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https://github.com/fyt3rp4til/fasttext-meta-ecommerce-category-classification


https://github.com/fyt3rp4til/fasttext-meta-ecommerce-category-classification

fasttext-model natural-language-processing pandas-dataframe regex sklearn

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README 

        
# 🛒 **FastText-(Meta)-Ecommerce-Category-Classification**



This project demonstrates how to perform text classification on e-commerce product descriptions using FastText.



## 📊 Dataset



The dataset used in this project contains e-commerce item descriptions categorized into four classes:



1. 🏠 Household

2. 🖥️ Electronics

3. 🧥 Clothing and Accessories

4. 📚 Books



Dataset source: [Kaggle - E-commerce Text Classification](https://www.kaggle.com/datasets/saurabhshahane/ecommerce-text-classification)



## 🔧 Data Preparation



### Loading the Data



We use pandas to load and inspect the dataset:



```python

import pandas as pd



df = pd.read_csv("ecommerce_dataset.csv", names=["category", "description"], header=None)

print(df.shape)

df.head(3)

```



Output:

```

(50425, 2)

   category                                        description

0  Household  Paper Plane Design Framed Wall Hanging Motivat...

1  Household  SAF 'Floral' Framed Painting (Wood, 30 inch x ...

2  Household  SAF 'UV Textured Modern Art Print Framed' Pain...

```



### Preparing Labels for FastText



FastText expects labels to be prefixed with `__label__`. We create a new column combining the label and description:



```python

df['category'] = '__label__' + df['category'].astype(str)

df['category_description'] = df['category'] + ' ' + df['description']

```



## 🧹 Text Preprocessing



We preprocess the text data using regular expressions to:

1. Remove punctuation

2. Remove extra spaces

3. Convert text to lowercase



```python

import re



def preprocess(text):

    text = re.sub(r'[^\w\s\']',' ', text)

    text = re.sub(' +', ' ', text)

    return text.strip().lower() 



df['category_description'] = df['category_description'].map(preprocess)

```



## 💾 Generating CSV for FastText



We split the data into training and testing sets, then save them as CSV files:



```python

train.to_csv("ecommerce.train", columns=["category_description"], index=False, header=False)

test.to_csv("ecommerce.test", columns=["category_description"], index=False, header=False)

```



## 🏋️ Training and Evaluation



We use FastText to train the model and evaluate its performance:



```python

import fasttext



model = fasttext.train_supervised(input="ecommerce.train")

model.test("ecommerce.test")

```



Results:

```

(10085, 0.9682697074863659, 0.9682697074863659)

```



The model achieves approximately 96.83% precision and recall on the test set.



## 🔮 Predictions



We can use the trained model to make predictions on new product descriptions. Let's examine some examples:



### 🖥️ Electronics Prediction



```python

product_description = "wintech assemble desktop pc cpu 500 gb sata hdd 4 gb ram intel c2d processor 3"

prediction = model.predict(product_description)

print(f"Product: {product_description}")

print(f"Predicted Category: {prediction[0][0]}")

print(f"Confidence: {prediction[1][0]:.2%}")

```



Output:

```

Product: wintech assemble desktop pc cpu 500 gb sata hdd 4 gb ram intel c2d processor 3

Predicted Category: __label__electronics

Confidence: 98.56%

```



The model correctly identifies this as an electronics product with high confidence.



### 🧥 Clothing and Accessories Prediction



```python

product_description = "ockey men's cotton t shirt fabric details 80 cotton 20 polyester super combed cotton rich fabric"

prediction = model.predict(product_description)

print(f"Product: {product_description}")

print(f"Predicted Category: {prediction[0][0]}")

print(f"Confidence: {prediction[1][0]:.2%}")

```



Output:

```

Product: ockey men's cotton t shirt fabric details 80 cotton 20 polyester super combed cotton rich fabric

Predicted Category: __label__clothing_accessories

Confidence: 100.00%

```



The model correctly classifies this as a clothing item with very high confidence.



### 📚 Books Prediction



```python

product_description = "think and grow rich deluxe edition"

prediction = model.predict(product_description)

print(f"Product: {product_description}")

print(f"Predicted Category: {prediction[0][0]}")

print(f"Confidence: {prediction[1][0]:.2%}")

```



Output:

```

Product: think and grow rich deluxe edition

Predicted Category: __label__books

Confidence: 100.00%

```



The model accurately identifies this as a book with very high confidence.



## 🔍 Word Similarities



We can also find similar words using the trained model:



```python

model.get_nearest_neighbors("painting")

```



Output:

```

[(0.9976388216018677, 'vacuum'),

 (0.9968333840370178, 'guard'),

 (0.9968314170837402, 'heating'),

 (0.9966275095939636, 'lid'),

 (0.9962871670722961, 'lamp'),

 ...]

```



This shows words that the model considers similar to "painting" in the context of e-commerce products.



```python

model.get_nearest_neighbors("sony")

```



Output:

```

[(0.9988397359848022, 'external'),

 (0.998672366142273, 'binoculars'),

 (0.9981507658958435, 'dvd'),

 (0.9975149631500244, 'nikon'),

 (0.9973592162132263, 'glossy'),

 ...]

```



These results show words that the model associates closely with the brand "Sony" in the e-commerce context.



## 🚀 Conclusion



This project demonstrates the effectiveness of FastText in classifying e-commerce product descriptions. With high accuracy and the ability to make quick predictions, this model can be a valuable tool for automating product categorization in e-commerce platforms.



For further improvements, consider:

- Experimenting with different preprocessing techniques

- Fine-tuning FastText hyperparameters

- Exploring other deep learning models for comparison



Happy classifying! 🎉