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PRESENTED BY:
M Farooq
PRESENTED TO:
SIR Dr. ABU-BAKAR
REG NO:
2018-GU-1950
SESSION:
BSIT(7TH)MORNING
TOPIC:
BUSINESS INTELLIGENCE SYSTEM
In this presentation we will discuss the following terms of my project which is
selected as a final year project.
 DESCRIPTION
 OBJECTIVES
 METHODOLOGY
 REQUIREMENTS
Description
 Business intelligence is the process by which enterprises use strategies and
technologies for analyzing current and historical data, with the objective of
improving strategic decision-making and providing a competitive advantage.
 Business intelligence systems combine data gathering, data storage, and
knowledge management with data analysis to evaluate and transform
complex data into meaningful, actionable information, which can be used to
support more effective strategic, tactical, and operational insights and
decision-making.
 Business intelligence environments consist of a variety of technologies,
applications, processes, strategies, products, and technical architectures
used to enable the collection, analysis, presentation, and dissemination of
internal and external business information.
 It is a GUI based Deskstop Application which calculates and gives
predictions on sales data derived from database and excel. Along with
predictions, it also provides graphs for better depiction and understanding of
data.
 It uses Machine Learning models like Linear Regression ,Logistic
Regression and Market Basket Analysis. It is a Business Oriented Project.
 The code for the entire project can be called through the main.py file. The
UI is done in PYQT5 and the files related are already attached with the
extension ".ui".
• Features:
 Self-serve reporting
 Fast implementation
 memory analysis
 Report scheduling
 Advanced security
Objectives
 Faster analysis, intuitive dashboards
 Increased organizational efficiency
 Data-driven business decisions
 Improved customer experience
 Improved employee satisfaction
 Trusted and governed data
Methodology
 Linear Regression
 , Linear Regression is the supervised Machine Learning model in which
the model finds the best fit linear line between the independent and
dependent variable i.e it finds the linear relationship between the dependent
and independent variable.
 Linear Regression is of two types: Simple and Multiple. Simple Linear
Regression is where only one independent variable is present and the
model has to find the linear relationship of it with the dependent variable
 Whereas, In Multiple Linear Regression there are more than one
independent variables for the model to find the relationship.
 A Linear Regression model’s main aim is to find the best fit linear line and
the optimal values of intercept and coefficients such that the error is
minimized.
 Error is the difference between the actual value and Predicted value and the
goal is to reduce this difference.
 Logistic Regression
 Logistic regression is a classification algorithm used to assign
observations to a discrete set of classes.
 Some of the examples of classification problems are Email spam or not
spam, Online transactions Fraud or not Fraud, Tumor Malignant or
Benign.
 Logistic regression transforms its output using the logistic sigmoid
function to return a probability value
 Analysis
 This is the step where all the data comes under a single platform. A
Business Intelligence software will enable you to collect as well as analyze
data with advanced analytical tools embedded in the same software.
 Analyzing the data collected through various methods helps an organization
to understand their customer’s opinions and find out areas needing
improvement. The software allows you to compare scores (such as NPS,
CES, CSAT) for varied periods and also among departments.
 Reporting
 After analysis, the next step is to understand what the metrics mean. This
step is the most important, as the wrong interpretation of the data can send
your organization down a cliff.
 Conversion into visual infographics can sometimes make it easier for a
person to understand.
 Such understanding will enable the organization to find answers to most
pressing business, operational and marketing questions.
Requirements
 An easy-to-use interface that doesn't require advanced training,
support or documentation. Automation for eliminating manual processes of
business functions related to Business Intelligence System.
 A reliable, secure database that provides accurate, real-time data.
 PC with 4GBRAM
 PC with 500 ROM
 Visual Studio with Python
 Sql server for database
•THANK
S

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Inventory System

  • 1.
  • 2. PRESENTED BY: M Farooq PRESENTED TO: SIR Dr. ABU-BAKAR REG NO: 2018-GU-1950 SESSION: BSIT(7TH)MORNING
  • 3. TOPIC: BUSINESS INTELLIGENCE SYSTEM In this presentation we will discuss the following terms of my project which is selected as a final year project.  DESCRIPTION  OBJECTIVES  METHODOLOGY  REQUIREMENTS
  • 4. Description  Business intelligence is the process by which enterprises use strategies and technologies for analyzing current and historical data, with the objective of improving strategic decision-making and providing a competitive advantage.  Business intelligence systems combine data gathering, data storage, and knowledge management with data analysis to evaluate and transform complex data into meaningful, actionable information, which can be used to support more effective strategic, tactical, and operational insights and decision-making.  Business intelligence environments consist of a variety of technologies, applications, processes, strategies, products, and technical architectures used to enable the collection, analysis, presentation, and dissemination of internal and external business information.
  • 5.
  • 6.  It is a GUI based Deskstop Application which calculates and gives predictions on sales data derived from database and excel. Along with predictions, it also provides graphs for better depiction and understanding of data.  It uses Machine Learning models like Linear Regression ,Logistic Regression and Market Basket Analysis. It is a Business Oriented Project.  The code for the entire project can be called through the main.py file. The UI is done in PYQT5 and the files related are already attached with the extension ".ui".
  • 7. • Features:  Self-serve reporting  Fast implementation  memory analysis  Report scheduling  Advanced security
  • 8.
  • 9. Objectives  Faster analysis, intuitive dashboards  Increased organizational efficiency  Data-driven business decisions  Improved customer experience  Improved employee satisfaction  Trusted and governed data
  • 10. Methodology  Linear Regression  , Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable i.e it finds the linear relationship between the dependent and independent variable.  Linear Regression is of two types: Simple and Multiple. Simple Linear Regression is where only one independent variable is present and the model has to find the linear relationship of it with the dependent variable  Whereas, In Multiple Linear Regression there are more than one independent variables for the model to find the relationship.
  • 11.  A Linear Regression model’s main aim is to find the best fit linear line and the optimal values of intercept and coefficients such that the error is minimized.  Error is the difference between the actual value and Predicted value and the goal is to reduce this difference.  Logistic Regression  Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.  Some of the examples of classification problems are Email spam or not spam, Online transactions Fraud or not Fraud, Tumor Malignant or Benign.  Logistic regression transforms its output using the logistic sigmoid function to return a probability value
  • 12.
  • 13.  Analysis  This is the step where all the data comes under a single platform. A Business Intelligence software will enable you to collect as well as analyze data with advanced analytical tools embedded in the same software.  Analyzing the data collected through various methods helps an organization to understand their customer’s opinions and find out areas needing improvement. The software allows you to compare scores (such as NPS, CES, CSAT) for varied periods and also among departments.
  • 14.  Reporting  After analysis, the next step is to understand what the metrics mean. This step is the most important, as the wrong interpretation of the data can send your organization down a cliff.  Conversion into visual infographics can sometimes make it easier for a person to understand.  Such understanding will enable the organization to find answers to most pressing business, operational and marketing questions.
  • 15.
  • 16. Requirements  An easy-to-use interface that doesn't require advanced training, support or documentation. Automation for eliminating manual processes of business functions related to Business Intelligence System.  A reliable, secure database that provides accurate, real-time data.  PC with 4GBRAM  PC with 500 ROM  Visual Studio with Python  Sql server for database