spam-or-ham.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":71,"outputs":[{"output_type":"stream","text":"/kaggle/input/sms-spam-collection-dataset/spam.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df=pd.read_csv('/kaggle/input/sms-spam-collection-dataset/spam.csv',encoding='latin-1')","execution_count":72,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df\n","execution_count":73,"outputs":[{"output_type":"execute_result","execution_count":73,"data":{"text/plain":"        v1                                                 v2 Unnamed: 2  \\\n0      ham  Go until jurong point, crazy.. Available only ...        NaN   \n1      ham                      Ok lar... Joking wif u oni...        NaN   \n2     spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n3      ham  U dun say so early hor... U c already then say...        NaN   \n4      ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n...    ...                                                ...        ...   \n5567  spam  This is the 2nd time we have tried 2 contact u...        NaN   \n5568   ham              Will Ì_ b going to esplanade fr home?        NaN   \n5569   ham  Pity, * was in mood for that. So...any other s...        NaN   \n5570   ham  The guy did some bitching but I acted like i'd...        NaN   \n5571   ham                         Rofl. Its true to its name        NaN   \n\n     Unnamed: 3 Unnamed: 4  \n0           NaN        NaN  \n1           NaN        NaN  \n2           NaN        NaN  \n3           NaN        NaN  \n4           NaN        NaN  \n...         ...        ...  \n5567        NaN        NaN  \n5568        NaN        NaN  \n5569        NaN        NaN  \n5570        NaN        NaN  \n5571        NaN        NaN  \n\n[5572 rows x 5 columns]","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>v1</th>\n      <th>v2</th>\n      <th>Unnamed: 2</th>\n      <th>Unnamed: 3</th>\n      <th>Unnamed: 4</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>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ham</td>\n      <td>Ok lar... Joking wif u oni...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5567</th>\n      <td>spam</td>\n      <td>This is the 2nd time we have tried 2 contact u...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5568</th>\n      <td>ham</td>\n      <td>Will Ì_ b going to esplanade fr home?</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5569</th>\n      <td>ham</td>\n      <td>Pity, * was in mood for that. So...any other s...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5570</th>\n      <td>ham</td>\n      <td>The guy did some bitching but I acted like i'd...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5571</th>\n      <td>ham</td>\n      <td>Rofl. Its true to its name</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5572 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df=df.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'],axis=1)","execution_count":74,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df=df.rename({'v1': 'target', 'v2': 'text'},axis=1)","execution_count":75,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df\n","execution_count":76,"outputs":[{"output_type":"execute_result","execution_count":76,"data":{"text/plain":"     target                                               text\n0       ham  Go until jurong point, crazy.. Available only ...\n1       ham                      Ok lar... Joking wif u oni...\n2      spam  Free entry in 2 a wkly comp to win FA Cup fina...\n3       ham  U dun say so early hor... U c already then say...\n4       ham  Nah I don't think he goes to usf, he lives aro...\n...     ...                                                ...\n5567   spam  This is the 2nd time we have tried 2 contact u...\n5568    ham              Will Ì_ b going to esplanade fr home?\n5569    ham  Pity, * was in mood for that. So...any other s...\n5570    ham  The guy did some bitching but I acted like i'd...\n5571    ham                         Rofl. Its true to its name\n\n[5572 rows x 2 columns]","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>target</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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5567</th>\n      <td>spam</td>\n      <td>This is the 2nd time we have tried 2 contact u...</td>\n    </tr>\n    <tr>\n      <th>5568</th>\n      <td>ham</td>\n      <td>Will Ì_ b going to esplanade fr home?</td>\n    </tr>\n    <tr>\n      <th>5569</th>\n      <td>ham</td>\n      <td>Pity, * was in mood for that. So...any other s...</td>\n    </tr>\n    <tr>\n      <th>5570</th>\n      <td>ham</td>\n      <td>The guy did some bitching but I acted like i'd...</td>\n    </tr>\n    <tr>\n      <th>5571</th>\n      <td>ham</td>\n      <td>Rofl. Its true to its name</td>\n    </tr>\n  </tbody>\n</table>\n<p>5572 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":77,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5572 entries, 0 to 5571\nData columns (total 2 columns):\n #   Column  Non-Null Count  Dtype \n---  ------  --------------  ----- \n 0   target  5572 non-null   object\n 1   text    5572 non-null   object\ndtypes: object(2)\nmemory usage: 87.2+ KB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"spam = df[df['target']=='spam']","execution_count":78,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spam","execution_count":79,"outputs":[{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"     target                                               text\n2      spam  Free entry in 2 a wkly comp to win FA Cup fina...