Understanding and Applying different algorithms and techniques to the iris dataset.

R programs using different techniques

Using the K-Nearest Neighbor (KNN) model to interpolate non-overlapping datasets in time and space

Linear, Polynomial and Logistic Regression

classification / clustering / regression analysis / data mining

Welcome to my repository!

The framework for finding multiresolution partitions that have homogeneous linear models from multiresolution dataset.

This is the repository of Machine Learning Internship I have done with CareerLauncher endorsed by AICTE and Intel Partnership

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression).

Practice Sets on ML concepts

Combined cycle power plant energy prediction using Random Forest Regressor.

First project at neuefische DS Bootcamp it was about the EDA and a multi regression real estate pricing on a dataset of King County (Washington, USA).

Final academic team project for "Data Science: R", a curricular course attended throughout the fall semester of my second master's degree in Business Analytics at Hult International Business School.

This notebook contains the EDA, data cleaning, modeling and prediction parts of the "House Price- Advance Regression Techniques" problem from Kaggle.

An R Companion for the Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, and Li (2005).

This is a POC on regression analysis from the simplest to advanced regression model. Notable things that is done is Data Preprocessing, Data Visualization, Feature Engineering and Statistical Modelling.

多元迴歸分析，用財報指標解讀當年股價的增長

An attempt to use regression to analyze the relationship between annual salary and some advanced metrics of NBA players.