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airline_ontime_performance_project

Different feature selection methods like SVD, SelectKBest, RFE are used to choose best features, then different machine learning algorithms like Random Forest, Gradient Boosting Tree, XGBoost together with GridsearchCV etc are applied and compared to choose the good model which is the best fit for the dataset.

alternative-assessment-2-s2037987-hii-yew-han-

analytics-using-clickstream-data

Analytics using clickstream data and visualise it using tableau

analyzing-open-university-learning-analytics-dataset

Developed to identify at-risk students on a huge Open University Learning Analytics Dataset (OULAD) using different user patterns and filtering techniques using Python. Used Association rule to determine relationships in unrelated data. K-Means Clustering is employed to determine groups of users using Weka V-3.8.2 and bring insights on the data.

awesome-public-datasets

A topic-centric list of HQ open datasets.

ba-data-analytics-oulad

Learning the impact of Learning Analytics with an authentic dataset

churn-prediction-oulad-python

This repository contains Python file and presentation for OULAD

clickstream-mining

Mining clickstream data to predict if a visitor will view another page or leave the website.

clickstream_data-events_analysis_predictions

E-Commerce | Binary Classification | User Behavior Analysis| Logistic Regression | Random Forest | Light GBM|Neural Net|KNN |Python (scikitlearn, seaborn, pandas, numpy, matplotlib)

clickstreamanalysis

Analyzing Clickstream Data using Markov Chains and data mining SPACE algorithm

data-mining-oulad

data-mining-project-for-oulad-at-risk-prediction

data-visualization-in-python-talk

deepcode

Deep learning using Recurrent Neural Networks on student code submissions; focusing on LSTMs to predict student success

deeplearning-models

A collection of various deep learning architectures, models, and tips

demographics_oulad

diabetes-prediction-

diabetes-prediction-using-kmeans-

In the beginning, the algorithm chooses k centroids in the dataset randomly after shuffling the data. Then it calculates the distance of each point to each centroid using the euclidean distance calculation method.

dockers

frieds.github.io

Tutorials on Python programming, data analysis, data visualizations and tech career advice

hiiyewhan-joshua.io

ibm-applied-data-science-capstone-project-

ibm-data-science-capstone-spacex

In this project, we predicted if the Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders.

ibm-machine-learning-professional-certificate

Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

keras-io

Keras documentation, hosted live at keras.io

learning-analytics

Evaluating the performance of hand crafted features that aim to capture higher level leaner properties for dropout prediction for a final year Learning Analytics module

lstm-3d-input-template

lstm_stock_prediction

This is an LSTM stock prediction using Tensorflow with Keras on top.

machine-learning-project

2nd Year: 1st - 92. A brief project and report on using the OULAD data set to predict and return a CSV of students final grades, from a variety of features, using a Random Forest or an SVC.

new-york-stock-exchange-predictions-rnn-lstm

BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!