Diwali Sales Data Analysis

Overview

This project involves a comprehensive analysis of Diwali sales data using Python and Jupyter Notebook. The primary goal is to clean the data and then perform various analytical tasks to uncover insights and trends related to Diwali sales. By analyzing sales data, the project aims to identify key factors that drive sales during the Diwali season, understand customer behavior, and make data-driven decisions to improve future sales strategies.

Project Structure

Features

• Data Cleaning: Handling missing values, removing duplicates, correcting data types, and standardizing formats.
• Data Analysis: Analyzing sales trends, identifying top-performing products, understanding customer demographics, and visualizing key insights.

Detailed Project Description

1. Data Cleaning:

   • Loading Data: Importing raw sales data into the notebook.
   • Handling Missing Values: Identifying and filling or removing missing values to ensure data integrity.
   • Removing Duplicates: Ensuring each record is unique to prevent skewed analysis.
   • Correcting Data Types: Converting data to appropriate types for accurate analysis.
   • Standardizing Formats: Ensuring consistent data formats for easy manipulation and analysis.

2. Data Analysis:

   • Sales Trends: Visualizing sales over time to identify peak periods and trends.
   • Product Performance: Analyzing which products are best-sellers and examining product categories.
   • Customer Insights: Understanding customer demographics, purchase behavior, and preferences.
   • Visualizations: Creating graphs and charts to present findings in an easily interpretable manner.

Tech Stack

• Python: The core programming language used for data manipulation, analysis, and visualization.
• Jupyter Notebook: An interactive computing environment for developing and presenting the project.
• Pandas: A Python library for data manipulation and analysis, particularly useful for handling tabular data.
• NumPy: A library for numerical computing, providing support for arrays and matrices.
• Matplotlib: A plotting library for creating static, interactive, and animated visualizations.
• Seaborn: A statistical data visualization library based on Matplotlib, used for making attractive and informative statistical graphics.

Installation

Prerequisites

• Python 3.x
• Jupyter Notebook

Clone the Repository

git clone https://github.com/Prabal-verma/Data-Analysis-Project-using-python.git
cd diwali-sales-analysis

###Contributing Contributions are welcome! Please fork this repository and submit a pull request with your improvements or new features.