Capital markets have existed for centuries, allowing companies to raise capital, and investors to invest in businesses through the use of equity-based and debt-based instruments. One of the most well known capital markets is the stock market. The stock market is a place where companies issue stock (shares of equity ownership) in exchange for cash. The owners of this stock, benefit from the long term price appreciation of the company, and from any distributions of returns on equity, commonly referred to as dividends. Company’s benefit from being able to raise funds to expand their business. Well established companies issue their stock on public exchanges. A public exchange is a centralized place (no longer solely physical) where investors can buy and sell their shares with other investors. Prices for recent trades, or exchanges of shares, are displayed publicly so that all market participants know the market value of a given company. The purpose of this paper is to explore the results of applying machine learning techniques to financial markets to predict stock price rise/fall and to alert stock holders via email.

Regression- In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Classification- In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, since a training set of data containing observations (or instances) whose category membership is known. classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance.

Lasso Regression- Lasso regression is a model that predicts real values output label, using L1 regularizer. It is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces.

A. Problem Statement A stock market crash is a sudden dramatic decline of stock prices across a significant cross-section of a stock market, resulting in a significant loss of paper wealth. Crashes are driven by panic as much as by underlying economic factors. They often follow speculative stock market bubbles. As traders constantly trying to make profitable stock trades and invest more wisely. We are primarily looking for ways to increase returns, decrease risk, and to make smarter decisions. One of the ways we make better trades and invest more wisely is with various trading and investing tools. Since stock prices vary throughout the day and while predicting the probability of their rise/fall, we have to consider 25 time segments within a day. Stock holders are unaware whether their stocks would rise or fall shortly. In order to overcome the ambiguity of stock prices, we developed an algorithm which predicts the probability with with a stock price could rise/fall and alert the stock holder if rise/fall probability is too high.# Stock-prediction-with-ML