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{
"cells": [
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"#import necessary libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split,KFold,cross_val_score, ShuffleSplit \n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import f1_score,accuracy_score,classification_report\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.feature_extraction.text import CountVectorizer"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ham</td>\n",
" <td>Go until jurong point, crazy.. Available only ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ham</td>\n",
" <td>Ok lar... Joking wif u oni...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>spam</td>\n",
" <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ham</td>\n",
" <td>U dun say so early hor... U c already then say...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ham</td>\n",
" <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type text\n",
"0 ham Go until jurong point, crazy.. Available only ...\n",
"1 ham Ok lar... Joking wif u oni...\n",
"2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n",
"3 ham U dun say so early hor... U c already then say...\n",
"4 ham Nah I don't think he goes to usf, he lives aro..."
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#read in file\n",
"df = pd.read_csv('sms_spam.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">text</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>unique</th>\n",
" <th>top</th>\n",
" <th>freq</th>\n",
" </tr>\n",
" <tr>\n",
" <th>type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ham</th>\n",
" <td>4827</td>\n",
" <td>4518</td>\n",
" <td>Sorry, I'll call later</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spam</th>\n",
" <td>747</td>\n",
" <td>642</td>\n",
" <td>Please call our customer service representativ...</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text \n",
" count unique top freq\n",
"type \n",
"ham 4827 4518 Sorry, I'll call later 30\n",
"spam 747 642 Please call our customer service representativ... 4"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# group by type of text/sms\n",
"df.groupby('type').describe()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>text</th>\n",
" <th>spam</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ham</td>\n",
" <td>Go until jurong point, crazy.. Available only ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ham</td>\n",
" <td>Ok lar... Joking wif u oni...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>spam</td>\n",
" <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ham</td>\n",
" <td>U dun say so early hor... U c already then say...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ham</td>\n",
" <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type text spam\n",
"0 ham Go until jurong point, crazy.. Available only ... 0\n",
"1 ham Ok lar... Joking wif u oni... 0\n",
"2 spam Free entry in 2 a wkly comp to win FA Cup fina... 1\n",
"3 ham U dun say so early hor... U c already then say... 0\n",
"4 ham Nah I don't think he goes to usf, he lives aro... 0"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#creating a new column named spam that classifies texts into spam or no spam messages/sms\n",
"# using the lambda function\n",
"df['spam'] = df['type'].apply(lambda x:1 if x == 'spam' else 0)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"#using the train test split to split our datasets in the ratio 75:25 or 3:1\n",
"x_train,x_test,y_train,y_test = train_test_split(df.text,df.spam,test_size=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0],\n",
" [0, 0, 0, ..., 0, 0, 0]])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Taking care of our text data by calling the count_vectorizer on them to change into a numerical data\n",
"# that the model will understand.\n",
"count = CountVectorizer()\n",
"x_train_count = count.fit_transform(x_train.values)\n",
"x_train_count.toarray()[:3]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making use of the MultiNomial Naive Bayes model\n",
"model = LogisticRegression()\n",
"model.fit(x_train_count,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9849354375896701"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Testing out our model's accuracy\n",
"x_test_pred = count.transform(x_test)\n",
"accuracy_score(model.predict(x_test_pred),y_test)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"classification report : precision recall f1-score support\n",
"\n",
" 0 1.00 0.98 0.99 1212\n",
" 1 0.90 0.99 0.95 182\n",
"\n",
" accuracy 0.98 1394\n",
" macro avg 0.95 0.99 0.97 1394\n",
"weighted avg 0.99 0.98 0.99 1394\n",
"\n"
]
}
],
"source": [
"# Classification report\n",
"print(f\"classification report : {classification_report(model.predict(x_test_pred),y_test)}\")"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"# Using the pipeline\n",
"clf = Pipeline([\n",
" ('vectorizer',CountVectorizer()),\n",
" ('nb',LogisticRegression())\n",
"])\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('vectorizer', CountVectorizer()),\n",
" ('nb', LogisticRegression())])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# fit our model\n",
"clf.fit(x_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9849354375896701"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Score our model\n",
"clf.score(x_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.97607656, 0.9784689 , 0.97727273, 0.98684211, 0.98325359])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv = ShuffleSplit(n_splits = 5, test_size = 0.2, random_state=0)\n",
"cross_val_score(MultinomialNB(),x_train_count,y_train, cv=cv)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# Saving our model as a pickle file\n",
"import pickle\n",
"with open(\"model_log.pkl\", \"wb\") as f:\n",
" pickle.dump(model, f)\n",
"\n",
"with open(\"model_log.pkl\", \"rb\") as f:\n",
" model = pickle.load(f)\n",
" \n",
"\n",
"# Saving our vectorizer\n",
"with open(\"vectorizer.pkl\", \"wb\") as vect:\n",
" pickle.dump(count, vect)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 1])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = [\"FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv\"\n",
" , \"Nah I don't think he goes to usf, he lives around here though\",\"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\",\n",
" \"URGENT! You have won a 1 week FREE membership in our £100,000 Prize Jackpot! Txt the word: CLAIM to No: 81010 T&C www.dbuk.net LCCLTD POBOX 4403LDNW1A7RW18\"]\n",
"test = count.transform(s).toarray()\n",
"model.predict(test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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