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Mobile app for emotion detection.

Mobile app for emotion detection.

Through a wide variety of mobile applications

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Overview: I designed and developed an innovative mobile chat app that tracks users’ emotions based on the text they type, suggesting emojis consistent with their emotional state. The app also features a dashboard that provides an overview of users’ emotions throughout the day.

Key Contributions:

Emotion Classification:
- Data Preparation: Cleaned and preprocessed training datasets to ensure accuracy and reliability in the emotion classification task.
- Model Implementation: Implemented the Word2Vec model to analyze and understand the emotional context of user messages.
Mobile App Development:
- Framework: Developed the mobile application using Flutter, ensuring a seamless and responsive user experience across various devices.
- User Interface: Designed an intuitive and engaging interface that allows users to easily interact with the app and view their emotional dashboard.
Backend and Deployment:
- API Development: Deployed the emotion classification model and built API endpoints using Flask, enabling real-time emotion tracking and emoji suggestions.
- Integration: Ensured smooth integration between the frontend and backend, providing users with accurate and timely feedback on their emotional state.

Impact: This project showcases my ability to combine data science and mobile development skills to create a unique and user-centric application. It highlights my expertise in emotion analysis, natural language processing, and mobile app development, demonstrating my capability to deliver innovative solutions that enhance user engagement and experience.

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Sentimental analysis of hotel reviews

Sentimental analysis of hotel reviews

Through a wide variety of mobile applications, we’ve developed a unique visual system and strategy that can be applied across the spectrum of available applications.

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Overview: I implemented a comprehensive business intelligence solution focused on analyzing Moroccan hotel reviews to help hotel owners better understand and meet customer expectations. This project involved several key steps, from data recovery to the creation of advanced interactive dashboards.

Key Contributions:

Data Collection:
- Review Recovery: Retrieved reviews from multiple hotel review sites, ensuring a diverse and comprehensive dataset.
Data Preprocessing:
- Cleaning and Preprocessing: Cleaned and preprocessed the extracted data to remove noise and inconsistencies, ensuring high-quality input for analysis.
Sentiment Analysis:
- Vader Implementation: Used Vader, a powerful sentiment analysis tool, to analyze the sentiment expressed in the reviews. This helped in categorizing customer feedback into positive, negative, and neutral sentiments.
Dashboard Development:
- Interactive Dashboards: Built advanced interactive dashboards using PowerBI. These dashboards provide a simple yet effective user interface for hotel owners to visualize and interpret the data.
- Graphical Representation: Designed intuitive graphical formats, such as bar charts, pie charts, and trend lines, to represent the sentiment analysis results and other key metrics.

Impact: This business intelligence solution provides hotel owners with valuable insights into customer feedback, enabling them to make data-driven decisions to improve their services and meet customer expectations. The interactive dashboards offer an easy-to-use interface for understanding complex data, facilitating better decision-making and strategic planning.

By including this project in your portfolio, you demonstrate your expertise in data analysis, sentiment analysis, and business intelligence. It highlights your ability to transform raw data into actionable insights, showcasing your skills in data preprocessing, analytical tool implementation, and advanced data visualization.

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Track the evolution of water in the great lakes of the world.

Track the evolution of water in the great lakes of the world.

Through a wide variety of mobile applications, we’ve developed a unique visual system and strategy that can be applied across the spectrum of available applications.

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Overview: I worked on a project focused on water body segmentation using advanced machine learning techniques. The project involved collecting and processing satellite imagery data, applying segmentation algorithms, and visualizing the results to make them accessible and understandable to stakeholders.

Key Contributions:

Data Collection:
- Datasets: Collected satellite imagery and other waterbody data using the Sentinel Hub dataset and the Resisc45 dataset.
- Manual Labeling: Manually labeled the data using an online tool, preparing it for subsequent analysis and training.
Data Preprocessing:
- Cleaning and Preparation: Preprocessed the collected data to ensure it was clean and suitable for machine learning applications. This included normalizing images and addressing any inconsistencies in the data.
Machine Learning Techniques:
- Model Implementation: Applied machine learning techniques such as U-net and Residual U-net for water body segmentation. These models were chosen for their effectiveness in image segmentation tasks, particularly in distinguishing water bodies from other land features.
Visualization and Analysis:
- Dash Framework: Used the Dash framework to create visualization and analysis tools. These tools made the data and segmentation results accessible and understandable to stakeholders, facilitating informed decision-making.
- Interactive Dashboards: Developed interactive dashboards that provided clear and actionable insights, allowing stakeholders to explore the segmented data visually.

Impact: This project demonstrates my ability to handle end-to-end machine learning workflows, from data collection and preprocessing to model implementation and result visualization. By making complex segmentation results accessible to stakeholders through intuitive dashboards, the project highlights my proficiency in both technical and communication skills, ensuring that the insights derived from the data can be effectively utilized.

Including this project in your portfolio showcases your expertise in remote sensing, image segmentation, and data visualization. It emphasizes your capability to leverage advanced machine learning techniques to address real-world challenges, transforming raw data into valuable and actionable insights.

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Detecting offensive speech in Arabic in social media.

Detecting offensive speech in Arabic in social media.

Through a wide variety of mobile applications, we’ve developed a unique visual system and strategy that can be applied across the spectrum of available applications.

