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The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidate…
This research advances credit card fraud detection by integrating machine learning and deep learning techniques. Key findings include improved model adaptability through hyperparameter tuning.
An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.
Classification Project for SDAIA T5 Data Science Bootcamp. This project will choose the best classification model to predict whether a loan is a short-term loan or a long-term loan, based on some features.
Predict the winning probability of white player in a chess game on the basis of first move of white player and first move of black player. In the dataset all the set of moves are given but I choose to predict the white winner the first move
Predicting Heart Disease with Python and Machine Learning. In this project, in the first part we will explore and prepare the data before starting the Machine Learning models. Let's try to predict which people have heart problems based on personal and health data. we use some Machine Learning models to make the predictions.
With this model: the amount of backlog would be reduced significantly, the amount of staff needed to do the job would be reduced drastically, the processing time would be shortened significantly and more cases of fraudulent transactions would be tracked down in a given amount of data processed - more than 40% increase in efficiency!