File size: 1,785 Bytes
629ff13 d5bb5f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
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)
|