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Meghdad-DTU

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Implementation of ML classifiers such as logistic regression, decision tree, random forest and gradient boosted tree with cross validation and parameter sweep

Probabilistic-Graphical-Models-Classification-Model

This project implemented binary logistic/probit regression, multinomial logistic regression and hierarchical logistic regression models on three different dataset using Pystan.

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Detecting-Heart-Arrhythmias-

This project aims at predicting if a heart beat from a ECG signal has an arrhythmia for each 0.4 second window centered on the peak of the heart beat. In this context, different classifiers including Random Forest, Logistic Regression, K Nearest Neighbors, Neural Networks and Decision Tree are used to detect abnormal beats. We use the MIH-BIH Arrythmia dataset from https://physionet.org/content/mitdb/1.0.0/ which is made available under the ODC Attribution License. This is a dataset with 48 half-hour two-channel ECG recordings measured at 360 Hz from the 1970s.

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Building-A-Scikit-Learn-Classification-Pipeline

A Scikit Learn classification Pipeline was developed for loan prediction.

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PySpark-and-taxi-demand-prediction

a simple example of taxi demand prediction using PySpark. Have a look at my "edX-course-Python-for-Data-Science-final-project" to understand data set and method!

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Discrete-Choice-Model

Advanced discrete choice modeling in Python

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PySpark-and-logistic-regression-for-loan-prediction-

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Meghdad-DTU/PySpark-and-predicting-taxi-demand-spike

Meghdad-DTU

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