KNN (K-Nearest Neighbor) Algorithm in Telugu
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- čas přidán 27. 08. 2024
- #KNN #knnalgorithm #machinelearningalgorithm
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import seaborn as sns
sns.set()
breast_cancer = load_breast_cancer()
X = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)
X = X[['mean area', 'mean compactness']]
y = pd.Categorical.from_codes(breast_cancer.target, breast_cancer.target_names)
y = pd.get_dummies(y, drop_first=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
y_pred = knn.predict(X_test)
sns.scatterplot(
x='mean area',
y='mean compactness',
hue='benign',
data=X_test.join(y_test, how='outer')
)
plt.scatter(
X_test['mean area'],
X_test['mean compactness'],
c=y_pred,
cmap='coolwarm',
alpha=0.7
)
confusion_matrix(y_test, y_pred)
Super Explanation _great feature.....
Thank you
Great Explanation. Thank you🙏🙏
Thank you so much for your wonderful explanation.
Thank you please do subscribe and share
Nice explanation
Thank you
Theoretical explanation was quite good 👏👏 but need more explanation regarding Code snippets especially for beginners
Sure
Superb akka
Thank you
tq mam finally i found a telugu video on knn
Keep watching
Super explanation.tq
Welcome 😊
Great explanation
Thank you
Excellent explanation madam
Thank you please do subscribe and share
Sweet voice
Really amazing your videos.can u please do STATISTICS videos also
Sure
Thanks for supb explanation akka
Please do subscribe and share
Mam excellent ga cheppparuu ,
Density estimation ,fuzzy classification chepparaa madam
Sure
Good explanation 👍
Category a and category b ani ela segregate ayyayi andi. Explain cheyagalara ah?
Good explanation..
Thank you madam
Nice explanation 😍
Thank you 🙂
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Super voice 😍❤️
Thank you please do subscribe and share
Akka super☝️👌
Mam if u don't mine
Evvaru ayina sudden gaa topics adiginapudu
Simple gaa cheppadaaniki
Cheppandi
Topics:
Features, classification - linear and nonlinear, Bayesian, perceptron, nearest neighbour classifier, logistic regression, naive bayes, decision trees, random forest, k-means clustering
E topics kavala?
@@Cseittutorials Ss mam
Already mee videos chusanu
But just main reason okkati sudden gaa adigite cheppadaaniki
Long short term memory topic explain in telugu pls
Sure
Mam important questions luuu cheppandhi mam
Theoretical explanation is good. However as s part of code import, execution, k value with code execution, test and train code execution for accurate test results needs some more precise explanation.
Here in algorithms, code writing and execution is the only key point and catch.
Hence code is very much important, to train and test module accuracy for refine outcome and accurate results.
Thank you
Arai jeevan ga ikkadi nuchai aa ga copy koti maku chaptanavu
What?
thanks akka
overall explanation is good
but i have doubt that is
without feature scaling is that better or not?
Feature scaling helps our model to predict more accurately
Madam time series analysis gurchi oka sari
How to select k value
medam, i want class in english pls translate
There are so many classes in english
@@Cseittutorials Implement RandomSearchCV with k fold cross validation on KNN,you have this class send link medam.
k-fold cross validation