordered by price each. Analysis and Visualization:
Total Sales: Sum up the total sales for the entire dataset. Sales Trends: Plot sales over time to identify trends and patterns. Best-Selling Products: Determine the products with the highest total sales and visualize the results. Revenue Indicators: Analyze key metrics such as average order value, total orders, and revenue by product category.
Tools and Libraries Python: The primary programming language for this analysis. jupyter NOtebook/Kaggle : Jupyter Needs to be installed but a replacement for Juppyter U can use the Kaggle - www.Kaggle.com Pandas: For data manipulation and analysis. Matplotlib and Seaborn: For creating visualizations.