rishabhathiya / repository

Stock-prediction

# Forecasting Stock Market Prices It is a **Time Series** dataset.A time series is simply a series of data points ordered in time.In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. ## PROBLEM STATEMENT: Our Aim is to create a model that can forecast the future stock price based on the model training and provided dataset. ### Data We will be using a [Huge stock market dataset](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs) from the Kaggle platform which has a very good collection of datasets.The file we will be using is present in following directory in the dataset zip file input\Data\Stocks\gs.us.txt The data is presented in CSV format as follows : Date, Open, High, Low, Close, Volume, OpenInt. Features: - Date - Open - High - Low - Close - Volume - OpenInt Note that prices have been adjusted for dividends and splits. ### LICENSE OF DATASET : [LICENSE](https://creativecommons.org/publicdomain/zero/1.0/) ### Requirements You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html) If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. This project requires **Python** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org/) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) - [statsmodels](https://www.statsmodels.org/stable/) ### Run In a terminal or command window, navigate to the top-level project directory `STOCK MARKET FORECASTING/` (that contains this README) and run one of the following commands: ipython notebook Forecasting_Stock_Market_Prices_task.ipynb or jupyter notebook Forecasting_Stock_Market_Prices_task.ipynb This will open the Jupyter Notebook software and project file in your browser. ### Steps : 1. Importing Libraries 2. Exploring the Dataset 3. Exploratory Data Analysis > * Univariate Analysis 4. Data Preprocessing 5. Model Building > * AUTOREGRESSIVE MODEL > * MOVING AVERAGE MODEL 6. Evaluation > * MEAN SQUARE ERROR > * MEAN ABSOLUTE ERROR > * ROOT MEAN SQUARE ERROR 7. Conclusion