https://github.com/keerthanapalanikumar/bike-sales-using-excel

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.
https://github.com/keerthanapalanikumar/bike-sales-using-excel

data-slicing datacleaning datavisualization-project pivot-tables sales-dashboard

Last synced: 6 days ago
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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.

• Host: GitHub
• URL: https://github.com/keerthanapalanikumar/bike-sales-using-excel
• Owner: KeerthanaPalanikumar
• Created: 2024-06-24T15:29:37.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-24T15:43:13.000Z (5 months ago)
• Last Synced: 2024-06-24T17:41:29.832Z (5 months ago)
• Topics: data-slicing, datacleaning, datavisualization-project, pivot-tables, sales-dashboard
• Homepage:
• Size: 189 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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.