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Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using Python

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RoobiyaKhan/Classification-Models-Using-Python

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README.md

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Social_Network_Ads.csv

Social_Network_Ads.csv

 
 

r.decisiontree.py

r.decisiontree.py

 
 

r.kernelsvm.py

r.kernelsvm.py

 
 

r.knn.py

r.knn.py

 
 

r.logistic reg.py

r.logistic reg.py

 
 

r.naivebayes.py

r.naivebayes.py

 
 

r.randomforest.py

r.randomforest.py

 
 

r.svm.py

r.svm.py

 
 

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Classification-Models-Using-Python

Social networks dataset contains the data about gender, age, estimated salary and purchased(binary 1 if purchase is made and 0 if there is no purchase). Our goal is to classify the customers based on their age, estimated salary and whether they made a purchase or not. Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification.

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Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using Python

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naive-bayes-classifier logistic-regression decision-tree-classifier svm-classifier random-forest-classifier knn-classifier kernel-svm-classifier

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