Heart disease or Cardiovascular disease is one of the biggest causes of mortality (i.e., causing 1 out of 4 deaths in the US) among the population of the world. Therefore, prediction of Cardiovascular disease is considered one of the important subjects in clinical data analysis. However, several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, etc. lead to cardiac arrest. So, the purpose of this work is to predict if any patient has the chance of having heart disease or not.

The objective is to detect patients with Parkinson’s Disease (PD) from the voice samples. The training data includes voice measurements such as average, maximum, and minimum vocal fundamental frequency, several measures of variation in fundamental frequency, variation in amplitudes, ratio of noise to tonal components, signal fractal scaling exponent and nonlinear measures of fundamental frequency variation. From these measurements, feature-selection (filter and wrapper method) is performed to select essential features for detecting Parkinson.

The objecive of this project is to predict house price from different features. The dataset includes 1460 instances and 80 features. The following algorithms are applied as on selected features from the data: Applied Algorithms: Linear Regression Decision Tree SVM Random Forest AdaBoost GradientBoost XGBoost Feature selection is performed using Parson Correlation. Feature imputation, encoding, and scaling is performed. The best performance achieved is R-square = 0.