Joshtrillions001/3MTT-Capstone-project

A hybrid Machine Learning prototype to detect counterfeit NAFDAC drug records in Nigeria for the 3MTT Capstone requirement. # Localized NAFDAC Drug Counterfeit Checker (MVP) An accountability-driven Machine Learning prototype built as a Capstone Project for the 3MTT NextGen cohort. This system helps protect patients in Nigeria by identifying suspicious or altered NAFDAC drug registration entries. ## 📌 Problem Statement Counterfeit pharmaceuticals with forged NAFDAC numbers present a critical threat to healthcare safety across Nigeria. This project provides a functional Minimum Viable Product (MVP) that leverages data-driven validation to instantly flag suspicious product entries. ## ⚙️ Core Technical Features - **Real-World Foundation:** Scaled entirely using an authentic dataset containing **18,110 official NAFDAC drug records**. - **Machine Learning Integration:** Uses `TfidfVectorizer` and a `MultinomialNB` (Naive Bayes) classification pipeline via Scikit-Learn. - **Hybrid Verification:** Combines statistical AI text pattern recognition with a deterministic database cross-reference scanner to maximize accuracy. - **Zero Ambiguity:** Returns clean, direct results: **Genuine** or **Suspicious**. ## 🛠️ Project Workspace Layout The code is divided logically into 3 distinct operational cells within Google Colab: 1. **Cell 1 (Data Prep):** Loads, strips, and cleans the NAFDAC dataset while injecting balanced synthetic anomalies for model training. 2. **Cell 2 (ML Engine):** Converts text profiles into mathematical feature vectors and trains the classifier. 3. **Cell 3 (Verification Dashboard):** Houses the primary execution function along with a 10-batch simulation loop for live demonstrations. ## 🚀 How to Run the Prototype 1. Open Google Colab and upload the `nafdac_drugs.csv` file to your session storage. 2. Execute the three cells sequentially. 3. Query any text profile using the test function: ```python check_my_drug("#Accu-Chek A3-100882") # Returns: Genuine check_my_drug("Fidson Healthcare Amoxicillin 250mg ERROR-23") # Returns: Suspicious ``` --- *Built with discipline and ship …