- Introduction to Data Science with R - Exploratory Modeling 1

Introduction to Data Science with R - Exploratory Modeling 1

Part 4 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from vi...
Part 4 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub.


☕ If you found this content useful and would like to support the channel, you can buy me a coffee: https://www.buymeacoffee.com/DaveOnData


★★★ My online data analysis courses ▶ https://school.daveondata.com


-----------------------------------------------------------------------------------------------------------------------
🤖 Looking for robust training in machine learning? 🤖
-----------------------------------------------------------------------------------------------------------------------
⚠ This video series is quite old now and incomplete. ⚠

👉 My new online course covers using tidymodels in R to do machine learning and covers everything you need to know (e.g., algorithms, math, bias-variance tradeoff, tuning, etc.).

Check out the following YouTube video to learn more:
📺 https://youtu.be/b6hiyLEFA_M



// THINGS HAVE CHANGED SINCE ORIGINAL VIDEO

⚠ - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.

💻 GitHub: https://github.com/EasyD/IntroToDataScience

#DataScience #Rprogramming #RprogrammingForDataScience

#Data Science #Data Analysis #Feature Engineering #Visualization #Data Wrangling #Data Exploration #R Programming #R Programming Tutorial #R Programming Training #Data Science with R #Data Scientist #Machine Learning with R #Programming #Tutorial #Training #Data Science Training #Data Science Tutorial #Machine Learning #Data Visualization #Data Science with R Programming
-	Introduction - Introduction to Data Science with R - Exploratory Modeling 1

- Introduction

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:00:00 - 00:01:38
-	Intuition Explantory modeling - Introduction to Data Science with R - Exploratory Modeling 1

- Intuition Explantory modeling

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:01:38 - 00:09:21
-	Intuition Random Forest algorithm - Introduction to Data Science with R - Exploratory Modeling 1

- Intuition Random Forest algorithm

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:09:21 - 00:16:34
<- as.factor(data.combined[, "title"])rf.label <- as.factor(train$survived) - Introduction to Data Science with R - Exploratory Modeling 1

<- as.factor(data.combined[, "title"])rf.label <- as.factor(train$survived)

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
<- data.Combined[,c("Pclass","title")]> rf.label<- as.factor(train$Survived)>> set.seed(123)> rf.1 <- randomForest(x = rf.train.1, y = rf.label, importance = TRUE, ntree = 500)Error in randomForest.default(x = rf.train.1, y = rf.label, importance = TRUE,  :NA/NaN/Inf in foreign function call (arg 1)In addition: Warning message:In data.matrix(x) : NAs introduced by coercion> rf.1Error: object 'rf.1' not found> varImpPlot(rf.1)Error in varImpPlot(rf.1) : object 'rf.1' not found - Introduction to Data Science with R - Exploratory Modeling 1

<- data.Combined[,c("Pclass","title")]> rf.label<- as.factor(train$Survived)>> set.seed(123)> rf.1 <- randomForest(x = rf.train.1, y = rf.label, importance = TRUE, ntree = 500)Error in randomForest.default(x = rf.train.1, y = rf.label, importance = TRUE, :NA/NaN/Inf in foreign function call (arg 1)In addition: Warning message:In data.matrix(x) : NAs introduced by coercion> rf.1Error: object 'rf.1' not found> varImpPlot(rf.1)Error in varImpPlot(rf.1) : object 'rf.1' not found

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
<- data.combined[, c("pclass", "title")] write c("title", "pclass"). I've already seen "flipping effect" in histograms (mine histograms: upper part 0, lower part 1; Daves: flipped) so maybe there is something in it!I'm trying different permutations to see which results will it yield, and I encourage you to do so as well! - Introduction to Data Science with R - Exploratory Modeling 1

<- data.combined[, c("pclass", "title")] write c("title", "pclass"). I've already seen "flipping effect" in histograms (mine histograms: upper part 0, lower part 1; Daves: flipped) so maybe there is something in it!I'm trying different permutations to see which results will it yield, and I encourage you to do so as well!

