This is my whole code:`import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline

data = pd.read_csv("diabetes.csv")

data.shape

data.head(5)

#check if any null value is present
data.isnull().values.any()

Correlation

import seaborn as sns
import matplotlib.pyplot as plt

get correlations of each feature in dataset

corrmat = data.corr()
top_corr_features = corrmat.index
plt.figure(figsize = (20,20))

plot heatmap

g = sns.heatmap(data[top_corr_features].corr(),annot=True,cmap="RdYlGn")

data.corr()

data.head(5)

diabetes_true_count = len(data.loc[data['Outcome']==True])
diabetes_false_count = len(data.loc[data['Outcome']==False])

(diabetes_true_count,diabetes_false_count) # Train test split
from sklearn.model_selection import train_test_split
feature_columns = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']
predicted_class = ['Outcome']

X=data[feature_columns].values
y=data[predicted_class].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=10)

Check how many other missing (zero) values

print("total no. of rows : {0}".format(len(data)))
print("no. of missing rows Pregnancies : {0}".format(len(data.loc[data['Pregnancies'] == 0])))
print("no. of missing rows Glucose :{0}".format(len(data.loc[data['Glucose'] == 0])))
print("no. of missing rows BloodPressure : {0}".format(len(data.loc[data['BloodPressure'] == 0])))
print("no. of missing rows SkinThickness : {0}".format(len(data.loc[data['SkinThickness'] == 0])))
print("no. of missing rows Insulin : {0}".format(len(data.loc[data['Insulin'] == 0])))
print("no. of missing rows BMI : {0}".format(len(data.loc[data['BMI'] == 0])))
print("no. of missing rows DiabetesPedigreeFunction : {0}".format(data.loc[data['DiabetesPedigreeFunction'] == 0]))
print("no. of missing rows Age : {0}".format(len(data.loc[data['Age'] == 0])))

import numpy as np
from sklearn.impute import SimpleImputer

np.isnan(X_test)

fill_values = SimpleImputer(missing_values=0, strategy='mean')
X_train = fill_values.fit_transform(X_train)
X_test = fill_values.fit_transform(X_test)

random_forest_model.fit(X_train, y_train.ravel())

predict_train_data = random_forest_model.predict(X_test)

from sklearn import metrics

print("Accuracy = {0:.3f}".format(metrics.accuracy_score(y_test, predict_train_data)))

Hyper Parameter Optimization

params={
"learning_rate" : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30],
"max_depth" : [ 3, 4, 5, 6, 8, 10, 12, 15 ],
"min_child_weight" : [ 1, 3, 5, 7 ],
"gamma" : [ 0.0, 0.1, 0.2, 0.3, 0.4 ],
"colsample_by_tree" : [ 0.3, 0.4, 0.5, 0.7 ]
}

Hyperparameter optimization using RandomizedSearchCV

from sklearn.model_selection import RandomizedSearchCV
import xgboost

use_label_encoder=False
classifier = xgboost.XGBClassifier()

random_search = RandomizedSearchCV(classifier,param_distributions = params,n_iter=5,scoring='roc_auc',n_jobs=-1, cv=5,verbose=3)

def timer(start_timer=None):
if not start_time:
start_time = datetime.now()
return start_time
elif start_time:
thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)
tmin, tsec = divmod(temp_sec,60)
print('\n Time taken: %i hours %i minutes %s seconds.' %(thour, tmin, round(tsec,2)))

from datetime import datetime

start_time = None #timing starts from this point for "start_time" variable
random_search.fit(X,y.ravel())
start_time #timing ends here for "start_time" variable

random_search.best_estimator_`
this is the dataset, I've used for my project:
diabetes.csv