Predicting Fraud Transactions Using Python | Machine Learning
In this coding tutorial, we will be Predicting Fraud Transactions Using Python. This machine learning model helps us to classify the transactions done to be as fraud and not fraud based on the given dataset, which includes payment type, account, amount, fraud, and not fraud. According to this data, we will train our model, and then we will predict the result accordingly.
Source Code: https://myprojectideas.com/predicting-fraud-transactions-using-machine-learning/
Fraud detection is a common application of machine learning. For predicting fraud transactions using machine learning, we can use several techniques, including supervised learning algorithms such as decision trees, random forests, and logistic regression, as well as unsupervised learning methods such as clustering and anomaly detection.
To predict fraudulent transactions using machine learning, we would need to first acquire a labeled dataset of past transactions, where the labels indicate whether a transaction was fraudulent or not. This dataset would be used to train a model, which can then be applied to new, unseen transactions to predict whether they are likely to be fraudulent or not.
The features used in the model will include information about the transaction itself, such as the payment type, account, amount, location, and time, as well as information about the customer, such as their past transaction history and behavior. It’s important to note that fraud detection is an ongoing process, and the model will require frequent updates and fine-tuning to remain effective as fraud patterns change over time.
Objectives:
The objectives of predicting fraud transactions using machine learning are as follows:
Identifying fraudulent transactions as early as possible to minimize financial losses.
Creating a robust model that can accurately detect fraud in real time, even as fraud patterns evolve over time.
Automating the fraud detection process to reduce the time and resources required to review transactions manually.
Reducing the number of false positives (legitimate transactions incorrectly flagged as fraudulent) to minimize customer inconvenience and improve customer satisfaction.
Generating actionable insights from the data to help identify patterns and trends in fraudulent activity, which can be used to improve the system’s overall security.
Incorporating Explainable AI (XAI) techniques to understand why certain transactions are predicted to be fraudulent and help improve the interpretability and transparency of the model.
Continuously monitor and evaluate the model’s performance to ensure that it meets the objectives over time.
Timestamp:
00:12 - Project Overview
00:45 - Code Explanation
05:23 - Demonstration
Requirements:
1. Python Programming Language
2. Jupyter Notebook or any other python IDE
Explanation of the code:
Initially, we have loaded all the necessary libraries through which our dataset is loaded, and accordingly, we will make the further model. Secondly, we have checked that if the dataset contains any null values we need to clean our dataset and then move with further operations.
As the dataset does not contain any null values, so we can move ahead with further operations and can select the algorithm to train our dataset.
In this code, we have used Naïve Bayes and Gaussian classifiers along with logistic regression are used to train our model, and accordingly, we will test it.
Through sklearn module of python, we are importing the pre-processing module so that we can make our raw data suitable for further analysis.
#machinelearning #python #frauddetection
In this coding tutorial, we will be Predicting Fraud Transactions Using Python. This machine learning model helps us to classify the transactions done to be as fraud and not fraud based on the given dataset, which includes payment type, account, amount, fraud, and not fraud. According to this data, we will train our model, and then we will predict the result accordingly.
Source Code: https://myprojectideas.com/predicting-fraud-transactions-using-machine-learning/
Fraud detection is a common application of machine learning. For predicting fraud transactions using machine learning, we can use several techniques, including supervised learning algorithms such as decision trees, random forests, and logistic regression, as well as unsupervised learning methods such as clustering and anomaly detection.
To predict fraudulent transactions using machine learning, we would need to first acquire a labeled dataset of past transactions, where the labels indicate whether a transaction was fraudulent or not. This dataset would be used to train a model, which can then be applied to new, unseen transactions to predict whether they are likely to be fraudulent or not.
The features used in the model will include information about the transaction itself, such as the payment type, account, amount, location, and time, as well as information about the customer, such as their past transaction history and behavior. It’s important to note that fraud detection is an ongoing process, and the model will require frequent updates and fine-tuning to remain effective as fraud patterns change over time.
Objectives:
The objectives of predicting fraud transactions using machine learning are as follows:
Identifying fraudulent transactions as early as possible to minimize financial losses.
Creating a robust model that can accurately detect fraud in real time, even as fraud patterns evolve over time.
Automating the fraud detection process to reduce the time and resources required to review transactions manually.
Reducing the number of false positives (legitimate transactions incorrectly flagged as fraudulent) to minimize customer inconvenience and improve customer satisfaction.
Generating actionable insights from the data to help identify patterns and trends in fraudulent activity, which can be used to improve the system’s overall security.
Incorporating Explainable AI (XAI) techniques to understand why certain transactions are predicted to be fraudulent and help improve the interpretability and transparency of the model.
Continuously monitor and evaluate the model’s performance to ensure that it meets the objectives over time.
Timestamp:
00:12 – Project Overview
00:45 – Code Explanation
05:23 – Demonstration
Requirements:
1. Python Programming Language
2. Jupyter Notebook or any other python IDE
Explanation of the code:
Initially, we have loaded all the necessary libraries through which our dataset is loaded, and accordingly, we will make the further model. Secondly, we have checked that if the dataset contains any null values we need to clean our dataset and then move with further operations.
As the dataset does not contain any null values, so we can move ahead with further operations and can select the algorithm to train our dataset.
In this code, we have used Naïve Bayes and Gaussian classifiers along with logistic regression are used to train our model, and accordingly, we will test it.
Through sklearn module of python, we are importing the pre-processing module so that we can make our raw data suitable for further analysis.
#machinelearning #python #frauddetection
