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https://github.com/sayed-ashfaq/walmart_dataanalysis

This project focuses on extracting insights from raw data through various analyses, including exploring distributions, visualizing data with plots, and calculating measures of central tendency. The goal is to uncover meaningful patterns and trends within the dataset.
https://github.com/sayed-ashfaq/walmart_dataanalysis

boxplot-visualization matplotlib-pyplot normal-distribution numpy-library pandas-dataframe python seaborn

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This project focuses on extracting insights from raw data through various analyses, including exploring distributions, visualizing data with plots, and calculating measures of central tendency. The goal is to uncover meaningful patterns and trends within the dataset.

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README 

        
# Walmart Black Friday Purchase Behavior Analysis



## About Walmart

Walmart, an American multinational retail corporation, operates a vast network of supercenters, discount departmental stores, and grocery stores worldwide. With over **100 million customers globally**, Walmart is a leader in the retail industry.



This project analyzes customer purchase behavior during **Black Friday**, a significant shopping event generating high transaction volumes.



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## Objective

The primary objective of this project was to:

- Explore customer purchase behavior during Black Friday.

- Visualize spending patterns and central tendencies using box plots and distribution analysis.

- Assess whether purchase amounts follow a normal distribution.



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## Dataset

The dataset used for this analysis contains transactional data of customers who purchased products at Walmart during Black Friday.



**Dataset Features**:

- **`User_ID`**: Unique customer identifier.

- **`Product_ID`**: Unique product identifier.

- **`Gender`**: Customer’s gender (Male/Female).

- **`Age`**: Customer’s age group (in bins).

- **`Occupation`**: Encoded occupation identifier.

- **`City_Category`**: City category (A, B, or C).

- **`StayInCurrentCityYears`**: Number of years the customer has lived in their current city.

- **`Marital_Status`**: Marital status (0 = Single, 1 = Married).

- **`ProductCategory`**: Encoded product category.

- **`Purchase`**: Amount spent by the customer on a product.



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## Key Insights

1. **Gender-Based Spending**:

   - Box plots revealed distinct spending patterns between male and female customers.



2. **Age and Purchase Behavior**:

   - Central tendency measures showed variations in spending across age groups.



3. **City Category Trends**:

   - Customers from category "A" cities demonstrated higher purchase amounts compared to "B" and "C" cities.



4. **Normal Distribution Check**:

   - Assessed purchase amounts for normality using visual distribution analysis.



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## Process Overview

### 1. **Data Cleaning**:

   - Addressed missing values and ensured dataset consistency.



### 2. **Exploratory Data Analysis (EDA)**:

   - Visualized purchase patterns using:

     - **Box plots** to understand distribution and detect outliers.

     - **Histograms** to examine overall data spread and normality.

   - Analyzed central tendencies (mean, median, mode) for different demographic groups.



### 3. **Insights**:

   - Focused on visual and descriptive statistics to identify patterns in the dataset.



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## Tools and Libraries

This project was implemented using:

- **Python**:

  - `Numpy` & `Pandas` for data manipulation.

  - `Matplotlib` and `Seaborn` for visualization.

- **Jupyter Notebook** for interactive analysis and documentation.



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## Repository Structure

- **`data/`**: Contains the dataset used for analysis.

- **`notebooks/`**: Jupyter Notebooks documenting the analysis process.

- **`visualizations/`**: Saved plots and charts used in the project.

- **`README.md`**: Overview of the project (this file).



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## Acknowledgments

- **Dataset Source**: Provided by Scaler for this project.

- **Libraries Used**: Thanks to the Python data science community for open-source tools.



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## License

This project is for educational and non-commercial use only. Please credit the repository if using its resources.



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## Next Steps

Future extensions of this project could include:

1. Applying feature engineering to enhance insights.

2. Conducting statistical testing to validate observed patterns.

3. Developing predictive models to forecast purchase behavior.