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IDB-FOR-DATASCIENCE

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This notebook is a one stop shop for ML based feature selection for classification purpose. Once the master data is prepared, we can use this notebook to try out 8 feature selection techniques and combine their results

Predictive-Excellence-Engine

In this notebook, we will create an AI and time serie driven forecasting engine based on a set of 5 AI models and 5 time series models and employ several algorithms to perform feature engineering and selection on a multivariate time series dataset.

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Unsupervised-ML-Modelling-for-Segmentation

Python Implementation of multiple unsupervised segmentation models and evaluating them through multiple evaluation metrics

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Data-Scientist-for-Enterprise-Nanodegree-Program

This repository has the solutions to multiple assignments done during the course on topics like supervised leering, deep learning

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Predicting-fit-of-a-candidate-for-a-job-using-tree-based-models

We use multiple Tree boosting models and compare their performance to calculate the fit percentage of a candidate for the job they apply for. Also try to handle categorical methods using various methods.

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Model-Monitoring

Let's talk Precision, Recall, F1 score, Reject Rate, PSI, CSI, KS and Gini along with their python implementation

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Deep_Learning

Code base for trying various DL models

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IDB-FOR-DATASCIENCE/ML-based-feature-selection

IDB-FOR-DATASCIENCE

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