In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, davies_bouldin_score
from sklearn.decomposition import PCA

import warnings
warnings.filterwarnings("ignore")
In [2]:
df = pd.read_csv('creditCardCust.csv')
df.head()
Out[2]:
CUST_ID BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 C10001 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 C10002 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 C10003 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 C10004 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 NaN 0.000000 12
4 C10005 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
In [3]:
df.describe()
Out[3]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
count 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8949.000000 8950.000000 8637.000000 8950.000000 8950.000000
mean 1564.474828 0.877271 1003.204834 592.437371 411.067645 978.871112 0.490351 0.202458 0.364437 0.135144 3.248827 14.709832 4494.449450 1733.143852 864.206542 0.153715 11.517318
std 2081.531879 0.236904 2136.634782 1659.887917 904.338115 2097.163877 0.401371 0.298336 0.397448 0.200121 6.824647 24.857649 3638.815725 2895.063757 2372.446607 0.292499 1.338331
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.019163 0.000000 6.000000
25% 128.281915 0.888889 39.635000 0.000000 0.000000 0.000000 0.083333 0.000000 0.000000 0.000000 0.000000 1.000000 1600.000000 383.276166 169.123707 0.000000 12.000000
50% 873.385231 1.000000 361.280000 38.000000 89.000000 0.000000 0.500000 0.083333 0.166667 0.000000 0.000000 7.000000 3000.000000 856.901546 312.343947 0.000000 12.000000
75% 2054.140036 1.000000 1110.130000 577.405000 468.637500 1113.821139 0.916667 0.300000 0.750000 0.222222 4.000000 17.000000 6500.000000 1901.134317 825.485459 0.142857 12.000000
max 19043.138560 1.000000 49039.570000 40761.250000 22500.000000 47137.211760 1.000000 1.000000 1.000000 1.500000 123.000000 358.000000 30000.000000 50721.483360 76406.207520 1.000000 12.000000
In [4]:
df.shape
Out[4]:
(8950, 18)
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8950 entries, 0 to 8949
Data columns (total 18 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   CUST_ID                           8950 non-null   object 
 1   BALANCE                           8950 non-null   float64
 2   BALANCE_FREQUENCY                 8950 non-null   float64
 3   PURCHASES                         8950 non-null   float64
 4   ONEOFF_PURCHASES                  8950 non-null   float64
 5   INSTALLMENTS_PURCHASES            8950 non-null   float64
 6   CASH_ADVANCE                      8950 non-null   float64
 7   PURCHASES_FREQUENCY               8950 non-null   float64
 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64
 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64
 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64
 11  CASH_ADVANCE_TRX                  8950 non-null   int64  
 12  PURCHASES_TRX                     8950 non-null   int64  
 13  CREDIT_LIMIT                      8949 non-null   float64
 14  PAYMENTS                          8950 non-null   float64
 15  MINIMUM_PAYMENTS                  8637 non-null   float64
 16  PRC_FULL_PAYMENT                  8950 non-null   float64
 17  TENURE                            8950 non-null   int64  
dtypes: float64(14), int64(3), object(1)
memory usage: 1.2+ MB
In [6]:
df.drop('CUST_ID', axis= 1, inplace=True)
In [7]:
df.head()
Out[7]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 NaN 0.000000 12
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
In [8]:
df.isna().sum()
Out[8]:
BALANCE                               0
BALANCE_FREQUENCY                     0
PURCHASES                             0
ONEOFF_PURCHASES                      0
INSTALLMENTS_PURCHASES                0
CASH_ADVANCE                          0
PURCHASES_FREQUENCY                   0
ONEOFF_PURCHASES_FREQUENCY            0
PURCHASES_INSTALLMENTS_FREQUENCY      0
CASH_ADVANCE_FREQUENCY                0
CASH_ADVANCE_TRX                      0
PURCHASES_TRX                         0
CREDIT_LIMIT                          1
PAYMENTS                              0
MINIMUM_PAYMENTS                    313
PRC_FULL_PAYMENT                      0
TENURE                                0
dtype: int64
In [9]:
medianValue = df['MINIMUM_PAYMENTS'].astype('float64').median(axis=0)
df['MINIMUM_PAYMENTS'].replace(np.nan, medianValue, inplace=True)
In [10]:
medianValue = df['CREDIT_LIMIT'].astype('float64').median(axis=0)
df['CREDIT_LIMIT'].replace(np.nan, medianValue, inplace=True)
In [11]:
df.isna().sum()
Out[11]:
BALANCE                             0
BALANCE_FREQUENCY                   0
PURCHASES                           0
ONEOFF_PURCHASES                    0
INSTALLMENTS_PURCHASES              0
CASH_ADVANCE                        0
PURCHASES_FREQUENCY                 0
ONEOFF_PURCHASES_FREQUENCY          0
PURCHASES_INSTALLMENTS_FREQUENCY    0
CASH_ADVANCE_FREQUENCY              0
CASH_ADVANCE_TRX                    0
PURCHASES_TRX                       0
CREDIT_LIMIT                        0
PAYMENTS                            0
MINIMUM_PAYMENTS                    0
PRC_FULL_PAYMENT                    0
TENURE                              0
dtype: int64
In [12]:
df.describe()
Out[12]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
count 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000
mean 1564.474828 0.877271 1003.204834 592.437371 411.067645 978.871112 0.490351 0.202458 0.364437 0.135144 3.248827 14.709832 4494.282473 1733.143852 844.906767 0.153715 11.517318
std 2081.531879 0.236904 2136.634782 1659.887917 904.338115 2097.163877 0.401371 0.298336 0.397448 0.200121 6.824647 24.857649 3638.646702 2895.063757 2332.792322 0.292499 1.338331
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.019163 0.000000 6.000000
25% 128.281915 0.888889 39.635000 0.000000 0.000000 0.000000 0.083333 0.000000 0.000000 0.000000 0.000000 1.000000 1600.000000 383.276166 170.857654 0.000000 12.000000
50% 873.385231 1.000000 361.280000 38.000000 89.000000 0.000000 0.500000 0.083333 0.166667 0.000000 0.000000 7.000000 3000.000000 856.901546 312.343947 0.000000 12.000000
75% 2054.140036 1.000000 1110.130000 577.405000 468.637500 1113.821139 0.916667 0.300000 0.750000 0.222222 4.000000 17.000000 6500.000000 1901.134317 788.713501 0.142857 12.000000
max 19043.138560 1.000000 49039.570000 40761.250000 22500.000000 47137.211760 1.000000 1.000000 1.000000 1.500000 123.000000 358.000000 30000.000000 50721.483360 76406.207520 1.000000 12.000000
In [13]:
sns.heatmap(df.corr(), cmap='coolwarm', center=0)
plt.title("Correlation Heatmap")
plt.show()
No description has been provided for this image
In [14]:
plt.figure(figsize=(15, 8))
for i, feat in enumerate(df.columns[0:6], 1):
    plt.subplot(2,3,i)
    sns.histplot(df[feat], bins=30, kde=True)
    plt.title(f"Dist. of {feat}")
plt.tight_layout()
plt.show()
No description has been provided for this image
In [15]:
scaler = StandardScaler()

