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 1 | A Comparative Analysis of Warehouse vs. Retail Sales Performance
 2 | Minor Project – INT375 | Data Science Specialization
 3 | 
 4 | Dataset Source
 5 | This analysis is based on a public dataset sourced from Data.gov. The dataset includes comprehensive sales records over time, categorized by item types, sales channels, and suppliers.
 6 | 
 7 | Dataset Overview
 8 | Rows: 307,000+
 9 | 
10 | Columns: Year, Month, Item Type, Retail Sales, Warehouse Sales, Transfers, etc.
11 | 
12 | Item Categories: WINE, BEER, LIQUOR, NON-ALCOHOL, STR_SUPPLIES, and more
13 | 
14 | Why Exploratory Data Analysis (EDA)?
15 | EDA is a critical initial step in any data science project. It allows us to:
16 | 
17 | Understand data distribution and structure
18 | 
19 | Detect missing or anomalous values
20 | 
21 | Discover relationships and patterns
22 | 
23 | Prepare data for further modeling or insights
24 | 
25 | Steps Performed in EDA:
26 | Cleaned data (missing values, duplicates)
27 | 
28 | Created Date and Total Sales columns
29 | 
30 | Performed univariate and bivariate analysis
31 | 
32 | Visualized trends using histograms, bar charts, line plots, and scatter plots
33 | 
34 | Project Objectives & Analysis
35 | 1. Monthly, Quarterly & Yearly Sales Trends
36 | Clear seasonality observed
37 | 
38 | Q2 and Q4 had highest average sales, likely due to seasonal demand
39 | 
40 | 2. Retail vs Warehouse Sales Comparison
41 | Warehouse sales contributed ~75% of total sales
42 | 
43 | Retail showed steadier performance over time
44 | 
45 | 3. Top-Selling Product Categories
46 | WINE and LIQUOR were top performers in sales revenue
47 | 
48 | BEER had high volume but moderate total value
49 | 
50 | 4. Price vs Quantity Sold Correlation
51 | A negative correlation suggests that as prices increase, quantity sold decreases
52 | 
53 | Reflects consumer price sensitivity
54 | 
55 | 5. Peak Sales Months
56 | November & December showed spikes in sales across categories
57 | 
58 | Likely driven by holiday season demands
59 | 
60 | Key Visualizations
61 | Histogram: Total Sales Distribution
62 | 
63 | Bar Chart: Sales by Item Type
64 | 
65 | Line Plot: Monthly Sales Trends
66 | 
67 | Scatter Plot: Price vs Quantity Correlation
68 | 
69 | Pie Chart: Channel-wise Contribution
70 | 
71 | Conclusion
72 | This EDA provided valuable insights into:
73 | 
74 | How different channels (retail vs. warehouse) perform
75 | 
76 | Seasonality in sales patterns
77 | 
78 | The role of product type and pricing on revenue and quantity
79 | 
80 | Customer behavior trends that can inform future strategy
81 | 
82 | It strengthened skills in:
83 | 
84 | Data preprocessing & cleaning
85 | 
86 | Visualization using Matplotlib and Seaborn
87 | 
88 | Analytical thinking and storytelling through data
89 | 
90 | Project Repository
91 | You can explore the full analysis and code here:
92 | GitHub Repository – Click Here ( https://github.com/ADARSH1805/warehouse-vs-retail-sales-analysis )
93 | 
94 | Let’s Connect!
95 | If you found this project interesting or helpful, feel free to ⭐️ the repo, give feedback, or connect with me on LinkedIn!
96 | I’m open to collaboration, ideas, and data discussions!
97 | 


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