diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 36870566..bb2b1127 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -89,7 +89,7 @@ In the `notebook.ipynb` file associated to this lesson, clear out all the cells
In this section, you will work with a small dataset about diabetes that is built into Scikit-Learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic Regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
-> ✅ There are many types of Regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use Linear Regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of recipe should be considered vegan or not, you're looking for a **category assignment** so you would use Logistic Regression. You'll learn more about Logistic Regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+> ✅ There are many types of Regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use Linear Regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use Logistic Regression. You'll learn more about Logistic Regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
Let's get started on this task.
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 169da288..ebfcd5db 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -10,7 +10,7 @@ Classification is a form of [supervised learning](https://wikipedia.org/wiki/Sup
Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
-Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
+Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
### Introduction
@@ -21,11 +21,11 @@ Before starting the process of cleaning our data, visualizing it, and prepping i
Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as 'smoker','weight', and 'age' to determine 'likelihood of developing X disease'. As a supervised learning technique similar to the Regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome.
-✅ Take a moment to imagine a dataset about recipes. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to contain Fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
+✅ Take a moment to imagine a dataset about cuisines. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to use fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
## Hello 'classifier'
-The question we want to ask of this recipe dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?
+The question we want to ask of this cuisine dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?
Scikit-Learn offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two lessons, you'll learn about several of these algorithms.
@@ -51,7 +51,7 @@ from imblearn.over_sampling import SMOTE
The next task will be to import the data:
```python
-df = pd.read_csv('../data/recipes.csv')
+df = pd.read_csv('../data/cuisines.csv')
```
Check the data's shape:
diff --git a/4-Classification/1-Introduction/notebook.ipynb b/4-Classification/1-Introduction/notebook.ipynb
index a98ae796..95cb84cd 100644
--- a/4-Classification/1-Introduction/notebook.ipynb
+++ b/4-Classification/1-Introduction/notebook.ipynb
@@ -19,7 +19,7 @@
"cells": [
{
"source": [
- "# Delicious Asian and Indian Recipes "
+ "# Delicious Asian and Indian Cuisines "
],
"cell_type": "markdown",
"metadata": {}
diff --git a/4-Classification/1-Introduction/solution/notebook.ipynb b/4-Classification/1-Introduction/solution/notebook.ipynb
index 2c6f0eb5..20737c96 100644
--- a/4-Classification/1-Introduction/solution/notebook.ipynb
+++ b/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"source": [
- "# Delicious Asian and Indian Recipes \n"
+ "# Delicious Asian and Indian Cuisines \n"
],
"cell_type": "markdown",
"metadata": {}
@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -25,10 +25,10 @@
"text": [
"Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
"Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
- "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
- "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
"Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+ "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
"Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+ "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
"\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
"You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -55,23 +55,23 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "df = pd.read_csv('../../data/recipes.csv')"
+ "df = pd.read_csv('../../data/cuisines.csv')"
]
},
{
"source": [
- "This dataset includes 385 columns indicating all kinds of ingredients in various recipes from a given set of cuisines."
+ "This dataset includes 385 columns indicating all kinds of ingredients in various cuisines from a given set of cuisines."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -104,7 +104,7 @@
"text/html": "
"
},
"metadata": {},
- "execution_count": 20
+ "execution_count": 19
}
],
"source": [
@@ -594,7 +594,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -618,7 +618,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -635,10 +635,10 @@
],
"metadata": {
"interpreter": {
- "hash": "dd61f40108e2a19f4ef0d3ebbc6b6eea57ab3c4bc13b15fe6f390d3d86442534"
+ "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
},
"kernelspec": {
- "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+ "name": "python3",
"display_name": "Python 3.7.0 64-bit ('3.7')"
},
"language_info": {
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index a91a9526..3eb9edec 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -1,6 +1,6 @@
-# Recipe Classifiers 1
+# Cuisine Classifiers 1
-In this lesson, you will use the dataset you saved from the last lesson full of balanced, clean data all about recipes. You will use this dataset with a variety of classifiers to predict a given national cuisine based on a group of ingredients. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
+In this lesson, you will use the dataset you saved from the last lesson full of balanced, clean data all about cuisines. You will use this dataset with a variety of classifiers to predict a given national cuisine based on a group of ingredients. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
# Preparation
@@ -11,8 +11,8 @@ Working in this lesson's `notebook.ipynb` folder, import that file along with th
```python
import pandas as pd
-recipes_df = pd.read_csv("../../data/cleaned_cuisine.csv")
-recipes_df.head()
+cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+cuisines_df.head()
```
The data looks like this:
@@ -37,8 +37,8 @@ import numpy as np
Divide the X and y coordinates into two dataframes for training. `cuisine` can be the labels dataframe:
```python
-recipes_label_df = recipes_df['cuisine']
-recipes_label_df.head()
+cuisines_label_df = cuisines_df['cuisine']
+cuisines_label_df.head()
```
It will look like this:
@@ -55,8 +55,8 @@ Name: cuisine, dtype: object
Drop that `Unnamed: 0` column and the `cuisine` column and save the rest of the data as trainable features:
```python
-recipes_feature_df = recipes_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
-recipes_feature_df.head()
+cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+cuisines_feature_df.head()
```
Your features look like this:
@@ -113,7 +113,7 @@ Let's focus on Logistic Regression for our first training trial since you recent
Let's train that model. Split your data into training and testing groups:
```python
-X_train, X_test, y_train, y_test = train_test_split(recipes_feature_df, recipes_label_df, test_size=0.3)
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
```
There are many ways to use the LogisticRegression library in Scikit-Learn. Take a look at the [parameters to pass](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
@@ -181,7 +181,7 @@ The result is printed - Indian cuisine is its best guess, with good probability:
| korean | 0.017277 | | | | | | | | | | | | | | | | | | | | |
| thai | 0.007634 | | | | | | | | | | | | | | | | | | | | |
-✅ Can you explain why the model is pretty sure this is an Indian recipe?
+✅ Can you explain why the model is pretty sure this is an Indian cuisine?
Get more detail by printing a classification report, as you did in the Regression lessons:
diff --git a/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
index cfaf7cf5..a819dbe5 100644
--- a/4-Classification/2-Classifiers-1/solution/notebook.ipynb
+++ b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -42,18 +42,18 @@
"text/html": "