BI_Final_Group_1
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The document discusses building a machine learning model to predict the cuisine of a dish based on its ingredients list. It explores various techniques like Naive Bayes classification and preprocessing steps. The best performing model was Naive Bayes, achieving an accuracy of 72.68%. While not the highest score on online challenges, the project provided valuable experience in natural language processing and machine learning.
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BI_Final_Group_1
- 2. OUTLINE Problem Statement Techniques Bernoulli Naïve Bayes Problems Encountered Data Mining Performance Conclusion
- 3. THE PROBLEM What if a machine behaves like a food connoisseur ? Essentially this machine needs to predict the category of a dish's cuisine given a list of ingredients We “humans” took up this challenge to build our own food connoisseur Employed Machine Learning and Natural Language Processing
- 5. Techniques Supervised Learning Text mining Preprocessing Stemming Stop word removal Sparse term removal Term frequency Inverse document frequency Feature Selection Bag of Words Algorithm Classification
- 6. Bernoulli Naïve Bayes Bayes Theorem (Prior and Posterior probabilities) Posterior: Probability (Italian/cheese) = (Probability (Italian) * Probability (cheese/ Italian))/ Probability (cheese) Decision rule: P(word|class)=(word_count_in_class + 1)/(total_words_in_class+total_unique_words_in_class) Real World Applications: Used for Spam Filtering, news classification, sentiment analysis
- 7. Problems Encountered Id Cuisine Ingredients 29 Thai Sugar, hot chilli, Asian fish sauce, lime juice. Possible Class Imbalance: Italian, Mexican, Southern U.S ~50% Accuracy on least frequent cuisines very low Counter Over fitting: Sampling Partitioning Preprocessing: Stemming eg. Almond vs almonds, thigh vs thighs Concatenation eg. Black olives vs black_olives
- 8. Problems Encountered Clustering of ingredients doesn’t work Disproportionate NULL values Poor Tf-idf accuracy Weighting of important ingredients Normalization: complex classification Least and most common ingredients Removed Sparse Terms
- 9. Load all the packages such as “tm”, “caret”, “wordcloud” etc. Convert Data from JSON format to Data Frame using “jsonlite” Set the levels target feature – cuisine for better classification cook$cuisine <- factor(cook$cuisine) Initial Preprocessing: “-” and “ ” into “_” for all the ingredients Create a corpus and then convert it to a document term matrix Data Mining (Import Data)
- 10. Final pre-processing using “tm” package Remove numbers, stop words, punctuation, and stemming Removing least and most common ingredients Draw random samples for train and test dataset Total – 39774 Training – 29830 Testing – 9944 Converting all the observations into Boolean using apply function From 0/1/2… to Yes/No Building a Naïve Bayes Model and confusion Matrix Data Mining (Finalize Data)
- 11. Decision Tree Multiple classes – only option Confusion Matrix Overall Accuracy of the model – 52.5%. Random Forest Accuracy Increased : 64.70% Out of Bag Error Estimate : 36.11% Performance
- 12. Linear Support Vector Machines SVM implemented in R Accuracy Decreased – 49.5% Naïve Bayes Model improves the accuracy for Multi-classification Accuracy improved to 72.68% Overall Statistics Accuracy – 0.7268 95% CI(0.7179, 0.7355) Performance
- 13. Word cloud A visual representation for text data, to visualize free form text. Tags are usually single words, and the importance of each tag is shown with font size or color. Italian Indian Task: Predict the cuisine for each recipe; Word cloud on the right are examples of our project results. Cheese is the most used ingredient in Italian food. Cumin is also very important in Indian food.
- 14. Conclusion Accuracy 72.68% , highest on kaggle for this problem is 82.17%. Got head start in Natural Language Processing and Machine Learning Looking forward to taking up more challenging and intriguing problems in these domains. First one would be doing sentiment analysis on Professor MacDonald’s Twitter/ Facebook/ LinkedIn Real and Fake profiles :D :p THANK YOU!!!