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| # -*- coding: utf-8 -*- | |
| """First_Text_Classification.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1sdLss09e3OxYVoeK3oBA6qrUSj_iOxp- | |
| <h3 align = "center">Importing Libraries</h3> | |
| """ | |
| import numpy as np | |
| import pandas as pd | |
| """<h3 align = "center">Importing Dataset</h3>""" | |
| data = pd.read_csv("spam.csv", encoding = "ISO-8859-1") | |
| """<h3 align = "center">Preliminary Data Checks</h3>""" | |
| data.head() | |
| data.isnull().sum() | |
| data.shape | |
| data['v1'].value_counts() | |
| data.info() | |
| """<h3 align = "center">Putting the Length of Characters of each row in a column.</h3>""" | |
| data["Unnamed: 2"] = data["v2"].str.len() | |
| """<h3 align = "center">Visualising Length of Characters for each category!</h3>""" | |
| """<h5>It is evident from the above plot that spam texts are usually longer in length!</h5> | |
| <h3 align = "center">Defining Variables</h3> | |
| """ | |
| X = data["v2"] | |
| y = data["v1"] | |
| """<h3 align = "center">Train Test Split</h3>""" | |
| 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) | |
| """<h3 align = "center">Vecrorizing Words into Matrix</h3>""" | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| count_vect = CountVectorizer() | |
| X_train_counts = count_vect.fit_transform(X_train) | |
| X_train_counts | |
| X_train.shape | |
| X_train_counts.shape | |
| from sklearn.feature_extraction.text import TfidfTransformer | |
| tfidf_transformer = TfidfTransformer() | |
| X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) | |
| X_train_tfidf.shape | |
| """<h3 align = "center">Using TDIF Vectorizer for optimum vectorization!</h3>""" | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| vectorizer = TfidfVectorizer() | |
| X_train_tfidf = vectorizer.fit_transform(X_train) | |
| X_train_tfidf.shape | |
| """<h3 align = "center">Creating Model</h3>""" | |
| from sklearn.svm import LinearSVC | |
| clf = LinearSVC() | |
| clf.fit(X_train_tfidf,y_train) | |
| """<h3 align = "center">Creating Pipeline</h3>""" | |
| from sklearn.pipeline import Pipeline | |
| text_clf = Pipeline([("tfidf",TfidfVectorizer()),("clf",LinearSVC())]) | |
| text_clf.fit(X_train,y_train) | |
| predictions = text_clf.predict(X_test) | |
| X_test | |
| from sklearn.metrics import confusion_matrix,classification_report,accuracy_score | |
| print(confusion_matrix(y_test,predictions)) | |
| print(classification_report(y_test,predictions)) | |
| """<h3 align = "center">Accuracy Score</h3>""" | |
| print(accuracy_score(y_test,predictions)) | |
| """<h3 align = "center">Predictions </h3>""" | |
| text_clf.predict(["Hi how are you doing today?"]) | |
| text_clf.predict(["Congratulations! You are selected for a free vouchar worth $500"]) | |
| """<h3 align = "center">Creating User Interface!</h3>""" | |
| import gradio as gr | |
| def first_nlp_spam_detector(text): | |
| list = [] | |
| list.append(text) | |
| arr = text_clf.predict(list) | |
| if arr[0] == 'ham': | |
| return "Your Text is a Legitimate One!" | |
| else: | |
| return "Beware of such text messages, It\'s a Spam! " | |
| interface = gr.Interface(first_nlp_spam_detector,inputs = gr.Textbox(lines=2, placeholder="Enter your Text Here.....!", show_label = False), | |
| outputs = gr.Label(value = "Predicting the Text Classification..!"),description = "Predicting Text Legitimacy!") | |
| first_nlp_spam_detector("Congratulations! You are selected for a free vouchar worth $500") | |
| interface.launch() |