label_encoder = LabelEncoder()
df["species"] = label_encoder.fit_transform(df["species"])

Now, we are splitting the dataset into features and target. df_features contain all four features from the df DataFrame. And df_target contains the species column of the dataset.

df_features = df.drop(labels=["species"], axis=1)
df_target = df.filter(items=["species"])

Now, we are splitting the dataset into train and test set. Please note that the shuffle=True parameter indicates that the dataset is shuffled before the split. And the random_state=1 parameter controls the random number generator that is used for shuffling.

X_train, X_test, y_train, y_test = train_test_split(df_features, df_target["species"], shuffle=True, random_state=1)

Now, we are initializing the classifier using the KNeighborsClassifier class. The n_neighbors parameter indicates k or the number of nearest neighbors that is being considered by the algorithm. Please note that a low value of k may introduce noise and a high value of k may make the boundaries between the classes less distinct.

After that, we are using the fit() and predict() methods to learn from the data and predict the target variable.

knn_classifier = KNeighborsClassifier(n_neighbors=5)
knn_classifier.fit(X_train, y_train)
y_test_pred = knn_classifier.predict(X_test)

Now, we can compare y_test_pred and y_test to measure the performance of the model. Please note that here we are measuring the accuracy score (What is accuracy score in machine learning?).

accuracy = accuracy_score(y_test, y_test_pred)

The output of the above program will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  150 non-null    float64
 1   sepal_width   150 non-null    float64
 2   petal_length  150 non-null    float64
 3   petal_width   150 non-null    float64
 4   species       150 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
None
   sepal_length  sepal_width  petal_length  petal_width  species
0           5.1          3.5           1.4          0.2        0
1           4.9          3.0           1.4          0.2        0
2           4.7          3.2           1.3          0.2        0
3           4.6          3.1           1.5          0.2        0
4           5.0          3.6           1.4          0.2        0
Accuracy Score:  1.0

As we can see we have obtained a very good accuracy score in this example.