Call Center Handle Time Prediction

Overview

This project aims to optimize Average Handle Time (AHT) and Average Call Allocation Time in a call center environment using a predictive model. The project includes two Jupyter notebooks for data analysis and model training, as well as a Flask application to serve predictions.

Presentation Link:-

https://www.canva.com/design/DAGTJm15k2k/UzHF9TO3m4twIsXDYgOlwQ/edit?utm_content=DAGTJm15k2k&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

Table of Contents

• Installation
• Usage
• Project Structure
• Endpoints
• Data Requirements
• How to Run the Application
• Example Requests
• License

Installation

1. Clone the repository:

   git clone <repository_url>
   cd <repository_directory>
   

Usage

Project Structure

• main.ipynb: Contains data analysis and exploration of the datasets.
• model.ipynb: Contains the predictive model for calculating handle time.
• app.py: The main Flask application for serving predictions.

Endpoints

1. Predict Endpoint

• URL: /predict

• Method: POST

• Request Body:

    json
    `{
      "customer_id": "12345",
      "primary_call_reason": "Flight Inquiry"
     }`
  

• Response:

    json
    `{
       "transferred_to_agent": "agent_id_x",
       "least_handle_time": 300.5
     }`
  

2. Call Complete Endpoint

• URL: /call_complete

• Method: POST

• Request Body:

    json
    `{
       "agent_id": "agent_id_x"
     }`
  

• Response:

    json
    `{
        "message": "Agent status updated to available."
     }`
  

3. Update Availability Endpoint

• URL: /update_availability

• Method: POST

• Request Body:

  json
  `{
     "agent_id": "agent_id_x",
     "availability": true
  }`
  

• Response:

    json
    `{
        "message": "Agent {agent_id} availability updated to {availability}."
     }`
  

Data Requirements

``Ensure you have the following datasets in the dataset directory:

`customer.csv`: Contains information about the customers.
`calls.csv`: Contains details about the calls.
`reason.csv`: Contains reasons for the calls.
`sentiment_statistics.csv`: Contains sentiment analysis statistics.``

How to Run the Application

1. Navigate to the project directory:

   `cd <repository_directory>`
   

2. Run the Jupyter notebooks main.ipynb and model.ipynb to perform data analysis and train the predictive model.

3. Run the Flask application:

   `python app.py`
   
   

Example Requests

Predict Handle Time

```bash
 `curl -X POST http://127.0.0.1:5000/predict \
  -H "Content-Type: application/json" \
  -d '{"customer_id": "12345", "primary_call_reason": "Flight Inquiry"}'`

Update Agent Status

```bash
`curl -X POST http://127.0.0.1:5000/call_complete \
-H "Content-Type: application/json" \
-d '{"agent_id": "agent_id_x"}'

License

This project is licensed under the MIT License. See the LICENSE file for more details.