Naive Bayes | Python div-bargali#205

Naive Bayes | Python

Author: div-bargali

Python/Algorithms/Machine Learning Algorithms/Naive Bayes in python.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[16]:
+
+
+#importing required packages
+import pandas as pd
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.naive_bayes import BernoulliNB
+clf=BernoulliNB()
+
+#loading the data
+dataset=pd.read_csv("C:/Users/ASUS/Downloads/train.csv")
+dataset.head()
+
+#getting the description of the dataset
+dataset.describe()
+dataset.describe().sum()
+
+#get some info about the data
+dataset.info()
+
+#getting the amount of null values in each column
+dataset.isnull().sum()
+
+#dropping the unimportant columns
+
+dataset=dataset.drop('PassengerId', axis=1)
+
+dataset=dataset.drop('Name', axis=1)
+
+dataset=dataset.drop('Ticket', axis=1)
+
+dataset=dataset.drop('Cabin', axis=1)
+
+dataset.head()
+
+#label encoding the categorical values which are of object type 
+le=preprocessing.LabelEncoder()
+dataset['Sex']=le.fit_transform(dataset['Sex'])
+dataset['Embarked']=le.fit_transform(dataset['Embarked'])
+dataset.head()
+
+"""this functions takes the independent variable column and trains the model after dividing the dataset into x and y
+and also spliting the dataset into training and testing data.This also prints the accuracy score and the confusion matrix"""
+ 
+def navbaiyes(value):
+ x=dataset.drop([value], axis=1)
+ y=dataset[value]
+ x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+ 
+ 
+ y_pred= clf.fit(x_train,y_train).predict(x_test)
+ print("The accuracy score is:")
+ print(accuracy_score(y_test, y_pred)*100)
+ print('--------------------------------------------')
+ print("The confusion matrix is:")
+ print(confusion_matrix(y_test,y_pred))
+ 
+#Calling the function
+navbaiyes('Survived')
+
+
+# In[ ]:
+
+
+
+