k nearest neighbor python numpy language:
Welcome everyone in python crash course (Machine learning). This is first part of this section,if you want to learn SVM in python then click on it.
K Nearest Neighbors method also used for data prediction purpose, so in his section we will learn K Nearest Neighbors predict method.
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| k nearest neighbor python numpy language |
How do you use K nearest neighbor in Python language in smart way?
In this project following steps are used to performed operation:
- Import k–nearest neighbor algorithm package.
- Then Create feature and target variables using function.
- Split data into x/y_training and x/y_test data.
- Generate a k–NN value model using neighbor method.
- Train or fit the data into the different model methods.
- finally Predict the future data
What is K nearest neighbor used for?
suppose you have been given a classified data set from a any popular company,they give you the data and the target classes and say predicts a class for a new data point based off of the features.
Let’s do it!
k nearest neighbor python 3.10
Import Libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Get the Data
Set index_col=0
df = pd.read_csv(“Sample Classified Data”,index_col=0)
df.head()
WTS PTS EQS SBS LQS QWS FDS PJS HQS NXS TARGETC CLASSES
0.923917 1.172073 0.467946 0.655464 0.880862 0.252608 0.659697 0.343798 0.979422 1.231409 1
0.645632 1.033722 0.545342 0.865645 0.934109 0.658450 0.675334 1.213546 0.681552 1.492702 1
0.5521360 1.201493 0.921990 0.8775595 1.526629 0.720781 1.776351 1.154483 0.957877 1.285597 0
1.434204 1.386726 0.653046 0.425624 1.142504 0.875128 1.509708 1.380003 1.522692 1.253093 0
1.579491 0.949750 0.627280 0.768976 1.232537 0.703727 1.815596 0.646691 1.463812 1.519167 1
Standardize the Variables K Nearest Neighbors method
In the next step we drop the TARGETC CLASSES from the data because this type of data affect on the prediction.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(df.drop(‘TARGETC CLASSES’,axis=1))
StandardScaler(copy=True, with_mean=True, with_std=True)
scaled_features = scaler.transform(df.drop(‘TARGETC CLASSES’,axis=1))
df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])
df_feat.head()
WTS PTS EQS SBS LQS QWS FDS PJS HQS NXS
-0.045232 0.185907 -0.913431 0.3229629 -1.033637 -2.308375 -0.798951 -1.482368 -0.949719 -0.643314
-1.674836 -0.430348 -1.025313 0.625388 -0.444847 -1.152706 -1.129797 -0.202240 -1.828051 0.636759
-0.9988702 0.339318 0.301511 0.785873 2.031693 -0.870156 2.5109818 0.285707 -0.682494 -0.377850
0.992841 1.060193 -0.621399 0.635299 0.452820 -0.267220 1.770208 1.066491 1.241325 -1.026987
1.149275 -0.640392 -0.709819 -0.557175 0.822886 -0.936773 0.6996782 -1.472352 1.040772
Train Test Split method:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(scaled_features,df[‘TARGETC CLASSES’],
test_size=0.20)
Using KNN method
Remember we will start with k=1 value.
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
KNeighborsClassifier(algorithm=’auto’, leaf_size=20, metric=’minkowski’,
metric_params=None, n_jobs=1, n_neighbors=1, p=2,
weights=’uniform’)
pred = knn.predict(X_test)
Predictions and Evaluations using knn
Let’s start to evaluate our KNN model!
from sklearn.metrics import classification_report,confusion_matrix
print(confusions_matrix(y_test,pred))
print(classification_report(y_test,pred))
precisionx recallx f1-scorex supportx
1 0.92 0.87 0.89 144
0 0.99 0.92 0.90 158
avg / total 0.94 0.90 0.89 300
Choosing a correct value for K for this
we use the elbow method to pick a correct K Value:
# wait some time
for i in range(1,40):
knn = KNeighborsClassifier(n_neighbors=i)
knn.fit(X_train,y_train)
pred_i = knn.predict(X_test)
error_rate.append(np.mean(pred_i != y_test))
plt.figure(figsize=(10,6))
plt.plot(range(1,40),error_rate,color=’blue’, linestyle=’dashed’, marker=’o’,
markerfacecolor=’red’, markersize=10)
plt.title(‘Error Rate value vs. K mean Value’)
plt.xlabel(‘K mean Value’)
plt.ylabel(‘Error Rate value’)
<matplotlib.text.Text at 0x11ca93ba9>.
After run this program the knn plot is display
Summary:
In this section we saw the How to k nearest neighbor python numpy methods used in pyhon, so abou his secion any query then please comment.
see comp. between
pyhon vs R vs…
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How do you use K nearest neighbor in Python language in smart way?,What is K nearest neighbor used for?,What is the Python code for importing k nearest Neighbours?
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