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https://github.com/steveee27/e-commerce-product-description-classification

Classify e-commerce product descriptions into categories (Household, Books, Electronics, Clothing & Accessories) using SVM and Random Forest models with TF-IDF and Word2Vec representations. Includes data preprocessing, hyperparameter tuning, and model evaluation for performance comparison.
https://github.com/steveee27/e-commerce-product-description-classification

e-commerce machine-learning nlp product-classification random-forest-classifier svm svm-classifier text-classification tf-idf word2vec

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Classify e-commerce product descriptions into categories (Household, Books, Electronics, Clothing & Accessories) using SVM and Random Forest models with TF-IDF and Word2Vec representations. Includes data preprocessing, hyperparameter tuning, and model evaluation for performance comparison.

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# E-commerce Product Classification



This project focuses on classifying product descriptions from an e-commerce website into four categories: Household, Books, Electronics, and Clothing & Accessories. The classification model uses two machine learning algorithms, SVM and Random Forest, and two text representation methods, TF-IDF and Word2Vec.



## Table of Contents

- [Project Overview](#project-overview)

- [Project Workflow](#project-workflow)

- [Results](#results)

- [Conclusion](#conclusion)

- [License](#license)



## Project Overview



- **Data Source**: Product descriptions from an e-commerce website with labels for four categories.

- **Text Representation Techniques**: TF-IDF and Word2Vec.

- **Machine Learning Algorithms**: Support Vector Machine (SVM) and Random Forest.

- **Performance Metrics**: Accuracy, Precision, Recall, and F1 Score.



## Project Workflow



1. **Data Preprocessing**: 

   - Clean the data by removing special characters, converting text to lowercase, and performing lemmatization.

   - Remove stopwords to focus on significant words.

2. **Text Representation**:

   - **TF-IDF**: Term Frequency-Inverse Document Frequency to represent text as numerical vectors.

   - **Word2Vec (CBOW)**: Continuous Bag of Words to capture the semantic similarity between words.

3. **Modeling and Hyperparameter Tuning**:

   - **Algorithms**: Apply SVM and Random Forest with hyperparameter tuning for each text representation method.

   - Tune at least two hyperparameters for each algorithm to improve performance.

4. **Evaluation and Comparison**:

   - Evaluate model performance on test data using Accuracy, Precision, Recall, and F1 Score.

   - Compare results across different models and text representation techniques to determine the most effective approach.



## Results



| Text Representation   | Algorithm     | Hyperparameters                              | Accuracy | Precision | Recall | F1 Score |

|-----------------------|---------------|----------------------------------------------|----------|-----------|--------|----------|

| **TF-IDF (Default)**  | **SVM**       | c = 1.0, kernel = rbf, gamma = scale         | 0.9547   | 0.9553    | 0.9547 | 0.9547   |

|                       | SVM (Tuning 1)| c = 0.5, kernel = linear, gamma = auto       | 0.9544   | 0.9546    | 0.9544 | 0.9543   |

|                       | SVM (Tuning 2)| c = 1.5, kernel = poly, gamma = scale        | 0.8346   | 0.8783    | 0.8346 | 0.8357   |

|                       | **Random Forest** | n_estimators = 100, max_depth = None, min_samples_split = 2 | 0.9345 | 0.9338 | 0.9345 | 0.9344 |

|                       | Random Forest (Tuning 1) | n_estimators = 150, max_depth = 20, min_samples_split = 5 | 0.8140 | 0.8630 | 0.8140 | 0.8140 |

|                       | Random Forest (Tuning 2) | n_estimators = 50, max_depth = 50, min_samples_split = 3 | 0.9218 | 0.9260 | 0.9218 | 0.9216 |

| **TF-IDF (Tuned)** 
 min_df = 5, max_df = 0.95 | **SVM** | c = 1.0, kernel = rbf, gamma = scale | **0.9551** | **0.9556** | **0.9551** | **0.9551** |

|                       | Random Forest | n_estimators = 100, max_depth = None, min_samples_split = 2 | 0.9361 | 0.9376 | 0.9361 | 0.9360 |

| **Word2Vec (CBOW)**   | **SVM**       | c = 1.0, kernel = rbf, gamma = scale         | 0.9290   | 0.9292    | 0.9290 | 0.9289   |

|                       | SVM (Tuning 1)| c = 1.5, kernel = linear, gamma = scale      | 0.9298   | 0.9298    | 0.9298 | 0.9297   |

|                       | SVM (Tuning 2)| c = 0.5, kernel = poly, gamma = auto         | 0.8544   | 0.8567    | 0.8544 | 0.8526   |

|                       | **Random Forest** | n_estimators = 100, max_depth = None, min_samples_split = 2 | 0.9405 | 0.9406 | 0.9405 | 0.9405 |

|                       | Random Forest (Tuning 1) | n_estimators = 75, max_depth = 45, min_samples_split = 4 | **0.9425** | **0.9426** | **0.9425** | **0.9424** |

|                       | Random Forest (Tuning 2) | n_estimators = 200, max_depth = 25, min_samples_split = 5 | 0.9406 | 0.9406 | 0.9406 | 0.9406 |



**Notes**:

- Hyperparameter tuning was performed to optimize the models for higher accuracy and F1 scores.

- The **SVM model with TF-IDF (Tuned)** and **Random Forest with Word2Vec (CBOW)** achieved the best performance overall.



## Conclusion



The results clearly demonstrate that the combination of TF-IDF with a tuned SVM model achieves the best performance across all metrics, including accuracy, precision, recall, and F1 score. Other combinations, such as Random Forest with Word2Vec or even TF-IDF, consistently lagged behind, with their highest scores reaching only around 0.92 compared to 0.95 achieved by the tuned SVM model. These findings highlight the importance of fine-tuning hyperparameters and selecting robust text representation techniques like TF-IDF for achieving superior results in e-commerce product classification. This approach proves particularly effective for tasks requiring both accuracy and consistency in categorization.



## License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.