ium_464913/IUM_2.ipynb at cc56865cc1f154c2538c03420b6cea22e0e9bea7

IUM 2

Installation of packages

%pip install kaggle
%pip install pandas
%pip install numpy
%pip install scikit-learn

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Note: you may need to restart the kernel to use updated packages.

Importing libraries

import pandas as pd
import numpy as np

# To preprocess the data
from sklearn.preprocessing import StandardScaler

# To split the data
from sklearn.model_selection import train_test_split

Downloading a dataset

!kaggle datasets download -d mlg-ulb/creditcardfraud

creditcardfraud.zip: Skipping, found more recently modified local copy (use --force to force download)

Uncompress a file

!unzip -o creditcardfraud.zip

Archive:  creditcardfraud.zip
  inflating: creditcard.csv

Load the data

df = pd.read_csv("creditcard.csv")
pd.set_option("display.max_columns", None)

Check missing values

df.isnull().sum()

Time      0
V1        0
V2        0
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
Amount    0
Class     0
dtype: int64

Size of the dataset

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB

Normalising the data

scaler = StandardScaler()

df["Amount"] = scaler.fit_transform(df["Amount"].values.reshape(-1, 1))

Summary statistics

df.describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	284807.000000	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	284807.000000
mean	94813.859575	1.168375e-15	3.416908e-16	-1.379537e-15	2.074095e-15	9.604066e-16	1.487313e-15	-5.556467e-16	1.213481e-16	-2.406331e-15	2.239053e-15	1.673327e-15	-1.247012e-15	8.190001e-16	1.207294e-15	4.887456e-15	1.437716e-15	-3.772171e-16	9.564149e-16	1.039917e-15	6.406204e-16	1.654067e-16	-3.568593e-16	2.578648e-16	4.473266e-15	5.340915e-16	1.683437e-15	-3.660091e-16	-1.227390e-16	2.913952e-17	0.001727
std	47488.145955	1.958696e+00	1.651309e+00	1.516255e+00	1.415869e+00	1.380247e+00	1.332271e+00	1.237094e+00	1.194353e+00	1.098632e+00	1.088850e+00	1.020713e+00	9.992014e-01	9.952742e-01	9.585956e-01	9.153160e-01	8.762529e-01	8.493371e-01	8.381762e-01	8.140405e-01	7.709250e-01	7.345240e-01	7.257016e-01	6.244603e-01	6.056471e-01	5.212781e-01	4.822270e-01	4.036325e-01	3.300833e-01	1.000002e+00	0.041527
min	0.000000	-5.640751e+01	-7.271573e+01	-4.832559e+01	-5.683171e+00	-1.137433e+02	-2.616051e+01	-4.355724e+01	-7.321672e+01	-1.343407e+01	-2.458826e+01	-4.797473e+00	-1.868371e+01	-5.791881e+00	-1.921433e+01	-4.498945e+00	-1.412985e+01	-2.516280e+01	-9.498746e+00	-7.213527e+00	-5.449772e+01	-3.483038e+01	-1.093314e+01	-4.480774e+01	-2.836627e+00	-1.029540e+01	-2.604551e+00	-2.256568e+01	-1.543008e+01	-3.532294e-01	0.000000
25%	54201.500000	-9.203734e-01	-5.985499e-01	-8.903648e-01	-8.486401e-01	-6.915971e-01	-7.682956e-01	-5.540759e-01	-2.086297e-01	-6.430976e-01	-5.354257e-01	-7.624942e-01	-4.055715e-01	-6.485393e-01	-4.255740e-01	-5.828843e-01	-4.680368e-01	-4.837483e-01	-4.988498e-01	-4.562989e-01	-2.117214e-01	-2.283949e-01	-5.423504e-01	-1.618463e-01	-3.545861e-01	-3.171451e-01	-3.269839e-01	-7.083953e-02	-5.295979e-02	-3.308401e-01	0.000000
50%	84692.000000	1.810880e-02	6.548556e-02	1.798463e-01	-1.984653e-02	-5.433583e-02	-2.741871e-01	4.010308e-02	2.235804e-02	-5.142873e-02	-9.291738e-02	-3.275735e-02	1.400326e-01	-1.356806e-02	5.060132e-02	4.807155e-02	6.641332e-02	-6.567575e-02	-3.636312e-03	3.734823e-03	-6.248109e-02	-2.945017e-02	6.781943e-03	-1.119293e-02	4.097606e-02	1.659350e-02	-5.213911e-02	1.342146e-03	1.124383e-02	-2.652715e-01	0.000000
75%	139320.500000	1.315642e+00	8.037239e-01	1.027196e+00	7.433413e-01	6.119264e-01	3.985649e-01	5.704361e-01	3.273459e-01	5.971390e-01	4.539234e-01	7.395934e-01	6.182380e-01	6.625050e-01	4.931498e-01	6.488208e-01	5.232963e-01	3.996750e-01	5.008067e-01	4.589494e-01	1.330408e-01	1.863772e-01	5.285536e-01	1.476421e-01	4.395266e-01	3.507156e-01	2.409522e-01	9.104512e-02	7.827995e-02	-4.471707e-02	0.000000
max	172792.000000	2.454930e+00	2.205773e+01	9.382558e+00	1.687534e+01	3.480167e+01	7.330163e+01	1.205895e+02	2.000721e+01	1.559499e+01	2.374514e+01	1.201891e+01	7.848392e+00	7.126883e+00	1.052677e+01	8.877742e+00	1.731511e+01	9.253526e+00	5.041069e+00	5.591971e+00	3.942090e+01	2.720284e+01	1.050309e+01	2.252841e+01	4.584549e+00	7.519589e+00	3.517346e+00	3.161220e+01	3.384781e+01	1.023622e+02	1.000000

