create knn.py

ML_Algorithms/KNN/iris_test.csv

@@ -0,0 +1,31 @@
+SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+6.1,2.8,4.7,1.2,Iris-versicolor
+5.7,3.8,1.7,0.3,Iris-setosa
+7.7,2.6,6.9,2.3,Iris-virginica
+6,2.9,4.5,1.5,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.4,3.4,1.5,0.4,Iris-setosa
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.9,3.1,5.1,2.3,Iris-virginica
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.5,3.2,5.1,2,Iris-virginica
+4.8,3,1.4,0.1,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+6.3,3.3,4.7,1.6,Iris-versicolor
+6.5,3,5.8,2.2,Iris-virginica
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.4,2.8,5.6,2.2,Iris-virginica
+4.7,3.2,1.6,0.2,Iris-setosa
+6.1,3,4.9,1.8,Iris-virginica
+5,3.4,1.6,0.4,Iris-setosa
+6.4,2.8,5.6,2.1,Iris-virginica
+7.9,3.8,6.4,2,Iris-virginica
+6.7,3,5.2,2.3,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+4.8,3,1.4,0.3,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa

ML_Algorithms/KNN/iris_train.csv

@@ -0,0 +1,121 @@
+SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+4.6,3.6,1,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+6.7,3.1,4.4,1.4,Iris-versicolor
+4.8,3.4,1.6,0.2,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+6.3,2.5,5,1.9,Iris-virginica
+6.4,3.2,4.5,1.5,Iris-versicolor
+5.2,3.5,1.5,0.2,Iris-setosa
+5,3.6,1.4,0.2,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.8,2.7,5.1,1.9,Iris-virginica
+6,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+5.4,3.9,1.3,0.4,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+5.5,2.4,3.7,1,Iris-versicolor
+6.3,2.8,5.1,1.5,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.6,3,4.4,1.4,Iris-versicolor
+7.2,3.6,6.1,2.5,Iris-virginica
+5.7,2.9,4.2,1.3,Iris-versicolor
+7.6,3,6.6,2.1,Iris-virginica
+5.6,3,4.5,1.5,Iris-versicolor
+5.1,3.5,1.4,0.2,Iris-setosa
+7.7,2.8,6.7,2,Iris-virginica
+5.8,2.7,4.1,1,Iris-versicolor
+5.2,3.4,1.4,0.2,Iris-setosa
+5,3.5,1.3,0.3,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+5,2,3.5,1,Iris-versicolor
+6.3,2.7,4.9,1.8,Iris-virginica
+4.8,3.4,1.9,0.2,Iris-setosa
+5,3,1.6,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.1,3.4,1.5,0.2,Iris-setosa
+5.7,3,4.2,1.2,Iris-versicolor
+7.7,3.8,6.7,2.2,Iris-virginica
+4.6,3.2,1.4,0.2,Iris-setosa
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.7,2.5,5,2,Iris-virginica
+5.5,4.2,1.4,0.2,Iris-setosa
+6,3,4.8,1.8,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6,2.2,4,1,Iris-versicolor
+5.4,3,4.5,1.5,Iris-versicolor
+6.2,3.4,5.4,2.3,Iris-virginica
+5.5,2.3,4,1.3,Iris-versicolor
+5.4,3.9,1.7,0.4,Iris-setosa
+5,2.3,3.3,1,Iris-versicolor
+6.4,2.7,5.3,1.9,Iris-virginica
+5,3.3,1.4,0.2,Iris-setosa
+5,3.2,1.2,0.2,Iris-setosa
+5.5,2.4,3.8,1.1,Iris-versicolor
+6.7,3,5,1.7,Iris-versicolor
+4.9,3.1,1.5,0.1,Iris-setosa
+5.8,2.8,5.1,2.4,Iris-virginica
+5,3.4,1.5,0.2,Iris-setosa
+5,3.5,1.6,0.6,Iris-setosa
+5.9,3.2,4.8,1.8,Iris-versicolor
+5.1,2.5,3,1.1,Iris-versicolor
+6.9,3.2,5.7,2.3,Iris-virginica
+6,2.7,5.1,1.6,Iris-versicolor
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3,6.1,2.3,Iris-virginica
+5.5,2.5,4,1.3,Iris-versicolor
+4.4,2.9,1.4,0.2,Iris-setosa
+4.3,3,1.1,0.1,Iris-setosa
+6,2.2,5,1.5,Iris-virginica
+7.2,3.2,6,1.8,Iris-virginica
+4.6,3.1,1.5,0.2,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+4.4,3,1.3,0.2,Iris-setosa
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.3,3.4,5.6,2.4,Iris-virginica
+4.6,3.4,1.4,0.3,Iris-setosa
+6.8,3,5.5,2.1,Iris-virginica
+6.3,3.3,6,2.5,Iris-virginica
+4.7,3.2,1.3,0.2,Iris-setosa
+6.1,2.9,4.7,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.2,2.8,4.8,1.8,Iris-virginica
+7,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,5.3,2.3,Iris-virginica
+5.1,3.8,1.6,0.2,Iris-setosa
+6.9,3.1,5.4,2.1,Iris-virginica
+5.9,3,4.2,1.5,Iris-versicolor
+6.5,3,5.2,2,Iris-virginica
+5.7,2.6,3.5,1,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+6.1,3,4.6,1.4,Iris-versicolor
+4.5,2.3,1.3,0.3,Iris-setosa
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+5.3,3.7,1.5,0.2,Iris-setosa
+5.6,3,4.1,1.3,Iris-versicolor
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+5.1,3.7,1.5,0.4,Iris-setosa
+4.9,2.4,3.3,1,Iris-versicolor
+6.7,3.3,5.7,2.5,Iris-virginica
+7.2,3,5.8,1.6,Iris-virginica
+4.9,3.1,1.5,0.1,Iris-setosa
+6.7,3.1,5.6,2.4,Iris-virginica
+4.9,3,1.4,0.2,Iris-setosa
+6.9,3.1,4.9,1.5,Iris-versicolor
+7.4,2.8,6.1,1.9,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.5,3,5.5,1.8,Iris-virginica
+6.3,2.3,4.4,1.3,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+5.6,2.8,4.9,2,Iris-virginica
+5.9,3,5.1,1.8,Iris-virginica
+5.4,3.4,1.7,0.2,Iris-setosa
+6.1,2.8,4,1.3,Iris-versicolor
+4.9,2.5,4.5,1.7,Iris-virginica
+5.8,4,1.2,0.2,Iris-setosa
+5.8,2.6,4,1.2,Iris-versicolor
+7.1,3,5.9,2.1,Iris-virginica

