K Means Clustering Project¶

K.D.P Kulugammana¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [2]:
df = pd.read_csv('College_Data',index_col=0)
In [3]:
df.head()
Out[3]:
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni Expend Grad.Rate
Abilene Christian University Yes 1660 1232 721 23 52 2885 537 7440 3300 450 2200 70 78 18.1 12 7041 60
Adelphi University Yes 2186 1924 512 16 29 2683 1227 12280 6450 750 1500 29 30 12.2 16 10527 56
Adrian College Yes 1428 1097 336 22 50 1036 99 11250 3750 400 1165 53 66 12.9 30 8735 54
Agnes Scott College Yes 417 349 137 60 89 510 63 12960 5450 450 875 92 97 7.7 37 19016 59
Alaska Pacific University Yes 193 146 55 16 44 249 869 7560 4120 800 1500 76 72 11.9 2 10922 15
In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 777 entries, Abilene Christian University to York College of Pennsylvania
Data columns (total 18 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Private      777 non-null    object 
 1   Apps         777 non-null    int64  
 2   Accept       777 non-null    int64  
 3   Enroll       777 non-null    int64  
 4   Top10perc    777 non-null    int64  
 5   Top25perc    777 non-null    int64  
 6   F.Undergrad  777 non-null    int64  
 7   P.Undergrad  777 non-null    int64  
 8   Outstate     777 non-null    int64  
 9   Room.Board   777 non-null    int64  
 10  Books        777 non-null    int64  
 11  Personal     777 non-null    int64  
 12  PhD          777 non-null    int64  
 13  Terminal     777 non-null    int64  
 14  S.F.Ratio    777 non-null    float64
 15  perc.alumni  777 non-null    int64  
 16  Expend       777 non-null    int64  
 17  Grad.Rate    777 non-null    int64  
dtypes: float64(1), int64(16), object(1)
memory usage: 115.3+ KB
In [5]:
df.describe()
Out[5]:
Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni Expend Grad.Rate
count 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.000000 777.00000
mean 3001.638353 2018.804376 779.972973 27.558559 55.796654 3699.907336 855.298584 10440.669241 4357.526384 549.380952 1340.642214 72.660232 79.702703 14.089704 22.743887 9660.171171 65.46332
std 3870.201484 2451.113971 929.176190 17.640364 19.804778 4850.420531 1522.431887 4023.016484 1096.696416 165.105360 677.071454 16.328155 14.722359 3.958349 12.391801 5221.768440 17.17771
min 81.000000 72.000000 35.000000 1.000000 9.000000 139.000000 1.000000 2340.000000 1780.000000 96.000000 250.000000 8.000000 24.000000 2.500000 0.000000 3186.000000 10.00000
25% 776.000000 604.000000 242.000000 15.000000 41.000000 992.000000 95.000000 7320.000000 3597.000000 470.000000 850.000000 62.000000 71.000000 11.500000 13.000000 6751.000000 53.00000
50% 1558.000000 1110.000000 434.000000 23.000000 54.000000 1707.000000 353.000000 9990.000000 4200.000000 500.000000 1200.000000 75.000000 82.000000 13.600000 21.000000 8377.000000 65.00000
75% 3624.000000 2424.000000 902.000000 35.000000 69.000000 4005.000000 967.000000 12925.000000 5050.000000 600.000000 1700.000000 85.000000 92.000000 16.500000 31.000000 10830.000000 78.00000
max 48094.000000 26330.000000 6392.000000 96.000000 100.000000 31643.000000 21836.000000 21700.000000 8124.000000 2340.000000 6800.000000 103.000000 100.000000 39.800000 64.000000 56233.000000 118.00000

Exploratory Data Analysis¶

In [7]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')

# Assuming 'df' is your DataFrame and it contains the columns 'Room.Board', 'Grad.Rate', and 'Private'
sns.lmplot(x='Room.Board', y='Grad.Rate', data=df, hue='Private', palette='coolwarm', height=6, aspect=1, fit_reg=False)

# Show the plot
plt.show()

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
In [9]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')

# Assuming 'df' is your DataFrame and it contains the columns 'Outstate', 'F.Undergrad', and 'Private'
sns.lmplot(x='Outstate', y='F.Undergrad', data=df, hue='Private', palette='coolwarm', height=6, aspect=1, fit_reg=False)

# Show the plot
plt.show()

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
In [11]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')

