SOFTWARE ENGINEERING PROJECT FOR AI AND APPLICATION

MACHINE LEARNING

1. SEARCH ENGINE Name: Oishi Sen, Shivam Lodh, Pritam Matya, Hariom Sharan Roll No: 12000121041, 12000121040, 12000121055, 12000121068 Dept. : Computer Science Engineering Year : 3rd Sem : 5th Subject : Software Engineering Code : ESC-591

2. Agenda "Today, we embark on a journey to understand the foundational elements of a search engine. We will cover:" 1. *Understanding the Project:* "The core idea behind our search engine." 2. *Setting Up:* "Ensuring we have the right tools ready." 3. *Code Walkthrough:* "A deep dive into the Python code." 4. *Execution and Testing:* "Bringing our search engine to life." 5. *Enhancements and Further Learning:* "Beyond the basics."

3. What's a Search Engine? "A search engine, in its essence, processes queries and searches through vast amounts of data to return relevant results." - "Our basic model will contain a set of predefined documents and will rank these documents based on their direct relevance to the user's query."

4. INTRODUCTION Data Collection: Gather a dataset of documents, articles, or web pages you want your search engine to index. You might want to use web crawlers or scrapers to collect data from the internet if it's a web search engine. Preprocessing: Tokenize the content: Break down the content into words or tokens. Remove stopwords: Words like "and", "the", and "is" are usually removed. Stemming/Lemmatization: Reduce words to their base or root form. For example, "running" becomes "run". Feature Extraction: Convert the textual data into a numerical format using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings like Word2Vec or FastText. Indexing: Store the processed documents in a structured format for efficient retrieval. You can use databases like Elasticsearch or Apache Solr for this. Ranking: Traditional search engines used keyword matching and handcrafted heuristics for ranking. With ML, you can train models to rank documents based on relevance. Collect user interactions as training data. For example, if a user clicks on the third link in response to a query, it indicates relevance. Train ranking models like RankNet, LambdaMART, or BERT for search.

5. Feedback Loop: Allow users to provide feedback on the relevance of search results. This can be implicit (clicks, time spent on a result) or explicit (thumbs up/down). Use this feedback to continually improve and retrain your ranking model. Scaling: As your dataset grows, you may need to consider distributed search and indexing systems like Elasticsearch or Apache Solr. Improvements: Consider using deep learning models, which can capture semantic meanings of words using embeddings (like Word2Vec or BERT). Incorporate other sources of data (like user behavior, click-through rates) to improve ranking. Incorporate query expansion, spell correction, and other techniques to handle diverse user inputs. Evaluation: Continuously evaluate the performance of your search engine using metrics like Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), etc. Deployment: Once satisfied, deploy your search engine. Depending on the scale, you might consider cloud-based solutions or dedicated search infrastructure. Building a search engine is a comprehensive project, and while ML can significantly improve search relevance and quality, keep in mind that building, training, and deploying an ML model requires careful consideration, especially regarding data privacy, infrastructure costs, and model interpretability.

6. Objective of the Project : The primary objective of this project is to design and implement an "Advanced SOP (Standard Operating Procedure) Search Engine" that efficiently searches through a collection of documents and presents the most relevant ones to the user. By leveraging the powers of Natural Language Processing (NLP) and modern search algorithms, this search engine aims to enhance the user's experience by providing accurate and quick results.

7. Software Development Life Cycle (SDLC): The SDLC is the backbone of any software project. By adhering to its structured phases, developers can ensure a holistic development approach. Requirement Analysis: This phase acts as the foundation. Understanding the problem and gathering requirements involves dialogues, discussions, and meetings with stakeholders and end-users. This ensures a product that aligns with user needs. System Design: Based on the requirements, a blueprint is drawn up. This involves deciding on the system's architecture, database design, and other technical specifications. Implementation: This phase involves breathing life into the design by writing code. For this project, tools like Python and Flask were instrumental. Testing: No software is flawless at the outset. This phase irons out any kinks, bugs, or glitches in the system. Deployment: Post-testing, the software is launched for the end-users. Maintenance: This is an ongoing phase. As users interact with the system, feedback is gathered, leading to periodic updates and enhancements.