Distribution of legitimate and fraudulent transactions

df["Class"].value_counts()

Class
0    284315
1       492
Name: count, dtype: int64

Undersampling the data

We will employ undersampling as one class significantly dominates the other.

# Determine the number of instances in the minority class
fraud_count = len(df[df.Class == 1])
fraud_indices = np.array(df[df.Class == 1].index)

# Select indices corresponding to majority class instances
normal_indices = df[df.Class == 0].index

# Randomly sample the same number of instances from the majority class
random_normal_indices = np.random.choice(normal_indices, fraud_count, replace=False)
random_normal_indices = np.array(random_normal_indices)

# Combine indices of both classes
undersample_indice = np.concatenate([fraud_indices, random_normal_indices])

# Undersample dataset
undersample_data = df.iloc[undersample_indice, :]

X_undersample = undersample_data.iloc[:, undersample_data.columns != "Class"]
y_undersample = undersample_data.iloc[:, undersample_data.columns == "Class"]

Size of undersampled dataset

undersample_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 984 entries, 541 to 141412
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Time    984 non-null    float64
 1   V1      984 non-null    float64
 2   V2      984 non-null    float64
 3   V3      984 non-null    float64
 4   V4      984 non-null    float64
 5   V5      984 non-null    float64
 6   V6      984 non-null    float64
 7   V7      984 non-null    float64
 8   V8      984 non-null    float64
 9   V9      984 non-null    float64
 10  V10     984 non-null    float64
 11  V11     984 non-null    float64
 12  V12     984 non-null    float64
 13  V13     984 non-null    float64
 14  V14     984 non-null    float64
 15  V15     984 non-null    float64
 16  V16     984 non-null    float64
 17  V17     984 non-null    float64
 18  V18     984 non-null    float64
 19  V19     984 non-null    float64
 20  V20     984 non-null    float64
 21  V21     984 non-null    float64
 22  V22     984 non-null    float64
 23  V23     984 non-null    float64
 24  V24     984 non-null    float64
 25  V25     984 non-null    float64
 26  V26     984 non-null    float64
 27  V27     984 non-null    float64
 28  V28     984 non-null    float64
 29  Amount  984 non-null    float64
 30  Class   984 non-null    int64  
dtypes: float64(30), int64(1)
memory usage: 246.0 KB

