Custom AI/ML
Model Solution for Predictive Ticket Booking System
Initial Conversation
About Project
The online ticket-booking firm is a well-established company of our long-term client. As the AI/ML trend accelerates and has future scope, clients have asked for AI-based functions in booking systems.
To scale in the AI/ML market and stand among the leading solutions, the client needs high profitability. Using past event price data and customer engagement rates, the client demands a top AI ticketing system that gives customized recent event price trends. Also, the online system benefits from the boosted productivity of AI-powered workflow and prevents human errors.
Additionally, the ML algorithms price forecasts based on the previous data, and the event organizers evaluate it and make quick decisions on advanced bookings. So, it will optimize the ticket pricing strategy and increase the profitability of the business.
Our Approach
Analyzing the Core Requirements
Started with discussing the business model and history of the online ticket booking system. The Team has prepared an overview of the project idea to understand the major needs. Along with AI/ML development, it is critical to improving the performance of the legacy system. So, the team has also worked to enhance system features.
Collecting Previous Years' Data
From the existing application and system records, the team has gathered the relevant data from about the last four years. Because the AI/ML solution needs ticket price trends to give accurate price predictions for events. Accordingly, the team has coordinated with the client and stakeholders to ensure complete data collection.
Selection of AI/ML Model
With the emerging AI/ML models, we have begun the research for the most appropriate model. Our purpose was to make custom AI/ML development for ticket price forecasting. Therefore, the development team has prepared the data for a linear regression model that best fits continuous outcomes. Its capability to use continuous independent and dependent variables gives supervised Machine learning operations (MLOps) higher accuracy.
Performing competitors analysis
The AI ML solution for the USA client, AWS (Amazon Web Services) is the major tool brought for the data preparation. Evaluating the potential competitors with an AI/ML solution edge has given us insights for implementation. Furthermore, it has helped in understanding the price trends and strengthening the data validation in MLOps.
Our Challenges
1. Unstructured Historical Data
The primary challenge with the project is unorganized historical data. Having real and categorical data is significant for such a data-driven ticketing system. It has stretched the turnaround time taken for task completion.
2. Missing Past Database
Secondly, the database is incomplete and misses the data for a certain duration in between. The AI/ML model for online ticketing needs entire past data from prices, venue, event type, and performers to design the new data processing model. However, the real-time data fetching will be insufficient due to the adverse impact on software speed.
3. Executing AI/ML in Legacy Systems
The legacy ticketing system has slowed performance and conventional functionality. Integrating cutting-edge MLOps and predictive AI methods doesn’t align with such a legacy system. Even after implementing the AI/ML solutions, maintainability and compatibility still remain questionable.
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Our Solutions
Data Scraping for Better Performance
As our custom AI ML development model needs to retain the performance standards, it is critical to retrain the model periodically. Data scraping in the AI ML model has proven highly beneficial for ticketing software. Our skilled developers have efficiently managed the quality of the model by scraping data from third-party websites and existing accumulated historical data.
Predictive Analytics
To structure the historical data into categories and meaningful information, we have employed text analytics in the system. It has aided in the classification of unstructured data efficiently. Additionally, the AI/ML team has undertaken the previous price pattern using predictive analytics. As a result, the system provides the price prediction for the upcoming week/month or specified duration. This allows the client to assume the best-selling events, seat cycle, etc.
Dynamic Pricing Method
Using a dynamic pricing strategy, the best AI models for online ticket booking systems have emerged as logistic regression. It allows us to research and examine all the external factors like recent demand, festive season, and real-time trends. Thus, our Artificial Intelligence model for the ticketing system provides 87% accurate price forecasts with regular training intervals for the model.
Customized Dashboard
The customized dashboard for managing the events price analysis gives various filters to narrow down the requirement. For example, if clients want to know the prices of events in the coming month last Friday, they can set the date and event availability time. Accordingly, our developed dynamic ticketing system displays the list of events with their sales probability (in %), seat availability, and price range
Our Process
Collecting the Historical & Current data
Firstly, our team started by gathering relevant data from past events and current market analysis. It has included all types of data such as raw and uncategorized which help to design an AI/ML model for online ticketing. So, we have undertaken the required strategies such as data scraping to collect sufficient data and train the model.
Setting up a New Data Pipeline
For a centralized online ticket booking system management, our team has set up a new data pipeline to easily transfer the historical data from the existing AWS S3 bucket to the new Process Data bucket. Using the AWS services, we have created a data pipeline. It will keep the continuous training of models from collecting, processing, and moving data periodically. So, we have planned for custom-made, best Artificial Intelligence models for online ticket booking systems with real-time data processing.
Data Preprocessing & Validation
As soon as the data pipeline has been set up, the actual data movement is initiated. For quick understanding, it has been divided into two i.e., data preprocessing and data validation. The appropriate data flow processes in each section are as follows;
(A) Data Preprocessing
The primary task as we have prepared the pipeline is to extract the raw data from the historical bucket. So, it starts with preprocessing before actual data preparation. The extracted data gets transformed into a suitable format and structure. Accordingly, the fresh data is prepared for training the best AI models for online ticket booking systems by examining the data correction further.
(B) Data Validation
Now, the automated data splitting, analysis, and monitoring are undertaken once it collects the new data. It’s the most crucial stage for MLOps for online ticketing system review, as the continuous model training will begin only after complete validation. As a result, the data has been moved ahead through the data pipeline for the Machine Learning model performance evaluation.
AI/ML Model Evaluation & Testing
Now, the Logistic regression model with initial learning of data is ready for data prediction. We have carried out the process of making a new S3 bucket for the collection of predictive data. With the proper control of the prediction data bucket, the development team can monitor the model. Thus, our Machine Learning models for the online ticket booking system example have offered precise price estimation. By the automated data quality checks using AWS services and verification, we have modified a few aspects.
Model Deployment
After the guaranteed testing and performance review, the model is deployed on the online ticketing system. In this AI ML solution for the USA client, AWS S3 has been the key tech solution that worked a wonderful job. It has seamlessly managed to deploy and deliver estimated results. Because of the dynamic nature of AI/ML online ticket booking software, the model has been continuously retraining to maintain or advance its workflow. Therefore, our AI/ML experts have simplified the project and achieved success!
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Final Outcome
Technology Stack
After detailed market and industry-based research, our AI/ML development team has decided to utilize the future-ready technology stack as given below.
AWS S3 Bucket
Mongo DB
Google Colab
AWS Lambda
AWS Glue DataBrew
Scikit-Learn
NumPy
Pandas
Matplotlib