8. Proposed System: Traditionally, search engines might merely look for keyword matches. The proposed system, however, dives deeper. It understands the weightage and relevance of terms using the TF-IDF Vectorization technique. This ensures results that are contextually aligned with the query. Design Philosophy: Traditional search engines often hinge on simple keyword matching, leading to search results that might be technically correct but contextually inappropriate. The proposed system, however, has been meticulously designed to go beyond mere keyword-based searches. It understands the context, relevance, and significance of words in relation to a set of documents, ensuring that the results returned are not just accurate but also meaningful. Architecture: The architecture of the proposed system is a blend of modern web technologies with advanced natural language processing techniques. At its core lies the TF-IDF Vectorization technique. This method ensures that each word's weight in a document is determined not just by its frequency in that document, but also its rarity across other documents. This, in essence, helps in ranking words based on their importance.

9. Methodology Used: The methodology of a project defines its approach towards achieving its objectives. It encompasses the processes, techniques, and tools used. In the context of the "Advanced SOP Search Engine", the methodology was strategically chosen to ensure the accurate retrieval of relevant documents in response to user queries. Let’s delve deeper into its intricacies. Foundation: Text Mining and NLP Before diving into the core technique, it's essential to understand the backdrop against which it operates. Text mining and Natural Language Processing (NLP) form the foundation. These fields deal with the extraction of meaningful information from vast amounts of text, making them pivotal for our search engine. TF-IDF (Term Frequency-Inverse Document Frequency): 1. Conceptual Framework: TF-IDF stands as one of the pillars of our methodology. At its heart, it's a statistical measure that evaluates the significance of a word in a document relative to a corpus or a collection of documents. The rationale behind this is simple: some words appear frequently across many documents and don't carry much unique meaning (like 'and', 'the'). In contrast, some words appear frequently in specific documents but are rare across others, making them more significant. 2. Breakdown: Term Frequency (TF): This represents the frequency of a term in a document. It's the ratio of the number of times the term appears in a document to the total number of terms in the document. Inverse Document Frequency (IDF): This denotes the importance of a term across a set of documents. If a term appears in many documents, its IDF value will be low, implying it's not a unique identifier. It's calculated as the logarithm of the total number of documents divided by the number of documents containing the term.

10. Cosine Similarity for Ranking: Once the documents and the user's query are transformed into vectors using TF-IDF, the next challenge is to determine which documents are most similar to the query. Enter cosine similarity. 1. Conceptual Overview: Cosine similarity calculates the cosine of the angle between two vectors. In our case, these vectors are the TF-IDF representations of the documents and the query. A smaller angle between them implies higher similarity and, consequently, higher relevance. 2. Practical Implication: By ranking documents based on their cosine similarity to the user's query, the search engine ensures that the top results are the ones most closely aligned with the user's intent. Iterative Refinement: An essential aspect of the methodology is iterative refinement. Initial tests with real users provide valuable feedback, leading to tweaks in the vectorization or similarity calculation processes, ensuring that the system becomes more refined and accurate over time.

11. Advantages: Precision: Returns results that are highly relevant, minimizing information noise. Speed: Optimized algorithms ensure quick search results, enhancing user experience. User-friendly Interface: A clutter-free design ensures even non-tech-savvy users can navigate with ease. Scalability: Designed with future expansions in mind, the system can handle increased data loads without a dip in performance. Disadvantages: Limited Document Collection: The current scope is limited to a set of predefined documents. Real-world applications might have a more dynamic set. Dependency on External Libraries: Any changes or discontinuation of Flask or Scikit-learn can impact the system's functionality.

12. Software Engineering Paradigms Applied Object-Oriented Programming (OOP): 1. Conceptual Overview: OOP is a software engineering paradigm based on the concept of "objects." These objects can represent real-world entities or abstract concepts, and they encapsulate data and functions that operate on that data. 2. Core Principles of OOP Applied: Encapsulation: Encapsulation involves bundling the data (attributes) and methods (functions) that operate on the data into single units called objects. For instance, the SOPSearchEngine class is a clear demonstration of encapsulation, where data about documents and methods to search through them are bundled together. Inheritance: Inheritance enables a class (child) to inherit properties and methods from another class (parent). While this specific project might not extensively utilize inheritance, the underlying libraries, especially those related to machine learning, often use inheritance to build upon base classes and extend functionalities. Polymorphism: This principle allows objects of different classes to be treated as objects of a common superclass. It's especially valuable when building scalable systems where new functionalities might be added, but existing interfaces need to remain consistent. Abstraction: Abstraction involves hiding complex implementations and showing only the essential features. The user does not need to know the intricacies of how TF-IDF or cosine similarity works; they only interact with the search method, showcasing abstraction.

