# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session








import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score, confusion_matrix, classification_report, RocCurveDisplay

from sklearn.linear_model import LogisticRegression





df = pd.read_csv("/kaggle/input/creditcardfraud/creditcard.csv")
df.head()








df.shape


df.info()


df.columns


df.isnull().sum().sort_values(ascending=False)


df["Class"].value_counts()


sns.countplot(x="Class", data=df)
plt.title("Class Distribution")
plt.show()


plt.figure(figsize=(8,4))
sns.histplot(df["Amount"], bins=50)
plt.title("Transaction Amount Distribution")
plt.show()








scaler = StandardScaler()

df["Amount"] = scaler.fit_transform(df[["Amount"]])
df["Time"] = scaler.fit_transform(df[["Time"]])





X = df.drop("Class", axis=1)
y = df["Class"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)








model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)








y_pred_proba = model.predict_proba(X_test)[:, 1]
roc_auc_score(y_test, y_pred_proba)





RocCurveDisplay.from_predictions(y_test, y_pred_proba)
plt.show()





y_pred = model.predict(X_test)

cm = confusion_matrix(y_test, y_pred)

sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.title("Confusion Matrix")
plt.show()





print(classification_report(y_test, y_pred))














import joblib

joblib.dump(model, "model.pkl")


np.save("X_test.npy", X_test)
np.save("y_test.npy", y_test)