metadata

dataset_info:
  features:
    - name: Date
      dtype: string
    - name: Open
      dtype: float64
    - name: High
      dtype: float64
    - name: Low
      dtype: float64
    - name: Volume
      dtype: int64
    - name: OpenInt
      dtype: int64
    - name: Close
      dtype: float64
  splits:
    - name: train
      num_bytes: 96470
      num_examples: 1582
  download_size: 56653
  dataset_size: 96470
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Stock Market Dataset

Description

This dataset contains stock market data for a specific stock over a period of time. The dataset includes daily stock prices and trading information, which can be used for financial analysis, time series forecasting, and stock price prediction.

Dataset Details

Columns:

Date: The trading date (MM/DD/YYYY format).
Open: The opening price of the stock on that day.
High: The highest price reached during the trading day.
Low: The lowest price reached during the trading day.
Volume: The number of shares traded on that day.
OpenInt: Open interest (often used in derivatives markets; for stocks, this might not be relevant).
Close: The closing price of the stock on that day.

Notes:

The column Unnamed: 6 contains only NaN values and should be ignored.
The dataset contains 1,582 entries.

Use Cases

Stock price trend analysis.
Predictive modeling using machine learning.
Time series forecasting for financial markets.

How to Use

You can load the dataset using the datasets library:

from datasets import load_dataset

dataset = load_dataset("Tarakeshwaran/Hackathon_Stock_Prediction")
print(dataset)