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| license: mit |
| language: |
| - en |
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| ## What is Transactify? |
| Transactify is an LSTM-based model designed to predict the category of online payment transactions from their descriptions. |
| By analyzing textual inputs like "Live concert stream on YouTube" or "Coffee at Starbucks," it classifies transactions into categories such as "Movies & Entertainment" or "Food & Dining." |
| This model helps users track and organize their spending across various sectors, providing better financial insights and budgeting. |
| Transactify is trained on real-world transaction data for improved accuracy and generalization. |
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| ## Table of contents |
| ## 1. Data Collection |
| The dataset consists of **5,000 transaction records** generated using ChatGPT, each containing a transaction description and its corresponding category. |
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| Example entries include: |
| - "Live concert stream on YouTube" (Movies & Entertainment) |
| - "Coffee at Starbucks" (Food & Dining) |
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| These records cover various spending categories such as **Lifestyle**, **Movies & Entertainment**, **Food & Dining**, and others. |
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| ## 2. Data Preprocessing |
| The preprocessing step involves several natural language processing (NLP) tasks to clean and prepare the text data for model training. These include: |
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| - Lowercasing all text. |
| - Removing digits and punctuation using regular expressions (regex). |
| - Tokenizing the cleaned text to convert it into a sequence of tokens. |
| - Applying `text_to_sequences` to transform the tokenized words into numerical sequences. |
| - Using `pad_sequences` to ensure all sequences have the same length for input into the LSTM model. |
| - Label encoding the target categories to convert them into numerical labels. |
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| After preprocessing, the data is split into training and testing sets to build and validate the model. |
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| ## 3. Model Building |
| - **Embedding Layer**: Converts tokenized transaction descriptions into dense vectors, capturing word semantics and relationships. |
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| - **LSTM Layer**: Learns sequential patterns from the embedded text, helping the model understand the context and relationships between words over time. |
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| - **Dropout Layer**: Introduces regularization by randomly turning off neurons during training, reducing overfitting and improving the model's generalization. |
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| - **Dense Layer with Softmax Activation**: Outputs a probability distribution across categories, allowing the model to predict the correct category for each transaction description. |
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| ### Model Compilation |
| - Compiled with the Adam optimizer for efficient learning. |
| - Sparse categorical cross-entropy loss for multi-class classification. |
| - Accuracy as the evaluation metric. |
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| ### Model Training |
| The model is trained for **50 epochs** with a batch size of **8**, using a validation set to monitor performance and adjust during training. |
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| ### Saving the Model and Preprocessing Objects |
| - The trained model is saved as `transactify.h5` for future use. |
| - The tokenizer and label encoder used during preprocessing are saved using joblib as `tokenizer.joblib` and `label_encoder.joblib`, respectively, ensuring they can be reused for consistent tokenization and label encoding when making predictions on new data. |
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| ## 4. Prediction |
| Once trained |
