This project involves the analysis and visualization of Diwali sales data to understand the purchasing behavior of customers across different states, gender, and age groups. The dataset used for this analysis is stored in the Diwali_Sales_Data.csv file.

The main goal of this project is to identify which group (state, gender, age group) orders the most items during the Diwali sales period. By analyzing the data and creating visualizations, we aim to gain insights into consumer behavior and preferences during this festive season.

• Diwali_Sales_Data.csv: The CSV file containing the raw data used for analysis.
• Diwali_Sales_Analysis.ipynb: Jupyter Notebook file containing the Python code for data analysis and visualization.
• README.md (this file): Provides an overview of the project and instructions for running the code.

To run the code in the Jupyter Notebook, you will need the following Python libraries installed:

• NumPy
• Pandas
• Matplotlib
• Seaborn

1. Data Loading: Load the Diwali sales data from the CSV file into a Pandas DataFrame.
2. Data Cleaning: Perform data cleaning and preprocessing to handle missing values, incorrect data types, and any inconsistencies in the dataset.
3. Exploratory Data Analysis (EDA): Analyze the data to identify patterns, trends, and insights. This includes examining the distribution of orders across different states, genders, and age groups.
4. Visualization: Create visualizations such as bar charts, histograms, and heatmaps to represent the findings of the analysis effectively.
5. Conclusion: Summarize the key findings from the analysis and draw conclusions about which group orders the most items during Diwali sales.

1. Ensure that you have Python installed on your system.
2. Install the required libraries by running pip install numpy pandas matplotlib seaborn.
3. Clone or download the repository to your local machine.
4. Open the Diwali_Sales_Analysis.ipynb file using Jupyter Notebook or any compatible platform.
5. Run the code cells in the notebook sequentially to perform the analysis and generate visualizations.
6. Review the findings and conclusions presented in the notebook.