ML_Algorithms/KNN/knn.py

@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+
+def euclidean_distance(x1,x2):
+    distance=0
+    for i in range(len(x1)):
+        distance+=np.square(x1[i]-x2[i])
+    return distance
+
+def knn(training_x,training_y,testing_x):
+    predictions=[]
+    for i in range(len(testing_x)):
+        distance=[]
+        for j in range(len(training_x)):
+            distance.append(euclidean_distance(testing_x[i],training_x[j]))
+        mydict={"class":training_y,"Distances":distance}
+        distance_df=pd.DataFrame.from_dict(mydict)
+        sorted_distance = distance_df.sort_values(by=["Distances"])
+        predict_labels=sorted_distance["class"][:5]
+        predicted_label=predict_labels.value_counts().index.tolist()[0]
+        predictions.append(predicted_label)
+    return predictions
+
+train_df=pd.read_csv("Code-Forge\ML_Algorithms\KNN\iris_train.csv")
+train_x=train_df.drop("Species",axis=1).to_numpy()
+train_y=train_df["Species"].to_numpy()
+
+test_df=pd.read_csv("Code-Forge\ML_Algorithms\KNN\iris_test.csv")
+test_x=test_df.drop("Species",axis=1).to_numpy()
+test_y=test_df["Species"].to_numpy()
+
+predictions=knn(train_x,train_y,test_x)