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

df_scaled.sample(10)
Out[15]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
6601 1.466558 0.518084 -0.469552 -0.356934 -0.454576 0.873476 -1.221758 -0.678661 -0.916995 -0.258913 0.696218 -0.591796 0.138993 -0.396811 4.440873 -0.525551 0.360680
1109 3.147168 0.518084 -0.290091 -0.356934 -0.030574 -0.466786 0.024043 -0.678661 0.131420 -0.675349 -0.476070 -0.310177 1.788050 0.086151 0.418783 -0.525551 0.360680
7163 -0.675422 -0.748323 -0.104776 0.112612 -0.454576 -0.466786 -0.225118 0.662184 -0.916995 -0.675349 -0.476070 -0.310177 -0.905410 -0.318171 -0.306057 -0.525551 -1.133803
3203 -0.591263 0.518084 1.720290 1.685324 0.970745 -0.466786 1.269843 2.673451 1.599199 -0.675349 -0.476070 1.822083 -0.548114 0.710507 -0.292632 2.893453 0.360680
8442 -0.749170 -2.935753 -0.445840 -0.356934 -0.398554 -0.466786 -0.806490 -0.678661 -0.707313 -0.675349 -0.476070 -0.511333 -0.410693 -0.598688 -0.228307 -0.525551 0.360680
5435 -0.750531 -3.100221 -0.469552 -0.356934 -0.454576 -0.453222 -1.221758 -0.678661 -0.916995 0.038543 -0.329534 -0.591796 -0.410693 -0.598688 -0.228307 -0.525551 -3.375526
7375 2.181509 0.518084 -0.353133 -0.356934 -0.179520 3.612365 -1.014125 -0.678661 -0.707313 3.072586 6.704190 -0.511333 1.238364 0.031606 0.569455 -0.525551 0.360680
4458 0.072269 0.518084 0.466552 0.848034 -0.454576 -0.466786 -0.944914 -0.306204 -0.916995 -0.675349 -0.476070 -0.551565 -0.685536 -0.443830 -0.212663 -0.525551 -1.881044
8411 2.099598 0.518084 -0.469552 -0.356934 -0.454576 -0.322450 -1.221758 -0.678661 -0.916995 0.573963 0.256610 -0.591796 0.413836 -0.230470 0.664192 -0.525551 0.360680
804 -0.712358 0.134325 -0.105857 -0.042763 -0.171945 -0.466786 0.854576 0.997395 0.970151 -0.675349 -0.476070 0.212830 0.826100 -0.338295 -0.292431 -0.525551 0.360680
In [16]:
k_values = range(2, 11)

