Projects

Machine Learning Nanodegree

During my Nanodegree at Udacity I've worked on projects using and learning about Machine Learning techniques.

Creating Customer Segments

Concepts used & learned:

  • Clustering
  • PCA/ICA

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 unsupervised analysis.

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Building a Student Intervention System

Concepts used & learned:

  • Regression vs. Classification problems
  • Decision Trees
  • Neural Networks
  • SVMs
  • Naive Bayes
  • K-Nearest Neighbors
  • Adaboosting

Investigated the factors that affect a student's performance in high school. Trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Selected the best model based on relative accuracy and efficiency.

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Predicting Boston Housing Prices

Concepts used & learned:

  • Statistical Analysis
  • Cross Validation & Metric Performance
  • Under- & Overfitting
  • Model Complexity & Tuning

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.

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