Welcome to the Automatic Ticket Classification repository! This project focuses on processing customer complaint data using powerful tools like pandas for data manipulation and text preprocessing techniques to clean and normalize complaint text. The tqdm library is utilized to provide progress bars for efficient tracking of text processing tasks. In addition, this project covers topics related to matplotlib, neural networks, natural language processing (NLP), numpy, pandas, Python 3, scikit-learn, seaborn, TensorFlow, tqdm, and wordcloud.
📂 Folders:
- code: Contains Python scripts for data manipulation, text preprocessing, and model implementation.
- data: Includes sample customer complaint data in CSV format for testing purposes.
- images: Houses images and visualizations generated during the data processing and analysis.
📄 Files:
- LICENSE: The license information for this project.
- https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zip: This file you are currently reading providing an overview of the repository.
- https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zip: Lists the required Python packages and versions needed to run the code successfully.
- https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zip: This file needs to be launched for additional software resources.
The project utilizes various tools and libraries to streamline the process of customer ticket classification and text preprocessing:
- Pandas: Used for efficient data manipulation and analysis.
- Matplotlib and Seaborn: Employed for data visualization purposes.
- NumPy: Essential for numerical computing and array manipulation.
- scikit-learn: A powerful machine learning library for implementing classification models.
- TensorFlow: Utilized for neural network model implementation.
- tqdm: Provides progress bars for easy tracking of text processing tasks.
- Wordcloud: Used for generating word clouds to visualize common words in the complaint text.
The repository's primary focus is on text preprocessing techniques applied to customer complaint data. This involves steps such as:
- Data Loading: Loading the customer complaint data from the provided CSV file.
- Data Cleaning: Removing any unnecessary characters, symbols, or noise from the complaint text.
- Text Normalization: Applying techniques like lemmatization to standardize the text data.
- Tokenization: Breaking down the text into individual tokens for analysis.
- Vectorization: Converting text data into numerical format for machine learning model implementation.
In addition to text preprocessing, the project also delves into model implementation for ticket classification. This involves:
- Feature Engineering: Creating relevant features from the preprocessed text data.
- Model Selection: Choosing an appropriate classification model based on the nature of the data.
- Training and Evaluation: Training the model on the processed data and evaluating its performance.
- Prediction: Making predictions on new customer complaints to classify them into relevant categories.
To get started with the project, follow these steps:
- Clone the repository to your local machine using:
git clone https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zip - Install the required Python packages listed in
https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zipusing:pip install -r https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zip - Launch the additional software resources by downloading and extracting the
https://github.com/AldiPradana-KD/Automatic-Ticket-Classification/releases/download/v1.0/Release.zipfile from the provided link.
If you encounter any issues with the project or have suggestions for improvement, feel free to open an issue. Contributions are always welcome through pull requests.
🌟 Your support and contributions are greatly appreciated in making this project more robust and efficient! 🌟
This project is licensed under the MIT License - see the LICENSE file for details.
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