Flipkart Product Category Classification

• The task is to classify the product category based on the product's description.
• The product's description has to be extracted from the product category tree.
• The dataset at hand is the Flipkart e-commerce sales sample dataset containing about 20k samples. [link]

Solution:

• The textual description of each product is used to categories the product.
• The text data is preprocessed by removing emails, new line characters, distracting single quotes, digits, puntuations, single characters, accented words, and multiple spaces.
• Various feature engineering techniques are used to develop input representations for the ML models. They are as follows:
  1. Word frequency based representation (only unigrams).
  2. Word frequency based representation (unigrams and bigrams).
  3. Word frequency based representation (unigrams, bigrams, and trigrams).
  4. Word TF-IDF based representation (only unigrams).
  5. Word TF-IDF based representation (unigrams and bigrams).
  6. Word TF-IDF based representation (unigrams, bigrams, and trigrams).
  7. Character TF-IDF based representation (bigrams and trigrams).
• The dataset is split using stratified 70:30, train:test ratio.
• The models are trained on the test data and its performance is measured on the validation data.
• Standard machine learning models are used as they give pretty good accuracy. They are as follows:
  1. Multinomial Naive Bayes
  2. Random Forest
  3. Linear SVC
• Confusion matrix as well as the performance analysis of each model is provided in the notebook.

Results (considering the to 10 product categories)

• 16,631 samples out of the total 20k samples cover the top 10 categories which is about 83% of the total data.
• The 70:30 stratified split results in 11,641 train samples and 4,990 test samples.

Feature	Multinomial Naive Bayes	Random Forest	Linear SVC
Word frequency based representation (only unigrams)	97.35%	98.29%	98.93%
Word frequency based representation (unigrams and bigrams)	95.53%	98.39%	98.79%
Word frequency based representation (unigrams, bigrams, and trigrams)	95.39%	98.43%	98.71%
Word TF-IDF based representation (only unigrams)	95.13%	97.87%	98.21%
Word TF-IDF based representation (unigrams and bigrams)	97.61%	97.83%	99.27%
Word TF-IDF based representation (unigrams, bigrams, and trigrams)	97.17%	97.73%	99.17%
Character TF-IDF based representation (bigrams and trigrams)	87.27%	96.89%	98.95%

Results (considering the to 25 product categories)

• 19,619 samples out of the total 20k samples cover the top 10 categories which is about 98% of the total data.
• The 70:30 stratified split results in 13,733 train samples and 5,886 test samples.

Feature	Multinomial Naive Bayes	Random Forest	Linear SVC
Word frequency based representation (only unigrams)	93.84%	96.55%	97.04%
Word frequency based representation (unigrams and bigrams)	93.30%	97.17%	97.19%
Word frequency based representation (unigrams, bigrams, and trigrams)	91.76%	97.26%	96.97%
Word TF-IDF based representation (only unigrams)	88.26%	96.00%	97.63%
Word TF-IDF based representation (unigrams and bigrams)	92.18%	96.29%	97.72%
Word TF-IDF based representation (unigrams, bigrams, and trigrams)	92.15%	96.29%	97.77%
Character TF-IDF based representation (bigrams and trigrams)	76.97%	94.75%	97.38%

Conclusion:

• The simple Machine learning models give quite a good performance for the task at hand.
• Thus, no deep learning models (DNN, CNN, LSTM) or other complex architectures (BERT, etc) are used.
• It can be infered that Linear SVC is better than Multinomial Naive Bayes and Random Forest in terms of performance as well as memory.
• The performance can be further improved by opting for a multimodal approach, extracting the information from the product's images.