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BATCH 1 FIRST REVIEW-1.pptx
1. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
BATCH -1
TITLE: STOCK MARKET PREDICTOR
FIRST PROJECT REVIEW
DATE : 17 – 01 – 2022
2. STOCK MARKET PREDICTOR
Jaswant Mishra 1SB18CS031
Tushar Singha 1SB18CS087
Aman Prabhat 1SB18CS005
Rishav Kumar 1SB18CS067
BATCH - 1
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Presented By
Under the Guidance of
PROF. M. LORATE SHINY
Assistant professor
Department of Computer Science and Engineering
3. CRITICAL LITERATURE SURVEY
STOCK MARKET PREDICTOR
SL
NO
PAPER AUTHOR TITLE OF THE
PAPER
OBJECTIVE METHOD INFERENCE
1
ICSSBE Paper Han Lock Slew “Regression Techniques for the
Prediction of Stock Price Trend,”
The authors of this paper examine
the theory and practice of
regression techniques for the
prediction of stock price trends by
using a transformed data set in an
ordinal data format.
Linear Regression The original pre-transformed data
source contains data of
heterogeneous data types used for
handling of currency values and
financial ratios
2
IEEE Paper Carol Hargreaves “Prediction of Stock Performance
Using Analytical Techniques,”
In this paper, the authors construct
a framework that enables them to
make class predictions about
industrial stock performances.
Logistic Regression they applied several analytical
techniques. A trading strategy is also
designed and the performance of the
stocks evaluated
4. CRITICAL LITERATURE SURVEY
STOCK MARKET PREDICTOR
SL
NO
PAPER AUTHOR TITLE OF THE PAPER OBJECTIVE METHOD INFERENCE
3
IEEE Paper Usman Hegazy "A machine learning model for stock market
prediction."
This paper proposes a machine
learning model to predict stock
market prices. The authors
proposed an algorithm in order to
integrate Particle swarm
optimization (PSO) and least
square support vector machine
(LS-SVM).
SVM-Classification The proposed model was applied
and evaluated using thirteen
benchmark financials datasets and
compared with artificial neural
networks with the Levenberg-
Marquardt (LM) algorithm.
4
Independent work report spring, 2015. Saahil Madge "Predicting stock price direction using support
vector machines."
This study uses day-to-day
ultimate prices for 34 technology
stocks to compute price volatility
and momentum for individual
stocks and for the whole sector.
SVM-Classification Their model tries to predict whether
a stock price erstwhile in the future
will be higher or lower than it is on a
given day.
5. CRITICAL LITERATURE SURVEY
STOCK MARKET PREDICTOR
SL
NO
PAPER AUTHOR TITLE OF THE PAPER OBJECTIVE METHOD INFERENCE
5
IEEE Paper Hakob Grigorian “A Stock Market Prediction Method Based on
Support Vector Machines (SVM) and
Independent Component Analysis (ICA),”
This research emphasizes on
financial time series prediction
problem. The authors combined
prediction model based on
support vector machines (SVM)
with independent component
analysis which they called (ICA)
(called SVM-ICA)
SVM-Classification They first uses the ICA technique to
remove important features from the
research data and then applies the
SVM technique to perform time
series prediction
6. Months/week
October November December January
Week1 Selecting the
Domain of Project.
Referring the IEEE papers to support
the problem statement.
Selecting OS and software to
implement
System design for
frontend and backend for
data integration
Week2 Selecting the
Domain of Project.
Referring the IEEE papers to support
the problem statement.
Selecting OS and software to
implement
System design for
frontend and backend for
data integration
Week3 Selecting the
problem Statement.
Critical survey on the IEEE papers
and Modifying the problem
statement.
Selecting a language to
implement supervised
machine learning algorithm
Week4 Selecting the
problem Statement.
Deciding the Platform to go with
whether the Web Application or
Mobile application.
