JOOD ALZAB

MARKET TREND DATA ANALYSIS

Performed a comprehensive analysis of market data to identify key trends, factors influencing the market, areas of growth, and potential opportunities. Developed visualizations to support decision-making. Toolkit: Python, Pandas, Matplotlib, Seaborn, Tableau, Google Colab

FITNESS SYSTEM PREDICTION USING ML

Utilized a dataset to build a classification model predicting the demand for fitness systems. Employed supervised learning techniques including logistic regression and random forest.

Toolkit: Python, Scikit-learn, Pandas, NumPy, Jupyter Notebook, Google Colab

ARABIC TEXT CLASSIFICATION WITH BERT

Cleaned Arabic text data and used BERT embeddings for word representation. Employed Naive Bayes classifier to categorize news titles into different topics.

Toolkit: Python, NLTK, BERT, PyTorch, Scikit-learn, Google Colab

CARD FRAUD DETECTION SYSTEM

Developed and trained an intelligent model for detecting credit card fraud using machine learning techniques such as decision trees and logistic regression.

Toolkit: Python, Scikit-learn, Pandas, NumPy, TensorFlow, Google Colab

SALES DATA ANALYSIS AND RECOMMENDATION Analyzed sales data to identify trends and patterns, providing actionable recommendations for future orders to optimize inventory and sales strategies. Toolkit: R, dplyr, ggplot2, RStudio