The market research team at AeroFit wants to identify the characteristics of the target audience for each type of treadmill offered by the company, to provide a better recommendation of the treadmills to the new customers. The team decides to investigate whether there are differences across the product with respect to customer characteristics.
Product Portfolio:
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The KP281 is an entry-level treadmill that sells for $1,500.
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The KP481 is for mid-level runners that sell for $1,750.
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The KP781 treadmill is having advanced features that sell for $2,500
We will use count of users, probabilities, conditional probabilities to evaluate the users and create a customer profile for each product
| Feature | Description |
|---|---|
| Product | Product Purchased: KP281, KP481, or KP781 |
| Age | Age of buyer in years |
| Gender | Gender of buyer (Male/Female) |
| Education | Education of buyer in years |
| MaritalStatus | MaritalStatus of buyer (Single or partnered) |
| Usage | The average number of times the buyer plans to use the treadmill each week |
| Income | Annual income of the buyer (in $) |
| Fitness | Self-rated fitness on a 1-to-5 scale, where 1 is the poor shape and 5 is the excellent shape |
| Miles | The average number of miles the buyer expects to walk/run each week |
- Uni-Variate Analysis
- Bi-Variate Analysis
- Marginal Probability
- Conditional Probability
- Loaded the dataset and performed basic EDA to understand its characterstics and structure.
- Performed data Cleaning like missing value handling, checking for duplicate values, fixing Structural Errors, handling outliers etc.
- Performed non graphical analysis such as descriptive analysis, frequency distribution, correlation analysis etc.
- Visualizations :
- Uni-Variate Analysis (distribution plots of all the continuous variable(s) barplots/countplots of all the categorical variables)
- Bivariate (Relationships between important variables such as gender and product)
- For continuous variable(s): Distplot, countplot, histogram for univariate analysis
- For categorical variable(s): Boxplot
- For correlation: Heatmaps, Pairplots
- Performed Cross-tabulation of Product against all other features, based on observation created the profile.
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Product - KP281:
- Age: Popular among young between 22 - 33 years range.
- Gender: Mostly preferred by Females (52% of all females brought this Product whereas 38% of all males brought this product)
- Marital Status: Both alike
- Fitness Level: People with poor and average fitness (fitness<=3) prefer more but overall have a good control across all fitness level.
- Usage Level: Average Number of Usage <= 4 per week.
- Income Range: Low, Medium and High income people (30000<=income<= 58000)
- Education: 14-16 years mostly
- Miles: popular among people who prefer to run less than 80 miles per week(running < 80 miles).
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Product - KP481:
- Age: Popular among young between 24 - 33 years range
- Gender: Almost similar popularity distribution in both genders.
- Marital Status: both alike
- Fitness Level: People with poor and average fitness (fitness<=3) prefer using this.
- Usage Level: Average Number of Usage <= 3 per week.
- Income Range: Low, Medium and High income people (30000<=income<= 58000)
- Education: 14-16 years mostly
- Miles: popular among people who prefer to run less than 120 miles per week(running < 120 miles).
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Product - KP781:
- Age: Popular among young between 25 - 30 years range
- Gender: Males prefer more than Females
- Marital Status: both alike
- Fitness Level: People with High fitness level like fitness==5
- Usage Level: Average Number of Usage >= 4 per week.
- Income Range: High and Very High income people (income>60000)
- Education: 16-18 years mostly
- Miles: popular among people who prefer to run more than 120 miles per week(running > 120 miles).