inertia_values = []
silhouette_scores = []
davies_bouldin_scores = []
for i in k_values:
    k_means = KMeans(n_clusters=i, random_state=42, n_init=10)
    k_means.fit(df_scaled)
    inertia_values.append(k_means.inertia_)
    silhouette_scores.append(silhouette_score(df_scaled, k_means.labels_))
    davies_bouldin_scores.append(davies_bouldin_score(df_scaled, k_means.labels_))

plt.figure(figsize=(18,5))

plt.subplot(1,3,1)
plt.plot(k_values, inertia_values, 'bo-')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.title('Elbow Method for Optimal k')

plt.subplot(1,3,2)
plt.plot(k_values, silhouette_scores, 'b.-')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Silhouette Score')
plt.title('Silhouette Score for Different k')

plt.subplot(1,3,3)
plt.plot(k_values, davies_bouldin_scores, 'b--')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Davies Bouldin Score')
plt.title('Davies Bouldin Score for Different k')

plt.show()
No description has been provided for this image
In [17]:
df_scaled.head()
Out[17]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 -0.731989 -0.249434 -0.424900 -0.356934 -0.349079 -0.466786 -0.806490 -0.678661 -0.707313 -0.675349 -0.476070 -0.511333 -0.960378 -0.528979 -0.302400 -0.525551 0.36068
1 0.786961 0.134325 -0.469552 -0.356934 -0.454576 2.605605 -1.221758 -0.678661 -0.916995 0.573963 0.110074 -0.591796 0.688678 0.818642 0.097500 0.234227 0.36068
2 0.447135 0.518084 -0.107668 0.108889 -0.454576 -0.466786 1.269843 2.673451 -0.916995 -0.675349 -0.476070 -0.109020 0.826100 -0.383805 -0.093293 -0.525551 0.36068
3 0.049099 -1.016953 0.232058 0.546189 -0.454576 -0.368653 -1.014125 -0.399319 -0.916995 -0.258913 -0.329534 -0.551565 0.826100 -0.598688 -0.228307 -0.525551 0.36068
4 -0.358775 0.518084 -0.462063 -0.347294 -0.454576 -0.466786 -1.014125 -0.399319 -0.916995 -0.675349 -0.476070 -0.551565 -0.905410 -0.364368 -0.257266 -0.525551 0.36068
In [18]:
model_kmeans = KMeans(n_clusters=3, random_state=111, n_init=10)
df['Cluster'] = model_kmeans.fit_predict(df_scaled)
In [19]:
print('Cluster Counts')
df['Cluster'].value_counts()

Cluster Counts
Out[19]:
Cluster
0    6105
1    1593
2    1252
Name: count, dtype: int64
In [20]:
df.head()
Out[20]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE Cluster
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12 0
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12 1
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12 0
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 312.343947 0.000000 12 0
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12 0
In [21]:
pca = PCA(n_components=2, random_state=42)
pca_coordinates = pca.fit_transform(df_scaled)

pca_df = pd.DataFrame(pca_coordinates, columns=["PCA1", "PCA2"], index=df_scaled.index)
pca_df["Cluster"] = df["Cluster"].values

df[["PCA1", "PCA2"]] = pca_df[["PCA1", "PCA2"]]

centers_2d = pca.transform(model_kmeans.cluster_centers_)

plt.figure(figsize=(8, 6))
for c in sorted(pca_df["Cluster"].unique()):
    subset = pca_df[pca_df["Cluster"] == c]
    plt.scatter(subset["PCA1"], subset["PCA2"], alpha=0.6, s=12, label=f"Cluster {c}")

plt.scatter(centers_2d[:, 0], centers_2d[:, 1], marker=".", s=100, edgecolor="black", linewidth=0.5, label="Centroids")

plt.title("PCA where k=3")
plt.xlabel("PCA 1")
plt.ylabel("PCA 2")
plt.legend()
plt.show()
No description has been provided for this image
In [22]:
print("Explained variance ratio (PC1, PC2):", np.round(pca.explained_variance_ratio_, 4))
print("Total variance explained by first 2 PCs:", np.round(pca.explained_variance_ratio_.sum(), 4))

