Apply multiple machine learning algorithms (supervised or unsupervised) on your data, examples include:
- Random forest
- Ensemble learning (e.g., XGBoost)
- KNN
- SVM
- Neural network
- Bayesian learning
- K-means
- PCA....
I am also highly experienced in processing image data, things to explore if you have image data:
- Image segmentation using Unet and other networks
- Image synthesis using generative neural network
- Computer vision
- Transfer style of one image to another
Machine learning can be done using local resources or cloud resources such as Colab