CardioGood Fitness Case Study - Descriptive Statistics¶
The market research team at AdRight is assigned the task to identify the profile of the typical customer for each treadmill product offered by CardioGood Fitness. The market research team decides to investigate whether there are differences across the product lines with respect to customer characteristics. The team decides to collect data on individuals who purchased a treadmill at a CardioGood Fitness retail store during the prior three months. The data are stored in the CardioGoodFitness.csv file.
The team identifies the following customer variables to study:¶
- product purchased, TM195, TM498, or TM798.
- gender.
- age, in years.
- education, in years.
- relationship status, single or partnered.
- annual household income.
- average number of times the customer plans to use the treadmill each week.
- average number of miles the customer expects to walk/run each week.
- and self-rated fitness on a 1-to-5 scale, where 1 is poor shape and 5 is excellent shape.
Perform descriptive analytics to create a customer profile for each CardioGood Fitness treadmill product line.¶
In [6]:
# Load the necessary packages
import numpy as np
import pandas as pd
In [7]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [9]:
# Load the Cardio Dataset
mydata = pd.read_csv('/content/drive/MyDrive/MIT - Data Sciences/Colab Notebooks/Week_One_-_Python_for_Data_Science/CardioGood_Analysis/CardioGoodFitness.csv')
In [10]:
mydata.head()
Out[10]:
| Product | Age | Gender | Education | MaritalStatus | Usage | Fitness | Income | Miles | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | TM195 | 18 | Male | 14 | Single | 3 | 4 | 29562 | 112 |
| 1 | TM195 | 19 | Male | 15 | Single | 2 | 3 | 31836 | 75 |
| 2 | TM195 | 19 | Female | 14 | Partnered | 4 | 3 | 30699 | 66 |
| 3 | TM195 | 19 | Male | 12 | Single | 3 | 3 | 32973 | 85 |
| 4 | TM195 | 20 | Male | 13 | Partnered | 4 | 2 | 35247 | 47 |
In [11]:
mydata.describe(include="all")
Out[11]:
| Product | Age | Gender | Education | MaritalStatus | Usage | Fitness | Income | Miles | |
|---|---|---|---|---|---|---|---|---|---|
| count | 180 | 180.000000 | 180 | 180.000000 | 180 | 180.000000 | 180.000000 | 180.000000 | 180.000000 |
| unique | 3 | NaN | 2 | NaN | 2 | NaN | NaN | NaN | NaN |
| top | TM195 | NaN | Male | NaN | Partnered | NaN | NaN | NaN | NaN |
| freq | 80 | NaN | 104 | NaN | 107 | NaN | NaN | NaN | NaN |
| mean | NaN | 28.788889 | NaN | 15.572222 | NaN | 3.455556 | 3.311111 | 53719.577778 | 103.194444 |
| std | NaN | 6.943498 | NaN | 1.617055 | NaN | 1.084797 | 0.958869 | 16506.684226 | 51.863605 |
| min | NaN | 18.000000 | NaN | 12.000000 | NaN | 2.000000 | 1.000000 | 29562.000000 | 21.000000 |
| 25% | NaN | 24.000000 | NaN | 14.000000 | NaN | 3.000000 | 3.000000 | 44058.750000 | 66.000000 |
| 50% | NaN | 26.000000 | NaN | 16.000000 | NaN | 3.000000 | 3.000000 | 50596.500000 | 94.000000 |
| 75% | NaN | 33.000000 | NaN | 16.000000 | NaN | 4.000000 | 4.000000 | 58668.000000 | 114.750000 |
| max | NaN | 50.000000 | NaN | 21.000000 | NaN | 7.000000 | 5.000000 | 104581.000000 | 360.000000 |
In [13]:
