https://github.com/shubhamgoyal575/diwali_sales_analysis
https://github.com/shubhamgoyal575/diwali_sales_analysis
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/shubhamgoyal575/diwali_sales_analysis
- Owner: shubhamgoyal575
- Created: 2024-06-01T10:01:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-01T10:20:53.000Z (over 1 year ago)
- Last Synced: 2024-06-01T11:23:04.717Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 589 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Diwali Sales Analysis (Python)
## Data Cleaning and Manipulation
In this step, the dataset is cleaned and manipulated to ensure it is suitable for analysis. The following actions are performed:
- Handling missing values
- Removing duplicates
- Converting data types
## Exploratory Data Analysis (EDA)
Using `pandas`, `matplotlib`, and `seaborn`, EDA is performed to uncover patterns and insights in the data. This includes:
- **Visualizing sales trends over time**
- Analyzing customer demographics
## Customer Insights
To improve customer experience, we analyze the data to identify potential customers based on various factors:
- **States**: Identify states with the highest number of potential customers.
- **Occupation**: Analyze occupations to find which groups are more likely to purchase.
- **Gender and Age Groups**: Understand the gender and age distribution of customers.
## Sales Insights
To enhance sales strategies, we identify the most selling product categories and products:
- **Product Categories**: Determine which categories are most popular.
- **Individual Products**: Identify top-selling products.
## Conclusion
The Diwali Sales Analysis project provides valuable insights that can help businesses improve customer experience and optimize sales strategies during the Diwali season. By understanding customer demographics and sales trends, businesses can make informed decisions to boost their performance.