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Book suggestions may be used by users to explore and search for books on the internet. Given a vast number of items and descriptions that correlate to the user's requirements, our recommendation system will assist the user in picking the book that best matches the description. The following criteria impact recommendation algorithms: rating, reviews, description, and author. The effectiveness of Book Recommendation Systems is greatly dependent on the classifier utilized. As a result, developing an accurate classifier is critical for improving the performance of recommendation systems. Decision Tree Classifiers stand out among many supervised learning approaches and algorithms due to their high accuracy, fast classification speed, strong learning ability, and ease of design. The framework for a Decision Tree-Based recommendation system is proposed in this study. Among the other significant supervised learning techniques and algorithms are Naïve Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor. [ABSTRACT FROM AUTHOR]

Stock market prediction helps to predict upcoming values of a company's stock or other economic instruments deals on exchange. Predicting the performance of the stock market is a difficult task. The construction of stock market is very fragile in nature. It discusses trends that are extremely varied and biased in nature. There are a lot of factors involved in causing the fall and rise of stock prices which include environmental factors, concrete factors, economic factors, behavioral factors, and industry performance. These factors combine to make stock price dynamic, volatile and tend to temporarily change the trend. The radical behavior of stock marks the high degree of inaccuracy of stock price. Unlike the materialistic influence of Parameters on stock market, human interference also plays a major role. Words and gestures of renowned personalities can bring about a drastic change and uplift the market trend. Commoners are mere spectators and overall, only a humongous crowd of people can change the trend. Not only historical data is sufficient to predict the future market value, but also short-term factors play a direct role. The project considers the prediction sector-wise keeping under consideration the specific factors that affect the same. Tweets extracted from social media are used to perform sentiment analysis to group them as positive, negative, or neutral. Historical tweets and factor analysis help to giving better results and understanding the trend. Thus, stock values can be predicted deftly and thus induce profit to the investors/stakeholders Subsequently reviewing the historical stock price and underlying factors as the epitome of core idea of the project, we deploy deep learning technique to deduce future stock value. We put-forth a model which employs a time series prediction-based algorithm LSTM to forecast the company's prospects. [ABSTRACT FROM AUTHOR]