# read in the employee data
import pandas as pd

employee_data = pd.read_csv('../Data/employee_data.csv')
employee_data.head()

# note the number of rows and columns
employee_data.shape

(1470, 10)

# view the data types of all the columns
employee_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 10 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   EmployeeID         1470 non-null   int64 
 1   Age                1470 non-null   int64 
 2   Gender             1470 non-null   object
 3   DistanceFromHome   1470 non-null   int64 
 4   JobLevel           1470 non-null   int64 
 5   Department         1470 non-null   object
 6   MonthlyIncome      1470 non-null   int64 
 7   PerformanceRating  1470 non-null   int64 
 8   JobSatisfaction    1470 non-null   int64 
 9   Attrition          1470 non-null   object
dtypes: int64(7), object(3)
memory usage: 115.0+ KB

# look at the numeric columns
employee_data.dtypes[employee_data.dtypes == 'int64']

EmployeeID           int64
Age                  int64
DistanceFromHome     int64
JobLevel             int64
MonthlyIncome        int64
PerformanceRating    int64
JobSatisfaction      int64
dtype: object

# look at the non-numeric columns
employee_data.dtypes[employee_data.dtypes != 'int64']

Gender        object
Department    object
Attrition     object
dtype: object

# create a copy of the dataframe
data = employee_data.copy()
data.head()

# look at the gender values
data.Gender.value_counts()

Gender
Male      882
Female    588
Name: count, dtype: int64

# change gender into a numeric field using np.where
import numpy as np

data.Gender = np.where(data.Gender == 'Female', 1, 0)
data.Gender.head()

0    1
1    0
2    0
3    1
4    0
Name: Gender, dtype: int64

# look at the attrition values
data.Attrition.value_counts()

Attrition
No     1233
Yes     237
Name: count, dtype: int64

# change attrition to a numeric field using np.where
data.Attrition = np.where(data.Attrition == 'Yes', 1, 0)
data.Attrition.head()

0    1
1    0
2    1
3    0
4    0
Name: Attrition, dtype: int64

# look at the department values
data.Department.value_counts()

Department
Research & Development    961
Sales                     446
Human Resources            63
Name: count, dtype: int64

# change department to a numeric field via dummy variables
pd.get_dummies(data.Department).astype('int').head()

# attach the columns back on to the dataframe
data = pd.concat([data, pd.get_dummies(data.Department).astype('int')], axis=1)
data.drop('Department', axis=1, inplace=True)
data.head()

# view the cleaned dataframe
data.head()

# note the number of rows and columns
data.shape

(1470, 12)

# what is the overall attrition for all employees in the data aka what percent of employees leave the company?
data.Attrition.mean() # 16% of employees leave the company

0.16122448979591836

# create a summary table to show the mean of each column for employees who stay vs leave - what are your takeaways?
data.groupby('Attrition').mean()

# create a new dataframe without the attrition column for us to model on
df = data.drop('Attrition', axis=1)
df.head()

# drop the employee column as well before modeling
df = df.drop(columns='EmployeeID')
df.head()

# note the number of rows and columns in the dataframe
df.shape

(1470, 10)

# create a pair plot comparing all the columns of the dataframe - what observations do you notice?
import seaborn as sns

sns.pairplot(df);

# scale the data using standardization
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
df_scaled.head()

# double check that all the column means are 0 and standard deviations are 1
df_scaled.describe()

# import kmeans and write a loop to fit models with 2 to 15 clusters
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# create an empty list to hold many inertia and silhouette values
inertia_values = []
silhouette_scores = []

# create 2 - 15 clusters, and add the intertia scores and silhouette scores to the lists
for k in range(2, 16):
    kmeans = KMeans(n_clusters=k, n_init=10, random_state=42) # changed from auto to 10
    kmeans.fit(df_scaled)
    inertia_values.append(kmeans.inertia_)
    silhouette_scores.append(silhouette_score(df_scaled, kmeans.labels_, metric='euclidean', sample_size=None))

