Participants will be provided with historical data from Facebook advertising campaigns, including information about the ad creative, targeting parameters, and performance metrics. The goal is to develop a predictive model that can accurately estimate the budget required for a new advertising campaign based on the given features.

**Data**:
The dataset will include information such as:

- Ad creative details (images, text, format).
- Targeting parameters (demographics, interests, location).
- Ad performance metrics (impressions, clicks, click-through rate, conversion rate).
Budget and spending details for previous campaigns.
- Participants will need to preprocess the data, perform feature engineering, and build a regression model to predict the budget for a new campaign.

**Prizes**:
Kaggle competitions typically offer cash prizes, Kaggle swag, and recognition on the Kaggle platform for top-performing participants.

**Benefits**:

1. **Practical Application**: Predicting campaign budgets is a real-world problem faced by digital marketers, making the competition relevant to industry needs.
2. **Data Analysis Skills**: Participants will enhance their skills in preprocessing data, feature engineering, and regression modeling.
3. **Insights for Marketers**: The competition results could provide valuable insights into the factors that most influence Facebook ad campaign budgets.
This competition aligns with the growing importance of data-driven decision-making in digital marketing and can attract participants interested in both machine learning and marketing domains.