The classification goal is to predict if the client will subscribe to a term deposit. The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns are based on phone calls.
ML describes methods that use statistical models, real-time analysis and algorithms to find patterns in data. This app detects the average petrol consumption based on entities like average income, tax, etc by using Random Forest.
Here the targets were free electrons in the ionosphere and we trained the system on 4 entities of the dataset. Based on them it predicts for the new data, whether it, is showing good or bad evidence by returning the label ‘g’ for good or ‘b’ for bad.
On the basis of mobile specifications like battery power, memory, screen, wifi, bluetooth, etc. we are predicting price range of mobile using SVM and SVC algorithms with sklearn. An accuracy of 80 % is achieved by the system.
Here, the dataset (a set of images of wine labels) was trained and associated on price, type, origin, and other notes from the web. Using machine learning and OpenCV we successfully predicted the labels of test images.
This application predicts positive and negative sentiments in a given text using NLP. The model was trained with two labels used for classification, ‘positive’ and ‘negative’ on huge datasets of twitter. Algorithm- Naive Bayes.
A model is trained over raw data from open source to predict if a person has heart disease. It has 4 levels of prediction based on severity level, approx. 14 different features were used to train the model. Use of prediction model with sklearn helped achieve accuracy up to 80%-85%.