abhisheks008 · abhisheks008 · Jan 27, 2024 · Jan 26, 2024
diff --git a/Global Retailers Analysis/Dataset/cos2017.csv b/Global Retailers Analysis/Dataset/cos2017.csv
diff --git a/Global Retailers Analysis/Dataset/cos2018.csv b/Global Retailers Analysis/Dataset/cos2018.csv
diff --git a/Global Retailers Analysis/Dataset/cos2019.csv b/Global Retailers Analysis/Dataset/cos2019.csv
diff --git a/Global Retailers Analysis/Images/Choropleth map - Retail revenue by country.png b/Global Retailers Analysis/Images/Choropleth map - Retail revenue by country.png
diff --git a/...ailers Analysis/Images/Distribution of Retail Revenue by Operational Format.png b/...ailers Analysis/Images/Distribution of Retail Revenue by Operational Format.png
diff --git a/Global Retailers Analysis/Images/Dominant Operational Formats Distribution.png b/Global Retailers Analysis/Images/Dominant Operational Formats Distribution.png
diff --git a/...alysis/Images/Radar Chart - Operational Format Performance Across Countries.png b/...alysis/Images/Radar Chart - Operational Format Performance Across Countries.png
diff --git a/Global Retailers Analysis/Images/Retail Revenue CAGR Over Time.png b/Global Retailers Analysis/Images/Retail Revenue CAGR Over Time.png
diff --git a/Global Retailers Analysis/Images/Retail Revenue by Country.png b/Global Retailers Analysis/Images/Retail Revenue by Country.png
diff --git a/Global Retailers Analysis/Images/Retail Revenue vs Net Income by Country.png b/Global Retailers Analysis/Images/Retail Revenue vs Net Income by Country.png
diff --git a/Global Retailers Analysis/Images/Retail Revenue vs Parent Company Revenue.png b/Global Retailers Analysis/Images/Retail Revenue vs Parent Company Revenue.png
diff --git a/Global Retailers Analysis/Images/Sunburst Chart - Retail Revenue Hierarchy.png b/Global Retailers Analysis/Images/Sunburst Chart - Retail Revenue Hierarchy.png
diff --git a/Global Retailers Analysis/Images/Top 10 Retailers by Revenue.png b/Global Retailers Analysis/Images/Top 10 Retailers by Revenue.png
diff --git a/... - Countries of Operation by Retail Revenue with Improved Hover Information.png b/... - Countries of Operation by Retail Revenue with Improved Hover Information.png
diff --git a/Global Retailers Analysis/Model/Global_Retailers_Analysis.ipynb b/Global Retailers Analysis/Model/Global_Retailers_Analysis.ipynb
diff --git a/Global Retailers Analysis/Model/README.md b/Global Retailers Analysis/Model/README.md
@@ -0,0 +1,69 @@
+<h1>Global Retailers Analysis</h1>
+
+**GOAL**
+
+To analyze the 'Global top 250 retailers' Dataset using Exploratory Data analysis.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/dhimananubhav/global-top-250-retailers
+
+**DESCRIPTION**
+
+Each year Deloitte publishes a list of the world’s 250 largest retailers. This dataset directory contains last 3 years of raw data retrieved from these reports.
+
+Out of these the top 250 retailers for year 2019 were analyzed for this project.
+
+The dataset contains the following columns:
+- rank
+- name	
+- country_of_origin
+- retail_revenue
+- parent_company_revenue
+- parent_company_net_income
+- dominant_operational_format
+- countries_of_operation
+- retail_revenue_cagr
+
+**WHAT I HAD DONE**
+
+* Checked for missing values and cleaned the data accordingly
+* Analyzed the data, found insights and visualized them accordingly.
+* Found detailed insights of different columns with one another using plotting libraries.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Seaborn
+4. Plotly
+
+
+**VISUALIZATION**
+![Retail Revenue by Country](<../Images/Retail Revenue by Country.png>)
+![Dominant Operational Formats Distribution](<../Images/Dominant Operational Formats Distribution.png>)
+![Retail Revenue vs Parent Company Revenue](<../Images/Retail Revenue vs Parent Company Revenue.png>)
+![Top 10 Retailers by Revenue](<../Images/Top 10 Retailers by Revenue.png>)
+![Retail Revenue CAGR Over Time](<../Images/Retail Revenue CAGR Over Time.png>)
+![Treemap - Countries of Operation by Retail Revenue](<../Images/Treemap - Countries of Operation by Retail Revenue with Improved Hover Information.png>)
+![Choropleth map - Retail revenue by country](<../Images/Choropleth map - Retail revenue by country.png>)
+![Radar Chart - Operational Format Performance Across Countries](<../Images/Radar Chart - Operational Format Performance Across Countries.png>)
+![Sunburst Chart - Retail Revenue Hierarchy](<../Images/Sunburst Chart - Retail Revenue Hierarchy.png>)
+
+For more visualizations and interactive plots, checkout .ipynb file :)
+
+**CONCLUSION**
+- Australia had the highest retail revenue in the year 2019, followed by US and Germany.
+- The Dominant operational formats for these retailers were supermarkets, hypermarkets, superstores, departmental stores etc.
+- Wal-Mart Stores, Inc. had the highest revenue, which was approximately five times to that of the retailer next to it, that was Costco Wholesale Corporation.
+- The correlation matrix shows the relation between parent company revenue and the net revenue generated by the retailer to be 0.88
+- The sunburst chart shows the retail revenue hierarchy in which the 4 out of top 5 spots are occupied by the US supermarket, hypermarkets, drugstores and home apparel retailers.
+
+
+
+**AUTHOR**
+
+- Code contributed by *Mariam* @ #JWoC_2024
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/mariam-m7084)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/mariam7084/)