Ahmad Ibrahim's repositories
Cryptocurrency-Market-Analysis
In this project, we delve into the world of cryptocurrencies using data from CoinMarketCap. Our analysis focuses on understanding the market capitalization, cleaning the data for accuracy, visualizing the top cryptocurrencies, and exploring price volatility. This project provides insights into the dynamics of the cryptocurrency Market.
Digit-Recognition---Keras-vs.-Pytorch
This notebook demonstrates the process of building and comparing digit recognition models using two popular deep learning frameworks: Keras and PyTorch. The goal is to showcase the strengths and differences between these frameworks in the context of a classic machine learning problem: recognizing handwritten digits from the MNIST dataset.
Movie-Recommendation-System-Using-Kmeans-and-HC
In this project, we embark on a journey to create a sophisticated movie recommendation system by harnessing the power of two distinctive clustering methods: K-Means clustering and hierarchical clustering.
Music-Genres-classifier-LogReg-vs-Tree
Music Genres classifier: Comparing between Logistic regression vs Decision Tree with and without Cross Validation
Predicting-Temperature-in-London-MLflow-
This project is an exploration into leveraging machine learning models to predict temperature in London. Using a dataset that contains multiple weather parameters, the notebook walks us through the entire process of building a predictive model: from the initial exploratory analysis to the final model deployment using MLflow.
Sentiment-Driven-Stock-Forecasting
In this project, we will perform sentiment analysis on news headlines related to stock market data. We aim to extract and analyze sentiment from news articles and visualize the sentiment trends for specific stocks and dates. To achieve this, we will follow a series of data preprocessing, sentiment analysis, and data visualization steps.
lighthouse
Auditing, performance metrics, and best practices for Progressive Web Apps
Sign-Language-Recognition-from-A-to-Z-with-Deep-Learning
This notebook focuses on developing a Convolutional Neural Network (CNN) for the classification of sign language gestures associated with the English alphabet. Utilizing the Sign Language dataset, the goal is to train a model capable of accurately recognizing hand signs corresponding to each letter.
Titanic_Predictive_Model
Titanic Survival Predictive Model