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https://github.com/prashhhant213/cardioflex-treadmill-analysis-using-descriptive-statistics-probability

Description Analysis and Visualization on CardioFlex Treadmill data to provide insights and recommendations to improve their userbase.
https://github.com/prashhhant213/cardioflex-treadmill-analysis-using-descriptive-statistics-probability

colab-notebook numpy pandas probability python stats

Last synced: 13 days ago
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Description Analysis and Visualization on CardioFlex Treadmill data to provide insights and recommendations to improve their userbase.

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README 

        
# CardioFlex Treadmill Analysis using Descriptive Statistics & Probability



  

  

  




## 🎯 Objective

Creating comprehensive customer profiles AeroFit treadmill product through descriptive analysis and Data Visualization. Analyzing data given to reach with the help of two-way contingency tables. Fiding out conditional and marginal probabilities to focus on customer characteristics, enhancing product marketing skills and facilitating improved product recommendations and informed business decisions.



## 📚 About Data

Aerofit is a leading brand in the field of fitness equipment. Aerofit provides a product range including machines such as treadmills, exercise bikes, gym equipment, and fitness accessories to cater to the needs of all categories of people.

The company collected the data on individuals who purchased a treadmill from the AeroFit stores during `three months`.The data is available in a single csv file 

**Product Portfolio**

- The `KP281` is an `entry-level` treadmill that sells for `USD 1,500`

- The `KP481` is for `mid-level` runners that sell for `USD 1,750`

- The `KP781` treadmill is having `advanced features` that sell for `USD 2,500`

  

| Feature | Description |

|:--------|:------------|

| Product Purchased: | KP281, KP481, or KP781 |

| Age | In years |

| Gender | Male/Female |

| Education | In years |

| MaritalStatus | Single or partnered |

| Usage | The average number of times the buyer plans to use the treadmill each week |

| Income | Annual income (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 | 

## Performed following Tasks

1. Data Cleaning

2. Analysis

3. Visualization

- Observations on the shape of data, data types of all the attributes, conversion of categorical attributes to 'category', missing value detection, statistical summary

- Non-Graphical Analysis: Value counts and unique attributes ​

- Visual Analysis - Univariate, Bivariate after pre-processing of the data

- For continuous variable(s): Distplot, countplot, histogram for univariate analysis 

- For categorical variable(s): Boxplot 

- Check the correlation among different factors: Heatmaps, Pairplots 

- Missing Value & Outlier check

- Insights based on Non-Graphical and Visual Analysis

  

    . Comments on the range of attributes

  

    . Comments on the distribution of the variables and relationship between them

  

    . Comments for each univariate and bivariate plot

- Representing the marginal probability like - what percent of customers have purchased KP281, KP481, or KP781 in a table (using pandas.crosstab)

- Calculated the Probability of buying a product based on each column

-  Check the correlation among different factors

  

- Business Insights & Recommendations : Includes patterns observed in the data along with what can infer from it

### Description about files in repository Backhand Index Pointing Down Light Skin Tone:

**Aerofit Business Case Study.ipynb** - Colaboratory notebook containing the code for analysis