# Stock Price Predictor
*Udacity - Machine learning Nano Degree Program : Project-6 (Capstone project)*
## Project Overview
*This is sixth and final capstone project in the series of the projects listed in Udacity- Machine Learning Nano Degree Program.*
Investment firms, hedge funds and even individuals have been using financial models to better understand market behavior and make profitable investments and trades. A wealth of information is available in the form of historical stock prices and company performance data, suitable for machine learning algorithms to process.
Can we actually predict stock prices with machine learning? Investors make educated guesses by analyzing data. They'll read the news, study the company history, industry trends and other lots of data points that go into making a prediction. The prevailing theories is that stock prices are totally random and unpredictable but that raises the question why top firms like Morgan Stanley and Citigroup hire quantitative analysts to build predictive models. We have this idea of a trading floor being filled with adrenaline infuse men with loose ties running around yelling something into a phone but these days they're more likely to see rows of machine learning experts quietly sitting in front of computer screens. In fact about 70% of all orders on Wall Street are now placed by software, we're now living in the age of the algorithm.
This project utilizes Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices. For data with timeframes recurrent neural networks (RNNs) come in handy but recent researches have shown that LSTM, networks are the most popular and useful variants of RNNs.
I have used Keras to build a LSTM to predict stock prices using historical closing price and trading volume and visualize both the predicted price values over time and the optimal parameters for the model.
![Stock Price Predictor](https://github.com/Rajat-dhyani/Stock-Price-Predictor/blob/master/data_visualization_lstm_improved.png)
## Problem Highlights
*The challenge of this project is to accurately predict the future closing value of a given stock across a given period of time in the future. For this project I have used a Long Short Term Memory networks – usually just called “LSTMs” to predict the closing price of the S&P 500 using a dataset of past prices*
* **Achievements:**
* Built a model to accurately predict the future closing price of a given stock, using Long Short Term Memory Neural net algorithm.
* Achieved Mean Squared Error rating of just 0.00093063.
Things i have learnt by completing this project:
* How to apply deep learning techniques: Long Short Term Memory Neural Network algorithms.
* How to use keras-tensorflow library.
* How to collect and preprocess given data.
* How to analyze model's performance.
* How to optimise Long Short Term Memory Neural Network algortithm, to ensure increase in postive results.
### Other Related Projects:
* <strong> Project 0 : </strong> *[Titanic Survivals Prediction](https://github.com/Rajat-dhyani/titanic_survival)*
* <strong> Project 1 : </strong> *[Boston's Houses Prediction](https://github.com/Rajat-dhyani/boston_housing)*
* <strong> Project 2 : </strong> *[Charity Donors Prediction](https://github.com/Rajat-dhyani/charity_donors)*
* <strong> Project 3 : </strong> *[Creating Customer Segments](https://github.com/Rajat-dhyani/creating_customer_segments)*
* <strong> Project 4 : </strong> *[Smart Cab](https://github.com/Rajat-dhyani/smart-cab)*
* <strong> Project 5 : </strong> *[ImageNetBot](https://github.com/Rajat-dhyani/ImageNetBot)*
## Software and Libraries
This project uses the following software and Python libraries:
* [Python 2.7](https://www.python.org/download/releases/2.7/)
* [NumPy](http://www.numpy.org/)
* [pandas](http://pandas.pydata.org/)
* [Keras](https://keras.io/)
* [Tensor-flow](https://www.tensorflow.org)
* [Jupyter Notebook](http://ipython.org/notebook.html)
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股票价格预测器:该项目旨在利用深度学习模型,长期记忆(LSTM)神经网络算法来预测股票价格
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股票价格预测 Udacity-机器学习纳米学位课程:Project-6(Capstone项目) 项目概况 这是Udacity-机器学习纳米学位计划中列出的一系列项目中的第六个也是最后一个顶点项目。 投资公司,对冲基金甚至个人一直在使用财务模型来更好地了解市场行为并进行有利可图的投资和交易。 历史股价和公司绩效数据的形式提供了大量信息,适用于机器学习算法进行处理。 我们真的可以通过机器学习预测股价吗? 投资者通过分析数据做出有根据的猜测。 他们将阅读新闻,研究公司的历史,行业趋势以及做出预测的其他许多数据点。 流行的理论是,股票价格是完全随机且不可预测的,但这提出了一个问题,为什么摩根士丹利和花旗集团这样的顶级公司会聘请定量分析师来建立预测模型。 我们的想法是,交易大厅里充斥着肾上腺素的男人,他们之间的联系松散,向电话里喊着什么,但如今,他们更有可能看到成排的机器学习专家静静地坐在电脑屏幕前。 实际上,现在华尔街上约70%的订单都是通过软件下达的,我们现在处在算法时代。 该项目利用深度学习模型,长期记忆(LSTM)神经网络算法来预测股票价格。 对于具有时间范围的数据,递归神经网络(
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Stock-Price-Predictor-master.zip (17个子文件)
Stock-Price-Predictor-master
visualize.py 3KB
googl.csv 629B
Stock_Price_Predictor.ipynb 410KB
data_visualization_lstm_basic.png 57KB
Project proposal.pdf 152KB
data_visualization_lstm_improved.png 58KB
data_visualization_benchmark.png 49KB
data_visualization.png 49KB
Project_Report.pdf 1.76MB
results.html 688KB
google_preprocessed.csv 196KB
LinearRegressionModel.py 1KB
preprocess_data.py 2KB
README.md 4KB
google.csv 140KB
stock_data.py 4KB
lstm.py 1KB
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