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ahmad-sawalqeh

About me

ai-generated-blog-generator

Developed a Streamlit app generates a blog post using AI models from Hugging Face. Users input a blog title, and the app creates an Introduction, Body, and Conclusion based on that title. It uses a text generation model to create the content and an image generation model to create the images.

airbnb-listing-eda

Performed an exploratory data analysis on the Airbnb listings dataset for Amsterdam, revealing that the highest average prices were recorded in 2023, while the lowest were in 2014. Prices peaked in April and were at their lowest in March, whereas availability was highest in June and lowest in September.

arabic_abstractive_summarization

Fine-tuning the mT5 transformer model for abstractive text summarization in Arabic, aiming to produce concise and meaningful summaries. The model's performance was evaluated with ROUGE metrics, showing preliminary results with ROUGE-1: 0.10, ROUGE-2: 0.02, and ROUGE-L: 0.08. This work is part of the Competition Military Technical College.

artificial_neural_networks

This repository is your comprehensive guide to building Artificial Neural Networks (ANN) using various frameworks in Python. Explore building ANNs from scratch using NumPy, implementing ANNs with TensorFlow, and creating ANNs with PyTorch.

auto-annotate

A simple tool for automatic image annotation using Roboflow API

automated-scrape-events

building an automated tool to scrape events content from local websites and have them ready to be inserted in a database which can eventually be used in our mobile app (examples: Visit Qatar, ILoveQatar, Qatar Calendar, QF Website, QU Website, etc.). We plan to use the NER (Named Entity Recognition) technique and/or a Large Language Model (LLM).

automated_eda

Developed an AutoEDA platform that streamlines the complete lifecycle of exploratory data analysis, including data acquisition, preprocessing and visualization. Designed for users with minimal data science expertise.

automated_ml

Developed an AutoML platform that optimizes the entire lifecycle of machine learning models for classification and regression tasks, from data acquisition to model saving, achieving 100% usability with zero-code features, designed for users with minimal data science expertise.

brazilian_e-commerce

The largest department store in Brazilian marketplaces, able to sell their products through the Store and ship them directly to the customers using logistics partners,Once the customer receives the product, or the estimated delivery date is due, the customer gets email where he can give a note for the purchase experience and write down some comment

card_match_challenge

Developed a Card Match game using Tkinter, where players swap cards to match their own hand or the main deck. The game ends when either all of the player's cards or the main deck's cards are identical, and the state is updated dynamically with each turn.

code-evaluator

Developed a Streamlit app to evaluate code solutions. Users can input task descriptions and code solutions to receive detailed feedback on task alignment, modularity, efficiency, and adherence to key concepts, as well as verify if the code is AI-generated.

computer_vision

This repository serves as your comprehensive guide to understanding and implementing Computer Vision techniques using Python. Dive into various topics, from basic introductions to advanced techniques, to gain a deeper understanding of processing and analyzing images.

customer_segmentation

Developed a K-means clustering model for customer segmentation and personalization in an e-commerce context. Utilized PCA for dimensionality reduction and WCSS for cluster validation, identifying three customer groups with similar preferences and behaviors.

cyrisxd

dashboard_electropi

Developed a Streamlit dashboards for visualizing and analyzing data within an organization's system. The dashboards provide interactive insights into user activity, subscriptions, course completion, capstone evaluations, coupon usage, and employment grant status.

deep_neural_networks

This repository is your comprehensive guide to building and working with deep neural networks (DNNs) in Python. Explore a wide range of topics, from generating and preprocessing data to training and inference, as well as advanced techniques such as embedding layers, convolutional neural networks (CNNs), long short-term memory networks (LSTMs).

detect-terrorists

Developed a YOLOv8x model with ultralytics to identify potential terrorists by classifying individuals as civilians or military and detecting weapons. Utilized AutoDistill for annotation and autolabeling, and defined ontology to enhance model training. Achieved a final accuracy of 0.738 mAP50 and 0.637 mAP50-95. Competition Air Defense College

detection-of-denial-of-service

devices-price-classification-system-using-python

Developed a Devices Price Classification System utilizing a pipeline that incorporates StandardScaler, SelectFromModel, and RandomForestClassifier, achieving an accuracy of approximately 93% in predicting device prices.

elsayedelmandoh

elsny3ya

Freelancer task

explority_data_analysis

This repository is dedicated to the exploration and analysis of datasets using Python. Whether you're a data scientist, analyst, or enthusiast, here you'll find resources, examples, and techniques for uncovering insights from data.

gemilla

GemiLla is a Streamlit-based project that allows two conversational agents, Gemini and LLaMA, to engage in interactive discussions. It demonstrates the integration of Google's Gemini and Meta's LLAMA-3, providing an engaging and flexible conversation interface.

generative-ai-for-beginners

18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

generative-ai-python

The Google AI Python SDK enables developers to use Google's state-of-the-art generative AI models (like Gemini and PaLM) to build AI-powered features and applications.

gimy

Instagram reels transcribe summarizers app

guardian-angel

Guardin Angel include more tasks: Activity Recognition, Feelings Recognition, kids_and_Caregivers_recognition and clothing_segmentation

lsi-labse-semantic-search-widebot

Developed a semantic search pipeline to extract relevant keywords from English movie plots. Utilized SpaCy for preprocessing and Gensim for loading corpora, TF-IDF, and LSI models, achieving a Mean Average Precision (MAP) of 87%. Additionally, a pretrained model (LaBSE) was used for comparison.

market_segmentation_neural_networks

Developed a neural network with 4 Dense layers for market segmentation using customer data. The model achieved a mean absolute error of 2.8181 on training data and 2.6434 on testing data. This model helps businesses tailor marketing strategies to specific customer segments.