Bike Sales Project

Overview:

This project involves analyzing bike sales data to extract meaningful insights. The project includes data cleaning, the creation of pivot tables, a sales dashboard, and data filtering using slicing techniques.

Project Structure:

• Data Cleaning: Initial step to clean and prepare the dataset for analysis.
• Pivot Tables: Used to summarize and analyze the cleaned data.
• Sales Dashboard: An interactive dashboard to visualize key sales metrics.
• Data Slicing: Applied to filter the data for more detailed analysis.

Steps:

1. Data Cleaning

Objective: Remove inconsistencies and prepare the dataset for analysis. Actions:

• Removed duplicate entries.
• Handled missing values.
• Corrected data types (e.g., dates, numerical values).
• Standardized categorical values.

2. Pivot Tables

Objective: Summarize and analyze the cleaned data. Actions:

• Created pivot tables to summarize sales by various dimensions such as date, product category, region, etc.
• Calculated key metrics such as total sales, average sales per unit, and sales trends over time.

3. Sales Dashboard

Objective: Visualize key sales metrics in an interactive and user-friendly manner. Actions:

• Created charts and graphs to visualize sales performance.
• Included key performance indicators (KPIs) such as total sales, top-selling products, and sales by region.
• Used slicers to allow users to filter the data by different dimensions such as date range, product category, and region.

4. Data Slicing

Objective: Enable detailed analysis through filtering. Actions:

• Implemented slicers and filters to allow users to view specific subsets of the data.
• Enabled detailed analysis by allowing users to drill down into specific aspects of the data.

How to Use

• Open the Excel File: Start by opening the Bike_Sales_Analysis.xlsx file.
• Explore the Pivot Tables: Navigate to the Pivot Tables sheet to see summarized data.
• Interact with the Dashboard: Use the slicers on the Sales Dashboard sheet to filter data and view different visualizations.
• Analyze Specific Data: Use data slicing features to filter the dataset based on specific criteria such as time period, product category, or region.