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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…
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
An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.
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!
Over the years, the company has collected basic bank details and gathered a lot of credit-related information. The management wants to build an intelligent system to segregate the people into credit score brackets to reduce the manual efforts.. You are hired as a data scientist to build a machine learning model that can classify the credit score.
Recent times the E-commerce websites took a rapid increase in their user stats, this project is a small help for all the people out there to have a confirmation on their delivery date to avoid any issues or problems while receiving the package.
Built a Machine Learning Supervised classification algorithm, for predicting the risk of cardiovascualar disease withing coming 10 years by analyzing Patients medical History.
ReneWind operates wind farms. Unexpected turbine failures are presenting operational and financial problems. This project uses machine learning to develop a model that accurately predict component failure, which will give the firm more control over maintenance scheduling, costs and power generation.