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
<- data.frame(title = as.factor(data.combined[, "title"])) - Introduction to Data Science with R - Exploratory Modeling 1

<- data.frame(title = as.factor(data.combined[, "title"]))

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
I wonder why you always use 'data.combined' (which includes the train and test data) when building the random forest models? Then, you always have to use [] to extract the train data again. I understood why you combined both for analysis in video 1 through 3 (to analyse both, the train and test data together), but wouldn't it be better / simpler / best practices to just use the separated train data set here in these code examples? - Introduction to Data Science with R - Exploratory Modeling 1

I wonder why you always use 'data.combined' (which includes the train and test data) when building the random forest models? Then, you always have to use [] to extract the train data again. I understood why you combined both for analysis in video 1 through 3 (to analyse both, the train and test data together), but wouldn't it be better / simpler / best practices to just use the separated train data set here in these code examples?

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
rf.train <- data.combined[, c("Title", "Pclass)]The difference I saw was about 0.5% over model rf.5. Is this actually a better predictive model or is it simply an artifact of how RandomForest calculates error rate? - Introduction to Data Science with R - Exploratory Modeling 1

rf.train <- data.combined[, c("Title", "Pclass)]The difference I saw was about 0.5% over model rf.5. Is this actually a better predictive model or is it simply an artifact of how RandomForest calculates error rate?

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
summary(data.combined[,"Age"]) - Introduction to Data Science with R - Exploratory Modeling 1

summary(data.combined[,"Age"])

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:15:51 - 00:53:00
-	Random Forest - Introduction to Data Science with R - Exploratory Modeling 1

- Random Forest

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:16:34 - 00:47:05
-	Comparison with Kaggle teams - Introduction to Data Science with R - Exploratory Modeling 1

- Comparison with Kaggle teams

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:47:05 - 00:52:09
1 submission with 100% accuracy, Little fishy - Introduction to Data Science with R - Exploratory Modeling 1

1 submission with 100% accuracy, Little fishy

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:49:06 - 00:53:00
P.D.  Your laughter at  is hilarious haha - Introduction to Data Science with R - Exploratory Modeling 1

P.D. Your laughter at is hilarious haha

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:49:09 - 00:53:00
-	Data Analysis matters :) - Introduction to Data Science with R - Exploratory Modeling 1

- Data Analysis matters :)

Introduction to Data Science with R - Exploratory Modeling 1
2016年01月25日 
00:52:09 - 00:53:00

David Langer

※本サイトに掲載されているチャンネル情報や動画情報はYouTube公式のAPIを使って取得・表示しています。

Timetable

動画タイムテーブル

動画数:138件

Intro - Does Python in Excel Replace Excel Charts?

Intro

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:00:00 - 00:01:05
Faceting Data Visualizations - Does Python in Excel Replace Excel Charts?

Faceting Data Visualizations

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:01:05 - 00:01:47
Faceted Histograms - Does Python in Excel Replace Excel Charts?

Faceted Histograms

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:01:47 - 00:04:31
Faceted Bar Charts - Does Python in Excel Replace Excel Charts?

Faceted Bar Charts

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:04:31 - 00:05:56
Faceted Scatter Plots - Does Python in Excel Replace Excel Charts?

Faceted Scatter Plots

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:05:56 - 00:07:27
Faceted Strip Plots - Does Python in Excel Replace Excel Charts?

Faceted Strip Plots

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:07:27 - 00:08:19
Violin Plots - Does Python in Excel Replace Excel Charts?

Violin Plots

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:08:19 - 00:09:13
Faceted Violin Plots - Does Python in Excel Replace Excel Charts?

Faceted Violin Plots

Does Python in Excel Replace Excel Charts?
2024年03月28日 
00:09:13 - 00:10:56
Into - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Into

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:00:00 - 00:00:58
Types of Machine Learning - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Types of Machine Learning

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:00:58 - 00:03:24
Decision Trees - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Decision Trees

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:03:24 - 00:05:08
Random Forests - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Random Forests

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:05:08 - 00:07:53
K-Means Clustering - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

K-Means Clustering

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:07:53 - 00:11:11
Logistic Regression - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Logistic Regression

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:11:11 - 00:13:15
Linear Regression - Python in Excel Makes Machine Learning a MUST-HAVE in 2024!

