Hire Best Machine Learning Engineers | Realcode4you

Realcode4you is the right choice if you are looking to Hire Best Machine Learning Engineers, Experts and Professionals. We are providing top rated services in all machine learning and data science topics. Below the some important topics in which you can get help.

Get Help In Fraud/Anomaly Detection

  • The concept of fraud includes a criminal and a victim.

  • It can be encountered in many different areas with various methods.

  • There are mainly two broad categories of fraud, traditional and digital

  • Further, even digital fraud activities are also quite diverse in themselves.

Some types of frauds are

  • Internet Fraud

  • Mail Fraud

  • Debit and Credit Card Fraud

  • Promotion Fraud

  • Application Fraud

Benefits of Fraud Detection

  • Losses can be prevented by detecting fraud attempts in real-time.

  • We can prioritize risk situations and respond to critical situations early.

  • By reducing manual reviews, we can reduce the workload of our fraud team, enable them to focus on more critical cases, and increase work efficiency.

400 billion dollar loss due to fraud?

Card industry will lose 400 billion dollars in this decade due to card frauds.

How will we do fraud detection?

In the last 10 years, fraud detection with the help of machine learning is quite trending. This is because ml increases efficiency when deployed in place of teams finding frauds manually.

Steps in Fraud Detection

  • Importing company transaction datasets

  • Preprocessing the data

  • Data Visualization

  • Model Building

  • Testing the Model

  • Deployment

Importing standard libraries

Import standard python libraries

  • Numpy : used by ml algorithms to perform matrix multiplications

  • Pandas : used for data-handling

  • Matplotlib : standard python library for graph plotting

  • Seaborn : advanced library with more aesthetic features

Importing standard libraries

Importing Dataset

Import the company transactions dataset

Data Preprocessing: Introduction

Data Preprocessing is the process of making data suitable for use while training a machine learning model.

Why to use data preprocessing

The dataset initially provided for training might not be in a ready-to-use state, for e.g. it might not be formatted properly, or may contain missing or null values.

Using a properly processed dataset while training will not only make life easier for you but also increase the efficiency and accuracy of our model


A dataset can be viewed as a collection of data objects, which are often also called as a records, points, vectors, patterns, events, cases, samples, observations, or entities.

Data objects are described by a number of features, that capture the basic characteristics of an object, such as the mass of a physical object or the time at which an event occurred, etc.

Features are often called as variables, characteristics, fields, attributes, or dimensions.

Categorical Features

Features whose values are taken from a defined set of values. For instance:

monthNames = [ "January", "February", "March", "April", "May", "June",
"July", "August", &q