Stock 2025 Kaggle This dataset contains the historical stock prices of google (googl) from january 2020 to march 2025. the data was fetched from yahoo finance using python’s yfinance library. This project aims to predict stock prices using linear regression and visualize the stock price trends for the google (googl) stock. the dataset used in this project is obtained from kaggle, which includes historical stock prices for googl.
Google Stock Data Kaggle #need to add dimension to because not only prescition with one stock price but other indicators (like other columns in dataset or other stocks that may affect this one ). In this project based blog, we will explore anomaly detection in google stock data from 2014 2022. the dataset used in this project is obtained from kaggle. the dataset is available on kaggle, and you can download it here. the dataset contains 106 rows and 7 columns. This comprehensive dataset encompasses google's stock price history over the past decade, ranging from january 1, 2013, to january 10, 2024. To further enable data scientist and manufacturing engineers, we publish an authentic industrial cloud data (aicd) dataset. the data was collected at an operating pick and place machine located in europe, and the data is not preprocessed.
Google Stock Data 2025 Kaggle This comprehensive dataset encompasses google's stock price history over the past decade, ranging from january 1, 2013, to january 10, 2024. To further enable data scientist and manufacturing engineers, we publish an authentic industrial cloud data (aicd) dataset. the data was collected at an operating pick and place machine located in europe, and the data is not preprocessed. Dataset contains historical google stock data for trainig ml and dl models. Download the "google stock prices training and test data" dataset from kaggle. load the training dataset (google stock price train.csv) using pandas. extract the 'open' stock price column for training. apply feature scaling (min max scaling) to normalize the training data between 0 and 1. We computed daily returns and analyzed the volatility of each stock. the findings indicated low volatility for the top tech companies, suggesting lower risk associated with investing in these stocks. This simple example will show you how lstm models predict time series data. stock market data is a great choice for this because it's quite regular and widely available via the internet.
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