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628module1-bodyfat

This is a group assignment of course STAT 628, University of Wisconsin-Madison. The main target is to predict a male's body fat percentage by some measurable variables.

analyse

Repository of the Learning Analytics tool ANALYSE for Open edX platform

analyze-the-performance-of-imd-s-students---unid3-ativ1

analyzing-graduate-students-data-with-pandas

The data set is downloaded by CDE (California Department of Education).

analyzing-student-performance

analyzing_students-_academic_performance

Educational Data Mining https://irfanalidv.github.io/Analyzing_Students-_Academic_Performance/

applying-data-science-to-data-analytics-online-courses

JBP040 - Introduction to Machine Learning

automated_essay_scorer

Deep learning & NLP based project, using word2vec model for creating word embeddings and using it to feed to LSTM neural network to predict the essay score awarded to a student

awesome-chinese-nlp

A curated list of resources for Chinese NLP 中文自然语言处理相关资料

awesome-cv-adversarial-attack-list

This repository is a curated list of papers and open source code about competition for CV Adversarial Attack.

bigdata-in-education-columbia-edx

Content (handouts and assignements) of the edX MOOC from Berkeley University.

breastcancerborm

Focused on designing classifier for breast cancer tumor if it is benign or Malignant using MCI Machine leaning dataset, established by university of Wisconsin. After applying expletory data analysis on the datasets, two methods – k nearest neighbors (KNN) and support vector machines (SVMs) - were applied to achieve the classification goals. Further investigation were done on the two methods to improve the classifier. The main concern was to improve the performance as much as possible and to reduce error type II (FN) which classify the patients as been healthy (Benign) while cancer classification is positive. We achieved 97% accuracy using SVMs and 96% accuracy using KNN.

cahl_remediation_rnn

The goal of this project is to create an algorithm that can predict and student learning pathway . When students are stuck on an exercise and a skillset, what content would most likely help the m unstuck. The exercise, video and hint consumption data will be trained using RNN models (GRU or LSTM), with validation on predicting unstuckness.

cal_ed_data

Examining the California Education Dataset

causal-inference-workshop

churn-prediction-oulad-python

This repository contains Python file and presentation for OULAD

classification-using-logistic-regression-and-xgboost

Students' Academic Performance Dataset

cogmodel

Modeling the performance of students in Cognitive (Problem Solving Tasks) in MOOC (EdX) using Machine Learning

collaborative-filtering-recommendation

Developed python code to recommend courses to students based on their previous performance

college-major-reccomendation-system

Provides the framework for data aggregation and model training of the startup I co-founded, allowing for a short personality test to determine suitable majors for undeclared college students and incoming freshman.

course-completion-rate-prediction

Will Enrolled Student Successfully Pass the Course? We are predicting Course Completion of Virtual Learning Environment by building Machine Learning Models in Apache Spark environment.

courseprediction

Prediciting number of enrollments for a course using regression

coursera-big-data-in-education-fall2013-ryanbaker

Coursera-big-data-in-education-Fall2013-RyanBaker

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

courseraforums

Anonymized versions of the discussion threads from the forums of 60 Coursera MOOCs

coursolve

Analytics for MOOC Learning via Coursolve + Oxford Internet Institute

data

Hack Education Datasets

data-analysis-project-using-python

Used various Python libraries such as numpy, pandas, seaborn, matplotlib and scikit-learn on Jupyter notebook to analyze various factors influencing student grades by performing Exploratory Data Analysis (EDA) and implemented machine learning algorithm , Logistic Regression on “alcohol consumption on student grades”

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

data-science-project-on-open-online-course-dataset

Predicting Online Course Grades with Neural Network