Fake bill detection tool with Python

Detailed Project Description:

For the last project of my data analysis training, I worked as a consultant for the National Organization for Counterfeit Money Control (ONCFM). The objective was to develop a Python-based model capable of identifying counterfeit euro bills by analyzing specific dimensions and features of the bills.

Key Tasks and Objectives:

1. Data Preprocessing and Analysis:

   • Clean and preprocess the dataset, handling missing or erroneous values, using linear regression to impute missing data.

   • Perform exploratory data analysis with Python to identify key features for modeling and understand the dataset.

2. Predictive Modeling:

   • Experiment with various machine learning models in Python, including logistic regression and k-means, to build a model capable of detecting counterfeit bills.

   • Test and evaluate the models to select the most accurate algorithm for final deployment.

3. Model Evaluation and Tuning:

   • Fine-tune the chosen model and validate it using a test dataset to ensure robustness and accuracy.

   • Prepare the Python model for live testing, ensuring it can handle new, unseen data effectively.

   • Build an executable application which encapsulate the trained model, enabling users to easily use it without needing to intercat with Python.

4. Reporting and Presentation:

   • Present the complete process, including data treatments, algorithms explored, and final model selection, to the ONCFM project lead.

   • Demonstrate the model’s performance using a new dataset provided during the presentation.

This project allowed me to apply my Python skills to predictive modeling and data processing, successfully creating a model to detect counterfeit bills.