Reviewing the Random
Forest Algorithm
WEEKLY REPORT
7. Selecting Problem statement &
Literature Survey
Choosing The Platform
Web/Application
Selecting the OS and software
to implement
System Design for frontend and
establishing the backend for
data integration
Data Collection and Filtering of
data
Dividing Data into training and
testing set
Feeding the data and training
the Algorithm
Testing, Labelling and
Documentation
Submission & Project
Completion
20/10/
2021
10/11/
2021
25/11/
2021
20/12/
2021
18/01/
2022
07/02/
2022
25/02/
2022
18/03/
2022
15/04/
2022
Current Progress
Completed
Completed
Complete
d
Ongoing
Planned
Planned
Planned
Planned
Planned
20/04/
2022
GANTT CHAT PRESENTATION
STOCK MARKET PREDICTOR
9. METHODOLOGY
STOCK MARKET PREDICTOR
1. User visits the website/web app
2. Previously savedmodel is loaded
3. User requests for acompany's stock data
4. Herequests for prediction to bemade
5. The Stock Market Prediction System trains a
model using the data from the database
6. Themodel is savedfor further use and closing
price is predicted
7. Result is displayed along with graph
11. STOCK MARKET PREDICTOR
MODELLING
Data collection is a very basic module and the initial step towards the project. It generally deals
with the collection of the right dataset. The dataset that is to be used in the market prediction
has to be used to be filtered based on various aspects. Data collection also complements to
enhance the dataset by adding more data that are external. Our data mainly consists of the
previous year stock prices. Initially, we will be analyzing the Kaggle dataset and according to
the accuracy, we will be using the model with the data to analyze the predictions accurately.
12. MODELLING
STOCK MARKET PREDICTOR
Data pre-processing is a part of data mining, which
involves transforming raw data into a more coherent
format.
Raw data is usually, inconsistent or incomplete and
usually contains many errors.
The data pre-processing involves checking out for
missing values, looking for categorical values,
splitting the data-set into training and test set and
finally do a feature scaling to limit the range of
variables so that they can be compared on common
environs
13. MODELLING
STOCK MARKET PREDICTOR
Training the machine is similar to feeding the data to the algorithm to touch up the test
data. The training sets are used to tune and fit the models. The test sets are untouched, as
a model should not be judged based on unseen data. The training of the model includes
cross-validation where we get a well-grounded approximate performance of the model
using the training data. Tuning models are meant to specifically tune the hyper parameters
like the number of trees in a random forest. We perform the entire cross-validation loop on
each set of hyper parameter values. Finally, we will calculate a cross-validated score, for
individual sets of hyper parameters. Then, we select the best hyper parameters. The idea
behind the training of the model is that we some initial values with the dataset and then
optimize the parameters which we want to in the model. This is kept on repetition until we
get the optimal values. Thus, we take the predictions from the trained model on the inputs
from the test dataset. Hence, it is divided in the ratio of 80:20 where 80% is for the training
set and the rest 20% for a testing set of the data.
17. PROJECT PLAN
STOCK MARKET PREDICTOR
Name Suraj.R Suraj.S Pradeep.GS Suraj.S
Role Project Planning Data Collection Data Training Data Testing
Responsibility
Project Planning and
interconnection of
module
Collecting data and
providing to team
members
Segregating and
feeding the data to
the machine
Testing the algorithm
and analyzing the
data
18. PROJECT PLAN
STOCK MARKET PREDICTOR
OUTPUT DELIVERABLES
To predict and build a machine learning algorithm which will be
more accurate reliable and will be able to cope with different uncertain
situation and give a better result. This will also help in overall growth of
economy and will establish faith of the investors in stock market
investment opportunities
19. PROJECT PLAN
STOCK MARKET PREDICTOR
Date of commencement of the Project was begin on
20 OCTOBER 2021
Probable date of completion of the project is expected on or before
20 APRIL 2022
21. CONCLUSION
STOCK MARKET PREDICTOR
• THE ALGORITHM WILL BE A GREAT ASSET FOR BROKERS AND
INVESTORS FOR INVESTING MONEY IN THE STOCK MARKET
SINCE IT WILL TRAIN ON A HUGE COLLECTION OF HISTORICAL
DATA AND WILL BE CHOSEN AFTER BEING TESTED ON A SAMPLE
DATA.
• THE PROJECT DEMONSTRATES THE MACHINE LEARNING MODEL
TO PREDICT THE STOCK VALUE WITH MORE ACCURACY AS
COMPARED TO PREVIOUSLY IMPLEMENTED MACHINE LEARNING
MODELS.
