README.md

---
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:  
```python
from datasets import load_dataset

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