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Random Forest

The document outlines a process for predicting breast cancer using the K-Nearest Neighbors (KNN) algorithm with the sklearn library. It includes steps for loading the dataset, standardizing features, splitting the data into training and testing sets, and evaluating model performance with different values of K. The results are visualized through error rate plots and classification reports for various K values.

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7alphaone
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25 views2 pages

Random Forest

The document outlines a process for predicting breast cancer using the K-Nearest Neighbors (KNN) algorithm with the sklearn library. It includes steps for loading the dataset, standardizing features, splitting the data into training and testing sets, and evaluating model performance with different values of K. The results are visualized through error rate plots and classification reports for various K values.

Uploaded by

7alphaone
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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import pandas as pd

import seaborn as sns


import matplotlib.pyplot as plt
import numpy as np
get_ipython ().run_line_magic ('matplotlib', 'inline')

#Get the Data


from sklearn.datasets import load_breast_cancer

cancer load_breast_cancer()

cancer.keys()

print (cancer [DESCR'])

cancer ['feature_names']

# Set up DataFrame
df feat pd.DataFrame (cancer ['data'], columns-cancer ['feature_names']) df
feat.info()
# In [9]:
cancer ['target']
# In [10]:
df_target pd.DataFrame (cancer ['target'], columns=['Cancer'])

df_feat.head()

# Standardizing the variables


from sklearn.preprocessing import StandardScaler
scaler StandardScaler()
scaler.fit(df_feat)
StandardScaler (copy-True, with mean-True, with_std=True)
scaled_features = scaler.transform(df_feat)

# In [25]:
df_feat_scaled= pd.DataFrame(scaled_features, columns=df_feat.columns)
In [26]:
dr_feat_scaled.head()
In [27]:
Train Test Split
from sklearn.model_selection import train_test_split
X_train, X_test, y train, y_test= train_test_split (scaled features, np.ravel
(df_target), test_size=0.30, random_state=105)

# In [20]:
#Using KNN Remember that we are trying to come up with a model to predict TARGET as
cancerouse or not. We'll start with k-1.
from sklearn.neighbors import KNeighborsClassifier
# In [29]:
knn KNeighborsClassifier (n_neighbors-1)
# In [30]:
knn.fit(X_train,y_train)
pred=knn.predict(X_test)
From sklearn.statistics import classification_report,confusion_matrix
print(confusion_matrix(y_test,pred))
print(classification_report(y_test,pred))
error_rate =[]

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))
In [36]:
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 vs. K Value')
plt.xlabel('K')
plt.ylabel('Error Rate')

# WITH K=1
knn=KNeighborsClassifier (n_neighbors=1)
knn.fit (X_train, y_train)
pred= knn.predict (X_test)
print('WITH K-1')
print('\n')
print(confusion matrix (y_test, pred))
print('\n')
print(classification_report (y_test, pred))

In [39]:
#NOW WITH K=21
knn= KNeighborsClassifier (n_neighbors=21)
knn.fit(X_train, y_train)
pred= knn.predict (X_test)
print('WITH k=21)
print('\n')
print (confusion matrix (y_test,pred))
print('\n')
print (classification_report (y_test,pred))

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