Summary statistics of the undersampled dataset

undersample_data.describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000	984.000000
mean	88501.498984	-2.445079	1.781022	-3.509406	2.214004	-1.477993	-0.713150	-2.787427	0.279073	-1.253108	-2.841500	1.930697	-3.124120	-0.026229	-3.502384	-0.039494	-2.097294	-3.304208	-1.128950	0.343668	0.175905	0.331911	0.049631	-0.031264	-0.037389	0.022812	0.027632	0.086286	0.046738	0.039676	0.500000
std	48996.269445	5.512352	3.713232	6.223001	3.231076	4.274632	1.789350	5.856197	4.857643	2.371055	4.563067	2.764745	4.595103	1.054377	4.653202	1.002911	3.465619	5.990033	2.412032	1.290973	1.126258	2.787884	1.167097	1.177562	0.551518	0.677541	0.476480	1.023332	0.479168	0.851800	0.500254
min	60.000000	-30.552380	-15.799625	-31.103685	-3.863126	-22.105532	-10.261990	-43.557242	-41.044261	-13.434066	-24.588262	-2.613374	-18.683715	-3.223045	-19.214325	-4.498945	-14.129855	-25.162799	-9.498746	-3.681904	-7.242879	-22.797604	-8.887017	-19.254328	-2.028024	-4.781606	-1.214960	-7.263482	-2.735623	-0.353229	0.000000
25%	45531.000000	-2.867222	-0.155438	-5.084967	-0.172018	-1.700260	-1.619179	-3.066415	-0.204192	-2.279453	-4.572043	-0.187147	-5.495221	-0.784589	-6.721799	-0.627097	-3.543426	-5.302111	-1.809496	-0.412430	-0.187708	-0.157259	-0.509376	-0.240064	-0.379825	-0.321251	-0.281187	-0.061809	-0.050194	-0.347302	0.000000
50%	83076.500000	-0.823244	0.957399	-1.381998	1.287041	-0.394605	-0.689473	-0.668321	0.147397	-0.694910	-0.948441	1.170286	-0.858094	-0.000686	-1.110717	-0.006070	-0.677801	-0.513640	-0.383038	0.221049	0.040630	0.155404	0.080270	-0.030318	0.009379	0.049923	-0.007475	0.063100	0.039464	-0.280984	0.500000
75%	135051.500000	0.919444	2.791569	0.356911	4.175332	0.616305	0.069620	0.265089	0.877002	0.134399	-0.016047	3.586502	0.190356	0.683977	0.110541	0.672903	0.250353	0.313841	0.334927	0.978754	0.445616	0.642724	0.624948	0.180735	0.365624	0.395001	0.324059	0.457194	0.226492	0.046539	1.000000
max	172733.000000	2.335833	22.057729	3.476268	12.114672	14.103918	6.474115	5.802537	20.007208	6.816732	11.732926	12.018913	2.534876	3.091328	3.442422	2.471358	3.139656	6.739384	3.790316	5.228342	11.059004	27.202839	8.361985	5.466230	1.208141	2.208209	2.745261	3.052358	4.975792	8.146182	1.000000

Distribution of legitimate and fraudulent transactions in an undersampled dataset

undersample_data["Class"].value_counts()

Class
1    492
0    492
Name: count, dtype: int64

Splitting whole data into training and test datasets

X = df.iloc[:, df.columns != "Class"]
y = df.iloc[:, df.columns == "Class"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

Statistical measures of the training dataset of whole data

pd.concat([X_train, y_train], axis=1).info()

