This project aims to predict the likelihood of admission to graduate school using R programming language. The project will use a dataset that contains information about previous applicants' academic records, GRE scores, and other variables to train a machine learning model to predict the probability of admission.
The first step in the project will be to explore and clean the data, removing any missing or incomplete values, and conducting exploratory data analysis to identify trends and patterns. Then, a machine learning model, such as logistic regression, decision trees, or random forests, will be trained using the cleaned data.
The performance of the model will be evaluated using metrics such as accuracy, precision, recall, and F1 score, and the model will be optimized by adjusting its hyperparameters to improve its performance.
Once the model has been trained and optimized, it will be used to predict the probability of admission for new applicants based on their academic records and GRE scores. The project will also explore the most important features that contribute to admission decisions, such as undergraduate GPA, GRE scores, and letters of recommendation.
Finally, the project will create an interactive dashboard using R Shiny that allows users to input their academic records and GRE scores to obtain a personalized prediction of their likelihood of admission to graduate school. This will provide a valuable tool for prospective students to evaluate their chances of admission and make informed decisions about their academic and career paths.
Overall, this project will leverage the power of machine learning and R programming to predict graduate school admission and provide a user-friendly tool for students to make informed decisions about their academic futures.