\n5      spam  FreeMsg Hey there darling it's been 3 week's n...\n8      spam  WINNER!! As a valued network customer you have...\n9      spam  Had your mobile 11 months or more? U R entitle...\n11     spam  SIX chances to win CASH! From 100 to 20,000 po...\n...     ...                                                ...\n5537   spam  Want explicit SEX in 30 secs? Ring 02073162414...\n5540   spam  ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...\n5547   spam  Had your contract mobile 11 Mnths? Latest Moto...\n5566   spam  REMINDER FROM O2: To get 2.50 pounds free call...\n5567   spam  This is the 2nd time we have tried 2 contact u...\n\n[747 rows x 2 columns]","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>target</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\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>5</th>\n      <td>spam</td>\n      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>spam</td>\n      <td>WINNER!! As a valued network customer you have...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>spam</td>\n      <td>Had your mobile 11 months or more? U R entitle...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>spam</td>\n      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5537</th>\n      <td>spam</td>\n      <td>Want explicit SEX in 30 secs? Ring 02073162414...</td>\n    </tr>\n    <tr>\n      <th>5540</th>\n      <td>spam</td>\n      <td>ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...</td>\n    </tr>\n    <tr>\n      <th>5547</th>\n      <td>spam</td>\n      <td>Had your contract mobile 11 Mnths? Latest Moto...</td>\n    </tr>\n    <tr>\n      <th>5566</th>\n      <td>spam</td>\n      <td>REMINDER FROM O2: To get 2.50 pounds free call...</td>\n    </tr>\n    <tr>\n      <th>5567</th>\n      <td>spam</td>\n      <td>This is the 2nd time we have tried 2 contact u...</td>\n    </tr>\n  </tbody>\n</table>\n<p>747 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ham = df[df['target']=='ham']","execution_count":80,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ham","execution_count":81,"outputs":[{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"     target                                               text\n0       ham  Go until jurong point, crazy.. Available only ...\n1       ham                      Ok lar... Joking wif u oni...\n3       ham  U dun say so early hor... U c already then say...\n4       ham  Nah I don't think he goes to usf, he lives aro...\n6       ham  Even my brother is not like to speak with me. ...\n...     ...                                                ...\n5565    ham                                       Huh y lei...\n5568    ham              Will Ì_ b going to esplanade fr home?\n5569    ham  Pity, * was in mood for that. So...any other s...\n5570    ham  The guy did some bitching but I acted like i'd...\n5571    ham                         Rofl. Its true to its name\n\n[4825 rows x 2 columns]","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>target</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>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    <tr>\n      <th>6</th>\n      <td>ham</td>\n      <td>Even my brother is not like to speak with me. ...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5565</th>\n      <td>ham</td>\n      <td>Huh y lei...</td>\n    </tr>\n    <tr>\n      <th>5568</th>\n      <td>ham</td>\n      <td>Will Ì_ b going to esplanade fr home?</td>\n    </tr>\n    <tr>\n      <th>5569</th>\n      <td>ham</td>\n      <td>Pity, * was in mood for that. So...any other s...</td>\n    </tr>\n    <tr>\n      <th>5570</th>\n      <td>ham</td>\n      <td>The guy did some bitching but I acted like i'd...</td>\n    </tr>\n    <tr>\n      <th>5571</th>\n      <td>ham</td>\n      <td>Rofl. Its true to its name</td>\n    </tr>\n  </tbody>\n</table>\n<p>4825 rows × 2 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('spam percentage:',(len(spam)/len(df))*100)","execution_count":82,"outputs":[{"output_type":"stream","text":"spam percentage: 13.406317300789663\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('ham percentage:',(len(ham)/len(df))*100)","execution_count":83,"outputs":[{"output_type":"stream","text":"ham percentage: 86.59368269921033\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#ham is of 86% whilw spam is of 13%","execution_count":84,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt","execution_count":85,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(df['target'])","execution_count":86,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n","name":"stderr"},{"output_type":"execute_result","execution_count":86,"data":{"text/plain":"<AxesSubplot:xlabel='target', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#we can see that there are more ham than