The Struggling Bike Company: Analyzing the Viability of Selling Bicycles 🚲📊

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Table of Contents 📘

1. Project Overview
2. Data
3. Project Goal
4. Data Exploration
5. Data Cleaning
6. Exploratory Data Analysis (EDA)
7. Key Insights
8. Conclusion

Project Overview 🚀

In this project, I investigate a bicycle retail business within the European market to identify opportunities for enhancing growth. Delving deep into the company's sales data, analyzing business performance, and uncovering pivotal shifts in strategies, I use detailed visualizations to paint a clear picture of the company's journey and draw insights on driving profitability.

Data 🗂️

The data consists of sales records with features like Date, Year, Month, Customer Age, Customer Gender, Country, State, Product Category, Sub Category, Quantity, Unit Cost, Unit Price, and Revenue. This rich dataset allows me to conduct comprehensive exploratory and profitability analyses.

Dataset Source

Project Goal 🎯

The primary goal is to develop strategies that minimize risk while boosting profits by 30%. This involves identifying the top-performing entities in the industry and focusing on factors driving their success.

Data Exploration 🔍

I start off by examining the distribution of categorical and numerical columns. During the business profitability analysis, I found negative values in the Margin column, indicating losses on some sales.

Data Cleaning 🧹

The cleaning process involved removing unnecessary columns, dropping missing values, and optimizing datatypes to prepare the data for further analysis.

Exploratory Data Analysis (EDA) 📊

I segmented the EDA into four phases, reflecting the company's journey from incurring losses to exponential growth and then to a sudden recession. Each phase involved analyzing margins, revenue, product introductions, market strategies, and much more.

Key Insights 💡

One of the major findings was that Germany stood out in terms of profitability in bike sales, thanks to dynamic pricing strategies and targeted campaigns. I also found certain demographic segments and product categories that were particularly profitable.

Conclusion 🏁

Through this project, I successfully identified opportunities for enhancing growth and developed strategies to increase profits. I highlighted the importance of adaptive market strategies, understanding customer preferences, and seasonal impacts.

Check out the complete notebook for an in-depth understanding. Remember, every bit of data tells a story!

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Please Note: The findings of this project are based on the given dataset and are intended for educational purposes. They may not reflect the current state of the bike retail industry.