mydata.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 180 entries, 0 to 179 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Product 180 non-null object 1 Age 180 non-null int64 2 Gender 180 non-null object 3 Education 180 non-null int64 4 MaritalStatus 180 non-null object 5 Usage 180 non-null int64 6 Fitness 180 non-null int64 7 Income 180 non-null int64 8 Miles 180 non-null int64 dtypes: int64(6), object(3) memory usage: 12.8+ KB
In [14]:
import matplotlib.pyplot as plt
%matplotlib inline
mydata.hist(figsize=(20,30))
Out[14]:
array([[<Axes: title={'center': 'Age'}>,
<Axes: title={'center': 'Education'}>],
[<Axes: title={'center': 'Usage'}>,
<Axes: title={'center': 'Fitness'}>],
[<Axes: title={'center': 'Income'}>,
<Axes: title={'center': 'Miles'}>]], dtype=object)
In [15]:
import seaborn as sns #importing seaborn library
sns.boxplot(x="Gender", y="Age", data=mydata)
Out[15]:
<Axes: xlabel='Gender', ylabel='Age'>
In [16]:
sns.boxplot(x="Product", y="Age", data=mydata)
Out[16]:
<Axes: xlabel='Product', ylabel='Age'>
In [17]:
pd.crosstab(mydata['Product'],mydata['Gender'] )
Out[17]:
| Gender | Female | Male |
|---|---|---|
| Product | ||
| TM195 | 40 | 40 |
| TM498 | 29 | 31 |
| TM798 | 7 | 33 |
In [18]:
pd.crosstab(mydata['Product'],mydata['MaritalStatus'] )
Out[18]:
| MaritalStatus | Partnered | Single |
|---|---|---|
| Product | ||
| TM195 | 48 | 32 |
| TM498 | 36 | 24 |
| TM798 | 23 | 17 |
In [19]:
sns.countplot(x="Product", hue="Gender", data=mydata)
Out[19]:
<Axes: xlabel='Product', ylabel='count'>
In [20]:
pd.pivot_table(mydata, index=['Product', 'Gender'],
columns=[ 'MaritalStatus'], aggfunc=len)
Out[20]:
| Age | Education | Fitness | Income | Miles | Usage | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MaritalStatus | Partnered | Single | Partnered | Single | Partnered | Single | Partnered | Single | Partnered | Single | Partnered | Single | |
| Product | Gender | ||||||||||||
| TM195 | Female | 27 | 13 | 27 | 13 | 27 | 13 | 27 | 13 | 27 | 13 | 27 | 13 |
| Male | 21 | 19 | 21 | 19 | 21 | 19 | 21 | 19 | 21 | 19 | 21 | 19 | |
| TM498 | Female | 15 | 14 | 15 | 14 | 15 | 14 | 15 | 14 | 15 | 14 | 15 | 14 |
| Male | 21 | 10 | 21 | 10 | 21 | 10 | 21 | 10 | 21 | 10 | 21 | 10 | |
| TM798 | Female | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 3 | 4 | 3 |
| Male | 19 | 14 | 19 | 14 | 19 | 14 | 19 | 14 | 19 | 14 | 19 | 14 | |
In [21]:
pd.pivot_table(mydata,'Income', index=['Product', 'Gender'],
columns=[ 'MaritalStatus'])
Out[21]:
| MaritalStatus | Partnered | Single | |
|---|---|---|---|
| Product | Gender | ||
| TM195 | Female | 46153.777778 | 45742.384615 |
| Male | 50028.000000 | 43265.842105 | |
| TM498 | Female | 49724.800000 | 48920.357143 |
| Male | 49378.285714 | 47071.800000 | |
| TM798 | Female | 84972.250000 | 58516.000000 |
| Male | 81431.368421 | 68216.428571 |
In [22]:
pd.pivot_table(mydata,'Miles', index=['Product', 'Gender'],
columns=[ 'MaritalStatus'])
Out[22]:
| MaritalStatus | Partnered | Single | |
|---|---|---|---|
| Product | Gender | ||
| TM195 | Female | 74.925926 | 78.846154 |
| Male | 80.190476 | 99.526316 | |
| TM498 | Female | 94.000000 | 80.214286 |
