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# importing libraries | |
from datasets import load_dataset, load_dataset_builder | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
import sklearn | |
from sklearn.compose import ColumnTransformer | |
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler | |
from sklearn.pipeline import Pipeline | |
from sklearn.model_selection import train_test_split | |
from sklearn.naive_bayes import GaussianNB | |
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, precision_score, recall_score, classification_report | |
import imblearn | |
from imblearn.under_sampling import RandomUnderSampler | |
from skops import hub_utils | |
import pickle | |
#from skops.card import Card, metadata_from_config | |
from pathlib import Path | |
import streamlit as st | |
from tempfile import mkdtemp, mkstemp | |
# Loading the dataset | |
dataset_name = "saifhmb/FraudPaymentData" | |
dataset = load_dataset(dataset_name, split = 'train') | |
dataset = pd.DataFrame(dataset) | |
dataset = dataset.dropna() | |
dataset = dataset.drop(['Time_step','Transaction_Id','Sender_Id', 'Sender_Account','Bene_Id','Bene_Account'], axis = 1) # deleting high cardinality features | |
y = dataset.iloc[:, 5].values | |
dataset = dataset.drop(['Label'], axis = 1) | |
dataset = dataset.drop(['Sender_lob', 'Sender_Sector'], axis = 1) # delete column since there is only a single unique value for 'Sender_lob' and 'Sender_sector' is a high cardinal feature | |
# Encoding the Independent Variables | |
categoricalColumns = ['Sender_Country', 'Bene_Country', 'Transaction_Type'] | |
onehot_categorical = OneHotEncoder(handle_unknown='ignore', sparse_output= False) | |
categorical_transformer = Pipeline(steps = [('onehot', onehot_categorical)]) | |
numericalColumns = dataset.select_dtypes(include = np.number).columns | |
sc = StandardScaler() | |
numerical_transformer = Pipeline(steps = [('scale', sc)]) | |
preprocessorForCategoricalColumns = ColumnTransformer(transformers=[('cat', categorical_transformer, categoricalColumns)], remainder ='passthrough') | |
preprocessorForAllColumns = ColumnTransformer(transformers=[('cat', categorical_transformer, categoricalColumns),('num',numerical_transformer,numericalColumns)], | |
remainder="passthrough") | |
# Spliting the datset into Training and Test set | |
X = dataset | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state = 42) # random state is 0 or 42 | |
# Train Naive Bayes Model using the Training set | |
# Handling imbalanced dataset | |
under_sampler = RandomUnderSampler() | |
X_under, y_under = under_sampler.fit_resample(X_train, y_train) | |
classifier = GaussianNB() # select the appropriate algorithm for the problem statement | |
model = Pipeline(steps = [('preprocessorAll', preprocessorForAllColumns),('classifier', classifier)]) | |
model.fit(X_under, y_under) | |
# Predicting the Test result | |
y_pred = model.predict(X_test) | |
# Making the Confusion Matrix and evaluating performance | |
cm = confusion_matrix(y_pred, y_test, labels=model.classes_) | |
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=np.array(['0 - Normal', '1 - Fraudulent'])) | |
disp.plot() | |
plt.show() | |
acc = accuracy_score(y_test, y_pred) | |
# Pickling the model | |
pickle_out = open("model.pkl", "wb") | |
pickle.dump(model, pickle_out) | |
pickle_out.close() | |
# Loading the model to predict on the data | |
pickle_in = open('model.pkl', 'rb') | |
model = pickle.load(pickle_in) | |
def welcome(): | |
return 'welcome all' | |
# defining the function which will make the prediction using the data which the user inputs | |
def prediction(Sender_Country, Bene_Country, USD_amount, Transaction_Type): | |
X = pd.DataFrame([[Sender_Country, Bene_Country, USD_amount, Transaction_Type]], columns = ['Sender_Country', 'Bene_Country', 'USD_amount', 'Transaction_Type']) | |
prediction = model.predict(X) | |
print(prediction) | |
return prediction | |
# this is the main function in which we define our webpage | |
def main(): | |
# giving the webpage a title | |
st.title("Fraud Detection ML App") | |
st.header("Model Description", divider = "gray") | |
multi = '''This is a Gaussian Naive Bayes model trained on a synthetic dataset, containing a large variety of transaction types representing normal activities | |
as well as abnormal/fraudulent activities. The model predicts whether a transaction is normal or fraudulent. | |
For more details on the model please refer to the model card at https://huggingface.co/saifhmb/fraud-detection-model | |
''' | |
st.markdown(multi) | |
st.markdown("To determine whether a transaction is normal or fraudulent, please **ENTER** the Sender Country, Beneficiary Country, Amount in USD and Transaction Type :") | |
col1, col2 = st.columns(2) | |
with col1: | |
Sender_Country = st.text_input("Sender Country") | |
with col2: | |
Bene_Country = st.text_input("Beneficiary Country") | |
col3, col4 = st.columns(2) | |
with col3: | |
USD_amount = st.number_input("Amount in USD") | |
with col4: | |
Transaction_Type = st.text_input("Transaction Type (Please enter one of the following: make-payment, quick-payment, move-funds, pay-check)") | |
result = "" | |
if st.button("Predict"): | |
result = prediction(Sender_Country, Bene_Country, USD_amount, Transaction_Type) | |
if result == 0: | |
st.success("The output is {}".format(result) + " This is a NORMAL transaction") | |
if result == 1: | |
st.success("The output is {}".format(result) + " This is a FRAUDULENT TRANSACTION") | |
if __name__=='__main__': | |
main() | |