Explained variance ratio (PC1, PC2): [0.273  0.2031]
Total variance explained by first 2 PCs: 0.4761
In [23]:
df.sample(10)
Out[23]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE Cluster PCA1 PCA2
133 9354.060450 1.000000 796.33 796.33 0.00 13748.471310 0.083333 0.083333 0.000000 0.666667 16 1 13000.0 10664.658660 2600.875721 0.083333 12 1 0.280847 8.098694
4213 12.423203 0.909091 2292.41 2003.99 288.42 96.310823 0.916667 0.916667 0.916667 0.083333 1 36 7500.0 2411.035394 41.854466 0.000000 12 2 2.423809 -0.855910
673 1162.770552 1.000000 533.36 0.00 533.36 19.292323 0.666667 0.000000 0.583333 0.083333 1 8 1200.0 632.227172 1395.576542 0.000000 12 0 -0.407868 -0.834288
2661 623.955961 0.818182 0.00 0.00 0.00 2119.702403 0.000000 0.000000 0.000000 0.166667 3 0 1200.0 1838.851003 174.872521 0.250000 12 0 -1.773447 0.147797
7507 215.475718 1.000000 670.65 300.00 370.65 0.000000 1.000000 0.083333 0.916667 0.000000 0 18 3000.0 366.634123 185.037922 0.300000 12 0 0.537639 -1.757736
4893 1764.083164 1.000000 329.47 0.00 329.47 58.536178 1.000000 0.000000 1.000000 0.166667 2 22 1800.0 789.269658 798.373689 0.000000 12 0 0.269730 -0.814377
1238 1548.224072 1.000000 696.63 696.63 0.00 0.000000 0.083333 0.083333 0.000000 0.000000 0 2 1800.0 588.329981 918.350562 0.000000 12 0 -1.230539 -0.424649
5936 2813.134224 0.818182 8834.96 8269.36 565.60 0.000000 0.750000 0.666667 0.583333 0.000000 0 24 7000.0 3788.138204 739.344097 0.000000 12 2 4.547892 0.119684
6627 1465.996562 1.000000 2498.92 222.99 2275.93 0.000000 1.000000 0.166667 1.000000 0.000000 0 36 3500.0 676.452393 421.840324 0.000000 12 2 1.995934 -1.309142
7021 222.957451 1.000000 0.00 0.00 0.00 1512.015822 0.000000 0.000000 0.000000 0.583333 14 0 1500.0 1856.738688 161.557965 0.166667 12 1 -1.974794 1.698326

Cluster 0: Shows less spender

Cluster 1: Shows medium/moderte spender

Cluster 2: Shows high spender

In [24]:
df_scaled.head()
Out[24]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 -0.731989 -0.249434 -0.424900 -0.356934 -0.349079 -0.466786 -0.806490 -0.678661 -0.707313 -0.675349 -0.476070 -0.511333 -0.960378 -0.528979 -0.302400 -0.525551 0.36068
1 0.786961 0.134325 -0.469552 -0.356934 -0.454576 2.605605 -1.221758 -0.678661 -0.916995 0.573963 0.110074 -0.591796 0.688678 0.818642 0.097500 0.234227 0.36068
2 0.447135 0.518084 -0.107668 0.108889 -0.454576 -0.466786 1.269843 2.673451 -0.916995 -0.675349 -0.476070 -0.109020 0.826100 -0.383805 -0.093293 -0.525551 0.36068
3 0.049099 -1.016953 0.232058 0.546189 -0.454576 -0.368653 -1.014125 -0.399319 -0.916995 -0.258913 -0.329534 -0.551565 0.826100 -0.598688 -0.228307 -0.525551 0.36068
4 -0.358775 0.518084 -0.462063 -0.347294 -0.454576 -0.466786 -1.014125 -0.399319 -0.916995 -0.675349 -0.476070 -0.551565 -0.905410 -0.364368 -0.257266 -0.525551 0.36068
In [25]:
new_sample = [[1820.0004, 1.000000, 0, 0.00, 0, 586, 0.97, 0.000000, 0, 0.0, 1, 0, 17000, 18000, 968.638394, 0.000000, 12]]
new_arr = np.array(new_sample)
try:
	new_scaled = scaler.transform(new_arr)
	predicted_cluster = model_kmeans.predict(new_scaled)
	if predicted_cluster[0] == 0: 
		print(f"Predicted cluster is {predicted_cluster[0]}, so the person is low spender." )
	elif predicted_cluster[0] == 1: 
		print(f"Predicted cluster is {predicted_cluster[0]}, so the person is medium/moderte spender." )	
	elif predicted_cluster[0] == 2: 
		print(f"Predicted cluster is {predicted_cluster[0]}, so the person is high spender." )
	else:
		print('Error! Please try again.')
except Exception as e:
	print('Error! Please try again.', e)

Predicted cluster is 1, so the person is medium/moderte spender.
In [ ]: