Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
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.