You can find the complete code here

Source: https://giphy.com/gifs/glitch-money-shopping-d3mmdNnW5hkoUxTG

In this fast-paced digital world, we are integrated into the digital transaction society. It is expected that in coming years there will be steady growth of non-cash transactions. As this digital transaction keep increasing every year, the number of credit card frauds also keeps increasing at an all-time high. 15.4 million people experienced credit fraud in 2016 alone in the U.S, according to a recent study.

There are few ways to stop these fraudulent activities but I am going to walk you through my machine learning approach here.

Collecting the Data

I used the Kaggle dataset


Getty Images; Reuters

Social media hot spells and fake news are becoming poison for our society in this digital era. Fake news from a fake profile may misguide us and can cause chaos and bring instability in our daily life.

Natural Language Processing (NLP) of machine learning is a great tool to understand people's sentiment and can scan a huge amount of text documents from any social platform in seconds.

The goal of this project is to develop a NLP project that can analyze tweets from two users and determines the probability of a specific post from a particular account. I will use…

Md Opu

Director of Stack Development Engineering, Oorja Corporation

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