Amid the worldwide wave of pandemic lockdowns, there has been a remarkable growth in E-learning. Online learning has become a challenge for students. It has become difficult for students to find the content they need. The mounting accessibility of textual content has necessitated comprehensive study in the areas of automatic text summarization and question generation. Multiple Choice Questions is very smooth for evaluations, and its assessment is implemented through computerized applications in order that results may be declared within some hours, and the evaluation system is 100% pure. The system proposes an interactive reading platform where the user can upload an E-Book and get textual summary and generates questions like MCQs, fill in the blanks and one word. The user can also evaluate the questions answered. The proposed system is an all-in-one interactive reading platform.

With ever increasing air route connectivity throughout the world, air travel has become a common, integral and faster way to travel. Predicting fares for airlines is an important as well as challenging task since a constant fluctuation in fares is observed and it is known to be dependent on varied set factors. With tremendous study in area, it is observed that using Machine Learning, Artificial Intelligence and Deep Learning techniques an estimation of flight fares at a given time can be obtained within seconds. In this paper, we use a Machine Learning Regression approach to predict flight fare by providing basic details of departure date and time, arrival time, source, destination, number of stops and name of the airline. The results show that Random Forest Regression Model provides highly optimal results. Keywords: Machine Learning, Random Forest, Prediction models, Airfare Prices, data analytics