DiwaliSales

This repository contains the code and analysis for the Diwali Sales Data project. The project involves exploring and analyzing sales data related to Diwali festivities. The dataset used for this analysis is named "Diwali Sales Data.csv".

Table of Contents

• Project Overview
• Dataset
• Analysis
• Visualizations
• Dependencies
• Usage
• Contributing
• License

Project Overview

The goal of this project is to perform data analysis on Diwali sales data to gain insights into various aspects such as sales amounts, product preferences, age group distribution, and more. The analysis aims to uncover patterns, trends, and relationships within the dataset.

Dataset

The dataset used for this project is named "Diwali Sales Data.csv". It contains the following columns:

• User_ID
• Cust_name
• Product_ID
• Gender
• Age Group
• Age
• Marital_Status
• State
• Zone
• Occupation
• Product_Category
• Orders
• Amount

Analysis

The analysis conducted in this project includes:

• Exploratory Data Analysis (EDA) to understand the dataset's structure and characteristics.
• Visualizations to showcase insights and patterns within the data.
• Correlation analysis to identify relationships between different variables.
• Distribution analysis to understand sales amount distribution across various factors.
• Product preferences analysis based on gender and other attributes.

Visualizations

Various visualizations have been created using libraries like Matplotlib, Seaborn, and Plotly. These visualizations include:

• Bar plots to visualize product category preferences.
• Box plots to display distribution and variability of sales amounts.
• Scatter plots to explore relationships between sales amount, age group, and state.

Dependencies

The following Python libraries were used for this project:

• Pandas
• Matplotlib
• Seaborn
• Plotly

Usage

To run the code and reproduce the analysis and visualizations, you need to have the required libraries installed. You can use the Jupyter Notebook or Python scripts provided in this repository.

1. Clone this repository to your local machine.

2. Install the required dependencies using the following command:

   Copy the code:

   pip install pandas matplotlib seaborn plotly

3. Open the Jupyter Notebook or Python script files to run the code.

Contributing

Contributions to this project are welcome! If you have any suggestions, improvements, or additional analyses to add, feel free to create a pull request.

License

This project is licensed under the MIT License