https://github.com/sumit-sinha9/sales-analysis
Analyzing 12 months worth fo Sales data
https://github.com/sumit-sinha9/sales-analysis
data-analysis pandas python visualization
Last synced: about 2 months ago
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Analyzing 12 months worth fo Sales data
- Host: GitHub
- URL: https://github.com/sumit-sinha9/sales-analysis
- Owner: sumit-sinha9
- Created: 2025-04-14T16:13:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-14T16:17:42.000Z (about 1 year ago)
- Last Synced: 2025-04-15T04:15:59.630Z (about 1 year ago)
- Topics: data-analysis, pandas, python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 6.14 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Sales-Analysis
Here I have taken 12 months worth of Sales Data and made some cleaning, preprocessing, analysis and visualisation on product sales.
## Datasets used
Monthly Sales data for all 12 months, files can be found in Sales Data folder
## Process
- As a first step I have gathered data from all 12 month datasets and stored it in all_data.csv file
- Next is Data cleaning & Preprocessing to get clean and proper dataset. As a part of pre processing I have added some new columns in datasets to make it more meaningfull
- After getting complete dataset, I started to explore deep into dataset to find answers to below questions.
- Finding which month produced max sales
- Finding which city produced maximum sales
- What time should we display advertisements to maximise sales
- What products are often sold together
- Finding which product had maximum sales and why
- And I visualized below data as graphs in ipynb file
- Visualizing monthly Sales data
- Visualizing Sales data on each city
- Sales acheived by hours (for all cities and specifically for San Fransisco)
- Product sales - count
- Product sales - count and adding Price as secondary y axis