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Frequently Asked Questions
Q. 1) Spam Email Classifier: Build a binary classifier to determine whether an email is spam (class 1) or not spam (class 0). Use train_emails.txt and train_labels.txt for training and test_emails.txt with test_labels.txt for testing. Preprocess the email text by removing stop words, tokenizing, and vectorizing before training a Naive Bayes classifier.
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Q. 2) Product Review Sentiment Analysis: Develop a classifier to analyze product reviews and categorize them into positive (class 1) or negative (class 0). Use files train_reviews.txt and train_labels.txt for training and test_reviews.txt with test_labels.txt for testing. Preprocess the reviews and train a Logistic Regression model. Report performance metrics like accuracy, precision, and recall.
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Q. 3) News Headline Classifier: Create a model to classify news headlines into either political (class 1) or non-political (class 0). Training data is in train_headlines.txt and train_labels.txt, while testing data is in test_headlines.txt and test_labels.txt. Use TF-IDF for feature extraction and evaluate using a Decision Tree classifier.
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Q. 4) Song Lyrics Genre Classifier: Build a model to classify song lyrics into pop (class 1) or rock (class 0). Use train_lyrics.txt and train_labels.txt for training and test_lyrics.txt with test_labels.txt for testing. Preprocess the lyrics, remove stop words, and train a Support Vector Machine (SVM).
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Q. 5) Customer Feedback Classifier: Create a binary classifier to identify whether customer feedback is actionable (class 1) or not (class 0). Training data is in train_feedback.txt and train_labels.txt, and testing data is in test_feedback.txt and test_labels.txt. Tokenize, preprocess, and use a Random Forest model to classify the feedback.
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Q. 6) Twitter Sentiment Analysis: Train a classifier to identify whether a tweet conveys a positive sentiment (class 1) or a negative sentiment (class 0). Use train_tweets.txt and train_labels.txt for training and test_tweets.txt and test_labels.txt for testing. Preprocess hashtags, mentions, and emojis, and train with a Neural Network.
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Q. 7) Resume Skill Classifier: Build a model to classify resumes into technical (class 1) or non-technical (class 0) categories based on their content. Use train_resumes.txt and train_labels.txt for training and test_resumes.txt and test_labels.txt for testing. Feature extraction should include identifying technical terms and training a K-Nearest Neighbors (KNN) model.
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Q. 8) Customer Complaint Classification: Develop a classifier to predict whether a complaint message requires escalation (class 1) or not (class 0). Use train_complaints.txt and train_labels.txt for training and test_complaints.txt and test_labels.txt for testing. Use TF-IDF and train using a Gradient Boosting model.
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Q. 9) Book Summary Genre Classifier: Train a model to classify book summaries into fiction (class 1) or non-fiction (class 0). Use train_summaries.txt and train_labels.txt for training and test_summaries.txt and test_labels.txt for testing. Preprocess the summaries and train a Naive Bayes classifier.
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Q. 10) Chat Message Intent Classifier: Build a binary classifier to determine whether a chat message contains a customer query (class 1) or a general statement (class 0). Use train_chats.txt and train_labels.txt for training and test_chats.txt and test_labels.txt for testing. Preprocess the messages and train a Logistic Regression model.
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