- 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]
- 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:
- Word frequency based representation (only unigrams).
- Word frequency based representation (unigrams and bigrams).
- Word frequency based representation (unigrams, bigrams, and trigrams).
- Word TF-IDF based representation (only unigrams).
- Word TF-IDF based representation (unigrams and bigrams).
- Word TF-IDF based representation (unigrams, bigrams, and trigrams).
- 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:
- Multinomial Naive Bayes
- Random Forest
- Linear SVC
- Confusion matrix as well as the performance analysis of each model is provided in the notebook.
- 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% |
- 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% |
- 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.