A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge.

In diagnosis centers the importance of detecting a cancer on time is vital. With the help of tools like x-ray, MRI machine, medical professionals can detect somatic mutations easily (a somatic mutation is an acquired change in a genetic code of one or more cells). Here we chose a disease that is liver cancer. This deployed model is given data via google Collab, then analyzed in real-time with machine learning model which was pretrained and the result is shown in the google Collab. Models that are used in our project are Logistic regression, Naive Bayes classifier and Random Forest etc., is used to carry out computation for prediction. And we compare these machine learning models accuracies. But we got good accuracy for machine learning model (random forest classifier). Early detection can help in identifying the risk of liver cancer. Our model is helpful for doctors to give timely medications for treatment.