Machine Learning Model for Corporate Acquisitions

## Machine Learning Model for Corporate Acquisitions

This project involved developing a machine learning model to predict corporate acquisitions within the European Union. The goal was to identify potential acquisition targets based on historical financial and operational data, providing insights for investment strategies.

### Data and Methodology

* **Dataset**: Analyzed over 8,000 companies spanning more than 20 years of data. The dataset included various financial metrics, market data, and corporate action histories.

* **Data Cleaning**: Performed extensive data cleaning to handle missing values, inconsistencies, and outliers, ensuring data quality for model training.

* **Feature Engineering**: Created new features from raw data that were hypothesized to be strong predictors of acquisition events. This included financial ratios, growth rates, and market-based indicators.

* **Model Selection**: Explored various machine learning algorithms, with a Random Forest model demonstrating the best performance.

* **Performance**: Achieved an Area Under the Curve (AUC) of 0.68, indicating a reasonable predictive capability for identifying acquisition targets.

### Technical Skills Demonstrated

* Proficiency in data cleaning and preprocessing techniques.

* Expertise in feature engineering for complex financial datasets.

* Advanced statistical modeling and machine learning implementation using Python (NumPy, Pandas, Scikit-learn).

* Model evaluation and interpretation.

### Impact

This model provides a data-driven approach to identifying potential M&A opportunities, which can be valuable for investment banks, private equity firms, and corporate development teams. It streamlines the target identification process and enhances strategic decision-making.