https://github.com/eshaagarwa/mukesh_store_data_analysis
Mukesh_store_dataAnalysis done by Ms-Excel
https://github.com/eshaagarwa/mukesh_store_data_analysis
kaggle-dataset ms-excel powerbi
Last synced: 3 months ago
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Mukesh_store_dataAnalysis done by Ms-Excel
- Host: GitHub
- URL: https://github.com/eshaagarwa/mukesh_store_data_analysis
- Owner: eshaagarwa
- Created: 2024-03-11T12:26:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-11T13:06:24.000Z (over 1 year ago)
- Last Synced: 2025-07-03T21:41:36.550Z (3 months ago)
- Topics: kaggle-dataset, ms-excel, powerbi
- Homepage:
- Size: 8.52 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Mukesh_Store_Data_Analysis
## Objective
Mukesh Store wants to create an Annual sales report so that, Vrinda can understand their customers and grow more sales.
This project focuses on analyzing the sales data of Vrinda Store using Excel. The dataset was cleaned, processed, and analyzed to extract insights about the store's performance.
## Questions (KPIs)
- Compare the sales and orders using single chart.
- Which month got the highest sales and orders?
- Who purchased more - Men or Women?
- What are different order status in 2022?
- List top 5 states contribute into the sales?
- Relation between age and gender based on number of orders.
- Which Channel is contributing maximum to the sales?
- Highest selling category?
## Dataset used
- Mukesh Store Data
## Methodologies Used
- Data cleaning techniques were applied to handle missing values, outliers, and inconsistencies.
- Data processing scripts were developed to transform the data into a suitable format for analysis.
- Excel was utilized for data analysis, including calculations, visualizations, and the use of slicers for interactive filtering.
## Data Cleaning
- We checked for null values in each column. In Gender column we there were some inconsistencies so we replaced M with Men and W with Women.
- In QTY column we replaced One with 1and Two with 2
Now our Dataset is cleaned.
## Data processing
we created a new column Age Group by applying a rule i.e.
- If age >= 50 then Senior
- If age >= 30 then Adult and
- If age <30 then Teenager
Now we created a new "Month" column.
## Data Analysis
# Order Vs Sales
- We find that sales and number of orders are highest for march.
# Sales Men Vs Women
- We find that Women purchased more than Men
# Order Status
# 
- We find that 92 % orders delivered while 2 % cancelled.
# Sales Top 5 States
- We can see the contribution of Top States in above diagram.
# Orders: Age Vs Gender
- We find adults have more orders.
- For Adults, Senior and Teenager in all cases Women have more orders than Men
# Orders : Channel
- We find that highest orders were requested by Amazon and second highest orders for Myntra etc.
# Dashboard
## Project Insight
- Women customers are more likely to buy products compared to men (~65%).
- The states of Maharashtra, Karnataka and Uttar Pradesh are the top 3 product buyers.
- The adult age group (30-49 yrs) is max contributing (~50%) and buys the most products.
- The maximum number of products customer orders from Amazon, Flipkart and Myntra channels