IDB-FOR-DATASCIENCE/Data-Scientist-for-Enterprise-Nanodegree-Program

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IDB-FOR-DATASCIENCE/Data-Scientist-for-Enterprise-Nanodegree-Program

IDB-FOR-DATASCIENCE

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

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.

Jupyter Notebook

Unsupervised-ML-Modelling-for-Segmentation

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

Jupyter Notebook

ML-based-feature-selection

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

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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.

Jupyter Notebook

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

Jupyter Notebook