| Male | 87.238095 | 91.100000 | |
| TM798 | Female | 215.000000 | 133.333333 |
| Male | 176.315789 | 147.571429 |
In [23]:
sns.pairplot(mydata)
Out[23]:
<seaborn.axisgrid.PairGrid at 0x797581eaef90>
In [24]:
mydata['Age'].std()
Out[24]:
6.943498135399795
In [25]:
mydata['Age'].mean()
Out[25]:
28.788888888888888
In [26]:
sns.displot(data=mydata, x='Age', kde=True)
Out[26]:
<seaborn.axisgrid.FacetGrid at 0x797581000b10>
In [27]:
mydata.hist(by='Gender',column = 'Age')
Out[27]:
array([<Axes: title={'center': 'Female'}>,
<Axes: title={'center': 'Male'}>], dtype=object)
In [28]:
mydata.hist(by='Gender',column = 'Income')
Out[28]:
array([<Axes: title={'center': 'Female'}>,
<Axes: title={'center': 'Male'}>], dtype=object)
In [29]:
mydata.hist(by='Gender',column = 'Miles')
Out[29]:
array([<Axes: title={'center': 'Female'}>,
<Axes: title={'center': 'Male'}>], dtype=object)
In [30]:
mydata.hist(by='Product',column = 'Miles', figsize=(20,30))
Out[30]:
array([[<Axes: title={'center': 'TM195'}>,
<Axes: title={'center': 'TM498'}>],
[<Axes: title={'center': 'TM798'}>, <Axes: >]], dtype=object)
In [32]:
# Select only the numerical columns before calculating the correlation.
numerical_data = mydata.select_dtypes(include=['number'])
corr = numerical_data.corr()
corr
Out[32]:
| Age | Education | Usage | Fitness | Income | Miles | |
|---|---|---|---|---|---|---|
| Age | 1.000000 | 0.280496 | 0.015064 | 0.061105 | 0.513414 | 0.036618 |
| Education | 0.280496 | 1.000000 | 0.395155 | 0.410581 | 0.625827 | 0.307284 |
| Usage | 0.015064 | 0.395155 | 1.000000 | 0.668606 | 0.519537 | 0.759130 |
| Fitness | 0.061105 | 0.410581 | 0.668606 | 1.000000 | 0.535005 | 0.785702 |
| Income | 0.513414 | 0.625827 | 0.519537 | 0.535005 | 1.000000 | 0.543473 |
| Miles | 0.036618 | 0.307284 | 0.759130 | 0.785702 | 0.543473 | 1.000000 |
In [33]:
sns.heatmap(corr, annot=True)
Out[33]:
<Axes: >
In [34]:
# Simple Linear Regression
#Load function from Scikit-learn
from Scikit-learn import linear_model
# Create linear regression object
regr = linear_model.LinearRegression()
y = mydata['Miles']
x = mydata[['Usage','Fitness']]
# Train the model using the training sets
regr.fit(x,y)
Out[34]:
LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
In [39]:
print(f"Coefficients: {regr.coef_}")
Coefficients: [20.21486334 27.20649954]
In [40]:
print(f"Intercept: {regr.intercept_}")
Intercept: -56.74288178464862
In [42]:
#MilesPredicted = -56.74 + 20.21*Usage + 27.20*Fitness
In [38]:
# Convert notebook to html
!jupyter nbconvert --to html "/content/drive/MyDrive/MIT - Data Sciences/Colab Notebooks/Week_One_-_Python_for_Data_Science/CardioGood_Analysis/Notebook+-+CardioGood+Fitness+Data+Analysis.ipynb"
[NbConvertApp] Converting notebook /content/drive/MyDrive/MIT - Data Sciences/Colab Notebooks/Week_One_-_Python_for_Data_Science/CardioGood_Analysis/Notebook+-+CardioGood+Fitness+Data+Analysis.ipynb to html [NbConvertApp] WARNING | Alternative text is missing on 10 image(s). [NbConvertApp] Writing 891271 bytes to /content/drive/MyDrive/MIT - Data Sciences/Colab Notebooks/Week_One_-_Python_for_Data_Science/CardioGood_Analysis/Notebook+-+CardioGood+Fitness+Data+Analysis.html