<class 'pandas.core.frame.DataFrame'>
Index: 199364 entries, 161145 to 117952
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    199364 non-null  float64
 1   V1      199364 non-null  float64
 2   V2      199364 non-null  float64
 3   V3      199364 non-null  float64
 4   V4      199364 non-null  float64
 5   V5      199364 non-null  float64
 6   V6      199364 non-null  float64
 7   V7      199364 non-null  float64
 8   V8      199364 non-null  float64
 9   V9      199364 non-null  float64
 10  V10     199364 non-null  float64
 11  V11     199364 non-null  float64
 12  V12     199364 non-null  float64
 13  V13     199364 non-null  float64
 14  V14     199364 non-null  float64
 15  V15     199364 non-null  float64
 16  V16     199364 non-null  float64
 17  V17     199364 non-null  float64
 18  V18     199364 non-null  float64
 19  V19     199364 non-null  float64
 20  V20     199364 non-null  float64
 21  V21     199364 non-null  float64
 22  V22     199364 non-null  float64
 23  V23     199364 non-null  float64
 24  V24     199364 non-null  float64
 25  V25     199364 non-null  float64
 26  V26     199364 non-null  float64
 27  V27     199364 non-null  float64
 28  V28     199364 non-null  float64
 29  Amount  199364 non-null  float64
 30  Class   199364 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 48.7 MB

pd.concat([X_train, y_train], axis=1).describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000	199364.000000
mean	94799.493936	0.000315	-0.002690	-0.001532	0.000721	-0.001494	-0.000210	-0.000870	-0.001980	0.000212	0.001357	-0.001039	-0.001565	0.000693	0.000137	0.000322	0.000084	0.000292	-0.000134	0.000490	0.000430	-0.000014	-0.000022	-0.000258	0.000362	0.000395	-0.000094	-0.000027	0.000015	0.001271	0.001731
std	47499.835491	1.963554	1.657379	1.516716	1.417138	1.368744	1.328673	1.226018	1.212338	1.102021	1.092801	1.020027	0.996526	0.997718	0.956938	0.916143	0.876131	0.852181	0.837556	0.814506	0.770257	0.743450	0.727625	0.629145	0.605298	0.521175	0.481842	0.401042	0.324849	0.983948	0.041563
min	0.000000	-46.855047	-63.344698	-33.680984	-5.560118	-42.147898	-23.496714	-43.557242	-73.216718	-13.434066	-24.588262	-4.797473	-17.769143	-5.791881	-19.214325	-4.498945	-14.129855	-25.162799	-9.498746	-7.213527	-23.646890	-34.830382	-10.933144	-44.807735	-2.822684	-10.295397	-2.534330	-22.565679	-11.710896	-0.353229	0.000000
25%	54126.000000	-0.921539	-0.601213	-0.892838	-0.848835	-0.692874	-0.769177	-0.554220	-0.209086	-0.644753	-0.535493	-0.762852	-0.407660	-0.648456	-0.425122	-0.583616	-0.467945	-0.484055	-0.498850	-0.456800	-0.211662	-0.229272	-0.544345	-0.162021	-0.354179	-0.316088	-0.327327	-0.070864	-0.052907	-0.330640	0.000000
50%	84633.500000	0.019705	0.063784	0.177888	-0.017852	-0.055832	-0.274397	0.039228	0.021803	-0.049633	-0.092069	-0.034135	0.137912	-0.013416	0.051179	0.049289	0.067772	-0.065113	-0.003217	0.004422	-0.062889	-0.029045	0.006744	-0.010915	0.040974	0.018014	-0.052287	0.001064	0.011119	-0.265271	0.000000
75%	139334.250000	1.316707	0.802437	1.025529	0.745566	0.609349	0.397928	0.569638	0.327023	0.597096	0.458129	0.738143	0.617393	0.664148	0.493925	0.649589	0.523095	0.401034	0.500436	0.460367	0.132834	0.187095	0.531017	0.147503	0.438953	0.350802	0.241082	0.090491	0.077989	-0.043058	0.000000
max	172792.000000	2.451888	22.057729	9.382558	16.715537	34.099309	23.917837	44.054461	20.007208	15.594995	23.745136	12.018913	7.848392	4.569009	10.526766	5.825654	7.059132	9.207059	5.041069	5.572113	39.420904	27.202839	10.503090	22.528412	4.022866	7.519589	3.463246	12.152401	22.620072	78.235272	1.000000

pd.concat([X_train, y_train], axis=1)["Class"].value_counts()

