🏆 1st Place (1/125) in a class competition for image classification using machine learning techniques, achieving an impressive accuracy of 79.4% on Kaggle without relying on neural networks.
The goal of this competition was to develop an effective image classifier using traditional machine learning methods to classify images with high accuracy.
This project was implemented using:
- Python:
- Pandas: For data manipulation and preprocessing.
- Scikit-Learn: To implement classifiers and optimize hyperparameters.
- NumPy: For numerical computations.
- StandardScaler: For feature scaling.
- Matplotlib: For visualizing data and results.
- ...
- Explored multiple classifiers:
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Support Vector Machines (SVM)
- Optimized hyperparameters using grid search and cross-validation techniques.
- Applied robust data preprocessing methods:
- Data Augmentation: To increase dataset diversity.
- Feature Extraction: To identify relevant patterns in the images.
Achieved a remarkable accuracy of 79.4% on Kaggle, outperforming other participants without using neural networks. The classifier demonstrated strong generalization capabilities, securing the 1st place in the competition.