# Assuming 'df' is your DataFrame and it contains the columns 'Outstate' and 'Private'
g = sns.FacetGrid(df, hue="Private", palette='coolwarm', height=6, aspect=2)
g = g.map(plt.hist, 'Outstate', bins=20, alpha=0.7)

# Add legend
g.add_legend()

# Show the plot
plt.show()

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
In [13]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')

# Assuming 'df' is your DataFrame and it contains the columns 'Grad.Rate' and 'Private'
g = sns.FacetGrid(df, hue="Private", palette='coolwarm', height=6, aspect=2)
g = g.map(plt.hist, 'Grad.Rate', bins=20, alpha=0.7)

# Add legend
g.add_legend()

# Show the plot
plt.show()

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)
In [16]:
df[df['Grad.Rate'] > 100]
Out[16]:
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni Expend Grad.Rate
In [17]:
df['Grad.Rate']['Cazenovia College'] = 100

C:\Users\ACER\AppData\Local\Temp\ipykernel_16820\2087630962.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['Grad.Rate']['Cazenovia College'] = 100
In [18]:
df[df['Grad.Rate'] > 100]
Out[18]:
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni Expend Grad.Rate
In [20]:
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('darkgrid')

# Assuming 'df' is your DataFrame and it contains the columns 'Grad.Rate' and 'Private'
g = sns.FacetGrid(df, hue="Private", palette='coolwarm', height=6, aspect=2)
g = g.map(plt.hist, 'Grad.Rate', bins=20, alpha=0.7)

# Add legend
g.add_legend()

# Show the plot
plt.show()

C:\Users\ACER\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

K Means Cluster Creation¶

In [21]:
from sklearn.cluster import KMeans
In [22]:
kmeans = KMeans(n_clusters=2)
In [23]:
kmeans.fit(df.drop('Private',axis=1))

C:\Users\ACER\anaconda3\Lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
Out[23]:
KMeans(n_clusters=2)
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.
KMeans(n_clusters=2)
In [24]:
kmeans.cluster_centers_
Out[24]:
array([[1.81323468e+03, 1.28716592e+03, 4.91044843e+02, 2.53094170e+01,
        5.34708520e+01, 2.18854858e+03, 5.95458894e+02, 1.03957085e+04,
        4.31136472e+03, 5.41982063e+02, 1.28033632e+03, 7.04424514e+01,
        7.78251121e+01, 1.40997010e+01, 2.31748879e+01, 8.93204634e+03,
        6.50926756e+01],
       [1.03631389e+04, 6.55089815e+03, 2.56972222e+03, 4.14907407e+01,
        7.02037037e+01, 1.30619352e+04, 2.46486111e+03, 1.07191759e+04,
        4.64347222e+03, 5.95212963e+02, 1.71420370e+03, 8.63981481e+01,
        9.13333333e+01, 1.40277778e+01, 2.00740741e+01, 1.41705000e+04,
        6.75925926e+01]])

Evaluation¶

In [25]:
def converter(cluster):
    if cluster=='Yes':
        return 1
    else:
        return 0
In [26]:
df['Cluster'] = df['Private'].apply(converter)
In [27]:
df.head()
Out[27]:
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad Outstate Room.Board Books Personal PhD Terminal S.F.Ratio perc.alumni Expend Grad.Rate Cluster
Abilene Christian University Yes 1660 1232 721 23 52 2885 537 7440 3300 450 2200 70 78 18.1 12 7041 60 1
Adelphi University Yes 2186 1924 512 16 29 2683 1227 12280 6450 750 1500 29 30 12.2 16 10527 56 1
Adrian College Yes 1428 1097 336 22 50 1036 99 11250 3750 400 1165 53 66 12.9 30 8735 54 1
Agnes Scott College Yes 417 349 137 60 89 510 63 12960 5450 450 875 92 97 7.7 37 19016 59 1
Alaska Pacific University Yes 193 146 55 16 44 249 869 7560 4120 800 1500 76 72 11.9 2 10922 15 1
In [28]:
from sklearn.metrics import confusion_matrix,classification_report
print(confusion_matrix(df['Cluster'],kmeans.labels_))
print(classification_report(df['Cluster'],kmeans.labels_))

[[138  74]
 [531  34]]
              precision    recall  f1-score   support

           0       0.21      0.65      0.31       212
           1       0.31      0.06      0.10       565

    accuracy                           0.22       777
   macro avg       0.26      0.36      0.21       777
weighted avg       0.29      0.22      0.16       777