Pinned Repositories
crab
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (python, numpy, scipy, matplotlib)
Dictionaries
Hunspell UTF8 dictionaries. These work with Sublime Text. [Spell check]
IBM-HR-Analytics-Employee-Attrition-Performance
The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.
implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
logistic-mf
Logistic Matrix Factorization for Implicit Feedback Data. http://stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf
predictive-analytics-hr-analytics
Why are our best and most experienced employees leaving prematurely?
zero_to_deep_learning_video
Repository for the Zero to Deep Learning® Video Course
FCHal's Repositories
FCHal/crab
Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (python, numpy, scipy, matplotlib)
FCHal/Dictionaries
Hunspell UTF8 dictionaries. These work with Sublime Text. [Spell check]
FCHal/IBM-HR-Analytics-Employee-Attrition-Performance
The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.
FCHal/implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
FCHal/logistic-mf
Logistic Matrix Factorization for Implicit Feedback Data. http://stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf
FCHal/predictive-analytics-hr-analytics
Why are our best and most experienced employees leaving prematurely?
FCHal/zero_to_deep_learning_video
Repository for the Zero to Deep Learning® Video Course