Fahad Masood Reda's Projects
ABSTRACT: Student dropout is of the utmost concern in higher education and machine learning techniques have become a powerful tool for proactively identifying students at-risk of dropping out. Data from more than 17,000 National University students were used to train nine machine learning algorithms to predict first-year dropout under four conditions, thus resulting in thirty-six models. The algorithms included were Logistic Regression, Naïve Bayes, Neural Networks, k-Nearest Neighbor, Support Vector Machine with linear and polynomial kernels, Decision Tree, Random Forest, and XGBoost. Modeling conditions varied with regard to class balancing and feature reduction. Models were evaluated based on ROC area and accuracy. Ensemble tree-methods XGBoost and Random Forest were superior across all modeling conditions. Overall, class balancing and feature reduction did not improve model performance. Feature importance was examined and many novel features proved to be useful for dropout prediction. Recommendations for
Deep Learning examples with Keras.
This notebook applies the architecture from Andrew Ng's Deep Learning Specialization from Coursera on the Titanic Survival data set from kaggle.
:memo: An awesome Data Science repository to learn and apply for real world problems.
A curated list of awesome network analysis resources.
The revision note for Coursera Andrew Ng's ML
Example data file for exercises in QUT's Big Data: Statistical Inference and Machine Learning MOOC on FutureLearn
Project for a school which wants to predict who of his students is likely to drop out so that they can react early and provide him/her help.
Introduction to Machine Learning and Azure Machine Learning Services. Hands on labs to show Azure Machine Learning features, developing experiments, feature engineering, R and Python Scripting, Production stage, publishing models as web service, RRS and BES usage
Student’s dropout prediction for Brazilian Federal Institutes of Education, Science and Technology
This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube
This is the code for "Learn Machine Learning in 3 Months" by Siraj Raval on Youtube
Oxford Deep NLP 2017 course
Content for Udacity's Machine Learning curriculum
A complete daily plan for studying to become a machine learning engineer.
Source code for 'Mastering Machine Learning with Python in Six Steps' by Manohar Swamynathan
This Repo was made to present the MISK DSI Course Exercises
Machine Learning Foundations: Algebra, Calculus, Statistics & Computer Science
Student Dropout Early Intervention System w. Supervised Machine Learning
In this repository you will find files that are used in my courses
Repository to store sample python programs for python learning
based on "Hands-On Machine Learning with Scikit-Learn & TensorFlow" (O'Reilly, Aurelien Geron)