model.py

# -*- coding: utf-8 -*-
"""
Created on Fri Jun 12 13:36:26 2020

@author: MARK
"""

import pandas as pd 
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB


df= pd.read_csv("spam.csv", encoding="latin-1")
df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
	# Features and Labels
df['label'] = df['class'].map({'ham': 0, 'spam': 1})
X = df['message']
y = df['label']
	
	# Extract Feature With CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X) # Fit the Data
    
pickle.dump(cv, open('tranform.pkl', 'wb'))
    
    
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
	#Naive Bayes Classifier
from sklearn.naive_bayes import MultinomialNB

clf = MultinomialNB()
clf.fit(X_train,y_train)
clf.score(X_test,y_test)
filename = 'nlp_model.pkl'
pickle.dump(clf, open(filename, 'wb'))