文件列表:

Temp
Transaction_fraud_detection_model.txt
decision_tree_classifier.pkl
untitled2.py

# TRANSACTIONS_FRAUD_DETECTION This project is an implementation of a Fraud Detection System using a Decision Tree algorithm, built with Python's scikit-learn library. It features a web interface created with Flask, allowing users to interactively predict and analyze fraudulent activities in financial transactions. ->What things you need to install the software and how to install them: Copy code Python 3.x Flask scikit-learn pandas numpy The Decision Tree model is trained on a dataset containing historical transaction data, where each transaction is labeled as fraudulent or legitimate. The model learns patterns from this data, enabling it to predict fraud in new transactions. Commands used: import pickle import sklearn from flask import Flask, request, render_template import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import accuracy_score, classification_report from sklearn.tree import DecisionTreeClassifier

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