README.md

Data-Science-Project

Medical Science Project - Breat Cancer Detection

import pandas as pd import numpy as np import seaborn as sns from sklearn.preprocessing import RobustScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split

dataset=pd.read_csv("breastcancerdetectionproject.csv") print(dataset.head())

print("Non missing values:",str(dataset.isnull().shape[0])) print("Missing values:",str(dataset.shape[0]-dataset.isnull().shape[0]))

y=dataset["fractal_dimension_worst"]#TARGET x=dataset.iloc[:,0:31]#FETAURES

from sklearn.preprocessing import LabelEncoder

Assuming 'fractal_dimension_worst' contains non-numeric values like 'M' or 'B'

label_encoder = LabelEncoder() y_encoded = label_encoder.fit_transform(y) label_encoder = LabelEncoder() x_encoded = label_encoder.fit_transform(x)

Now 'y_encoded' can be used as the target variable for KNN

x_train,x_test,y_train,y_test = train_test_split(x, y_encoded, test_size=0.2, random_state=42) knn = KNeighborsClassifier(n_neighbors=4) knn.fit(x_train,y_train) y_res = knn.predict(x_test)

x=dataset.data

y=dataset.target

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1)

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=1)

knn=KNeighborsClassifier(n_neighbors=4)

knn.fit(x_train,y_train)

y_res=knn.predict(x_test)