added lesson_4.html

2-Regression/4-Logistic/solution/R/lesson_4.Rmd

@@ -14,7 +14,7 @@ output:
 
 ![Infographic by Dasani Madipalli](../../images/logistic-linear.png){width="600"}
 
-#### ** [Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**
+#### **[Pre-lecture quiz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**
 
 ####  Introduction
 
@@ -56,7 +56,7 @@ suppressWarnings(if (!require("pacman"))install.packages("pacman"))
 pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)
 ```
 
-## ** Define the question**
+## **Define the question**
 
 For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
 
@@ -148,7 +148,7 @@ pumpkins_select %>%
 
 The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.
 
-Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).
+Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3.html).
 
 Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
 

README.md

@@ -97,7 +97,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |         <ul><li>[Python](2-Regression/1-Tools/README.md)</li><li>[R](2-Regression/1-Tools/solution/R/lesson_1.html)</li></ul>         |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
 |      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |          <ul><li>[Python](2-Regression/2-Data/README.md)</li><li>[R](2-Regression/2-Data/solution/R/lesson_2.html)</li></ul>          |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
 |      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |        <ul><li>[Python](2-Regression/3-Linear/README.md)</li><li>[R](2-Regression/3-Linear/solution/R/lesson_3.html)</li></ul>        |      <ul><li>Jen and Dmitry</li><li>Eric Wanjau</li></ul>       |
-|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb)</li></ul>      |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
+|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |     <ul><li>[Python](2-Regression/4-Logistic/README.md) </li><li>[R](2-Regression/4-Logistic/solution/R/lesson_4.html)</li></ul>      |      <ul><li>Jen</li><li>Eric Wanjau</li></ul>       |
 |      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |                                                 [Python](3-Web-App/1-Web-App/README.md)                                                  |                         Jen                          |
 |      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            | <ul><li> [Python](4-Classification/1-Introduction/README.md) </li><li>[R](4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb)  | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |
 |      11       |             Delicious Asian and Indian cuisines 🍜             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | <ul><li> [Python](4-Classification/2-Classifiers-1/README.md)</li><li>[R](4-Classification/2-Classifiers-1/solution/R/lesson_11.html) | <ul><li>Jen and Cassie</li><li>Eric Wanjau</li></ul> |