Welcome to the Aerofit Descriptive Statistics & Probability project repository! 🚀
Aerofit is not just a fitness equipment brand; it's a lifestyle! We provide a wide range of fitness products, including top-notch treadmills, exercise bikes, gym equipment, and all the fitness accessories you need. We're here to cater to the diverse fitness needs of people from all walks of life. 💥
The brilliant minds at AeroFit have a mission: to understand our customers better and provide tailored treadmill recommendations to newcomers. To do this, we're diving deep into data to find out if there are specific characteristics that differentiate our treadmill products. Our journey consists of two main goals:
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Perform descriptive analytics to craft unique customer profiles for each AeroFit treadmill product, illustrated through compelling tables and charts. 📈📊
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Create two-way contingency tables and calculate conditional and marginal probabilities, shining a light on how these insights impact our business. 🤓📉
Our data is gold. It's collected from individuals who've purchased AeroFit treadmills in the past three months. It contains essential features, such as:
- Product Purchased: KP281, KP481, or KP781
- Age: In years
- Gender: Male/Female
- Education: In years
- Marital Status: Single or partnered
- Usage: How often the treadmill is used weekly
- Income: Annual income (in $)
- Fitness: Self-rated fitness on a scale from 1 (poor shape) to 5 (excellent shape)
- Miles: The average number of miles expected to be walked/run each week
Here's a quick overview of our treadmill products:
- KP281: Entry-level treadmill at $1,500.
- KP481: Mid-level runner's choice at $1,750.
- KP781: Advanced treadmill with all the bells and whistles at $2,500.
In this repository, you will find a series of Python scripts and Jupyter notebooks that guide you through this exciting data exploration. What you can expect to accomplish:
- Import and analyze the dataset, getting to know the data's structure and characteristics.
- Uncover outliers through box plots and descriptive statistics.
- Investigate the impact of features like marital status and age on the product purchased.
- Calculate and represent marginal probabilities.
- Examine correlations among various factors using heat maps and pair plots.
With these steps, you can answer intriguing questions, like "What's the probability of a male customer buying a KP781 treadmill?" or categorize users into various segments.
Our project is just getting started, and we invite you to explore the world of fitness and data with us. Whether you're a fitness enthusiast or a data geek, this project has something for everyone.
We look forward to your contributions, insights, and recommendations, and we're excited to see where this journey takes us! 🚴♀️🏃♀️💻
Stay tuned for updates, and let's make Aerofit even better for our customers! 🌟🤸♂️
Keep sweating and coding! 💪👩💻👨💻