The coding has been done on Python 3.65 using Jupyter Notebook. This program fetches LIVE data from TWITTER using Tweepy. Then we clean our data or tweets ( like removing special characters ). After that we perform sentiment analysis on the twitter data and plot it for better visualization. The we fetch the STOCK PRICE from yahoo.finance and add it to the data-set to perform prediction. We apply many machine learning algorithms like (random forest, MLPClassifier, logistic regression) and train our data-set. Then we perform prediction on untrained data and plot it with the real data and see the accuracy.

The Stock Price Prediction App is a Streamlit-based web application that provides users with tools to analyze historical stock price data, visualize technical indicators, and make short-term price predictions using different machine learning models.

A web-based stock analysis application built with React and Flask, offering interactive visualizations and machine learning-based stock price predictions.

The project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.

Stock price prediction visualization app powered by LSTMs for forecasting and FinBERT for sentiment analysis

Real-time stock price prediction app using LSTM, Streamlit, and historical data (2010–2023). Forecasts next 10 days & visualizes trends.

This project is a Stock Market Prediction App built using Python, Keras, Streamlit, and yFinance. It predicts stock prices based on historical data and visualizes trends with moving averages and ML-based predictions.

Stock market price prediction is a vastly researched topic. By predicting the price of the share for the future, companies will make huge profits. Stock market prices depend on lots of factors which makes it hard to predict. Different machine learning algorithms are able to predict stock prices with good accuracy. This project aims to use stock information supplied by Google Finance, and use it for technical analysis, visualization, and prediction with different machine learning algorithms. By looking at stock market data, especially some stocks of gigantic technology and others. Used pandas to obtain stock data from Google Finance, visualized various elements of it, and lastly looked at a few ways to analyze a stock's data trends, based on its prediction.

This project is a stock price prediction model for Apple Inc. (AAPL) using historical stock price data. The goal is to build a predictive model that can forecast Apple's stock prices for a specified time horizon. The project focuses on time series analysis, machine learning, and data visualization.

Implemented and trained XGBoost, LSTM, and WGAN-GP models for stock price forecasting, achieving robust predictive performance. Developed data preprocessing pipelines with normalization, splitting, and Fourier transforms, enhancing accuracy and efficiency. Optimized hyperparameters and visualized predictions using RMSE.

🔮 Stock Price Prediction using LSTM, GRU, and CNN A deep learning project to predict future stock prices using historical data of Apple Inc. (AAPL), built with LSTM, GRU, and 1D CNN models. The project includes data fetching via yfinance, time series preprocessing, training, evaluation, and visualization of model predictions.

An Interactive Financial Dashboard for investors who are interested in obtaining stock insights of various companies including various financial institution stocks. Power BI Business Analytics is the platform used to present the Visualization of the data. The Dashboard allow investors to interactively analyze various stocks prices to current market Machine Learning for price prediction using Facebook Prophet and Long Short Term Memory models

Stock Price Prediction predicts the stock price for next 5 years, data is fetched from Yahoo finance. The webapp uses Facebook Prophet model which has an MAPE of 10%.

This project analyzes and visualizes stock data for the top 10 fast-food companies using Power BI, showcasing key insights like stock trends, market performance, and trading volumes through interactive dashboards.

This project implements a stock price prediction model using a Recurrent Neural Network (RNN) in Python. The model is trained on historical stock price data to predict future prices. The key steps include data preprocessing, model training, and visualization of predictions.

A Machine Learning project that analyzes stock data from CSV or yfinance, predicts future prices using Random Forest Regressor, detects trends, and provides visualizations like moving averages, price charts, and prediction graphs.

The project aims to analyze the sentiment of news articles related to specific stocks, visualize the sentiment trends over time, and make predictions on stock prices using machine learning algorithms.

This project is an interactive web application that uses a Long Short-Term Memory (LSTM) deep learning model to predict stock prices. It features automated hyperparameter optimization with Keras Tuner and a real-time dashboard built with Streamlit for visualizing predictions against live data from Yahoo Finance.

• Weather Forecasting is the process of making predictions of the future, based on past and present data of the weather. • We used ARIMA model(Auto Regressive Integrated Moving Average) to analyze and predict the time-series data and we shall also perform rigorous exploratory data analysis and visualizations on the dataset. • Feature Engineering – selecting required attributes. • Data cleaning – renaming attributes and filling missing data. • Check rolling mean and standard deviation (graph must not vary too much for stationarity). • Perform Augmented Dickey–Fuller test (to check for stationarity) • plotting PACF(partial auto correlation function) and ACF(auto correlation function) to find p and q values of ARIMA model. • Fitting and forecasting the model for temperature data. • This could be also be used other types of time series data such as stock prices, market price variations, etc.

This project predicts stock prices using a Long Short-Term Memory (LSTM) neural network. It fetches historical stock data using Yahoo Finance API, preprocesses data for training, trains an LSTM model, a recurrent neural network (RNN), for prediction, and visualizes actual vs. predicted stock prices.

Stock price prediction using LSTM neural networks with historical data analysis and visualization

Stock price prediction application with interactive web interface. Features multiple ML models (Linear Regression, Random Forest), 19+ technical indicators, alerts system, and real-time market data visualization.

The Stock Price Prediction using LSTM Model is a Python program that utilizes LSTM to forecast future stock prices. It preprocesses historical data, trains the LSTM model, and evaluates prediction accuracy. The program aids in making informed investment decisions by generating predictions and providing visualizations and performance metrics.

StockVision is an advanced stock price prediction and analysis tool that leverages machine learning to provide insightful forecasts. It offers detailed data visualizations and comprehensive stock analysis for informed decision-making.

Implementing a machine learning trading agent using an ensemble(Bootstrapping) of Random Forest Learners that would learn a strategy using past stock price data and generate stock orders for each day and give a visualization of the growth of the Portfolio with those predictions. Evaluate the model against different approaches and fine tune the model for better performance.

Enhanced real-time stock price prediction by 12% using sentiment analysis on 5M+ daily tweets via Twitter API, polygon.io, and yfinance; built a scalable pipeline with Google Cloud Pub/Sub, Apache Beam, Dataflow, and visualized insights with Google BigQuery and Looker Studio.

A powerful, interactive tool for analyzing multiple stocks and predicting future price movements using machine learning and technical analysis. Built with a modern dark-mode UI, this dashboard provides real-time insights for traders and investors.

PennApps Top 30 finalist: stock price predictions + visualization using Machine Learning

A web app showcasing machine learning models for stock price prediction with interactive visualizations.

Stock Market Analysis and Prediction is the project on technical analysis, visualization, and prediction using data provided by Google Finance. Predicted future stock prices using a Recurrent Neural Network architecture