PROJECT1: RED WINE QUALITY PREDICTION It's Red Wine Quality Prediction which uses supervised Machine Learning to make predictions whether wine has a good quality or bad quality. Data pre-processing, Data cleaning, Data Visualization and Model Training and Model Prediction are the key fundamentals of this task. Random Forest Classifier is used to make predictions.
PROJECT2: IRIS CLASSIFICATION For the Iris classification problem, the model defines the relationship between the sepal and petal measurements and the predicted Iris species. Some simple models can be described with a few lines of algebra, but complex machine learning models have a large number of parameters that are difficult to summarize. Logistic Regression is used to make predictions.