anilkumar-krishna Goto Github PK

anilkumar-krishna

Config files for my GitHub profile.

bank-marketing-campaign_eda

boombikes-sharing_multiple-linear-regression-model

Building a multiple linear regression model to predict the demand for shared bikes and determine how well the variables describe the demand

cars

covariants

Real-time updates and information about key SARS-CoV-2 variants, plus the scripts that generate this information.

covid-policy-tracker

Systematic dataset of Covid-19 policy, from Oxford University

covidpolicyliterature

credi-case-study_eda

dress-data-set

house-price-prediction-in-australian-market

House price prediction in Australian market - Ridge and Lasso regression model

iris

lead-score-logistic-regression-model

Building a logistic regression model to predict whether lead will be converted or not and assigning lead score to each of the leads

mhealth

Research articles on mHealth for the period 2017-2021

nfhs-4-analysis

This project explores factors affecting child stunting and influence of contraceptive awareness in women on other health indicators using NFHS-4 (National Family Health Survey - 4, India) data.

nfhs-5

NFHS-5: National Family Health Survey (2019-20). CSV fact sheets (states, districts) for key indicators from http://rchiips.org/nfhs/ | https://doi.org/10.7910/DVN/42WNZF

nlp-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

nlp_public_policy

NLP: An Application for Public Policy, PyCon Ireland 2018

object-detection

pybliometrics

Python-based API-Wrapper to access Scopus

python-public-policy

Python for Public Policy course

semantic_processing

smart-literature-review

This is the repository for the files and documents used in the Smart Literature Review paper from (Boye, Møller, 2019)

syntactic-processing

telecom-churn-case-study---advanced-machine-learning

In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, we will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.