Code for building a spam filtering bot.

detector.py

import cPickle as c
import os
from sklearn import *
from collections import Counter


def load(clf_file):
    with open(clf_file) as fp:
        clf = c.load(fp)
    return clf


def make_dict():
    direc = "emails/"
    files = os.listdir(direc)
    emails = [direc + email for email in files]
    words = []
    c = len(emails)

    for email in emails:
        f = open(email)
        blob = f.read()
        words += blob.split(" ")
        print c
        c -= 1

    for i in range(len(words)):
        if not words[i].isalpha():
            words[i] = ""

    dictionary = Counter(words)
    del dictionary[""]
    return dictionary.most_common(3000)


clf = load("text-classifier.mdl")
d = make_dict()


while True:
    features = []
    inp = raw_input(">").split()
    if inp[0] == "exit":
        break
    for word in d:
        features.append(inp.count(word[0]))
    res = clf.predict([features])
    print ["Not Spam", "Spam!"][res[0]]

spam.py

import os
from collections import Counter
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split as tts
from sklearn.metrics import accuracy_score
import cPickle as c


def save(clf, name):
    with open(name, 'wb') as fp:
        c.dump(clf, fp)
    print "saved"


def make_dict():
    direc = "emails/"
    files = os.listdir(direc)
    emails = [direc + email for email in files]
    words = []
    c = len(emails)
    for email in emails:
        f = open(email)
        blob = f.read()
        words += blob.split(" ")
        print c
        c -= 1

    for i in range(len(words)):
        if not words[i].isalpha():
            words[i] = ""

    dictionary = Counter(words)
    del dictionary[""]
    return dictionary.most_common(3000)


def make_dataset(dictionary):
    direc = "emails/"
    files = os.listdir(direc)
    emails = [direc + email for email in files]
    feature_set = []
    labels = []
    c = len(emails)

    for email in emails:
        data = []
        f = open(email)
        words = f.read().split(' ')
        for entry in dictionary:
            data.append(words.count(entry[0]))
        feature_set.append(data)

        if "ham" in email:
            labels.append(0)
        if "spam" in email:
            labels.append(1)
        print c
        c = c - 1
    return feature_set, labels


d = make_dict()
features, labels = make_dataset(d)

x_train, x_test, y_train, y_test = tts(features, labels, test_size=0.2)

clf = MultinomialNB()
clf.fit(x_train, y_train)

preds = clf.predict(x_test)
print accuracy_score(y_test, preds)
save(clf, "text-classifier.mdl")

I am getting this error

File "G:\ML proj\Email spam classification\detector.py", line 42, in
clf = load("text_classifier.mdl")

File "G:\ML proj\Email spam classification\detector.py", line 19, in load
clf = c.load(fp)

File "C:\Users\Kalaivani\anaconda3\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]

UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 52: character maps to

How to clear this error

check this:--> https://stackoverflow.com/questions/9233027/unicodedecodeerror-charmap-codec-cant-decode-byte-x-in-position-y-character