Канал: Waldyn Martinez

Intro to Colab

Intro to Colab

Regression House Market Example Part 9

Regression House Market Example Part 9

Regression House Market Example Part 8

Regression House Market Example Part 8

Regression House Market Example Part 7

Regression House Market Example Part 7

Regression House Market Example Part 4

Regression House Market Example Part 4

Regression House Market Example Part 5

Regression House Market Example Part 5

Regression House Market Example Part 6

Regression House Market Example Part 6

Regression House Market Example Part 2

Regression House Market Example Part 2

Regression House Market Example Part 3

Regression House Market Example Part 3

Regression House Market Example Part 1

Regression House Market Example Part 1

The Least Squares Method R Code

The Least Squares Method R Code

Least Squares Method in Regression

Least Squares Method in Regression

Intro to Multiple Linear Regression R Code

Intro to Multiple Linear Regression R Code

Introduction to Multiple Linear Regression

Introduction to Multiple Linear Regression

M2 - Modeling a Response in Linear Regression

M2 - Modeling a Response in Linear Regression

Setting Working Directories in R

Setting Working Directories in R

R Programming Basics

R Programming Basics

Canvas Notes Colab

Canvas Notes Colab

NumPy Intro

NumPy Intro

Data Structures: Lists, Tuples and Dictionaries

Data Structures: Lists, Tuples and Dictionaries

Modules

Modules

Functions

Functions

Functions

Functions

Loops

Loops

Conditionals

Conditionals

Class Types

Class Types

Identifiers in Python

Identifiers in Python

Accessing R with Google Colab

Accessing R with Google Colab

MLR (Adding Quadratic Terms Example in R)

MLR (Adding Quadratic Terms Example in R)

MLR (Explaining the Effect of Variable with a Quadratic Term)

MLR (Explaining the Effect of Variable with a Quadratic Term)

MLR (Introduction to Polynomial Regression)

MLR (Introduction to Polynomial Regression)

MLR (Interactions between Qualitative Variables)

MLR (Interactions between Qualitative Variables)

MLR (Interactions between Quantitative and Qualitative Variables)

MLR (Interactions between Quantitative and Qualitative Variables)

MLR (Interaction Effect Between Two Quantitative Variables)

MLR (Interaction Effect Between Two Quantitative Variables)

MLR (Introduction to Interaction Effects)

MLR (Introduction to Interaction Effects)

Including Qualitative Predictors (Example 1 part 1)

Including Qualitative Predictors (Example 1 part 1)

Including Qualitative Variables (Example 1 part 3)

Including Qualitative Variables (Example 1 part 3)

Including Qualitative Predictors (Example 1 part 2)

Including Qualitative Predictors (Example 1 part 2)

Categorical Variable Encoding

Categorical Variable Encoding

Model Degrees of Freedom (Multiple Linear Regression)

Model Degrees of Freedom (Multiple Linear Regression)

Changing Variable Types in R (Multiple Linear Regression)

Changing Variable Types in R (Multiple Linear Regression)

Dummy Variable Encoding (Mutiple Linear Regression)

Dummy Variable Encoding (Mutiple Linear Regression)

Qualitative Variables (Multiple Linear Regression)

Qualitative Variables (Multiple Linear Regression)

Residual Analysis

Residual Analysis

Confidence and Prediction Intervals

Confidence and Prediction Intervals

The F Hypothesis Test

The F Hypothesis Test

ANOVA R Output

ANOVA R Output

Explained and Unexplained Variation

Explained and Unexplained Variation

Multicollinearity

Multicollinearity

Regression in High Dimensions

Regression in High Dimensions

Multiple Linear Regression Example 1

Multiple Linear Regression Example 1

Multicollinearity Part 2 R Code

Multicollinearity Part 2 R Code

Multicollinearity

Multicollinearity

Matrix Representation of Linear Reg. (in R)

Matrix Representation of Linear Reg. (in R)

Matrix Formulation of Linear Regression

Matrix Formulation of Linear Regression

Introduction to Matrices in R

Introduction to Matrices in R

Get Percentiles in R

Get Percentiles in R

2019 08 30 15 26 49

2019 08 30 15 26 49

Understanding the pnorm function in R

Understanding the pnorm function in R

Introduction to the Normal Distribution

Introduction to the Normal Distribution

Graphics in R

Graphics in R

Bagging Ensemble p2

Bagging Ensemble p2

Bagging Ensemble

Bagging Ensemble

V1 Review of Tree Models

V1 Review of Tree Models

Paralyzed Veterans - Confusion Matrix and Cutoff

Paralyzed Veterans - Confusion Matrix and Cutoff

Paralyzed Veterans - Choosing Champion Model

Paralyzed Veterans - Choosing Champion Model

Paralyzed Veterans - Model 3 Stepwise model with Interactions

Paralyzed Veterans - Model 3 Stepwise model with Interactions

Paralyzed Veterans - Model 2 Stepwise Variable Selection

Paralyzed Veterans - Model 2 Stepwise Variable Selection

Paralized Veterans - Model 1 Full Model

Paralized Veterans - Model 1 Full Model

Paralyzed Veterans - Data Partitioning

Paralyzed Veterans - Data Partitioning

Paralyzed Veterans - Preprocessing (dropping variables)

Paralyzed Veterans - Preprocessing (dropping variables)

Paralyzed Veterans - Creating missing value indicators cont'd

Paralyzed Veterans - Creating missing value indicators cont'd

Paralyzed Veterans - Creating missing value indicators

Paralyzed Veterans - Creating missing value indicators

Paralyzed Veterans - Introduction to the data

Paralyzed Veterans - Introduction to the data

Gain and Lift Chart

Gain and Lift Chart

lift chart understanding lift

lift chart understanding lift

lift chart releveling the response variable

lift chart releveling the response variable

lift chart sorting the propensities

lift chart sorting the propensities

creating dataset with propensities

creating dataset with propensities

logistic regression predicting validation data set

logistic regression predicting validation data set

how probabilities are calculated using logistic regression

how probabilities are calculated using logistic regression

logistic regression introduction

logistic regression introduction

splitting the data

splitting the data

some preprocessing

some preprocessing