changing classification file name to cleaned_cuisines

4-Classification/1-Introduction/README.md

@@ -275,7 +275,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
     ```
 
     This fresh CSV can now be found in the root data folder.

4-Classification/1-Introduction/solution/notebook.ipynb

@@ -622,7 +622,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transformed_df.to_csv(\"../../data/cleaned_cuisine.csv\")"
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
    ]
   },
   {

4-Classification/1-Introduction/translations/README.tr.md

@@ -275,7 +275,7 @@ Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../../data/cleaned_cuisines.csv")
     ```
 
     Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.

4-Classification/1-Introduction/translations/README.zh-cn.md

@@ -269,7 +269,7 @@ Scikit-learn项目提供多种对数据进行分类的算法，你需要根据
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
     ```
 
     这个全新的CSV文件可以在数据根目录中被找到。

4-Classification/2-Classifiers-1/README.md

@@ -15,7 +15,7 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     ```python
     import pandas as pd
-    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
     cuisines_df.head()
     ```
 

4-Classification/2-Classifiers-1/solution/notebook.ipynb

@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },

4-Classification/3-Classifiers-2/README.md

@@ -6,7 +6,7 @@ In this second classification lesson, you will explore more ways to classify num
 
 ### Prerequisite
 
-We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisine.csv_ in the root of this 4-lesson folder.
+We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisines.csv_ in the root of this 4-lesson folder.
 
 ### Preparation
 

4-Classification/3-Classifiers-2/notebook.ipynb

@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },

4-Classification/3-Classifiers-2/solution/notebook.ipynb

@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },

4-Classification/4-Applied/README.md

@@ -40,7 +40,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Then, work with your data in the same way you did in previous lessons, by reading a CSV file using `read_csv()`:
 
     ```python
-    data = pd.read_csv('../data/cleaned_cuisine.csv')
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
     data.head()
     ```
 

4-Classification/4-Applied/solution/notebook.ipynb

@@ -115,7 +115,7 @@
     }
    ],
    "source": [
-    "data = pd.read_csv('../../data/cleaned_cuisine.csv')\n",
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
     "data.head()"
    ]
   },