Class
0    199019
1       345
Name: count, dtype: int64

Statistical measures of the test dataset of whole data

pd.concat([X_test, y_test], axis=1).info()

<class 'pandas.core.frame.DataFrame'>
Index: 85443 entries, 183484 to 240913
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Time    85443 non-null  float64
 1   V1      85443 non-null  float64
 2   V2      85443 non-null  float64
 3   V3      85443 non-null  float64
 4   V4      85443 non-null  float64
 5   V5      85443 non-null  float64
 6   V6      85443 non-null  float64
 7   V7      85443 non-null  float64
 8   V8      85443 non-null  float64
 9   V9      85443 non-null  float64
 10  V10     85443 non-null  float64
 11  V11     85443 non-null  float64
 12  V12     85443 non-null  float64
 13  V13     85443 non-null  float64
 14  V14     85443 non-null  float64
 15  V15     85443 non-null  float64
 16  V16     85443 non-null  float64
 17  V17     85443 non-null  float64
 18  V18     85443 non-null  float64
 19  V19     85443 non-null  float64
 20  V20     85443 non-null  float64
 21  V21     85443 non-null  float64
 22  V22     85443 non-null  float64
 23  V23     85443 non-null  float64
 24  V24     85443 non-null  float64
 25  V25     85443 non-null  float64
 26  V26     85443 non-null  float64
 27  V27     85443 non-null  float64
 28  V28     85443 non-null  float64
 29  Amount  85443 non-null  float64
 30  Class   85443 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 20.9 MB

pd.concat([X_test, y_test], axis=1).describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000	85443.000000
mean	94847.378896	-0.000734	0.006277	0.003574	-0.001682	0.003486	0.000489	0.002030	0.004620	-0.000495	-0.003167	0.002424	0.003652	-0.001616	-0.000319	-0.000751	-0.000195	-0.000682	0.000312	-0.001144	-0.001004	0.000033	0.000052	0.000602	-0.000845	-0.000922	0.000220	0.000062	-0.000036	-0.002966	0.001720
std	47461.120548	1.947325	1.637050	1.515182	1.412908	1.406722	1.340636	1.262562	1.151291	1.090691	1.079574	1.022315	1.005413	0.989553	0.962457	0.913388	0.876542	0.842669	0.839626	0.812957	0.772484	0.713266	0.721198	0.613394	0.606464	0.521520	0.483126	0.409616	0.341987	1.036492	0.041443
min	0.000000	-56.407510	-72.715728	-48.325589	-5.683171	-113.743307	-26.160506	-28.215112	-50.943369	-9.481456	-20.949192	-4.568390	-18.683715	-3.888606	-18.493773	-4.391307	-13.303888	-22.883999	-9.287832	-6.938297	-54.497720	-22.665685	-9.499423	-32.828995	-2.836627	-8.696627	-2.604551	-9.793568	-15.430084	-0.353229	0.000000
25%	54354.000000	-0.916858	-0.591858	-0.883828	-0.848202	-0.688280	-0.766664	-0.553479	-0.207216	-0.638926	-0.535400	-0.761716	-0.400087	-0.648761	-0.426516	-0.581015	-0.468312	-0.483139	-0.498660	-0.455027	-0.211881	-0.226184	-0.537704	-0.161490	-0.355671	-0.319736	-0.326068	-0.070797	-0.053129	-0.331280	0.000000
50%	84850.000000	0.013238	0.070185	0.185047	-0.024109	-0.051627	-0.273686	0.042343	0.023782	-0.053821	-0.094949	-0.029129	0.144948	-0.013803	0.049248	0.045291	0.062957	-0.066955	-0.004245	0.002229	-0.061529	-0.030687	0.006971	-0.011789	0.040976	0.013508	-0.051695	0.001984	0.011561	-0.265271	0.000000
75%	139277.500000	1.313257	0.806615	1.031155	0.737784	0.618067	0.399864	0.572423	0.328337	0.597388	0.443126	0.743511	0.620694	0.657826	0.491916	0.647117	0.523608	0.396799	0.501455	0.455249	0.133608	0.184846	0.523689	0.147923	0.441093	0.350617	0.240657	0.092224	0.078900	-0.047356	0.000000
max	172788.000000	2.454930	15.876923	4.079168	16.875344	34.801666	73.301626	120.589494	18.748872	9.272376	15.331742	11.669205	4.406338	7.126883	7.439566	8.877742	17.315112	9.253526	4.712398	5.591971	38.117209	22.579714	7.220158	20.803344	4.584549	5.826159	3.517346	31.612198	33.847808	102.362243	1.000000

