callmekofi/cactus

An image classification problem identifying the presence or absence of cactus in a 32 by 32 pixel image. The 17,500-image database was split 60% train, 20% validation, 20% test. Parallel processing was implemented for image processing and model building. Feature engineering included the creation edge features using the difference between adjacent pixels. Support vector machine, random forest, Xgboost, and convolutional neural network (CNN) models were fit. The CNN fit with TensorFlow and Keras outperformed all other models with 97.5% classification accuracy. By using a weighted average of the Xgboost, Random Forest, and CNN models a classification accuracy of 97.7% was achieved. This was a team project. I was responsible for the image processing, feature engineering, Random Forest, Xgboost, and weighted average models.

Hi there, I'm Kofi

I'm a Computer Vision, Data Scientist, Machine Learning Engineer and a Researcher!!

I have a passion for learning and sharing my knowledge with others in a way that's as public as possible.

• 🔭 I’m currently working on the application of machine learning in Satellite Imaging
• 🌱 I’m currently learning Reinforcement learning
• 👯 I’m looking to collaborate on Machine learning Flood detection models
• 💬 Ask me about Data Science, Computer Vision, Machine Learning
• 📫 How to reach me: [email protected]
• 🥅 2022 Goals: Contribute more to Machine learning / Data science Open Source projects.

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