Sportswear Manufacturer Sales Analysis

Data Analysis Project: Tracking Sales Trends for a Sportswear Manufacturer

This project aims to analyze the sales performance of a sportswear manufacturer in the US over a two-year period. The analysis will leverage a dataset containing more than 9,000 records and 13 columns.

The project will utilize a publicly available dataset downloaded from Kaggle.This project will combine the power of Python for in-depth analysis and visualization with the user-friendly interface of Excel for creating an engaging dashboard. By combining these approaches, the project will provide valuable insights into the sportswear manufacturer's sales performance to help in guiding future sales strategies

Part 1: Exploratory Data Analysis (EDA), Data Cleaning, and Analysis in Python

Libraries

Pandas, Matplotlib, Seaborn, datetime

Objectives

Gain initial insights into the data through visualization and summary statistics.

Identify and address any data quality issues such as missing values, outliers, or inconsistencies.

Analyze key sales metrics including sales by state, sales method, sales by product category, and sales over time.

Investigate relationships between variables like sales, geolocation (state and region), product, retailer, and sales method.

Expected Outcomes

A comprehensive understanding of the data distribution and potential trends.

Cleaned and prepared data ready for further analysis and visualization.

Descriptive statistics and visualizations (e.g.,bar charts, pie charts, line plots) to explore sales patterns.

Identification of factors influencing sales performance.

Part 2: Sales Performance Dashboard in Excel

Objectives

Create an interactive dashboard to visualize key sales metrics and trends identified in the Python analysis.

Track sales performance by location (state, region), retailer, product category, and sales method for a two-year period.

Allow users to explore and understand sales patterns easily.

Charts

Sales by State: Analyze regional sales performance and identify potential target markets.

Sales by Sales Method: Compare performance across in-store, online, and outlet channels.

Sales by Product: Explore which product categories contribute most to overall sales.

Sales by Retailer: Identify top-performing retailers and potential areas for partnership growth.

Sales Over Time: Track sales trends and identify seasonal patterns.

Expected Outcomes

An interactive dashboard providing a comprehensive overview of the sportswear manufacturer's sales performance.

Ability for users to drill down into specific sales metrics and gain deeper insights.

Improved decision-making through data-driven insights into sales trends and customer behavior.