KNN-Regressor-Evalution

Implementation of KNN in Python KNN-Regressor & KNN Predict & Evalution

KNN-Regressor-Practice

Implementation of KNN in Python KNN-Regressor & KNN Predict & Evalution

[[3, 4, 5], [6, 9, 7], [2, 4, 5],[1, 3, 2], [7, 7, 7], [5, 6,7], [4, 4 ,8],[2, 2, 2],[3 ,5 ,1]]

With This Label for Classification=> [1,2,1,1,2,2,2,1,1]

Test Data ==> [5 ,5 ,5] , [6 , 3 ,2]

"""

import pandas as pd
import numpy as np

Load the Dataset

Create DataFrame from Train Data

Use Pandas lib for Create Dataframe

TrainData = pd.DataFrame([[3, 4, 5, 1],
                         [6, 9, 7, 2],
                         [2, 4, 5, 1],
                         [1, 3, 2, 1],
                         [7, 7, 7, 2],
                         [5, 6, 7, 2],
                         [4, 4 ,8, 2],
                         [2, 2, 3, 1],
                         [3, 5, 1, 1]],columns=['F1', 'F2', 'F3','Label'])
print('Show Train Data')
TrainData

Create DataFrame from Test Data

TestData = pd.DataFrame([[5, 5, 5, 0],
                         [6, 3, 2, 0]],columns=['F1', 'F2', 'F3','Label'])
print('Show Test Data')
TestData

Select the Features

Train Data

X_train = TrainData.iloc[:,[0,1,2]].values # Features Data
Y_train = TrainData.iloc[:,[3]].values     # Labeled Data

# Test Data
X_test = TestData.iloc[:,[0,1,2]].values
Y_test = TestData.iloc[:,[3]].values

# Show Train Data
X_train,Y_train

# Show Test Data
X_test,Y_test

Define Error Metrics

As this is a regression problem, we have defined MAPE as the error metrics as shown below

def MAPE(Y_actual,Y_Predicted):
    Mape = np.mean(np.abs((Y_actual - Y_Predicted)/Y_actual))*100
    return Mape

Build the Model of KNN Classification

#Building the KNN.Regressor Model on our dataset

k=3
from sklearn.neighbors import KNeighborsClassifier
KNN_model = KNeighborsClassifier(n_neighbors=k,metric='euclidean') # euclidean & minkowski & manhattan & 
KNN_model.fit(X_train,Y_train.ravel())

The following lists the string metric identifiers and the associated distance metric classes:

Metrics intended for real-valued vector spaces:

“euclidean” = > sqrt(sum((x - y)^2))

“manhattan” => sum(|x - y|)

“chebyshev” => max(|x - y|)

“minkowski” => sum(|x - y|^p)^(1/p)

“wminkowski” => sum(|w * (x - y)|^p)^(1/p)

“seuclidean” => sqrt(sum((x - y)^2 / V))

“mahalanobis” => sqrt((x - y)' V^-1 (x - y))

Predict the testing Data

KNN_predict = KNN_model.predict(X_test)  # Predictions on Testing data

X_test

Y_test = KNN_predict # Set Predicted label put on Y_Test
Y_test   # Predicted Values

Accuracy Check For KNN Classification !

Using MAPE error metrics to check for the error rate and accuracy level

KNN_MAPE = MAPE(Y_train,KNN_predict)
Accuracy_KNN = 100 - KNN_MAPE
print("MAPE: ",KNN_MAPE)
print('Accuracy of KNN model: {:0.2f}%.'.format(Accuracy_KNN))

Build the Model of KNN Regressor Classification

#Building the KNN.Regressor Model on our dataset

k=3
from sklearn.neighbors import KNeighborsRegressor
KNN_model = KNeighborsRegressor(n_neighbors=k).fit(X_train,Y_train)

Predict the testing Data

KNN_predict = KNN_model.predict(X_test) #Predictions on Testing data

X_test

Y_test = KNN_predict # Set Predicted label put on Y_Test
Y_test   # Predicted Values

Accuracy Check For KNN Regressor Classification!

