gopi3e / Amazon-Food-Reviews-Analysis-and-Modelling

[Machine Learning | Data Analysis] Data Analysis on Amazon Fine Food Reviews dataset.

Amazon-Food-Reviews-Analysis-and-Modelling

Performed Exploratory Data Analysis, Data Cleaning, Data Visualization and Text Featurization(BOW, tfidf, Word2Vec). Build several ML models like KNN, Naive Bayes, Logistic Regression, SVM, Random Forest, etc.

Objective:

Given a text review, determine the sentiment of the review whether its positive or negative.

Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

Notebooks in more readable form on Jupyter Nbviewer[https://nbviewer.jupyter.org/github/cyanamous/Amazon-Food-Reviews-Analysis-and-Modelling/tree/master/]

About Dataset

The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.

Number of reviews: 568,454
Number of users: 256,059
Number of products: 74,258
Timespan: Oct 1999 - Oct 2012
Number of Attributes/Columns in data: 10

Attribute Information:

1. Id
2. ProductId - unique identifier for the product
3. UserId - unqiue identifier for the user
4. ProfileName
5. HelpfulnessNumerator - number of users who found the review helpful
6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
7. Score - rating between 1 and 5
8. Time - timestamp for the review
9. Summary - brief summary of the review
10. Text - text of the review

---

1 Amazon Food Reviews EDA, NLP and Text Preprocessing

1. Defined Problem Statement
2. Performed Exploratory Data Analysis(EDA) on Amazon Fine Food Reviews Dataset plotted Word Clouds, Distplots, Histograms, etc.
3. Performed Data Cleaning & Data Preprocessing by removing unneccesary and duplicates rows and for text reviews removed html tags, punctuations, Stopwords and Stemmed the words using Porter Stemmer
4. Documented the concepts clearly
5. Plotted TSNE plots for Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec

---

2 KNN

1. Applied K-Nearest Neighbour on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec
2. Used both brute & kd-tree implementation of KNN
3. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne

Performance Table

Conclusions:

1. Best Accuracy of 85.107% is achieved by Avg Word2Vec Featurization
2. The kd-tree and brute implementation of KNN gives relatively similar results
3. KNN is a very slow Algorithm compared to others takes alot of time to train
4. KNN did not fair in terms of precision and F1-score. Overall KNN was not that good for this dataset

---

3 Naive Bayes

1. Applied Naive Bayes using Bernoulli NB and Multinomial NB on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec
2. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne
3. Printed Top 25 Important Features for both Negative and Positive Reviews

Performance Table

Conclusions:

1. The best thing about Naive Bayes it much quicker than algorithms amazingly fast training times
   
2. Best Models are Bi-Gram with accuracy of 89.53% and precision of 0.594
3. Multinomial Naive Bayes does not work with negative values
4. Naive Bayes fails miserably with featurization of Word2Vec and tfidf Word2Vec as Word2Vec feature are completely dependent while Naive Bayes is based on assumption of feature independence

---

4 Logistic Regression

1. Applied Logistic Regression on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec
2. Used both Grid Search & Randomized Search Cross Validation
3. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne
4. Showed How Sparsity increases as we increase lambda or decrease C when L1 Regularizer is used for each featurization
   
5. Did pertubation test to check whether the features are multi-collinear or not

Performance Table

Conclusions:

1. Features are multi-collinear i.e. they are co-related
2. Bigram Featurization performs best with accuracy of 93.704 and F1-Score of 0.808
3. Sparsity increases as we increase lambda or decrease C when L1 Regularizer is used
4. Algorithms like SVM & Logistic Regression performed best on this data

---

5 SVM

1. Applied SVM with rbf(radial basis function) kernel on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec
2. Used both Grid Search & Randomized Search Cross Validation
3. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne
4. Evaluated SGDClassifier on the best resulting featurization

Performance Table

Conclusions:

1. Support Vector Machine(SVM) gave the best result better than other algos close to Logistic Regression
2. Tf-idf Featurization(C=1000,gamma=0.005) gave the best results with accuracy of 91.667% and F1-score of 0.733
3. SVM with RBF kernel the separating plane exists in another space - a result of kernel transformation of the original space. Its coefficients are not directly related to the input space. Hence we can't get the feature importance
4. Also tried SGDClassifier with the best result i.e. with tfidf featurization it was very quick and gave around same score in just seconds with accuracy of 91.04% and F1-score of 0.734 with (alpha=1e-05,penalty='l1')

---

6 Decision Trees

1. Applied Decision Trees on Different Featurization of Data viz. BOW(uni-gram,bi-gram), tfidf, Avg-Word2Vec(using Word2Vec model pretrained on Google News) and tf-idf-Word2Vec
2. Used both Grid Search with random 30 points for getting the best max_depth
3. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne
4. Plotted feature importance recieved from the decision tree classifier

Performance Table

Conclusions:

1. Decision Trees on Uni-gram, bi-gram and tfidf would have taken forever if had taken all the dimensions as it had huge dimension and hence tried with max 300 as max_depth
2. Bi-gram Featurization(max_depth=73) gave the best results with accuracy of 85.11% and F1-score of 0.513
3. Plotted feature importance for Uni-gram, bi-gram and tfidf but not for Avg Word2Vec and Tfidf Avg Word2Vec as Word2Vec featurizations are highly co-related hence can't directly get the feature importance

---