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nanoresearch's Projects

2014

Official content for the Fall 2014 Harvard CS109 Data Science course

awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-machine-learning-deep-learning-mathematics

A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning

awesome-materials-informatics

Curated list of current known efforts in materials informatics

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going).

awesome-quantum-deep-learning

A curated list of study materials , discussion forums and ideas about the huge emerging topic

awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

awesome-quantum-processing

Here you can get all the Quantum Projects and the descriptions of the projects around the web

axa-insurance-telematics-kaggle

I developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two versions of my code (test and full execution). Since AWS costs have exceeded my budget I sopped to train my model(s) all dataset for full dataset execution. There is also a ppt that presents my outputs in test execution. Full Data Execution code is more production ready and slightly different version. I had to use Databricks Table Caching to TRAIN and TEST data tables to obtain acceptable performance in production ready version.