IDB-FOR-DATASCIENCE/Predictive-Excellence-Engine IDB-FOR-DATASCIENCE/Predictive-Excellence-Engine

  • Stars
    star
    12
  • Rank 1,597,372 (Top 32 %)
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created about 4 years ago
  • Updated over 3 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
IDB-FOR-DATASCIENCE/Predictive-Excellence-Engine
github

IDB-FOR-DATASCIENCE

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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.

More Repositories

1

Unsupervised-ML-Modelling-for-Segmentation

Python Implementation of multiple unsupervised segmentation models and evaluating them through multiple evaluation metrics
Jupyter Notebook
8
star
2

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
Jupyter Notebook
5
star
3

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
Jupyter Notebook
3
star
4

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
2
star
5

Model-Monitoring

Let's talk Precision, Recall, F1 score, Reject Rate, PSI, CSI, KS and Gini along with their python implementation
Jupyter Notebook
2
star
6

Deep_Learning

Code base for trying various DL models
Jupyter Notebook
2
star