Project Title: AI Based Automated Service Hunting Tool
Category: AI / Web Application
Project File: Download Project File
Muhammad Zamar Khan
zamar.khan@vu.edu.pk
zamar.khan@live.com
AI Based Automated Service Hunting Tool
Project Domain / Category
AI/ Web technology
Abstract / Introduction
The project involves building a user-friendly application for students to explore in-demand services and understand how to list their own services for potential hiring opportunities. Using the app, students can input specific keywords, seller types, and countries to search a service provider website. The app then collects data on listings, such as titles, descriptions, sales, and ratings, and analyzes them to identify popular services and keywords. Visualizations are provided to help students grasp trends and make informed decisions about offering their own services.
The project will progress through five stages, starting with user interface development using Flask framework to create a user-friendly form for inputting search parameters. The subsequent stage involves implementing web scraping to extract data from the service provider website, including title, description, sales, rating, and more. Data analysis follows, where algorithms, including NLP techniques, are developed to analyze extracted data, perform keyword analysis, and calculate aggregate statistics. Visualizations are then created using libraries like Matplotlib to present analysis results such as focus keywords and total sales. Finally, thorough testing and debugging are conducted to ensure the application's reliability and performance, including unit testing and user acceptance testing to address any issues and optimize functionality.
Functional Requirements:
User Registration and Authentication:
Users can register for an account.
Users can log in securely.
Authentication mechanisms ensure account security.
Input Form for Search Parameters:
Users can input search parameters including keywords, seller types, and seller countries.
Input form validates user input for completeness and accuracy.
Web Scraping and Data Extraction:
Application extracts data from the service provider website based on user-defined search parameters.
Relevant information is collected from listing pages, including title, description, sales, rating, industry, platform, last delivery, and seller rank.
Data Analysis and Insights:
Algorithms analyze extracted data to derive insights.
Keyword analysis identifies focus keywords, unique keywords, and other patterns within listing titles and descriptions.
Aggregate statistics such as total sales and average rating are calculated for the collected listings.
Customized Analysis and Filtering:
Users can apply filters and conditions for customized analysis of listing data.
Filtering options include criteria such as listing start date, sales volume, or seller rating.
Visualization and Presentation:
Visualizations present analysis results, including focus keywords, unique keywords, total sales, and other relevant metrics.
Visualizations are responsive and compatible across different devices and screen sizes.
User Interaction and Feedback:
Intuitive navigation and interface design facilitate user interaction.
Users can provide feedback on search results and analysis insights.
Tools:
Python programming language (latest version).
Flask web development framework.
Web scraping libraries such as BeautifulSoup or Scrapy.
Database management system (e.g., SQLite, MySQL).
IDEs or text editors for coding (e.g., Visual Studio Code, PyCharm).
Libraries for data analysis and visualization (e.g., Pandas, Matplotlib, Plotly).
For Natural Language Processing (NLP) in Python, you can use libraries such as NLTK, spaCy, Gensim, Transformers, TextBlob, and StanfordNLP.
Supervisor:
Name: Muhammad Zamar Khan
Email ID: zamar.khan@vu.edu.pk
Skype ID: zamar.khan@live.com
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