Final Year B.tech Project on Machine Learning Stock Prediction through Deep Learning

Top Class Stock Price Prediction Project through Machine Learning Algorithms for Google. Easy Understanding and Implementation. B.tech Final Year College Project.

The basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.

Stock price prediction. UNC 2018 Fall machine learning course final project.

This is my University final year research project which is about a stock price prediction system using machine learning, reinforcement learning and also NLP techniques to analyze news articles about stock market.

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Final team project was a Stock Price Prediction Model using Deep Learning neural networks and Python. Deliverables included pulling data from Yahoo Finance Library, processing the data for database storage and retrieval, connecting PgAdmin database and AWS cloud storage to store and retrieve the data; retrieving data from the database for preprocessing and use in an LSTM Machine Learning model for prediction. Also, prepared data visualization describing the project and displaying our project deliverables on an active website using JavaScript.

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Analysis And Prediction Of Stock Prices : Final Year Project for B.E. @ Government Engg. College, Modasa

Final year team Project. Stock Price Prediction using Sentiment Analysis for Indian Markets. Our aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data.

Final Year Project on STOCK PRICE PREDICTION AND ANALYSIS USING TWITTER SENTIMENTS by Jairaj Saraf, Hardik Lad, Siddhi Pawar, guided by Prof. Vidya Chitre.

Stock Price Prediction Using LSTM is a sophisticated financial analysis tool developed as a final year project. The system utilizes Long Short-Term Memory (LSTM) neural networks to analyze historical stock market data and predict future price trends

This is the final project for Introduction to Machine Learning, Supervised Learning. This project involves developing a stock price prediction and trading signal generation system using the k-Nearest Neighbors (kNN) algorithm.

The facebook Stock Price that uses the Machine Learning to predict the next day closing price of the Facebook Stock price. Its is based on the supervised learning that implements number of regressors that are trained on the dataset that is collected from the online repository " Quandl.com" This project also have a very basic GUI is also designed to ease the process of prediction and visualization. The GUI is designed using the Tkinter library in Python. To run this just download all the files and copy all the files to a folder and run the "google_final_gui.py" and you will get the GUI designed Some drawbacks:- 1.Opening two graphs at same time may overwrite each other. 2.Different windows will open for each graph

In this HW, we will implement a very aged prediction problem from the financial field. Given a series of stock prices, including daily open, high, low, and close prices, decide your daily action and make your best profit for the future trading. Can you beat the simple “buy-and-hold” strategy? Please check the sample data. You will see each line contains four tuples: open-high-low-close. The sample data is NASDAQ:GOOG. The data, called training_data.csv, contains more-than-five-year daily prices, whose line number corresponds to the time sequence. Another data, called testing_data.csv, contains one-year daily prices, which corresponds to the time period next to the final date in training_data.csv. In this project, we ignore the transaction cost, meaning that you can do an action every day if you want without extra expense (at most one action can be executed within one day, as the open price)

In our final ML-to-WebApp project, we will create a stock market prediction application behind a real paywall service. We will apply the power of TensorFlow, a high-performance numerical computation framework, on stock market data to offer predictions of future prices. In this hypothetical application, viewers will have to purchase subscriptions for your service through the powerful web plugin Memberful.com. This is a critical point I can't emphasize enough, whenever you are dealing with sensitive data that isn't your main business, push that work onto others that do specialize in it. The last thing you want is to be responsible for stolen customer data. Here, I will show you a great way to monetize your business without unnecessary risks in a fully professional ecosystem.

Stock Price prediction Project - Final year Project

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Stock price prediction using Multiple Linear Regression

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Stock Market Price Prediction Final Project Group 2

Final Project of Rutgers ECE568: Stock Prices Prediction

Final Project - Stock Price Prediction Streamlit Web Based

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