pd.concat([X_test, y_test], axis=1)["Class"].value_counts()

Class
0    85296
1      147
Name: count, dtype: int64

Splitting undersampled data into training and test datasets

X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = (
    train_test_split(X_undersample, y_undersample, test_size=0.3, random_state=0)
)

Statistical measures of the training dataset of undersampled data

pd.concat([X_train_undersample, y_train_undersample], axis=1).info()

<class 'pandas.core.frame.DataFrame'>
Index: 688 entries, 6870 to 208266
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Time    688 non-null    float64
 1   V1      688 non-null    float64
 2   V2      688 non-null    float64
 3   V3      688 non-null    float64
 4   V4      688 non-null    float64
 5   V5      688 non-null    float64
 6   V6      688 non-null    float64
 7   V7      688 non-null    float64
 8   V8      688 non-null    float64
 9   V9      688 non-null    float64
 10  V10     688 non-null    float64
 11  V11     688 non-null    float64
 12  V12     688 non-null    float64
 13  V13     688 non-null    float64
 14  V14     688 non-null    float64
 15  V15     688 non-null    float64
 16  V16     688 non-null    float64
 17  V17     688 non-null    float64
 18  V18     688 non-null    float64
 19  V19     688 non-null    float64
 20  V20     688 non-null    float64
 21  V21     688 non-null    float64
 22  V22     688 non-null    float64
 23  V23     688 non-null    float64
 24  V24     688 non-null    float64
 25  V25     688 non-null    float64
 26  V26     688 non-null    float64
 27  V27     688 non-null    float64
 28  V28     688 non-null    float64
 29  Amount  688 non-null    float64
 30  Class   688 non-null    int64  
dtypes: float64(30), int64(1)
memory usage: 172.0 KB

