Welcome to our final project for the Data Analytics BootCamp! In this project, we tackled the pressing issue of mold detection using machine learning techniques. Mold poses significant health risks and can cause damage to buildings and infrastructure. Our project aims to develop a robust machine learning model capable of accurately recognizing and classifying mold in images.

Project Overview Problem Statement: Mold detection is traditionally a time-consuming and labor-intensive process, often requiring manual inspection by experts. Objective: Our goal is to automate the mold detection process using machine learning algorithms, reducing the time and effort required for inspection while improving accuracy. Approach: We collected a dataset of images containing both mold-infested and mold-free samples. We then preprocessed the data, extracted relevant features, and trained several machine learning models to classify images as either mold or mold-free. Results: After extensive experimentation and evaluation, we identified the most effective model and fine-tuned its parameters to achieve optimal performance. Conclusion: Our project demonstrates the potential of machine learning in streamlining mold detection processes, offering a scalable and efficient solution for identifying and addressing mold-related issues. Repository Structure data/: Contains the dataset used for training and testing the machine learning models. notebooks/: Jupyter notebooks detailing the data preprocessing, model training, and evaluation processes. models/: Saved trained models for future use and deployment. src/: Source code files for data preprocessing, feature extraction, model training, and evaluation. requirements.txt: List of Python dependencies required to run the project code. Getting Started To get started with the project, follow these steps:

Clone the repository to your local machine. Navigate to the project directory. Install the required dependencies using pip install -r requirements.txt. Explore the Jupyter notebooks in the notebooks/ directory to understand the project workflow and methodology. Run the provided Python scripts in the src/ directory to preprocess data, train models, and evaluate performance.