Code

#importing the Libraies
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Reading the Dataset
dataset = pd.read_csv('Social_Network_Ads.csv')

dataset

dataset

dataset=pd.get_dummies(dataset,drop_first=True)

dataset

indep=dataset[['Age', 'EstimatedSalary','Gender_Male']]
dep=dataset['Purchased']

#split into training set and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(indep, dep, test_size = 1/3, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.linear_model import LogisticRegression

#<https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter>

from sklearn.model_selection import GridSearchCV

param_grid = {'solver':['newton-cg', 'lbfgs', 'liblinear', 'saga'],
             'penalty':['l2']} 

grid = GridSearchCV(LogisticRegression(), param_grid, refit = True, verbose = 3,n_jobs=-1,scoring='f1_weighted') 
   
# fitting the model for grid search 
grid.fit(X_train, y_train) 
 

# print best parameter after tuning 
#print(grid.best_params_) 
re=grid.cv_results_
#print(re)
grid_predictions = grid.predict(X_test) 
   

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, grid_predictions)

# print classification report 
from sklearn.metrics import classification_report
clf_report = classification_report(y_test, grid_predictions)

from sklearn.metrics import f1_score
f1_macro=f1_score(y_test,grid_predictions,average='weighted')
print("The f1_macro value for best parameter {}:".format(grid.best_params_),f1_macro)

print("The confusion Matrix:\\n",cm)

print("The report:\\n",clf_report)

from sklearn.metrics import roc_auc_score

roc_auc_score(y_test,grid.predict_proba(X_test)[:,1])

table=pd.DataFrame.from_dict(re)

table

age_input=float(input("Age:"))
bmi_input=float(input("BMI:"))
children_input=float(input("Children:"))
sex_male_input=int(input("Sex Male 0 or 1:"))
smoker_yes_input=int(input("Smoker Yes 0 or 1:"))

Future_Prediction=grid.predict([[age_input,bmi_input,children_input,sex_male_input,smoker_yes_input]])# change the paramter,play with it.
print("Future_Prediction={}".format(Future_Prediction))

Dataset

User Purchase Data