This project aims to analyze bike sales data using Microsoft Excel. The data was processed and analyzed using various techniques, including merging and cleaning tables, removing duplicates, normalizing data, creating pivot tables, and generating a dashboard vizualization. The final output is a comprehensive dashboard that explores the bike sales data and provides filtering options.
The initial dataset had the following structure:
| ID | Marital Status | Gender | Income | Children | Education | Occupation | Home Owner | Cars | Commute Distance | Region | Age | Purchased Bike |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12496 | M | F | $40.000,00 | 1 | Bachelors | Skilled Manual | Yes | 0 | 0-1 Miles | Europe | 42 | No |
| 24107 | M | M | $30.000,00 | 3 | Partial College | Clerical | Yes | 1 | 0-1 Miles | Europe | 43 | No |
To prepare the data for analysis, the following steps were performed:
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Merge and clean tables using VLOOKUP: Relevant tables were merged using the VLOOKUP function to combine information from multiple sources into a single dataset.
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Removing duplicates: Duplicate entries in the dataset were identified and removed to ensure data integrity and avoid redundancy.
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Cleaning and normalizing data: The dataset was cleaned by fixing inconsistencies, such as formatting issues, misspelled values, or incorrect data types. Data normalization techniques were applied to ensure consistent representation across the dataset.
The cleaned and prepared dataset was then analyzed using the following techniques:
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Pivot tables: Pivot tables were created to summarize and aggregate the data based on various dimensions and measures, allowing for deeper insights and easy exploration of the dataset.
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Chart analysis: Visualizations were generated to gain a better understanding of the data and identify patterns or trends related to bike sales.
A comprehensive dashboard was developed to provide an interactive and user-friendly interface for exploring the bike sales data. The dashboard incorporates the analyzed data, including pivot tables, charts. It allows users to apply various filters to customize their analysis and gain specific insights based on their requirements.
To use this project, follow the instructions below:
- Open the provided Excel file containing the prepared dataset and analysis results.
- Navigate to the "dashboard" sheet/tab within the Excel file.
- Interact with the dashboard by applying filters, exploring pivot tables, and analyzing the visualizations.
- Customize the filters and settings according to your analysis needs.