pd.concat([X_train_undersample, y_train_undersample], axis=1).describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000	688.000000
mean	88546.635174	-2.443642	1.748210	-3.490693	2.161294	-1.466909	-0.737723	-2.759190	0.361773	-1.222417	-2.808144	1.937783	-3.131850	-0.001132	-3.568854	-0.022936	-2.145811	-3.365430	-1.137238	0.377690	0.127157	0.446495	0.012945	-0.069031	-0.020203	0.031782	0.022154	0.114684	0.041557	0.036592	0.501453
std	48529.661753	5.382638	3.616426	6.020391	3.198221	4.227553	1.829535	5.498995	4.741154	2.336555	4.417548	2.771137	4.560753	1.081826	4.641960	0.981683	3.458663	6.062216	2.462689	1.287256	1.072960	2.749354	1.143940	1.283882	0.549485	0.689015	0.474411	0.923161	0.487077	0.834360	0.500362
min	117.000000	-30.552380	-15.799625	-31.103685	-3.863126	-22.105532	-10.261990	-37.060311	-37.353443	-11.126624	-23.228255	-2.613374	-18.431131	-3.223045	-19.214325	-4.498945	-13.563273	-25.162799	-9.498746	-3.602657	-7.242879	-16.922016	-8.887017	-19.254328	-2.028024	-4.781606	-1.214960	-7.263482	-2.735623	-0.353229	0.000000
25%	45531.000000	-2.867222	-0.164478	-5.049001	-0.212543	-1.703845	-1.691031	-3.105154	-0.220868	-2.205996	-4.731895	-0.194163	-5.643631	-0.767631	-6.767749	-0.562582	-3.612856	-5.277726	-1.816368	-0.373523	-0.197730	-0.142520	-0.510247	-0.246005	-0.373302	-0.320463	-0.281449	-0.061809	-0.050983	-0.346113	0.000000
50%	82526.500000	-0.874057	0.984845	-1.482880	1.285768	-0.400360	-0.741307	-0.740952	0.141389	-0.694910	-0.981569	1.154879	-0.845463	0.008049	-1.132761	0.001558	-0.750918	-0.495063	-0.392743	0.246478	0.030556	0.163323	0.076684	-0.027143	0.014360	0.046511	-0.026232	0.059798	0.036635	-0.273188	1.000000
75%	135096.750000	0.945582	2.850947	0.348579	4.166857	0.599892	0.033569	0.240843	0.919999	0.196633	-0.001047	3.625262	0.163104	0.744021	0.086669	0.665736	0.219809	0.314206	0.371481	0.978754	0.443495	0.680597	0.629109	0.174862	0.382076	0.406056	0.306403	0.482488	0.235549	0.046539	1.000000
max	172573.000000	2.335833	19.167239	3.228978	11.927512	14.103918	6.355986	5.802537	20.007208	6.816732	11.732926	12.018913	2.534876	3.091328	3.442422	2.364199	3.139656	6.739384	3.790316	5.228342	7.907378	27.202839	5.774087	5.303607	1.208141	2.208209	2.745261	3.052358	4.975792	8.146182	1.000000

pd.concat([X_train_undersample, y_train_undersample], axis=1)["Class"].value_counts()

Class
1    345
0    343
Name: count, dtype: int64

Statistical measures of the test dataset of undersampled data

pd.concat([X_test_undersample, y_test_undersample], axis=1).info()

<class 'pandas.core.frame.DataFrame'>
Index: 296 entries, 102782 to 57921
Data columns (total 31 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Time    296 non-null    float64
 1   V1      296 non-null    float64
 2   V2      296 non-null    float64
 3   V3      296 non-null    float64
 4   V4      296 non-null    float64
 5   V5      296 non-null    float64
 6   V6      296 non-null    float64
 7   V7      296 non-null    float64
 8   V8      296 non-null    float64
 9   V9      296 non-null    float64
 10  V10     296 non-null    float64
 11  V11     296 non-null    float64
 12  V12     296 non-null    float64
 13  V13     296 non-null    float64
 14  V14     296 non-null    float64
 15  V15     296 non-null    float64
 16  V16     296 non-null    float64
 17  V17     296 non-null    float64
 18  V18     296 non-null    float64
 19  V19     296 non-null    float64
 20  V20     296 non-null    float64
 21  V21     296 non-null    float64
 22  V22     296 non-null    float64
 23  V23     296 non-null    float64
 24  V24     296 non-null    float64
 25  V25     296 non-null    float64
 26  V26     296 non-null    float64
 27  V27     296 non-null    float64
 28  V28     296 non-null    float64
 29  Amount  296 non-null    float64
 30  Class   296 non-null    int64  
dtypes: float64(30), int64(1)
memory usage: 74.0 KB