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Fine classification of offensive content written in Arabic dialect on Facebook and visualization

the main goal of this project is to classify offensive content written in Arabic dialect (Egyptian dialect, Levantine dialect, Gulf dialect, and Moroccan dialect) on Facebook and visualize the results.

Definition of offensive content

Offensive content is content that reasonably causes another to experience extreme anger, insult, or disrespect. Examples of offensive content

Content that is threatening, harassing, or incites violence.

Hate speech

Hate speech often emerges from an “us vs. them” conceptual framework, in which individuals differentiate the group they believe they belong to, or the “in-group,” from the “out-group.” Hate speech toward the out-groups is segmented into three major categories in this analysis.

– Dehumanization and demonization

Dehumanization involves belittling groups and equating them to culturally despised subhuman entities, such as pigs, rats, monkeys, or even germs or dirt/filth. A widely known recent manifestation of this phenomena involved calling the Tutsi minority in Rwanda cockroaches in the lead up and during the 1994 genocide.

Demonization, on the other hand, involves portraying a group as superhuman, like a monster, robot, or even fatal diseases like cancer that are a mortal threat to the in-group.

Racism
Sexism (Misogyny,Misandry)
religion off (againt religion)

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Recommendation system to enhance user experience.

Recommendation system to enhance user experience.

Through a wide variety of mobile applications, we’ve developed a unique visual system and strategy that can be applied across the spectrum of available applications.

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Overview: I developed a recommendation system designed to enhance the eCommerce customer experience. The system was built using a small dataset of book ratings and focused on providing tailored recommendations to both new and existing users. The project emphasized the back-end development of the recommendation engine, utilizing collaborative filtering techniques to deliver personalized suggestions.

Key Contributions:

User Identification and Sign-Ups:
- New vs. Existing Users: Implemented functionality to differentiate between new and existing users.
- Promotion of Sign-Ups: Designed mechanisms to encourage new users to sign up, enhancing user engagement and data collection.
Recommendation Strategies:
- For New Users: Recommended popular items to new users based on general popularity metrics, ensuring immediate value and engagement.
- For Existing Users: Implemented both item-based and user-based collaborative filtering techniques to provide personalized recommendations.
Collaborative Filtering Techniques:
- Item-Based Filtering: Utilized the Item Similarity Index to recommend items similar to those previously liked or purchased by users.
- User-Based Filtering: Employed the User Similarity Index to recommend items based on the preferences of similar users.
Data Analysis and Recommendation Logic:
- Past Purchases and Likes: Analyzed users’ past purchases and likes to generate accurate and relevant recommendations.
- Similarity Indices: Calculated item and user similarity indices to enhance the precision of the recommendation system.

Impact: This recommendation system significantly improved the eCommerce customer experience by providing tailored product suggestions, leading to increased user satisfaction and engagement. By focusing on collaborative filtering techniques, the system effectively leveraged user data to deliver relevant recommendations, fostering a more personalized shopping experience.

Including this project in your portfolio highlights your expertise in developing advanced recommendation engines and your ability to apply collaborative filtering techniques to real-world datasets. It demonstrates your proficiency in data analysis, machine learning, and back-end development, showcasing your capability to create impactful solutions that enhance user experiences in the eCommerce domain.

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Restaurant Mobile Application Figma Design.

Through a wide variety of mobile applications, we’ve developed a unique visual system and strategy that can be applied across the spectrum of available applications.

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A web-based, responsive application designed to revolutionize the online education experience. It features a comprehensive learning management system (LMS) that includes dedicated panels for admins, instructors, and students. LMSZAI is a fully ready-to-use SaaS solution, perfect for educational institutions, corporate training, and independent educators.

Key Features:

Admin Panel:
- Technology Stack: Built with modern web technologies for reliability and performance.
- Centralized Control: Manage courses, users, and content from a single, intuitive dashboard.
- Analytics and Reporting: Track student progress, course completions, and generate insightful reports.
- User Management: Handle user roles, permissions, and access levels effortlessly.
- Customization: Customize the platform to reflect your brand with configurable settings and themes.
Instructor Panel:
- Course Creation: Easily create and manage courses with rich multimedia content, quizzes, and assignments.
- Student Engagement: Communicate with students through discussion forums, messaging, and announcements.
- Grading and Feedback: Efficiently grade assignments and provide detailed feedback to students.
- Performance Tracking: Monitor student progress and adjust teaching strategies accordingly.
- Resource Management: Upload and manage course materials, resources, and supplementary content.
Student Panel:
- User-Friendly Interface: Navigate courses, assignments, and resources with ease.
- Progress Tracking: Keep track of learning progress, grades, and deadlines through a personalized dashboard.
- Interactive Learning: Engage with instructors and peers through forums, chats, and collaborative projects.
- Access Anywhere: Enjoy a seamless learning experience on any device, anytime, anywhere.
- Certifications: Earn and download certificates upon course completion.

Benefits:

Scalability: This is designed to grow with your educational needs, whether for a small class or a large institution.
Efficiency: Streamline administrative tasks, improve communication, and enhance learning experiences.
Flexibility: Support for various teaching and learning styles, from self-paced courses to instructor-led sessions.
Engagement: Foster a vibrant learning community with interactive tools and collaborative features.
Data-Driven Decisions: Utilize comprehensive analytics to improve course content and teaching methods.