Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home

Udacity Machine Learning Nano degree Program. Project Predicting House prices in Boston

Python code for the GP-RC algorithm presented in "Genetic Programming with Rademacher Complexity for Symbolic Regression" (CEC-2019). Paper Link: https://ieeexplore.ieee.org/document/8790341

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Reduce the model complexity by 612 times, and memory footprint by 19.5 times compared to base model, while achieving worst case accuracy threshold.

Domain Adaptation by Transferring Model-Complexity Priors Across Tasks Paper Experiments

A wide variety of supervised and unsupervised machine learning methods using the scikit-learn library

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Predicting Boston Housing Prices using Machine Learning

Bias/Variance dilemma, cross-validation and work on Iris Data Set from UCI Machine Learning Repository

Practice Machine Learning Model Complexity in Linear Model

Pipeline for training and evaluating CNNs as well as analyzing layerwise computational complexity

Predicting Boston Housing Prices

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervise…

Compute Lyapunov exponents and Covariant-Lyapunov-Vectors of an RNN update trajectory