Added KNeighbors model to predict iris values

Iris/iris_kneighbours.py

@@ -0,0 +1,42 @@
+#Loading the necessary modules
+import numpy as np
+import pandas as pd
+
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import mean_absolute_error
+
+#Loading the iris dataset
+iris = datasets.load_iris()
+iris_df = pd.DataFrame(
+    data= np.c_[iris['data'],
+    iris['target']],
+    columns= iris['feature_names'] + ['target']
+
+)
+
+neighbors = 4 #Best number of neigbors
+
+#Setting our feature matrix
+X = iris_df[["sepal length (cm)", "sepal width (cm)", "petal length (cm)", "petal width (cm)"]]
+y = iris_df["target"]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+def score(X_train, X_test, y_train, y_test, neighbors):
+    sample_model = KNeighborsRegressor(n_neighbors=neighbors)
+    sample_model.fit(X_train, y_train)
+    return mean_absolute_error(sample_model.predict(X_test), y_test)
+
+def getOptimumNeighbors(X_train, X_test, y_train, y_test):
+    for neighbors in range(3, 15):
+        print("Nodes: {} -> {}".format(neighbors, score(X_train, X_test, y_train, y_test, neighbors)))
+
+#Uncomment this to check which number of neighbors perform best
+#getOptimumNeighbors(X_train, X_test, y_train, y_test)
+
+model = KNeighborsRegressor(n_neighbors=3)
+model.fit(X_train, y_train)
+
+print(mean_absolute_error(model.predict(X_test), y_test))