The Google stock dataset contains historical data of Google's stock prices and related attributes. It consists of 14 columns and 1257 rows. Each column represents a specific attribute, and each row contains the corresponding values for that attribute.
The columns in the dataset are as follows:
- Symbol: The name of the company, which is GOOG in this case.
- Date: The year and date of the stock data.
- Close: The closing price of Google's stock on a particular day.
- High: The highest value reached by Google's stock on the given day.
- Low: The lowest value reached by Google's stock on the given day.
- Open: The opening value of Google's stock on the given day.
- Volume: The trading volume of Google's stock on the given day, i.e., the number of shares traded.
- adjClose: The adjusted closing price of Google's stock, considering factors such as dividends and stock splits.
- adjHigh: The adjusted highest value reached by Google's stock on the given day.
- adjLow: The adjusted lowest value reached by Google's stock on the given day.
- adjOpen: The adjusted opening value of Google's stock on the given day.
- adjVolume: The adjusted trading volume of Google's stock on the given day, accounting for factors such as stock splits.
- divCash: The amount of cash dividend paid out to shareholders on the given day.
- splitFactor: The split factor, if any, applied to Google's stock on the given day. A split factor of 1 indicates no split.
This dataset can be used for a variety of purposes, such as:
- Analyzing the historical performance of Google's stock.
- Building predictive models to forecast Google's future stock prices.
- Developing trading strategies to profit from the movement of Google's stock prices.
Dataset: https://www.kaggle.com/datasets/shreenidhihipparagi/google-stock-prediction
Solution: https://github.com/Premrufus/BharatIntenRepo/blob/main/Stock_Price_Prediction.ipynb
Problem Statement: Handwritten Digit Recognition Using MNIST Dataset with the Help of Neural Network.
The MNIST dataset is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It contains 60,000 training images and 10,000 testing images. Each image is a 28x28 pixel grayscale image of a handwritten digit from 0 to 9. The images have been size-normalized and centered in a fixed-size image.
The MNIST dataset is a popular dataset for machine learning because it is relatively simple to understand and implement. The dataset is also large enough to train a variety of machine learning models, and it is diverse enough to capture the different ways that people write digits.
The MNIST dataset can be used to train a variety of machine learning models, such as:
- Supervised learning models, such as support vector machines (SVMs) and decision trees.
- Unsupervised learning models, such as k-means clustering and principal component analysis (PCA).
- Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
The MNIST dataset has been used to develop a variety of applications, such as:
- Handwritten digit recognition systems.
- Fraud detection systems.
- Image classification systems.
- Natural language processing systems.
The MNIST dataset is a valuable resource for researchers and practitioners in the field of machine learning. It is a simple, yet effective dataset for training and testing machine learning models
Dataset: Imported from Tensorflow package and Keras API
Solution: https://github.com/Premrufus/BharatIntenRepo/blob/main/Number_Recognition.ipynb