# plot the inertia values
import matplotlib.pyplot as plt

# turn the list into a series for plotting
inertia_series = pd.Series(inertia_values, index=range(2, 16))

# plot the data
inertia_series.plot(marker='o')
plt.xlabel("Number of Clusters (k)")
plt.ylabel("Inertia")
plt.title("Number of Clusters vs. Inertia");

# plot the silhouette scores

# turn the list into a series for plotting
silhouette_series = pd.Series(silhouette_scores, index=range(2, 16))

	EmployeeID	Age	Gender	DistanceFromHome	JobLevel	MonthlyIncome	PerformanceRating	JobSatisfaction	Human Resources	Research & Development	Sales
Attrition
0	2027.656123	37.561233	0.406326	8.915653	2.145985	6832.739659	3.153285	2.778589	0.041363	0.671533	0.287105
1	2010.345992	33.607595	0.367089	10.632911	1.637131	4787.092827	3.156118	2.468354	0.050633	0.561181	0.388186

	Age	Gender	DistanceFromHome	JobLevel	MonthlyIncome	PerformanceRating	JobSatisfaction	Human Resources	Research & Development	Sales
0	0.446350	1.224745	-1.010909	-0.057788	-0.108350	-0.426230	1.153254	-0.211604	-1.374051	1.515244
1	1.322365	-0.816497	-0.147150	-0.057788	-0.291719	2.346151	-0.660853	-0.211604	0.727775	-0.659960
2	0.008343	-0.816497	-0.887515	-0.961486	-0.937654	-0.426230	0.246200	-0.211604	0.727775	-0.659960
3	-0.429664	1.224745	-0.764121	-0.961486	-0.763634	-0.426230	0.246200	-0.211604	0.727775	-0.659960
4	-1.086676	-0.816497	-0.887515	-0.961486	-0.644858	-0.426230	-0.660853	-0.211604	0.727775	-0.659960

	Age	Gender	DistanceFromHome	JobLevel	MonthlyIncome	PerformanceRating	JobSatisfaction	Human Resources	Research & Development	Sales
count	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03
mean	-3.504377e-17	-4.350262e-17	4.350262e-17	-2.658493e-17	-4.471102e-17	-6.114534e-16	-9.183886e-17	6.767074e-17	2.900174e-17	8.458842e-17
std	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00
min	-2.072192e+00	-8.164966e-01	-1.010909e+00	-9.614864e-01	-1.167343e+00	-4.262300e-01	-1.567907e+00	-2.116037e-01	-1.374051e+00	-6.599598e-01
25%	-7.581700e-01	-8.164966e-01	-8.875151e-01	-9.614864e-01	-7.632087e-01	-4.262300e-01	-6.608532e-01	-2.116037e-01	-1.374051e+00	-6.599598e-01
50%	-1.011589e-01	-8.164966e-01	-2.705440e-01	-5.778755e-02	-3.365516e-01	-4.262300e-01	2.462002e-01	-2.116037e-01	7.277751e-01	-6.599598e-01
75%	6.653541e-01	1.224745e+00	5.932157e-01	8.459113e-01	3.986245e-01	-4.262300e-01	1.153254e+00	-2.116037e-01	7.277751e-01	1.515244e+00
max	2.526886e+00	1.224745e+00	2.444129e+00	2.653309e+00	2.867626e+00	2.346151e+00	1.153254e+00	4.725816e+00	7.277751e-01	1.515244e+00

Loading¶

K-Means Clustering¶

a. Standardize the data¶

b. Write a loop to fit models with 2 to 15 clusters and record the inertia and silhouette scores¶

	EmployeeID	Age	Gender	DistanceFromHome	JobLevel	Department	MonthlyIncome	PerformanceRating	JobSatisfaction	Attrition
0	1001	41	Female	1	2	Sales	5993	3	4	Yes
1	1002	49	Male	8	2	Research & Development	5130	4	2	No
2	1004	37	Male	2	1	Research & Development	2090	3	3	Yes
3	1005	33	Female	3	1	Research & Development	2909	3	3	No
4	1007	27	Male	2	1	Research & Development	3468	3	2	No