Machine Learning Engineer Nanodegree (Udacity)

This repo contains the projects I did for Udacity Machine Learning Engineering Nanodegree.

Project 1: Predicting Boston Housing Prices (boston_housing/)

Overview

Built a regression 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.

Keywords: statistical analysis, metric performance, cross validation, bias/variance tradeoffs, learning curves, model complexity

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Project 2: Finding Donors for CharityML (finding_donors/)

Overview

Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.

Keywords: classification, logistic regression, decision trees, ensemble methods, model tuning

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Project 3: Build a Student Intervention System (student_intervention/)

Overview

Built and compared supervised classification models (logistic regression, support vector machines, and random forest) that predicts the likelihood that a given student will pass, quantifying whether an intervention is necessary. Fine tuned the selected best model - support vector machine and achieved 0.8337 training F1 score and 0.8228 test F1 score.

Keywords: supervised learning, binary classification, cross validation, model fine-tuning

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Project 4: Creating Customer Segments (customer_segments/)

Overview

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.

Keywords: clustering, pca, feature selection, k means, gaussian mixture model

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Project 5: Classify Images using Neural Network (image_classification/)

Overview

Classified images from the CIFAR-10 dataset. The dataset was preprocessed (image normalization, label one-hot encoding), then trained a convolutional neural network with convolutional layer, max pool layer and fully connected layer on all the samples. The model was then evaluated on test samples.

Keywords: classification, convolutional neural network, tensorflow

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Project 6: Classify Images using Neural Network (smartcab/)

Overview

Applied reinforcement learning to build a simulated vehicle navigation agent. Identified the environment the agent operates in and the possible states the agent can be in. Implemented and optimized a Q-Learning algorithm to allow the agent to automatically learn an optimal driving strategy based on rewards and penalties.

Keywords: reinforcement learning, q-learning

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Project 7: Machine Learning for Employee Retention (capstone/)

Overview

Capstone project for this Nanodegree. Explored a machine learning approach for an effective employee retention program. Built and evaluated a variety of supervised classification algorithms to predict whether an employee with given features is leaving or not. Identified the most important features that lead to employee turnover. This project provides actional insights to the management and human resource team to ensure their key workers remain in place.

Keywords: supervised learning, binary classification, cross validation, model refinement, feature importance