LSTM_Stock_Price_Prediction_tf

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LSTM_Stock_Price_Prediction_tf

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In this repository by using tensorflow, I build, train and evaluate LSTM models for predicting Google stock prices

LSTM_Stock_Price_Prediction_tf 최신버젼 다운로드

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# <p align="center">📈 Google Stock Price Prediction (tf) 📉</center></p>

<p align="center">Thanks for visiting my notebook</p>

***
<br>
<p align="center"><img src='https://i.postimg.cc/m2S3hjtP/google-stock.jpg' height=400px width=700px align="center"></p><br>

<p align="center">
    📌 Feel free to fork or edit the notebook for your own convenience. If you liked the notebook, consider upvoting. It helps other people discover the notebook as well. Your support inspires me to produce more of these kernel.😊
</p><br><br>

### 🎯 Objectives of Notbook

This notebook aims to
- Load and normalize dataset
- Visualize some image of dataset
- Build, train and test LSTM model
- Evaluate result of Models and find best of them
- Visualize results

<p align="center">
    <b>📌 Like this notebook? You can support me by giving upvote</b> 😆👍🔼<br>
    👉 <i>More about myself: <a href="https://www.kaggle.com/arezalo">www.kaggle.com/arezalo</a></i>
</p>
<hr>

### <p align="center"><strong>Google's Stock</strong></p>

Google's stock, or GOOGL, is a popular choice for investors due to the company's strong financial performance and position as a leader in the technology industry. While subject to market volatility, Google's stock is considered a strong long-term investment due to the company's financials and position in the industry.

The Google Stock Price dataset contains historical price information for Google's stock, covering the period from 2016-06-14 to today. The dataset is made up of seven columns, each providing unique information about Google's stock performance.

The following is the **structure of the data set**:

<div align="center">
<table style="width:100%">
<thead>
<tr>
<th style="text-align:center; font-weight: bold; font-size:20px">Variable Name</th>
<th style="text-align:center; font-weight: bold; font-size:20px">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><b><center>Open</center></b></td>
<td>displays the opening price of the stock on the given date</td>
</tr>
<tr>
<td><b><center>High</center></b></td>
<td>represents the highest price at which the stock was traded during the day</td>
</tr>
<tr>
<td><b><center>Low</center></b></td>
<td>displays the lowest price at which the stock was traded during the day</td>
</tr>
<tr>
<td><b><center>Close</center></b></td>
<td>represents the closing price of the stock on the given date</td>
</tr>
<tr>
<td><b><center>Volume</center></b></td>
<td>displays the total number of shares traded on the given date</td>
</tr>
<tr>
<td><b><center>Dividends</center></b></td>
<td>Cash payments to shareholders that can reduce the stock price</td>
</tr>
<tr>
<td><b><center>Stock Splits</center></b></td>
<td>Dividing shares to increase affordability and liquidity without changing overall value</td>
</tr>
</tbody>
</table>
</div>
<br>
<div style="font-family:Georgia;line-height:25px">

The dataset provides a wealth of information for investors and analysts looking to gain insights into Google's stock performance. By analyzing the data over time, users can identify patterns and trends, evaluate the impact of external factors on stock price, and make informed investment decisions.

The dataset is ideal for conducting advanced data analysis, including regression analysis, time series analysis, and predictive modeling. It can also be used to create informative visualizations, such as line graphs and candlestick charts, to better understand trends and patterns in Google's stock performance.

</div>

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