Stock Market Prediction using Data Mining technique

Introduction

Today, data Mining becomes highly important among industrial areas and many other organizations. It has become one of the leading technology in today’s world. It helps to analyze the data and predict the result. Data can be dug from a very big database with minimum efforts. Collecting data, computing data, analyzing data are an indivisible area of business methodology. If you want to develop a project using data mining the Skyfi Labs will help you with such an exciting journey.

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1. Data Analytics using R

Project Description

It is very difficult to predict the stock market, so we are using some machine learning algorithms for the betterment of the project. So here we are using analysis by charts and history of the stock market. We have to use historical data for analysis purposes. You can download datasets from various sites like Kaggle etc. It is a very simple project for second-year engineering students. Who can learn the practical implementation of the concept by developing such a project? In this article, we will give basic guidelines like platform, libraries used, algorithm, etc.

Implementation Guidelines

1. So here you can use jupyter notebook for the implementation of the project. There are many other IDEs but we will prefer jupyter notebook which is highly convenient for beginners.
2. The next step is finding a dataset. You can find a dataset for stock market prediction on any of the website like Kaggle etc,
3. Looking at the dataset, it has many columns like date, open, close, high, low, and turnover, etc. High and low are the boundaries of the stock prices, where a close column gives the closing value for the end of the day.
4. Start with the cleaning of the dataset. If there are all dates with pubic holidays etc. Then you have to remove them accordingly.
5. Profit and loss totally depend upon the closing price column.
6. You can use regression algorithms for the prediction. Here we are using a moving average algorithm for the training testing and prediction of the data.
7. We have to import the packages like NumPy for the arrays, pandas for the sorting, and matplotlib for the two-dimensional graphs. 
8. After plotting the graph we have to create a data frame with the available data and target variable which is closing value.
9. The next part is training and testing of the model. If there are 1000 rows, you can use 50% for training and 50% for testing,
10. After that, we have to use a machine learning algorithm for the prediction. It is stored in an array. You can search for the implementation of a moving average algorithm which is very common and easy to implement.
11. You can further use the root mean square for the comparison of the actual values and predicted values.
12. So you can again plot the graph using matplotlib with different colors for training and predicted graph.

Benefits

You can learn the basic implementation of algorithms

Use of python libraries

So here are the basic guidelines for the project implementation. You can improve it by using the front end and can develop an overall application with the Django framework and TensorFlow packages. Skyfi Labs provide the course for python and Django, so learn more and keep improving.

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• Data Mining
• Machine Learning

• Data Mining

Stock Market Prediction using Data Mining technique