Predicting-Ad-Clicks-Classification-by-Using-Machine-Learning
🥈SilverIn this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.
1220Updated 4d ago
Intermediate30min to implementmarketing
Saves ~240 min per use
Quick InstallView Source
git clone https://github.com/farrellwahyudi/Predicting-Ad-Clicks-Classification-by-Using-Machine-Learning.gitWorks with:
Claude
Overview
About This Skill
In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/farrellwahyudi/Predicting-Ad-Clicks-Classification-by-Using-Machine-LearningRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the following ad campaign data to predict click-through rates using machine learning. Provide recommendations for optimizing ad content, targeting by age, and income levels. [COMPANY]: [COMPANY_NAME], [INDUSTRY]: [INDUSTRY_NAME], [DATA]: [DATASET_DESCRIPTION].
Example Output
# Ad Campaign Performance Analysis ## Predicted Click-Through Rates - **Age Group 18-24**: 12.5% predicted CTR - **Age Group 25-34**: 18.7% predicted CTR - **Age Group 35-44**: 10.2% predicted CTR - **Age Group 45+**: 5.8% predicted CTR ## Recommendations ### Content Personalization - Focus on interactive content for younger demographics - Highlight product benefits over features for older demographics ### Age-Targeted Ads - Increase ad spend for the 25-34 age group by 20% - Reduce ad spend for the 45+ age group by 15% ### Income-Level Targeting - Create premium content for households earning $100k+ - Offer discounts and promotions for households earning $50k-75k
Apply to these tools
Browse all toolsCompatible MCP servers
Browse all MCP servers
Find the right skills for your stack
Take a free 3-minute scan and get personalized AI skill recommendations.
Take free scan