Zeyad Sayed

Machine Learning SpecialistFreelance / Project

• Job Details:I've had the privilege to complete 13 diverse and challenging jobs, each one adding a new dimension to my skill set. Every project has been a step forward in my professional development. I'm thrilled to share that I've achieved a 100% Job Success Score (JSS) and maintained a stellar ⭐⭐⭐⭐⭐ rating. These accomplishments reflect the dedication and hard work I put into every project, and I'm deeply appreciative of the positive feedback from my clients.

Data ScientistInternship

• Job Details:During the internship at CodeClause, I focused on projects in Natural Language Processing (NLP) domains: Fake News Detection: Developed a model to identify fake news articles using NLP techniques like text preprocessing, feature extraction, and classification algorithms such as Naive Bayes or LSTM. Sentiment Analysis: Conducted sentiment analysis on textual data to determine the sentiment polarity (positive, negative, or neutral) using techniques like tokenization, feature engineering, and sentiment lexicons. Detection of Parkinson’s Disease: Utilized NLP techniques to analyze speech and textual data for early detection of Parkinson’s disease, employing methods such as feature extraction, dimensionality reduction, and classification algorithms. Prediction of the Next Word: Built a language model using NLP techniques to predict the next word in a sequence of text, employing methods like n-grams, recurrent neural networks (RNN), or transformer-based models like GPT.

Data ScientistInternship

• Job Details:During the internship at Codsoft, I completed projects in various machine learning domains: Titanic Survival Prediction: Developed a classification model to predict survival on the Titanic using algorithms like Logistic Regression and Decision Trees. Iris Flower Classification: Built a multi-class classification model using KNN, SVM, and neural networks to classify iris flowers. Sales Prediction: Developed time-series forecasting models like SARIMA and LSTM to predict future sales figures. Credit Card Fraud Detection: Implemented fraud detection using algorithms like Logistic Regression and Autoencoders, dealing with imbalanced datasets.