This repository contains a Python notebook demonstrating a simple image classification task using a Support Vector Machine (SVM) classifier. The TensorFlow "cats_vs_dogs" dataset is utilized for training and testing. To address potential memory issues, a percentage of the dataset is loaded, and images are resized and flattened on-the-fly to reduce memory consumption. Hyperparameter optimization is performed using random search with cross-validation. The SVM model is trained, and its accuracy is evaluated on a test set.

• Python 3.x
• TensorFlow
• TensorFlow Datasets
• scikit-learn
• OpenCV (opencv-python)

Install dependencies using (if not installed):

pip install tensorflow tensorflow-datasets scikit-learn opencv-python

1. Clone the repository:

   git clone https://github.com/dipeshbabu/PRODIGY_ML_03.git

2. Navigate to the project directory:

   cd PRODIGY_ML_03

3. Run the notebook:

   jupyter notebook PRODIGY_ML_03.ipynb

   Make sure to run the notebook in an environment that supports GPU acceleration for faster execution.

• Adjust the percentage of the dataset loaded in the split argument of tfds.load based on your available memory.
• Modify hyperparameter ranges and distribution in the param_dist dictionary for random search in the code based on your experimentation.