PPT for stock price prediction project using SVR algorithm

1. Visualizing and forecasting
stocks using Dash

2. Relevance
▪ Stock investments provide one of the highest returns in the market. Even
though they are volatile in nature, one can visualize share prices and other
statistical factors which helps the keen investors carefully decide on which
company they want to spend their earnings on.
▪ Artificial Neural networks and Machine Learning are very effective tools
for the implementation of forecasting stock prices, returns, and stock
modelling. With the help of statistical analysis, the relation between the
selected factors and share price is formulated which can help in forecasting
accurate results.
▪ Although, share market can never be predicted due to its vague domain,
this project aims at applying the concept of prediction and analysis of data
for forecasting the stock prices.

3. Objectives
▪ Make the main website's structure using mainly Dash HTML Components and
Dash Core Components.
▪ Enhance the site's UI by styling using CSS
▪ Generate plots of data using the plot ly library of Python. The data is
fetched using yfinance python library
▪ Implement a machine learning model to predict the stock price for
the dates requested by the user

4. Proposed System
• Proposed system is an online web-based application using learning
model for predicting the price of a given stock. 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 we will be
using a SVR – Support Vector Regression is a supervised learning
algorithm that is used for predicting continuous values.

5. Support Vector Regression
• Support Vector Regression as the name suggests is a
regression algorithm that supports both linear and non-linear
regressions.
• Support Vector Regression uses the same principle as the SVMs. The
basic idea behind SVR is to find the best fit line. In SVR, the best fit
line is the hyperplane that has the maximum number of points.
• Hyperplane: It is a separation line between two data classes in
a higher dimension than the actual dimension. In SVR it is
defined as the line that helps in predicting the target value.

6. • Boundary Lines: These are the two lines that are drawn
around the hyperplane at a distance of ε (epsilon). It is used to
create a margin between the data points.
• Support Vector: It is the vector that is used to define the
hyperplanein SVR support vector is used to define the linear
regression.

7. Dataset
• Since the stock is live and the values of stock changes day by day. We
are fetching the data from Yahoo Finance(yfinanace)
• Feature list: date, open, close, high, low, volume

9. Expected output
• This project collects input stock code from the user and provide output
as line graphs which shows the stock prices of the company in various
years
• SVR algorithm enable the user to get predicted stock price for the
number of days inputted by the user.