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Hussien A.

@Hussein732001

4.4
1
0.0
0.0
100%

Python | AI & Machine Learning | API Deployment

$20 USD / Hour
・
Flag of
Egypt (1:01 PM)
・
Joined on July 7, 2024
$20 USD / Hour
・
I am a computer engineer with experience in computer science and machine learning. Proficient in Python, with a strong background in data analysis and problem-solving. I've developed many projects using python and AI like: 8 puzzle game, Restaurant Management System, Emotional Analysis, Hate Speech Detection and more.
Verifications
On time
100%
On budget
100%
Accept rate
100%
Freelancer Python Developers Egypt
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Portfolio

Portfolio

12383011
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
12033438
Project description. The system receives a question from the user, then the question is passed to a Large Language Model (LLM) to generate a preliminary answer and the generated answer is then passed to an my API for further processing finally the final answer is formulated and delivered back to the user. The Project Link: https://github.com/HussienAdel/Auto-Answering-App
Auto Answering using LLM
Project description. The system receives a question from the user, then the question is passed to a Large Language Model (LLM) to generate a preliminary answer and the generated answer is then passed to an my API for further processing finally the final answer is formulated and delivered back to the user. The Project Link: https://github.com/HussienAdel/Auto-Answering-App
Auto Answering using LLM
12033440
Project description. Developed a sophisticated hate speech detection model aimed at identifying and classifying hate speech in textual data. This project employed advanced machine learning and natural language processing (NLP) techniques to enhance the accuracy and reliability of detecting harmful content. The Project Link: https://github.com/hussienadel/hate-speech-detection-model
Hate Speech Detection using ML
12033445
Project description. Developed an advanced emotional analysis model designed to classify and interpret human emotions from textual data into six distinct emotional categories: "love", "fear", "joy", "anger", "surprise", and "sadness". This project leveraged a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture implemented in Keras to enhance the accuracy and depth of sentiment analysis. The Project Link: https://github.com/hussienadel/emotional_analysis_model
Emotional Analysis using DL
Project description. Developed an advanced emotional analysis model designed to classify and interpret human emotions from textual data into six distinct emotional categories: "love", "fear", "joy", "anger", "surprise", and "sadness". This project leveraged a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture implemented in Keras to enhance the accuracy and depth of sentiment analysis. The Project Link: https://github.com/hussienadel/emotional_analysis_model
Emotional Analysis using DL
12156446
Recommendation System for Movies - Dashboard
12181068
This project focuses on developing an advanced agricultural dashboard that integrates machine learning models to predict and visualize various agricultural conditions. The dashboard is designed to assist farmers and agricultural professionals in making data-driven decisions to optimize crop yield, manage resources efficiently, and enhance overall productivity. Skills: Python · Scikit-Learn · DASH · Plotly · Pandas The Project Link: https://github.com/HussienAdel/Optimizing-Aricultural-Project
Optimizing Agricultural Dashboard Using Machine Learning
12383011
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
A cutting-edge web application designed to analyze and support mental health using advanced Machine Learning and Web Development techniques. Features: - Mental Health Analysis: Scrapes tweets using Selenium and predicts mental health scores with models like Random Forest, Logistic Regression, Naive Bayes, and GRU. - Sentiment Analysis: Classifies text to determine sentiment and provide emotional insights. - Interactive Chatbot: Integrated Groq API (Llama3) to answer mental health-related questions with real-time support. Tech Stack: TensorFlow, Scikit-learn, Flask, HTML, CSS, JavaScript, Selenium, NLTK, Pandas, and NumPy. This application showcases innovation and practical impact, merging AI-driven insights with user-focused web solutions.
Mental Health Analysis Web App
12033438
Project description. The system receives a question from the user, then the question is passed to a Large Language Model (LLM) to generate a preliminary answer and the generated answer is then passed to an my API for further processing finally the final answer is formulated and delivered back to the user. The Project Link: https://github.com/HussienAdel/Auto-Answering-App
Auto Answering using LLM
Project description. The system receives a question from the user, then the question is passed to a Large Language Model (LLM) to generate a preliminary answer and the generated answer is then passed to an my API for further processing finally the final answer is formulated and delivered back to the user. The Project Link: https://github.com/HussienAdel/Auto-Answering-App
Auto Answering using LLM
12033440
Project description. Developed a sophisticated hate speech detection model aimed at identifying and classifying hate speech in textual data. This project employed advanced machine learning and natural language processing (NLP) techniques to enhance the accuracy and reliability of detecting harmful content. The Project Link: https://github.com/hussienadel/hate-speech-detection-model
Hate Speech Detection using ML
12033445
Project description. Developed an advanced emotional analysis model designed to classify and interpret human emotions from textual data into six distinct emotional categories: "love", "fear", "joy", "anger", "surprise", and "sadness". This project leveraged a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture implemented in Keras to enhance the accuracy and depth of sentiment analysis. The Project Link: https://github.com/hussienadel/emotional_analysis_model
Emotional Analysis using DL
Project description. Developed an advanced emotional analysis model designed to classify and interpret human emotions from textual data into six distinct emotional categories: "love", "fear", "joy", "anger", "surprise", and "sadness". This project leveraged a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture implemented in Keras to enhance the accuracy and depth of sentiment analysis. The Project Link: https://github.com/hussienadel/emotional_analysis_model
Emotional Analysis using DL
12156446
Recommendation System for Movies - Dashboard
12181068
This project focuses on developing an advanced agricultural dashboard that integrates machine learning models to predict and visualize various agricultural conditions. The dashboard is designed to assist farmers and agricultural professionals in making data-driven decisions to optimize crop yield, manage resources efficiently, and enhance overall productivity. Skills: Python · Scikit-Learn · DASH · Plotly · Pandas The Project Link: https://github.com/HussienAdel/Optimizing-Aricultural-Project
Optimizing Agricultural Dashboard Using Machine Learning
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4.4 · 1 Review
Reviews
User Avatar
Flask Web App for Mental Health Sentiment Analysis • $10 USD
Answered all my queries on time.
Madeeha A.
@madeeha2373
•
Flag of
Jaipur, India
•
3 months ago
Share
Experience
Vice Head of Machine Learning
Oct, 2022 - Jul, 2024
•
1 year, 9 months
Az-SENCS | Al-Azhar University | Cairo, Egypt
Oct, 2022 - Jul, 2024
•
1 year, 9 months
I joined the team as a machine learning member, and my responsibilities were to cooperate with my colleagues for a successful and smooth qualifying process in machine learning, learn its algorithms, and be a machine learning instructor and presentations maker to teach and help more people outside the team.
Oct, 2022 - Jul, 2024
•
1 year, 9 months
Education
Al-Azhar University
2019 - 2024
•
5 years
Bachelors in Computer Engineering
Flag of
Egypt
2019 - 2024
•
5 years
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