The exponential growth of online music streaming has given birth to many new platforms among which, the widely used platform is Spotify. The most popular music streaming app's data can be used to predict the capability of a song to be popular before its release with the help of attributes like loudness, energy, acousticness, etc. which is defined when the song is being made. This study helps to predict the popularity of the song using the song metrics available in Spotify by applying Random Forest classifier, K-Nearest neighbour classifier and Linear Support Vector classifier to compare which of these models can effectively predict the popularity. The results found that Random Forest works the best for predicting popularity with high accuracy, precision, recall and F1-score.

The Covid-19 outburst seemed in Wuhan in December 2019 and spread rapidly all over the world. The Covid-19 ailment does now have clinically proven vaccines and medication for treatment [1]. WHO recommends that initial vaccination should arrange groups at highest risk of introduction to infection in each country, including health workers, older persons and those with other health issues? In this study we going to apply ARIMA and LSTM. ARIMA (Autoregressive Integrated Moving Average), It is a class of classical that captures a suite of unlike standard temporal structures in period series data. Long Short-Term Memory (LSTM) networks are a type of recurrent neural network proficient of knowledge order dependence in arrangement prediction problems. This is a behavior required in multifaceted problem domains like machine translation, speech recognition, and more. LSTMs are a composite area of deep learning.

India is an agricultural country, much of the economy is dependent on productivity growth. Agriculture is heavily dependent on rainwater and depends on various soil conditions, namely nitrogen, phosphorus, potassium, and climates such as temperatures and rainfall. The growth of agricultural technology will increase crop production. Machine learning is a promising area for research to anticipate yield based on data patterns. The proposed learning algorithms apply to the machine learning algorithms: Random Forest, Logistic Regression, Decision Tree, and Support Vector Machine. Predictions of plants that are most relevant to the current environment are being made. This work gives producers a strong prediction of planting what types of crops in their area on the farm according to the above-mentioned parameters to grow a smart agricultural product. four different algorithms are applied in this project system. With the help of the ROC-AUC-SCORE, the accuracy of all the models is compared and other factors like precision, recall, F1 score, and support are also compared. And from all these results we can know which model is perfect and from that, we can know which crop is suitable for the given soil and climatic condition.

The stock market is growing abundantly due to the rise of investors for their passive income is This article aims to develop an innovative artificial recurrent neural network approach for better stock market forecasts. The stock 611market is receiving a lot of attention from investors. Capturing the regularity of stock market changes has always been a key point for investors and investment firms. Investors are very interested in the field of stock price forecasting research. To make a successful investment, many investors want to know the future of the stock market. The data is pulled from the livestock market for analysis and visualization and results in analysis in real-time and offline. Predictive methods can be divided into two broad categories: statistical methods and artificial intelligence methods. Statistical methods include the logistic regression model and ARIMA, UCM model. Artificial intelligence techniques include multi-layer perceptron's, accumulative neural networks, Naïve Bayes, back-propagation networks, single-layer LSTMs, vector bearers, cyclic neural networks, and more. From this research, LSTM is achieving less Error percentage than any other model. LSTM helps investors, analysts, or anyone interested in investing in the stock market to get a better understanding of the future state of the stock market.