pd.concat([X_test_undersample, y_test_undersample], axis=1).describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	V11	V12	V13	V14	V15	V16	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000	296.000000
mean	88396.587838	-2.448419	1.857288	-3.552900	2.336519	-1.503755	-0.656035	-2.853058	0.086851	-1.324446	-2.919028	1.914227	-3.106154	-0.084562	-3.347887	-0.077981	-1.984526	-3.161909	-1.109686	0.264590	0.289212	0.065582	0.134902	0.056521	-0.077336	0.001963	0.040364	0.020281	0.058781	0.046845	0.496622
std	50147.105326	5.812072	3.934323	6.680660	3.308417	4.389263	1.693893	6.622008	5.121293	2.451914	4.891517	2.754439	4.681722	0.986937	4.683458	1.051296	3.484989	5.826410	2.293910	1.298310	1.235841	2.862463	1.216935	0.877975	0.555090	0.650752	0.481822	1.224166	0.460841	0.892432	0.500835
min	60.000000	-29.876366	-8.402154	-30.558697	-2.956827	-21.665654	-5.773192	-43.557242	-41.044261	-13.434066	-24.588262	-2.383066	-18.683715	-3.076318	-17.620634	-3.092108	-14.129855	-22.541652	-9.090892	-3.681904	-5.225849	-22.797604	-8.887017	-5.988806	-1.742803	-2.079928	-1.170476	-7.263482	-1.931920	-0.353229	0.000000
25%	45977.500000	-2.867766	-0.130600	-5.417818	-0.118496	-1.667035	-1.477544	-2.835885	-0.168935	-2.345829	-4.445615	-0.144802	-5.340188	-0.815218	-6.363108	-0.729637	-3.303237	-5.358990	-1.747789	-0.563676	-0.165023	-0.178103	-0.483530	-0.212828	-0.405811	-0.324214	-0.270853	-0.056831	-0.042639	-0.349231	0.000000
50%	84069.000000	-0.740915	0.941852	-1.139964	1.340723	-0.369227	-0.596589	-0.501864	0.169642	-0.696902	-0.875521	1.267304	-0.938658	-0.060414	-1.059352	-0.012904	-0.547678	-0.527389	-0.318904	0.169827	0.056998	0.130060	0.081904	-0.035614	-0.010232	0.068890	0.031911	0.073702	0.046030	-0.300834	0.000000
75%	135023.500000	0.879511	2.700371	0.394765	4.305361	0.624459	0.139244	0.306788	0.833392	0.011527	-0.051012	3.542336	0.234752	0.609629	0.173916	0.685300	0.351119	0.309636	0.237358	0.948371	0.461180	0.568611	0.617588	0.200328	0.317653	0.386804	0.355382	0.395412	0.192766	0.028048	1.000000
max	172733.000000	2.306769	22.057729	3.476268	12.114672	9.880564	6.474115	3.791907	19.587773	4.866316	6.367661	11.152491	1.725185	2.897044	2.654275	2.471358	2.696475	6.443649	2.591846	4.851255	11.059004	27.202839	8.361985	5.466230	1.077407	2.156042	1.458828	2.706566	3.042406	5.663610	1.000000

pd.concat([X_test_undersample, y_test_undersample], axis=1)["Class"].value_counts()

Class
0    149
1    147
Name: count, dtype: int64

126 KiB Raw Blame History

IUM 2

Installation of packages

Importing libraries

Downloading a dataset

Uncompress a file

Load the data

Check missing values

Size of the dataset

Normalising the data

Summary statistics

Distribution of legitimate and fraudulent transactions

Undersampling the data

Size of undersampled dataset

Summary statistics of the undersampled dataset

Distribution of legitimate and fraudulent transactions in an undersampled dataset

Splitting whole data into training and test datasets

Statistical measures of the training dataset of whole data

Statistical measures of the test dataset of whole data

Splitting undersampled data into training and test datasets

Statistical measures of the training dataset of undersampled data

Statistical measures of the test dataset of undersampled data

126 KiB

Raw Blame History