Diwali-Sales-Analysis-Prediction

Overview: This project focuses on analyzing the Diwali Sales dataset obtained from Kaggle through exploratory data analysis (EDA). The goal is to gain valuable insights from the data and use them to improve business strategies effectively. Additionally, an interactive sales dashboard was created using Power BI to visualize key insights.

Objective:

• Improve customer experience by analyzing sales data.
• Conduct an in-depth analysis to increase revenue.
• Identify potential customers based on demographic insights.
• Recognize top-selling product categories to optimize inventory and sales strategies.

Dataset Information:

1. Customer details: Name, Gender, Age, Marital Status, Location, and Occupation.
2. Order details: Product ID, Product Name, Product Category, and Purchase Amount.

Problem Statement: The objective of this analysis is to identify patterns and trends in the Diwali sales data to improve customer targeting and maximize business profitability. By understanding customer demographics, purchase behaviors, and top-selling products, businesses can enhance customer experience, plan inventory efficiently, and optimize sales strategies for future Diwali sales.

Features:

1. Customer Segmentation: Identify potential customers across different states, occupations, genders, and age groups to enhance targeted marketing.
2. Sales Insights: Determine the most popular product categories and best-selling products to optimize inventory management.
3. Data Visualization: Use graphical representations to analyze trends in sales, customer demographics, and product preferences.

Tasks

1. Data Cleaning & Manipulation: Ensure data accuracy and consistency using Pandas and NumPy.
2. Exploratory Data Analysis (EDA): Utilize Python libraries like Pandas, NumPy, Matplotlib, and Seaborn to analyze and visualize data trends.
3. Customer Segmentation: Analyze customer attributes to facilitate targeted marketing strategies.
4. Sales Improvement Strategies: Identify top-selling products and customer preferences for better sales planning.
5. Dashboard Creation: Develop an interactive Power BI dashboard to present key insights.

Repository Contents:

1. Diwali_Sales_Analysis.ipynb - Jupyter Notebook containing the entire analysis.
2. dataset/ - Folder containing the Diwali Sales dataset.
3. PowerBI_Dashboard.pbix - Power BI file for interactive sales dashboard.
4. README.md - Detailed project documentation.

How to Run and Test the Analysis:

• 1.Clone this repository: Install Python and required libraries (numpy, pandas, etc.) using pip. https://github.com/Parthadee/Diwali-Sales-Analysis-Prediction.git cd Diwali-Sales-Analysis-Prediction
• 2.Install dependencies: pip install -r requirements.txt
• 3.Run the Jupyter Notebook or Python script: The script will generate a dataset in .csvformat for further analysis.
• 4.Testing: jupyter notebook analysis.ipynb OR Google Colab. Run the Diwali_Sales_Analysis.ipynb file

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Dataset

• https://www.kaggle.com/datasets/nazishjaveed/diwale-sale-dataset
• The dataset used for this project is sourced from Kaggle. It contains details of customers and their purchases made during Diwali sales.

  Output Screen-shots

  Dashboard in PoweBI

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