Linear Regression

Python in Excel Makes Machine Learning a MUST-HAVE in 2024!
2024年03月20日 
00:13:15 - 00:16:17
Intro - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

Intro

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:00:00 - 00:01:03
Power Query Introduction - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

Power Query Introduction

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:01:03 - 00:06:09
SQL Introduction - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

SQL Introduction

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:06:09 - 00:11:00
Power Query Pros & Cons - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

Power Query Pros & Cons

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:11:00 - 00:14:22
SQL Pros & Cons - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

SQL Pros & Cons

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:14:22 - 00:18:15
Which You Should Use - SQL vs. Power Query – What You Should Use for Python in Excel in 2024!

Which You Should Use

SQL vs. Power Query – What You Should Use for Python in Excel in 2024!
2024年03月14日 
00:18:15 - 00:20:42
Intro - Python in Excel vs. VBA - What You Should Learn in 2024!

Intro

Python in Excel vs. VBA - What You Should Learn in 2024!
2024年03月06日 
00:00:00 - 00:00:37
VBA Overview - Python in Excel vs. VBA - What You Should Learn in 2024!

VBA Overview

Python in Excel vs. VBA - What You Should Learn in 2024!
2024年03月06日 
00:00:37 - 00:04:16
Python in Excel Overview - Python in Excel vs. VBA - What You Should Learn in 2024!

Python in Excel Overview

Python in Excel vs. VBA - What You Should Learn in 2024!
2024年03月06日 
00:04:16 - 00:07:33
The Question is Answered - Python in Excel vs. VBA - What You Should Learn in 2024!

The Question is Answered

Python in Excel vs. VBA - What You Should Learn in 2024!
2024年03月06日 
00:07:33 - 00:10:05
Intro - Python in Excel Makes Power Query a MUST-HAVE in 2024!

Intro

Python in Excel Makes Power Query a MUST-HAVE in 2024!
2024年02月28日 
00:00:00 - 00:01:22
Sizing the Data - Python in Excel Makes Power Query a MUST-HAVE in 2024!

Sizing the Data

Python in Excel Makes Power Query a MUST-HAVE in 2024!
2024年02月28日 
00:01:22 - 00:05:59
Loading All the Data - Python in Excel Makes Power Query a MUST-HAVE in 2024!

Loading All the Data

Python in Excel Makes Power Query a MUST-HAVE in 2024!
2024年02月28日 
00:05:59 - 00:08:54
Visualize the Data with a Count Plot - Python in Excel Makes Power Query a MUST-HAVE in 2024!

Visualize the Data with a Count Plot

Python in Excel Makes Power Query a MUST-HAVE in 2024!
2024年02月28日 
00:08:54 - 00:10:20
Visualize the Data with Histograms - Python in Excel Makes Power Query a MUST-HAVE in 2024!

Visualize the Data with Histograms

Python in Excel Makes Power Query a MUST-HAVE in 2024!
2024年02月28日 
00:10:20 - 00:13:19
Intro - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

Intro

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:00:00 - 00:01:03
The Data - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

The Data

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:01:03 - 00:02:23
Logistic Regression Using Solver - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

Logistic Regression Using Solver

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:02:23 - 00:04:38
Loading the Data into Python - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

Loading the Data into Python

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:04:38 - 00:06:31
Wrangling the Data - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

Wrangling the Data

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:06:31 - 00:08:21
The Logistic Regression Model - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

The Logistic Regression Model

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:08:21 - 00:09:56
The Model Summary - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

The Model Summary

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:09:56 - 00:11:49
Interpreting the Model - Solver vs. Python in Excel - Which is Best for Logisitc Regression?

Interpreting the Model

Solver vs. Python in Excel - Which is Best for Logisitc Regression?
2024年02月21日 
00:11:49 - 00:14:41
Intro - Python Crash Course: Part 13 -  Using Lambdas

Intro

Python Crash Course: Part 13 - Using Lambdas
2024年02月20日 
00:00:00 - 00:00:30
Your First Lambda - Python Crash Course: Part 13 -  Using Lambdas

Your First Lambda

Python Crash Course: Part 13 - Using Lambdas
2024年02月20日 
00:00:30 - 00:02:45
Lambdas Are Objects - Python Crash Course: Part 13 -  Using Lambdas

Lambdas Are Objects

Python Crash Course: Part 13 - Using Lambdas
2024年02月20日 
00:02:45 - 00:05:07
Passing Lambdas to Functions - Python Crash Course: Part 13 -  Using Lambdas

Passing Lambdas to Functions

Python Crash Course: Part 13 - Using Lambdas
2024年02月20日 
00:05:07 - 00:07:39