Volume 14 | Issue 5 | May

 Abstract

The stock market is one of the most complex and dynamic financial systems, influenced by numerous factors such as economic conditions, political events, company performance, and investor sentiment. Predicting stock prices accurately is a challenging task due to the high volatility and non-linear nature of financial data. This paper presents a machine learning-based approach for stock market prediction using historical stock data.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Stock Market Prediction, Linear Regression, Decision Tree, Random Forest, Data Analysis, EDA

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This report presents an overview of the Full Stack Development Internship undertaken at Codtech from 15th January 2026 to 12th February 2026. The internship was designed as a structured learning opportunity to enhance practical knowledge and industry- relevant skills in full stack web development. During the internship, emphasis was placed on understanding both frontend and backend technologies, including user interface design, server- side logic, database management, and application integration. The program focused on hands-on implementation of concepts through real-world tasks and guided project work, enabling a deeper understanding of the complete software development lifecycle. The internship also aimed to improve problem-solving abilities, coding standards, and professional work ethics while working under lawful and reasonable guidance. Exposure to modern development practices helped bridge the gap between theoretical knowledge and practical application.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Full Stack Development, Web Development, Frontend Technologies, Backend Technologies, HTML, CSS, JavaScript, Server-Side Programming, Database Management, Software Development Lifecycle

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Full Stack Python development refers to the process of building complete web applications using Python for the backend and modern web technologies for the frontend. This project focuses on designing and developing a dynamic, user-friendly, and database-driven web application using Python-based frameworks such as Django or Flask along with HTML, CSS, JavaScript, and Bootstrap for the frontend.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Full Stack Development, Web Development, Frontend Technologies, Backend Technologies, HTML, CSS, JavaScript, Server-Side Programming, Database Management, Software Development Lifecycle, Hands-on Learning, Application Development, Sumago Infotech Internship Training, Industry-Oriented Skills.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the era of digital commerce, customer reviews have become a significant factor influencing purchasing decisions. These reviews serve as a rich source of information that reflects user experience, satisfaction levels, and product performance. The objective of this study is to analyze online product reviews using basic sentiment analysis techniques and present the findings through interactive visualizations. The process involves data collection, preprocessing, sentiment classification, and dashboard creation using Power BI. The results help in understanding customer opinions and identifying trends that can assist businesses in improving their products and services.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sentiment Analysis, Data Visualization, Power BI, Customer Feedback, Text Mining, E-commerce Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

House price prediction is an important application of Machine Learning that helps estimate property prices based on various features such as location, area, number of rooms, amenities, and market conditions. Accurate prediction models are useful for buyers, sellers, real estate companies, and investors in making better financial decisions. Traditional methods of property valuation often depend on manual analysis and experience, which can be time-consuming and less accurate. Machine Learning techniques provide a data-driven approach that improves prediction accuracy and efficiency.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

"House Price Prediction using Machine Learning"

