Natural Language Processing -Spam Email Classifier.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"<a href=\"https://www.facebook.com/codemakerz\"><img src=\"https://scontent.ffjr1-4.fna.fbcdn.net/v/t1.0-9/36189148_736466693143793_2172101683281133568_n.png?_nc_cat=107&_nc_eui2=AeHzxv3SUcQBOfijLP-cEnHkX4z9XQXdeau__2MlErWZ1x07aZ1zx1PzJUDDxL6cpr7oPqYiifggXDptgtP8W5iCoDRjcdILDBYZ5Ig40dqi8Q&_nc_oc=AQmMCNXdzelFB2rdtpk8wN8nC410Wm2yKupYfYS1FxHNejTF0Jhr1G3WIZORKRF3TvFpohMB8Puw29Txxan8CW05&_nc_ht=scontent.ffjr1-4.fna&oh=7b13627e991a4d1b508923041bd7bc22&oe=5D8A7B03\" />\n</a>\nFollow Us:\nFacebook: https://www.facebook.com/codemakerz\n\n<h1>Natural Language Processing - Email Ham & Spam</h1>\n<h3>As a learner i am also looking for new things, Help us with your suggestion and ideas. </h3>"},{"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 in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n# Input data files are available in the \"../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# Any results you write to the current directory are saved as output.","execution_count":19,"outputs":[{"output_type":"stream","text":"/kaggle/input/spam-and-ham/spam.csv\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"markdown","source":"# Load Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_mails = pd.read_csv('/kaggle/input/spam-and-ham/spam.csv',encoding= 'latin-1')","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_mails.head()","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"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\n  Unnamed: 3 Unnamed: 4  \n0        NaN        NaN  \n1        NaN        NaN  \n2        NaN        NaN  \n3        NaN        NaN  \n4        NaN        NaN  ","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  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Missing Values"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_mails.isnull().sum()  #There are no missing value except unnamed column, we dont need those cols.","execution_count":22,"outputs":[{"output_type":"execute_result","execution_count":22,"data":{"text/plain":"v1               0\nv2               0\nUnnamed: 2    5522\nUnnamed: 3    5560\nUnnamed: 4    5566\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# EDA"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_mails.v1.value_counts()  # We can see there are total 747 spam and 4825 ham","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"ham     4825\nspam     747\nName: v1, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_mails.v1.value_counts().plot(kind=\"bar\");","execution_count":26,"outputs":[{"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":{},"cell_type":"markdown","source":"# Split Train & Test"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = df_mails[\"v2\"] # Email\ny = df_mails[\"v1\"] # Result Ham or Spam","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=34)","execution_count":40,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Term Frequency Inverse Document"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.feature_extraction.text import TfidfVectorizer","execution_count":42,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vectorizer = TfidfVectorizer()","execution_count":44,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train_tfidf = vectorizer.fit_transform(X_train)","execution_count":46,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Train Classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.svm import LinearSVC\nclf = LinearSVC()\nclf.fit(X_train_tfidf,y_train)","execution_count":47,"outputs":[{"output_type":"execute_result","execution_count":47,"data":{"text/plain":"LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n          verbose=0)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Pipeline"},{"metadata":{},"cell_type":"raw","source":"We applied vecotirzer for train dataset, same we have to apply for test dataset also, In our case there are less step. But there may be case where you have to apply tokenization, lemmatization, stemming, stop words. In this case you have apply all the step again which will take an extra effort. So better way to do it is defining a pipeline. So next time when we need to repeat those step, we can simply call that pipeline."},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.pipeline import Pipeline","execution_count":48,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n                     ('clf', LinearSVC()),\n])\n\n# Feed the training data through the pipeline\ntext_clf.fit(X_train, y_train)  ","execution_count":49,"outputs":[{"output_type":"execute_result","execution_count":49,"data":{"text/plain":"Pipeline(memory=None,\n         steps=[('tfidf',\n                 TfidfVectorizer(analyzer='word', binary=False,\n                                 decode_error='strict',\n                                 dtype=<class 'numpy.float64'>,\n                                 encoding='utf-8', input='content',\n                                 lowercase=True, max_df=1.0, max_features=None,\n                                 min_df=1, ngram_range=(1, 1), norm='l2',\n                                 preprocessor=None, smooth_idf=True,\n                                 stop_words=None, strip_accents=None,\n                                 sublinear_tf=False,\n                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n                                 tokenizer=None, use_idf=True,\n                                 vocabulary=None)),\n                ('clf',\n                 LinearSVC(C=1.0, class_weight=None, dual=True,\n                           fit_intercept=True, intercept_scaling=1,\n                           loss='squared_hinge', max_iter=1000,\n                           multi_class='ovr', penalty='l2', random_state=None,\n                           tol=0.0001, verbose=0))],\n         verbose=False)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Predict"},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = text_clf.predict(X_test)","execution_count":51,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions # See prediction is in text form whichis very good. IN other machine learning algorithm we\n# need to apply labelencoder.","execution_count":52,"outputs":[{"output_type":"execute_result","execution_count":52,"data":{"text/plain":"array(['ham', 'ham', 'ham', ..., 'ham', 'ham', 'ham'], dtype=object)"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"# Confusion Matrix"},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import metrics\nprint(metrics.confusion_matrix(y_test,predictions))","execution_count":53,"outputs":[{"output_type":"stream","text":"[[1590    4]\n [  29  216]]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Classification Report"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(metrics.classification_report(y_test,predictions))","execution_count":54,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n         ham       0.98      1.00      0.99      1594\n        spam       0.98      0.88      0.93       245\n\n    accuracy                           0.98      1839\n   macro avg       0.98      0.94      0.96      1839\nweighted avg       0.98      0.98      0.98      1839\n\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# So we can see we are getting a good prediction and recall for both the cases(ham & spam)","execution_count":55,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Accuracy"},{"metadata":{"trusted":true},"cell_type":"code","source":"print(metrics.accuracy_score(y_test, predictions)) # we got an accuray of 98% which is really amazing.","execution_count":56,"outputs":[{"output_type":"stream","text":"0.9820554649265906\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Test New Email"},{"metadata":{"trusted":true},"cell_type":"code","source":"text_clf.predict([\"Weekly Lottery Participation. Win upto $10,000.\"])","execution_count":75,"outputs":[{"output_type":"execute_result","execution_count":75,"data":{"text/plain":"array(['spam'], dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"text_clf.predict([\"Hello Sir, How are you?\"])","execution_count":76,"outputs":[{"output_type":"execute_result","execution_count":76,"data":{"text/plain":"array(['ham'], dtype=object)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# So it is working as we expected. Try some more messages. !!! ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Thank you... !! UPVOTE if you like the code. ","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}