├── Global Superstore for python (Ca-2).csv ├── README.md ├── Screenshot 2025-04-12 125119.png ├── Screenshot 2025-04-12 125129.png ├── Screenshot 2025-04-12 125139.png ├── Screenshot 2025-04-12 125150.png ├── Screenshot 2025-04-12 125200.png └── programm of project.py /Global Superstore for python (Ca-2).csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Global Superstore for python (Ca-2).csv -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Global-Superstore-python-project 2 | 📦 Global Superstore Sales Analysis 3 | This project explores sales and profit trends using the Global Superstore dataset, sourced from GitHub. It uncovers insights across regions, product categories, and customer segments. The analysis identifies key performance drivers and reveals patterns to support smarter business decisions. 4 | 5 | 📊 Project Objectives 6 | 7 | Monthly Sales Trend 8 | 9 | Top 10 Most Profitable Products 10 | 11 | Average Sales by Ship Mode 12 | 13 | Sales and Profit by Region 14 | 15 | Total Profit by Region 16 | 17 | 🗂️ Data Includes 18 | 19 | Product names and categories 20 | 21 | Sales and profit values 22 | 23 | Region, country, and segment data 24 | 25 | Order and ship dates 26 | 27 | Ship modes 28 | 29 | 🛠️ Tools & Libraries 30 | Python 🐍 – Pandas, NumPy, Matplotlib, Seaborn, and Python IDLE were used to analyze, clean, and visualize the data. 31 | 32 | 📈 Visualizations & Insights 33 | 34 | Line Chart: Monthly Sales Trend 35 | 36 | Bar Charts: Top 10 Profitable Products, Average Sales by Ship Mode, and Total Profit by Region 37 | 38 | Combo Chart: Sales and Profit by Region 39 | Key findings show that Office Supplies and Technology drive the most profit, the US leads in revenue, Standard Class is the most used shipping mode, and profit margins vary across categories and regions. 40 | 41 | 📌 Purpose 42 | The goal is to help businesses understand profitability by product and region, optimize inventory and marketing strategies, and discover actionable insights for data-driven decisions. 43 | 44 | -------------------------------------------------------------------------------- /Screenshot 2025-04-12 125119.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Screenshot 2025-04-12 125119.png -------------------------------------------------------------------------------- /Screenshot 2025-04-12 125129.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Screenshot 2025-04-12 125129.png -------------------------------------------------------------------------------- /Screenshot 2025-04-12 125139.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Screenshot 2025-04-12 125139.png -------------------------------------------------------------------------------- /Screenshot 2025-04-12 125150.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Screenshot 2025-04-12 125150.png -------------------------------------------------------------------------------- /Screenshot 2025-04-12 125200.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Maheshkumar08032005/Global-Superstore-python-project/e95249050e7052edb808fa3b58e890430b4db9fa/Screenshot 2025-04-12 125200.png -------------------------------------------------------------------------------- /programm of project.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | 6 | sns.set(style="whitegrid") 7 | 8 | # Load dataset 9 | df = pd.read_excel(r"E:\Python Report\Global Superstore for python (Ca-2).xlsx") 10 | 11 | # Dataset Overview 12 | print("\nDataset Overview\n", df) 13 | print("\nHead\n", df.head()) 14 | print("\nTail\n", df.tail()) 15 | print("\nSummary\n", df.describe()) 16 | print("\nInfo\n") 17 | df.info() 18 | print("\nColumns\n", df.columns) 19 | print("\nShape\n", df.shape) 20 | print("\nNull Values\n", df.isnull().sum()) 21 | 22 | 23 | # Objective 1: Monthly Sales Trend 24 | df['Order Date'] = pd.to_datetime(df['Order Date']) 25 | monthly_sales = df.groupby(df['Order Date'].dt.to_period('M'))['Sales'].sum().to_timestamp() 26 | top4 = monthly_sales.nlargest(4) 27 | plt.figure(figsize=(10, 5)) 28 | plt.plot(monthly_sales, label='Monthly Sales', color='steelblue') 29 | plt.scatter(top4.index, top4.values, color='green', s=100, label='Top 4 Peaks') 30 | plt.title("Monthly Sales Trend") 31 | plt.xlabel("Month") 32 | plt.ylabel("Total Sales") 33 | plt.xticks(rotation=45) 34 | plt.legend() 35 | plt.tight_layout() 36 | plt.show() 37 | 38 | 39 | 40 | # Objective 2: Top 10 Most Profitable Products 41 | top_products = df.groupby('Product Name')['Profit'].sum().nlargest(10).reset_index() 42 | plt.figure(figsize=(10, 6)) 43 | sns.barplot(data=top_products, x='Profit', y='Product Name', color='teal') 44 | plt.title("Top 10 Most Profitable Products") 45 | plt.tight_layout() 46 | plt.show() 47 | 48 | 49 | 50 | # Objective 3: Average Sales by Ship Mode 51 | print("\nObjective 3: Average Sales by Ship Mode") 52 | avg_sales_shipmode = df.groupby('Ship Mode')['Sales'].mean().reset_index() 53 | 54 | plt.figure(figsize=(8, 5)) 55 | plt.bar(avg_sales_shipmode['Ship Mode'], avg_sales_shipmode['Sales'], color='purple', edgecolor='black') 56 | plt.title("Average Sales by Ship Mode") 57 | plt.xlabel("Ship Mode") 58 | plt.ylabel("Average Sales") 59 | plt.tight_layout() 60 | plt.show() 61 | 62 | 63 | 64 | # Objective 4: Sales and Profit by Region 65 | region_data = df.groupby('Region')[['Sales', 'Profit']].sum().reset_index() 66 | x = np.arange(len(region_data)) 67 | w = 0.35 68 | plt.figure(figsize=(10, 6)) 69 | plt.bar(x - w/2, region_data['Sales'], w, label='Sales', color='orange') 70 | plt.bar(x + w/2, region_data['Profit'], w, label='Profit', color='blue') 71 | plt.xticks(x, region_data['Region'], rotation=30, ha='right') 72 | plt.title("Sales and Profit by Region") 73 | plt.legend() 74 | plt.tight_layout() 75 | plt.show() 76 | 77 | 78 | # Objective 5: Total Profit by Region 79 | print("\nObjective 5: Total Profit by Region") 80 | df.groupby('Region')['Profit'].sum().plot(marker='o', linestyle='-', color='blue', figsize=(8,5)) 81 | plt.title("Profit by Region") 82 | plt.xlabel("Region") 83 | plt.ylabel("Total Profit") 84 | plt.grid(True) 85 | plt.tight_layout() 86 | plt.show() 87 | 88 | --------------------------------------------------------------------------------