Neural Network for House Price Prediction

Oct 2024 - Nov 2024

GitHub Repo

Synopsis¶

For my Year 3 ML module at Imperial College London I was tasked with:

• Implementing a decision tree algorithm, using it to determine an indoor location based on WIFI signal strengths collected from a mobile phone.

• Developing and optimising a neural network architecture to predict the price of houses in California using the California House Prices Dataset.

Skills¶

Below are the skills and experiences gained from the project:

• Implemented the following:

  • K-NN Classifiers

  • Decision Trees

  • Evaluation of ML models (Confusion matrix, performance metrics, etc)

  • Hyperparameter tuning (dataset split + crossvalidation)

  • Principles of overfitting and mitigation

  • NNs

    • Linear regression

    • Activation functions

    • Backpropagation

    • Gradient Descent

    • Overfitting

• Competency with the following libraries:

  • PyTorch

  • Numpy

  • Pandas

  • Scikit-learn