Pinned Repositories
Churn-Prediction
This project is the Incident driven contract conversion modelling based on Logit probability.There were two datasets one with conversion information and other past incidence information of a company that provides professional services. The proposed model helps business leaders to take informed decisions about chances of contract renewals based on past service incidents, escalations, number of visits missed,etc.
CookSmart
Android application that suggest recipes based on user'sinventory- Google Hackathon project(NSBE 2018)
Forecasting-Retail-Food-Services-Time-Series-Data-
Time series data(monthly) pertaining to the US Retail and Food Services was analyzed and SARIMA techniques were used for forecasting.
Happiness-Score-of-Countries
Happiness score of various countries were used to visualize the state of happiness of the world in a world map and future prediction of happiness score of countries were also performed based on the existing dataset.
intro_to_sprinting_codeless_project
A sample "code-less" project in support of the "Intro to Sprinting" workshop.
kaggle-hr
Shiny app for https://www.kaggle.com/ludobenistant/hr-analytics dataset
Regression-Modelling-in-R
Rebate offers, Advertising spending and seasonality were used to model the sales prediction of a product .Diminishing returns , effects of interaction among variables were also studied . 'nlminb' routine was used to optimize the parameters and hypothesis testing was done to gain insights.
RossMann-Store-Sales
SocialMediaAnalytics
Sentimental analysis ,word cloud and topic modeling on social media data .Live tweets were collected and the polarity,subjectivity on the tweets were measured .Sci-kit learn's NMF and Gensim modules were used to conduct topic modelling.
Titanic-Disaster-Machine-Learning-Apache-Spark
This is my pilot project on Apache Spark's ML library on the well known Titanic disaster dataset from Kaggle
muthu-tech's Repositories
muthu-tech/CookSmart
Android application that suggest recipes based on user'sinventory- Google Hackathon project(NSBE 2018)
muthu-tech/Churn-Prediction
This project is the Incident driven contract conversion modelling based on Logit probability.There were two datasets one with conversion information and other past incidence information of a company that provides professional services. The proposed model helps business leaders to take informed decisions about chances of contract renewals based on past service incidents, escalations, number of visits missed,etc.
muthu-tech/Forecasting-Retail-Food-Services-Time-Series-Data-
Time series data(monthly) pertaining to the US Retail and Food Services was analyzed and SARIMA techniques were used for forecasting.
muthu-tech/Happiness-Score-of-Countries
Happiness score of various countries were used to visualize the state of happiness of the world in a world map and future prediction of happiness score of countries were also performed based on the existing dataset.
muthu-tech/intro_to_sprinting_codeless_project
A sample "code-less" project in support of the "Intro to Sprinting" workshop.
muthu-tech/kaggle-hr
Shiny app for https://www.kaggle.com/ludobenistant/hr-analytics dataset
muthu-tech/Regression-Modelling-in-R
Rebate offers, Advertising spending and seasonality were used to model the sales prediction of a product .Diminishing returns , effects of interaction among variables were also studied . 'nlminb' routine was used to optimize the parameters and hypothesis testing was done to gain insights.
muthu-tech/RossMann-Store-Sales
muthu-tech/SocialMediaAnalytics
Sentimental analysis ,word cloud and topic modeling on social media data .Live tweets were collected and the polarity,subjectivity on the tweets were measured .Sci-kit learn's NMF and Gensim modules were used to conduct topic modelling.
muthu-tech/Titanic-Disaster-Machine-Learning-Apache-Spark
This is my pilot project on Apache Spark's ML library on the well known Titanic disaster dataset from Kaggle
muthu-tech/yellowbrick
Visual analysis and diagnostic tools to facilitate machine learning model selection.