Predicting if a NBA rookie player will last at least 5 years in the league

mini machine learning projects

Random forest regression + ARIMA timeseries modeling to impute metric values and forecast revenue for reporting purposes.

This repository encompasses my research conducted at the CPS Lab, South Campus, University of Delhi, during my tenure as a research intern. The focus of our study involved identifying unique phenotypes of complications arising from myocardial infarction using k-means clustering. and this dataset is taken from UCI repository

Aplicación de gestión de proyectos, horas, usuarios y clientes para la empresa Hodeia Digital (Bilbao)

A simulation study looking at which combinations of missing data handling methods across a prediction model's pipeline are compatible, and which ones lead to bias.

Contributing to Open Source code

Analyzing the data of air quality using traditional data analytical methods with the help of R studio

From missing mechanism of data to data imputation

Dealing with Missing values using ML

Bankruptcy Prediction project, exploring sampling and imputation techniques, aimed at improving recall, for MTL782 (Data Mining) course offered in Spring 2021

A method for cold-deck imputation of a continuous distribution from binned incomes, using a real-world reference data set

Cleaning data using decision tree and k-nn techniques

In this notebook, i show a examples to implement imputation methods for handling missing values.

Predicting solar energy production

Predicting sales volume at various stores

Comparing effectiveness of data imputations techniques using simulation in R.

EDA or Extrapolatory Data Analysis is the process of reading the data , importing data so we can analyse data , extract important information from it and clean it if necessary then perform the predictive model building on it.

Fall 2020 - Computational Medicine - course project

Prediction of x_e_out [-] on Heat Flux Dataset where ~15% of 7 out of 8 features have been nulled (lost) required careful data preparation including imputation of missing data points.