<jupyter_start><jupyter_text>Importing DataSet<jupyter_code>dt = pd.read_csv("Diabities.csv")
dt.head()<jupyter_output><empty_output><jupyter_text>Analyzing DataSet<jupyter_code>dt.info()
dt.isnull().sum()
x=dt.iloc[: , : -1]
y=dt.iloc[: ,-1]
x
y<jupyter_output><empty_output><jupyter_text>Splitting DataSet<jupyter_code>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)<jupyter_output><empty_output><jupyter_text>Applying Classifiers and Evaluation   Random Forest<jupyter_code>from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=6,criterion='entropy')
classifier.fit(x_train,y_train)
y_pred=classifier.predict(x_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_pred)<jupyter_output><empty_output><jupyter_text>Logistic Regression<jupyter_code>from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(solver='lbfgs',max_iter=1000)
lr.fit(x_train,y_train)
y_pred=lr.predict(x_test)
accuracy_score(y_test,y_pred)<jupyter_output><empty_output><jupyter_text>K Neighbor Classifier<jupyter_code>from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier()
knn.fit(x_train,y_train)
y_pred=knn.predict(x_test)
accuracy_score(y_test,y_pred)<jupyter_output><empty_output>
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