Learn-ML

Notes and Insights on ML

Technologies used:

• Google Colab (for coding environment)
• Python
• Tensorflow
• NumPy
• Pandas
• Sklearn
• XGBoost
• Matplotlib/Seaborn (for data visualization)

Projects

HousePricePred: ML Regression problem that predicts the price of a house given various features of the home.

• Models used:
  • Random Forest Regressor
  • XGBoost Regressor
  • KMeans/KNeighbors Regressor (in progress)
• Data cleaning and processing techniques:
  • Simple Imputer
  • One Hot Encoder
  • Feature Engineering with mutual information and interaction effects (in progress)
• Code and model optimization:
  • Pipelines
  • Cross validation for choosing best hyperparameters