1. Abstract

In the digital era, online advertisements play an important role in marketing and business growth. Companies invest heavily in online ads, but not all users view or engage with them. Therefore, predicting whether a user will view an advertisement is very useful for improving marketing strategies.

This project focuses on predicting advertisement views using supervised machine learning techniques and IBM Watson services. The system analyzes user behavior and demographic data to determine the probability of a user viewing an advertisement.

The dataset is loaded into IBM Watson Studio, where data preprocessing and model training are performed. Various machine learning algorithms are tested, and the best-performing model is selected. After training, the model is deployed on IBM Cloud and integrated with a web interface using APIs. The complete application is deployed on Heroku Cloud.

This project helps businesses improve ad targeting, reduce marketing costs, and increase customer engagement.

2. Objectives

The main objectives of this project are:

1. To understand online advertisement behavior.
2. To study supervised machine learning techniques.
3. To learn IBM Watson Studio and Cloud services.
4. To preprocess and analyze advertisement data.
5. To build a prediction model for ad views.
6. To compare different machine learning algorithms.
7. To deploy the trained model on IBM Cloud.
8. To integrate APIs with a web interface.
9. To host the application on Heroku.
10. To support data-driven digital marketing.

3. Existing System

Traditional advertisement analysis methods mainly depend on basic statistics and manual analysis.

The limitations of the existing system are:

1. Manual data analysis is time-consuming.
2. Low prediction accuracy.
3. Limited personalization.
4. Poor real-time prediction.
5. High marketing costs.
6. Inefficient ad targeting.
7. Lack of automation.
8. Difficulty in handling large datasets.

These drawbacks reduce the effectiveness of online advertising.

4. Proposed System

The proposed system uses machine learning and cloud services for automated ad view prediction.

In this system:

• User and advertisement data are collected.

• Data is uploaded to IBM Watson Studio.

• Data is cleaned and preprocessed.

• Machine learning models are trained.

• Best model is selected and deployed.

• APIs are generated for predictions.

• Web interface is developed.

• Application is deployed on Heroku.

This system provides fast, accurate, and scalable predictions.

5. Implementation Procedure

The project is implemented using the following steps:

Step 1: Data Collection

The advertisement dataset is collected from online sources.

Step 2: IBM Cloud Setup

IBM Cloud account is created and Watson Studio is configured.

Step 3: Data Upload

Dataset is uploaded to Watson Studio.

Step 4: Data Preprocessing

Missing values and outliers are handled.

Step 5: Model Training

Algorithms such as Logistic Regression, Decision Tree, and Random Forest are trained.

Step 6: Model Evaluation

Models are evaluated using accuracy and precision.

Step 7: Model Deployment

Best model is deployed on IBM Cloud.

Step 8: Application Deployment

Web app is integrated with API and hosted on Heroku.

6. Software Requirements

The software tools required for this project are:

• IBM Watson Studio

• IBM Cloud Platform

• Python

• Flask

• Heroku

• Jupyter Notebook

• Web Browser

7. Hardware Requirements

The hardware requirements include:

• Processor: Intel i5 or higher

• RAM: 8 GB or higher

• Storage: 256 GB or higher

• System: Laptop/Desktop

• Internet Connection: Stable broadband

Optional:

• Cloud storage and computing services

8. Advantages of the Project

1. Improves advertisement targeting.
2. Reduces marketing expenses.
3. Increases user engagement.
4. Enhances prediction accuracy.
5. Supports digital marketing strategies.
6. Provides automated decision-making.
7. Easy cloud deployment.
8. Scalable for large datasets.
9. User-friendly web interface.
10. Helps maximize return on investment (ROI).