ANN for Classification and Regression: Project Overview

Classification: Deep Learning models that predict which passengers survived the Titanic shipwreck

Regression: Deep Learning models that predict sales prices of houses

Key Features

• Highlighted the workflow stages of a Machine Learning project:
  1. Define problem statement
  2. Importing/collecting data
  3. EDA analysis
  4. Data preprocessing
  5. Build, train, evaluate models
  6. Tune model
  7. Make predictions

Models performance

Classification: accuracy=83%

Regression: MAE=13.464

Code and Resources Used

• Python version: 3.9
• Packages: pandas, numpy, sklearn, matplotlib, seaborn, tensorflow
• Data source: classification: (https://www.kaggle.com/c/titanic/data), regression: (https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)
• Reference: (https://github.com/mrdbourke/tensorflow-deep-learning)