Simple ML Model to Predict NVDA Stock Price

Predicting stocks using Facebook prophet. for some big tech companies (TSLA, GOOG ,MSFT, FB, AAPL, NVDA, PYPL, ADBE, NFLX )

This project applies machine learning to predict NVIDIA (NVDA) stock movements. Using yfinance data or a local CSV, it builds a Random Forest model with engineered features (ratios, trends) and backtests predictions. The goal is to forecast if the next day’s close will rise or fall.

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This project utilizes LSTM (Long Short-Term Memory) neural networks to predict stock prices for four different symbols: Apple (AAPL), Nvidia (NVDA), Google (GOOGL), and Amazon (AMZN). The LSTM models are trained using historical stock price data fetched from Yahoo Finance and evaluated based on various performance metrics.

You have stock data for four semiconductor companies: Analog Devices (ADI), Intel (INTC), Nvidia (NVDA), and Xilinx (XLNX). This assignment will walk through some basic exploratory data analysis (EDA), preprocessing data and feature engineering, and comparing the performance of two models. This assignment is based on results reported by (Basak, Kar, Saha, Khaidem, and Dey 2019). The assignment consists of seven parts arranged in three sections: I. EDA Working with Dictionaries Plotting Time Series Preliminary Analysis II. Predicting Price Movement Exponential Smoothing Logistic Regression vs Random Forests Feature Engineering with Technical Indicators III. Analysis Short Answer Each stock data set is a .csv formatted file covering a twenty-year history of trading data between January 15, 2000 and January 15, 2020. The next cell loads each into a pandas dataframe. Following convention, we use the notation df to denote a pandas dataframe. You of course can use the type command to identify an object's datatype. You may also find the command whos helpful to list what variables are currently stored in memory, their size, and type.

Nvidia price prediction using random forest

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NVDA Price Stock Prediction ( Keras / Tensorflow / Matplatlib / Python )

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Using a Stacked LSTM Model to Predict Nvidia's Stock Prices.

This project will introduce a RNN using LSTM to predict the stock price of NVDA

AI-powered NVDA stock predictor with sentiment analysis, technical indicators, and ML - hosted on GitHub Pages

NVDA Addon to predict the language of every spoken text and change the spoken languge, if available.

Predicted next day returns for AAPL, AMZN, GOOGL, MSFT, and NVDA using XGBoost, Bagging, Random Forest, SVM, Lasso, and Linear Modeling, achieving greatest accuracy of 55.96%, a regression problem focusing on feature engineering

This repository contains a Python-based tool that scrapes 10k documents from the SEC website and analyzes them to predict stock price movements for NVDA using natural language processing techniques like cosine and Jaccard similarity.

NewsToAlpha predicts next-day stock price direction by analyzing recent financial news alongside price trends for popular tickers like AAPL, NVDA, and TSLA. Users get up/down forecasts with supporting news highlights via an easy web dashboard.

This repository contains Jupyter Notebook code for predicting stock prices of QQQ, NVDA, and AAPL using LSTM models. The code includes data visualization to analyze the results.The LSTM models are trained on historical stock price data from 2015-01-01 until 2023-04-01,and the predictions are made on the basis of that data.

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Simple ML Model to Predict NVDA Stock Price

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Using the ETS model in R, I ran a prediction on the stock of NVDA for the next 12 months

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A machine learning project that predicts NVIDIA (NVDA) stock trends using advanced predictors like rolling averages, stock ratios, and backtesting. This repository provides a step-by-step guide for beginners to transition to intermediate-level stock prediction modeling, featuring Random Forest Classifiers and custom prediction function.

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NVDA stock price predictor (WIP)

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Use machine learning to predict the closing price of NVDA at a user defined number of days in the future

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