13. Modularity: Though modularity can be seen as an offshoot of OOP, it's foundational enough to merit its own discussion. The entire project is designed with modularity in mind. Each component, be it the user interface, the search mechanism, or the database of documents, has been created as distinct modules. This ensures that changes in one module do not cascade and create unforeseen complications in others. Procedural Programming: While OOP formed the backbone, certain aspects of the system, especially utility functions, could have been developed using the procedural paradigm. This paradigm is based on the concept of procedure calls, where you define code as reusable functions or procedures. These procedures are then called to perform a task in a sequence, which can be a simpler and more straightforward approach for specific tasks within larger OOP projects.

14. Description of Language Used Python, since its inception in the late 1980s by Guido van Rossum, has progressively cemented its place as one of the most versatile and user-friendly programming languages. Its philosophy emphasizes code readability, allowing developers to express concepts in fewer lines of code than might be possible in other languages. This emphasis on simplicity without compromising on power makes it an ideal choice for diverse applications. Key Features and Their Relevance to the Project: Readability and Syntax: Python's clear and readable syntax promotes easy collaboration. Different developers, regardless of their familiarity with the initial project, can understand and contribute to the code. This feature was vital for ensuring that the "Advanced SOP Search Engine" remains maintainable and scalable in the long run. Extensive Standard Library: Python's vast standard library is akin to a treasure trove. For the search engine, libraries like Flask facilitated web development, while Scikit-learn offered tools for machine learning and text processing. Dynamic Typing: Python is dynamically typed, which means that the type of data an object can store can change at runtime. This flexibility aids rapid development and iterative testing, crucial for the agile development process of our search engine. Platform Independence: Python is cross-platform. Whether the deployment target is Windows, MacOS, Linux, or even some embedded systems, Python ensures consistent performance. This was essential for the "Advanced SOP Search Engine" to reach a diverse user base.

15. DFD (Data Flow Diagram: A DFD offers a graphical representation of the system's flow. Imagine a series of interconnected entities - the User, the Web Interface, the Search Engine, and the Document Database. The user's query traverses this network, getting processed and refined before the results make their way back. Levels of DFD: Level 0 (Context Diagram): Entities: User Process: SOP Search Engine System Data Stores: None at this level. Data Flow: User provides a query to the SOP Search Engine System and receives search results. Level 1 (Detailed DFD): Entities: User Processes: User Query Input Interface Search Processing Result Compilation Data Stores: Document Database Data Flow: User provides a query to the User Query Input Interface. The query is processed by the Search Processing module, which interacts with the Document Database to fetch relevant documents. Result Compilation arranges the results and sends them back to the user.

16. User Input & Display "Interactivity is key. We've set up a block of code to capture user input for the query and then use our 'search' function to find matches." - "Depending on the search results, our program will either display the relevant documents or kindly inform the user that no matches were found."

17. The Data & Searching Function "At the heart of our engine lies the data it searches. For simplicity, we're using a 'documents' list with sample text data." - "The 'search' function is where the magic happens. It loops through each document and checks if it matches our user's query."

18. Execution & Testing "With our code ready, it's time to see it in action. By navigating to our script's directory and running it, we bring our search engine to life." - "Testing is crucial. Try out various queries to see how our engine responds!"

19. Future Scope & Enhancements "Today's journey was a foundational step. But the world of search engines is vast and intriguing." - *Tokenizing Words:* "Splitting text into individual 'tokens' or words for more refined searching." - *TF-IDF:* "A more advanced method to determine document relevance based on term frequencies." - *Advanced Libraries:* "Exploring tools like NLTK can open up sophisticated text processing possibilities."

20. Conclusion "Today, we peeled back the curtain on search engines, understanding their core essence and building a basic one ourselves." - "Search engines power the digital age, guiding us through oceans of data. What we learned today is the stepping stone to more advanced projects in this domain."

21. THANK YOU