PrinceParvez
/

customer-support-ai

@@ -17,7 +17,7 @@ A machine learning model for classifying banking customer-support messages into
 This project demonstrates a complete machine-learning workflow, including dataset inspection, data cleaning, train/validation/test splitting, model training, evaluation, error analysis, and prediction.
-## 🚀 Project Overview
 Customer-support systems receive a large number of messages every day. Automatically identifying the intent behind each message can help route customer queries to the correct support workflow.
@@ -53,7 +53,7 @@ cancel_transfer
 ---
-## 🎯 Task
 **Text Classification / Customer Intent Classification**
@@ -69,7 +69,7 @@ One of 77 predefined banking customer-support intents.
 ---
-## 📊 Dataset
 The model was trained using the **Banking77** dataset.
@@ -97,7 +97,7 @@ Final Test Evaluation
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-## 🧠 Model
 The trained model is a **scikit-learn text-classification model** saved using `joblib`.
@@ -129,7 +129,7 @@ Expected output:
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-## 📈 Model Performance
 The model was evaluated using separate validation and test datasets.
@@ -173,7 +173,7 @@ The final test evaluation was performed on data that was not used during model t
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-## 🔍 Error Analysis
 Error analysis was performed on the validation dataset to understand where the model makes incorrect predictions.
@@ -207,7 +207,7 @@ reports/validation_predictions.csv
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-## 🏦 Supported Intent Categories
 The model supports 77 banking customer-support intents, including:
@@ -293,7 +293,7 @@ wrong_exchange_rate_for_cash_withdrawal
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-## 🛠️ Project Features
 The complete project contains:
@@ -311,7 +311,7 @@ The complete project contains:
 ---
-## 📁 Project Structure
 ```text
 customer-support-annotation/
@@ -351,7 +351,7 @@ customer-support-annotation/
 ---
-## 💻 Interactive Prediction
 The project includes an interactive prediction script.
@@ -373,7 +373,7 @@ Predicted Intent: card_arrival
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-## 🌐 Live Demo
 The model is also integrated into a web application that allows users to enter customer-support messages and receive predicted intents.
@@ -387,7 +387,7 @@ Add your Streamlit application URL here.
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-## 📦 Installation
 Clone the project:
@@ -410,7 +410,7 @@ python scripts/predict.py
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-## 🔗 Source Code
 The complete source code, training scripts, evaluation scripts, dataset-processing pipeline, and application code are available on GitHub.
@@ -420,7 +420,7 @@ https://github.com/princechouhan3/customer-support-ai
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-## 💡 Potential Applications
 This type of intent-classification model can be used as a component of:
@@ -436,7 +436,7 @@ The model itself is a classification component and can be integrated into a larg
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-## ⚠️ Limitations
 This model was trained and evaluated using the Banking77 dataset.
@@ -456,7 +456,7 @@ The model is intended primarily as a machine-learning project and portfolio demo
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-## 🔬 Future Improvements
 Possible future improvements include:
@@ -473,7 +473,7 @@ Possible future improvements include:
 ---
-## 👨‍💻 Project
 **Customer Support AI — Banking Intent Classification**
@@ -481,6 +481,6 @@ This project demonstrates an end-to-end machine-learning workflow from raw custo
 ---
-## 📄 License
 This project is released under the MIT License.

 This project demonstrates a complete machine-learning workflow, including dataset inspection, data cleaning, train/validation/test splitting, model training, evaluation, error analysis, and prediction.
+## Project Overview
 Customer-support systems receive a large number of messages every day. Automatically identifying the intent behind each message can help route customer queries to the correct support workflow.
 ---
+## Task
 **Text Classification / Customer Intent Classification**
 ---
+## Dataset
 The model was trained using the **Banking77** dataset.
 ---
+## Model
 The trained model is a **scikit-learn text-classification model** saved using `joblib`.
 ---
+## Model Performance
 The model was evaluated using separate validation and test datasets.
 ---
+## Error Analysis
 Error analysis was performed on the validation dataset to understand where the model makes incorrect predictions.
 ---
+## Supported Intent Categories
 The model supports 77 banking customer-support intents, including:
 ---
+## Project Features
 The complete project contains:
 ---
+## Project Structure
 ```text
 customer-support-annotation/
 ---
+## Interactive Prediction
 The project includes an interactive prediction script.
 ---
+## Live Demo
 The model is also integrated into a web application that allows users to enter customer-support messages and receive predicted intents.
 ---
+## Installation
 Clone the project:
 ---
+## Source Code
 The complete source code, training scripts, evaluation scripts, dataset-processing pipeline, and application code are available on GitHub.
 ---
+## Potential Applications
 This type of intent-classification model can be used as a component of:
 ---
+## Limitations
 This model was trained and evaluated using the Banking77 dataset.
 ---
+## Future Improvements
 Possible future improvements include:
 ---
+## Project
 **Customer Support AI — Banking Intent Classification**
 ---
+## License
 This project is released under the MIT License.