Binary Classification Models in Machine Learning

Read the Dataset
import pandas as pd
df=pd.read_csv(path)
print(df.shape)
Convert categorical to numerical:
from sklearn.preprocessing import LabelEncoder
df[[columns]]=df[columns]].apply(LabelEncoder().fit_transform)
X and Y
X=df.iloc[:,:-1]
Y=df.iloc[:,-1]
from sklearn.model_selection import train_test_split
X_train,X_val,Y_train,Y_val=train_test_split(X,Y,test_size=0.2,random_state=42)
To create more than one model
models = {} //dictionary
Logistic Regression
from sklearn.linear_model import LogisticRegression
models['Logistic Regression'] = LogisticRegression()
#similary create other models
from sklearn.metrics import accuracy_score, precision_score, recall_score
accuracy, precision, recall = {}, {}, {}
for key in models.keys():
Fit the classifier model
models[key].fit(X_train, Y_train)
Prediction
predictions = models[key].predict(X_val)
Calculate Accuracy, Precision and Recall Metrics
accuracy[key] = accuracy_score(predictions, Y_val)
precision[key] = precision_score(predictions, Y_val)
recall[key] = recall_score(predictions, Y_val)
Y_predict = models[key].predict(X_val)
auc = roc_auc_score(Y_val, Y_predict)
print('Classification Report:',key)
print(classification_report(Y_val,predictions))
false_positive_rate, true_positive_rate, thresholds = roc_curve(Y_val, predictions)
print('ROC_AUC_SCORE is',roc_auc_score(Y_val, predictions))
#fpr, tpr, _ = roc_curve(y_test, predictions[:,1])
plt.plot(false_positive_rate, true_positive_rate)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC curve')
plt.show()
sns.heatmap(confusion_matrix(Y_val,predictions),fmt='',annot=True) What is a binary classifier in machine learning?
Binary Classification: In binary classification, the goal is to classify the input into one of two classes or categories. Example - On the basis of the given health conditions of a person, we have to determine whether the person has a certain disease or not.

[@codewithkhurram313]

Its 5.08 AM and today is my midterm exam and our AI teacher teaches us by copying the lectures of Stanford university. He didn't even tell us that Logistic Regression ,..... Neutral Networks are algorithms used for Binary Classification. And he is going to give us problems in exams related to all these. But thanks for showing us how things are practically done.

[@yashodharpathak189]

Thanks for the wonderful video mam! I have a doubt. Do we require to do feature scaling of all numerical variables before fitting the models or these models take care of it automatically?

[@investime247]

Need to do for all numerical variables

[@larbi-belaissaoui]

THANKS YOU FOR THIS

[@Go-yg9rg]

predictions = models[key].predict(X_val)
Y_predict = models[key].predict(X_val)
why do you use two variable, acctully are same ?

[@investime247]

Yes it's same

[@fireninjagaming2499]

Dataset need him mam please share you mam

[@rajeshvermarehariya7299]

Mam please share the link of colab

[@mudhassir.]

Hi mam enakku DL la orusila doubts irukku mam
Unga insta id kudunga mam
Naa voice note mooliyama explain panren. Enakku help pannunga mam please mam please

[@investime247]

Hema_david2510

[@mudhassir.]

@@investime247 mam insta la ping pannirukken paarunga mam pls

[@mudhassir.]

@@investime247 mam insta la msg pannirukken. Please reply pannunga mam