{"repo":"madhurimarawat/Stock-Market-Prediction","free":true,"listed":false,"github":"https://github.com/madhurimarawat/Stock-Market-Prediction","clone":"git clone https://github.com/madhurimarawat/Stock-Market-Prediction.git","description":"This repository began as a 7th-semester minor project and evolved into our 8th-semester major project, \"Advanced Stock Price Forecasting Using a Hybrid Model of Numerical and Textual Analysis.\" It utilizes Python, NLP (NLTK, spaCy), ML models, Grafana, InfluxDB, and Streamlit for data analysis and visualization.","language":"Jupyter Notebook","stars":20,"topics":["documentation","flask","flask-app","grafana-dashboard","grafana-influxdb","hybrid-model","influxdb-database","machine-learning","machine-learning-algorithms","minor-project"],"license":"MIT","category":"machine-learning","readme_excerpt":"Stock-Market-Prediction This repository began as a 7th-semester minor project and evolved into our 8th-semester major project , \"Advanced Stock Price Forecasting Using a Hybrid Model of Numerical and Textual Analysis.\" It utilizes Python, NLP (NLTK, spaCy), ML models, Grafana, InfluxDB, and Streamlit for data analysis and visualization. Project Description The Advanced Stock Price Forecasting Using a Hybrid Model of Numerical and Textual Analysis project involves a comprehensive approach to predicting stock prices using both numerical data and textual analysis. The project components include: 1. Data Collection and Storage : We gathered historical stock data of major companies and stored it in an InfluxDB database to efficiently handle large-scale time-series data. 2. Data Visualization : A Grafana dashboard has been set up for real-time visualization of stock prices and analysis results, enhancing data interpretation and decision-making processes. 3. Textual Analysis for Enhanced Forecasting : We utilized Natural Language Processing (NLP) libraries, such as NLTK and spaCy, to analyze financial news and reports. This component complements numerical analysis to improve the accuracy of our hybrid forecasting model. 4. Machine Learning Models : The project used models including Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, and Random Forest to process both numerical and textual data, creating a robust and comprehensive stock prediction system. 5. Reddit Chatbot","default_branch":null,"files":null,"tree":[],"storefront":"/r/madhurimarawat","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/madhurimarawat/Stock-Market-Prediction/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}