Using MAPE error metrics to check for the error rate and accuracy level

KNN_MAPE = MAPE(Y_train.reshape(1, -1),KNN_predict)
Accuracy_KNN = 100 - KNN_MAPE
print("MAPE: ",KNN_MAPE)
print('Accuracy of KNN model: {:0.2f}%.'.format(Accuracy_KNN))

Authors

Amin Zayeromali

Twitter: @AminZayeromali

Instagram: aminzayer

Linkedin: aminzayeromali

Google Scolar: Amin Zayeromali

Email : Amin {dot} zayeromali {At} gmail {dot} com

Connect with me

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
KNN_Python.ipynb		KNN_Python.ipynb
KNN_Regressor_Practice.ipynb		KNN_Regressor_Practice.ipynb
KNN_Regressor_Practice.pdf		KNN_Regressor_Practice.pdf
LICENSE		LICENSE
README.md		README.md
knn_python.py		knn_python.py
knn_regressor_practice.py		knn_regressor_practice.py

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KNN-Regressor-Evalution

KNN-Regressor-Practice

Implementation of KNN in Python KNN-Regressor & KNN Predict & Evalution

[[3, 4, 5], [6, 9, 7], [2, 4, 5],[1, 3, 2], [7, 7, 7], [5, 6,7], [4, 4 ,8],[2, 2, 2],[3 ,5 ,1]]

With This Label for Classification=> [1,2,1,1,2,2,2,1,1]

Test Data ==> [5 ,5 ,5] , [6 , 3 ,2]

Load the Dataset

Create DataFrame from Train Data

Use Pandas lib for Create Dataframe

Create DataFrame from Test Data

Select the Features

Train Data

Define Error Metrics

As this is a regression problem, we have defined MAPE as the error metrics as shown below

Build the Model of KNN Classification

“euclidean” = > sqrt(sum((x - y)^2))

“manhattan” => sum(|x - y|)

“chebyshev” => max(|x - y|)

“minkowski” => sum(|x - y|^p)^(1/p)

“wminkowski” => sum(|w * (x - y)|^p)^(1/p)

“seuclidean” => sqrt(sum((x - y)^2 / V))

“mahalanobis” => sqrt((x - y)' V^-1 (x - y))

Predict the testing Data

Accuracy Check For KNN Classification !

Using MAPE error metrics to check for the error rate and accuracy level

Build the Model of KNN Regressor Classification

Predict the testing Data

Accuracy Check For KNN Regressor Classification!

Using MAPE error metrics to check for the error rate and accuracy level

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KNN-Regressor-Evalution

KNN-Regressor-Practice

Implementation of KNN in Python KNN-Regressor & KNN Predict & Evalution

[[3, 4, 5], [6, 9, 7], [2, 4, 5],[1, 3, 2], [7, 7, 7], [5, 6,7], [4, 4 ,8],[2, 2, 2],[3 ,5 ,1]]

With This Label for Classification=> [1,2,1,1,2,2,2,1,1]

Test Data ==> [5 ,5 ,5] , [6 , 3 ,2]

Load the Dataset

Create DataFrame from Train Data

Use Pandas lib for Create Dataframe

Create DataFrame from Test Data

Select the Features

Train Data

Define Error Metrics

As this is a regression problem, we have defined MAPE as the error metrics as shown below

Build the Model of KNN Classification

“euclidean” = > sqrt(sum((x - y)^2))

“manhattan” => sum(|x - y|)

“chebyshev” => max(|x - y|)

“minkowski” => sum(|x - y|^p)^(1/p)

“wminkowski” => sum(|w * (x - y)|^p)^(1/p)

“seuclidean” => sqrt(sum((x - y)^2 / V))

“mahalanobis” => sqrt((x - y)' V^-1 (x - y))

Predict the testing Data

Accuracy Check For KNN Classification !

Using MAPE error metrics to check for the error rate and accuracy level

Build the Model of KNN Regressor Classification

Predict the testing Data

Accuracy Check For KNN Regressor Classification!

Using MAPE error metrics to check for the error rate and accuracy level

Authors

Connect with me

License

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