The present project has successfully executed various analytical and learning algorithms on the MS admission dataset. The dataset's features have been effectively visualized. The dataset was utilized to train multiple machine learning models, including linear regression, decision tree, and random forest algorithms, which are predictive models in machine learning. Upon providing the dataset to the three different models, it was determined that the Random Forest algorithm demonstrated the highest level of accuracy and is therefore deemed the optimal model for predicting the probability of a student's admission to one of the top five universities in the US for a Master's degree program.
https://www.kaggle.com/datasets/mohansacharya/graduate-admissions
- Exploratory Data Analysis
- Data preprocessing
- Building predictive models using Linear Regression, Decision Tree and Random Forest
The data was used to train multiple models using the three following algorithms and the achieved accuracies are as mentioned below:
- Linear Regression : 81.97
- Decision Tree : 86.64
- Random Forest : 93.12