spam","execution_count":87,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords","execution_count":88,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['target']=np.where(df['target']=='spam',1,0)","execution_count":89,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spam=[]\nham=[]\nspam_class=df[df['target']==1]['text']\nham_class=df[df['target']==0]['text']","execution_count":90,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def extract_ham(ham_class):\n    global ham\n    words = [word.lower() for word in word_tokenize(ham_class) if word.lower() not in stopwords.words(\"english\") and word.lower().isalpha()]\n    ham=ham+words","execution_count":91,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def extract_spam(spam_class):\n    global spam\n    words = [word.lower() for word in word_tokenize(spam_class) if word.lower() not in stopwords.words(\"english\") and word.lower().isalpha()]\n    spam=spam+words","execution_count":92,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"spam_class.apply(extract_spam)\nham_class.apply(extract_ham )","execution_count":93,"outputs":[{"output_type":"execute_result","execution_count":93,"data":{"text/plain":"0       None\n1       None\n3       None\n4       None\n6       None\n        ... \n5565    None\n5568    None\n5569    None\n5570    None\n5571    None\nName: text, Length: 4825, dtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from nltk.stem import SnowballStemmer\nimport string\nstemmer = SnowballStemmer(\"english\")\n\ndef cleanText(message):\n    \n    message = message.translate(str.maketrans('', '', string.punctuation))\n    words = [stemmer.stem(word) for word in message.split() if word.lower() not in stopwords.words(\"english\")]\n    \n    return \" \".join(words)\n\ndf[\"text\"] = df[\"text\"].apply(cleanText)\ndf.head(n = 10) ","execution_count":94,"outputs":[{"output_type":"execute_result","execution_count":94,"data":{"text/plain":"   target                                               text\n0       0  go jurong point crazi avail bugi n great world...\n1       0                              ok lar joke wif u oni\n2       1  free entri 2 wkli comp win fa cup final tkts 2...\n3       0                u dun say earli hor u c alreadi say\n4       0          nah dont think goe usf live around though\n5       1  freemsg hey darl 3 week word back id like fun ...\n6       0      even brother like speak treat like aid patent\n7       0  per request mell mell oru minnaminungint nurun...\n8       1  winner valu network custom select receivea å£9...\n9       1  mobil 11 month u r entitl updat latest colour ...","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>target</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>go jurong point crazi avail bugi n great world...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>ok lar joke wif u oni</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>free entri 2 wkli comp win fa cup final tkts 2...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>u dun say earli hor u c alreadi say</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>nah dont think goe usf live around though</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>freemsg hey darl 3 week word back id like fun ...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0</td>\n      <td>even brother like speak treat like aid patent</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0</td>\n      <td>per request mell mell oru minnaminungint nurun...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>winner valu network custom select receivea å£9...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1</td>\n      <td>mobil 11 month u r entitl updat latest colour ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y=df['target']\nX=df['text']","execution_count":95,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":96,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)","execution_count":97,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics import accuracy_score\ncv=CountVectorizer()\nlr=LogisticRegression(max_iter=10000)\nX_train=cv.fit_transform(X_train)","execution_count":98,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr.fit(X_train,y_train)\npred_1=lr.predict(cv.transform(X_test))\nscore_1=accuracy_score(y_test,pred_1)\nscore_1","execution_count":99,"outputs":[{"output_type":"execute_result","execution_count":99,"data":{"text/plain":"0.9808612440191388"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.naive_bayes import MultinomialNB\nnb=MultinomialNB()\nnb.fit(X_train,y_train)\npred_2=nb.predict(cv.transform(X_test))\nscore_2=accuracy_score(y_test,pred_2)\nscore_2","execution_count":100,"outputs":[{"output_type":"execute_result","execution_count":100,"data":{"text/plain":"0.9844497607655502"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.svm import SVC\nsvm=SVC()\nsvm.fit(X_train,y_train)\npred_3=svm.predict(cv.transform(X_test))\nscore_3=accuracy_score(y_test,pred_3)\nscore_3","execution_count":101,"outputs":[{"output_type":"execute_result","execution_count":101,"data":{"text/plain":"0.9814593301435407"},"metadata":{}}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}