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Modern enterprise networks suffer from severe security risks, including unauthorized access, distributed denial-of-service (DDoS) attacks, and malware infiltration. Traditional perimeter defense mechanisms, such as static firewalls, are no longer sufficient to detect and mitigate sophisticated, dynamic cyber threats. This paper presents the design, implementation, and evaluation of a robust Network Intrusion Detection System (NIDS) utilizing open-source technologies to enhance network visibility and security. The proposed system enables real-time traffic analysis and packet logging across IP networks, utilizing signature- based detection to identify malicious activities. The architecture leverages a centralized monitoring engine configured with custom rule sets to identify specific attack vectors while minimizing false positives. Experimental analysis, conducted by simulating various attack scenarios (e.g., port scanning, brute-force attempts, and payload injections), demonstrates the system's high efficiency, low latency in alert generation, and minimal impact on network throughput. The framework is highly suitable for deployment in environments requiring stringent security monitoring, such as corporate networks, academic institutions, and data centers.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Intrusion Detection System, Network Security, Signature-Based Detection, Packet Sniffing, Threat Mitigation, Cybersecurity, NIDS, Traffic Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of e-commerce platforms has made online product reviews one of the most influential factors in customer purchase decisions. Consumers increasingly rely on product ratings, written reviews, and verified purchase indicators before selecting items from online marketplaces. However, the large volume of customer-generated feedback makes manual analysis inefficient and often impractical. This paper presents an internship-based analytical study titled "Online Product Review Analysis Using Power BI," which focuses on transforming raw product and customer review data into actionable business insights through interactive dashboarding and review-oriented analytics. The project integrates structured product attributes such as product category, price, discount percentage, rating, review count, brand, city, age group, delivery days, order status, and verified review status. Power BI was used as the primary business intelligence tool for data import, transformation, modeling, DAX-based KPI generation, and dashboard creation. The analytical framework supports category-wise product analysis, rating distribution, customer segmentation, verified review evaluation, and delivery-performance impact assessment. The internship dataset indicates approximately 3892 total reviews, an average rating of 3.24, and nearly 85% verified reviews, suggesting a moderately positive and comparatively reliable review environment. Results show that customer satisfaction is influenced not only by product quality and ratings but also by pricing strategy, discounts, delivery timelines, and category-specific expectations. The study demonstrates that even without complex machine learning pipelines, a well-designed Power BI dashboard can provide practical sentiment- oriented insights and decision support for e-commerce businesses. This paper highlights the importance of combining descriptive analytics with review intelligence to improve customer understanding, product strategy, and operational performance in digital marketplaces.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Online Product Review Analysis, Power BI, Business Intelligence, E-commerce Analytics, Sentiment Analysis, Customer Reviews, Data Visualization, Product Ratings.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Interior design is a complex process that involves spatial planning, aesthetic arrangement, and efficient utilization of available resources. Traditional interior design approaches rely on manual sketches, 2D layouts, or expensive professional software tools, which often fail to provide real-time interaction and accurate visualization. These limitations lead to inefficient decision-making, increased costs, and unsatisfactory design outcomes.This paper presents the design and implementation of a Web-Based Virtual Interior Designer, an interactive system that enables users to create, manipulate, and visualize interior spaces in a real-time 3D environment. The system leverages Three.js, a WebGL-based JavaScript library, for rendering high-performance 3D graphics directly within a browser. The backend is developed using Node.js and Express, providing RESTful APIs for data processing and communication. User data and design configurations are stored in MongoDB, ensuring scalability and efficient data retrieval.The system supports advanced interaction features such as object selection using raycasting, translation along multiple axes, rotational transformations, and persistent storage of user designs. Secure authentication is implemented using JSON Web Tokens (JWT) to ensure data integrity and access control. Experimental evaluation demonstrates that the system achieves smooth rendering performance, low latency interaction, and efficient database operations. The proposed solution is scalable, cost-effective, and suitable for applications in interior design, real estate visualization, and architectural planning.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

3D Visualization, WebGL, Three.js, Interior Design System, JWT Authentication, MongoDB, Human-Computer Interaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Online shopping applications have transformed the traditional retail system by enabling users to purchase products anytime and anywhere. This paper presents the design and development of an Online Shopping Application that provides a user-friendly interface, secure payment methods, and efficient product management. The system is developed using modern web technologies to ensure scalability, security, and performance. The rapid advancement of internet technologies has significantly transformed traditional commerce into electronic commerce (e- commerce). Online shopping applications have become an essential part of modern life, enabling users to purchase products conveniently from anywhere. This research paper presents the design, development, and implementation of an Online Shopping Application using modern web technologies. The system focuses on providing a secure, user-friendly, and efficient platform for both customers and administrators. Key features include product browsing, user authentication, shopping cart functionality, secure payment processing, and order management. The application aims to enhance user experience while maintaining high performance and security standards.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web Application, Django, Payment Gateway, User Interface, E-commerce, Online Shopping, Django Framework, Web Development, Secure Payment, User Experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Traditional shopping systems require physical presence, limited accessibility, and consume significant time and effort. With the rapid advancement of internet technologies, E-commerce platforms have become an essential solution for modern business systems. This paper presents the design and development of a secure and efficient E-commerce website using HTML, CSS, and JavaScript. The proposed system enables users to browse products, add items to the cart, and manage purchases through an interactive interface. The system focuses on responsiveness, usability, and performance optimization to improve the user experience. The implementation demonstrates enhanced accessibility, efficient product management, and faster transaction flow. The system is suitable for small-scale businesses, startups, and online marketplaces.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-commerce Website, Online Shopping, Cart System, Web Development, HTML, CSS, JavaScript, Responsive Design

  License

Creative Commons Attribution 4.0 and The Open Definition