I'm an explorer. Passionate about deciphering the patterns orchestrating the universe, I wish to leverage data exploration to improve people's life. Machine learning and data vizualization are my companions. I eat code for breakfast.

Feat of arms

Dog breed classifier

  • CNN
  • Face detection
  • VGG16
  • VGG19
  • InceptionV3
  • Resnet-50
  • Python

I built an algorithm capable of identifying canine breed given an image of a dog. If the given image features a human, the algorithm identifies a resembling dog breed. In this project, I leverage the power of Convolutional Neural Networks (CNN) for the dog breed classifier.

Train a Smartcab to drive

  • Reinforcement Learning
  • Q-Learning
  • Optimization
  • Modeling
  • Model Tuning
  • Python
  • Algebra
  • Statistics
  • Calculus

I applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

Customer segments

  • Clustering
  • sklearn
  • Visualizing Data
  • Feature Selection

I reviewed unstructured data to understand the patterns and natural categories that the data fits into. Multiple algorithms were compared both empirically and theoretically. I made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Finding donors to Charity

  • Naive Bayes
  • sklearn
  • Regression vs Classification type problems
  • Model Fitting and Prediction
  • Decision Trees
  • Regression
  • Neural Networks
  • Support Vector Machines
  • K-Nearest Neighbors
  • Adaboosting

Factors that affect the likelihood of charity donations were investigated based on real census data. I developed a naive classifier, trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. The best model was selected based on accuracy, a modified F-scoring metric, and algorithm efficiency.

Predicting housing prices

  • Bias/underfitting & Variance/overfitting
  • Learning Curves
  • Model Complexity
  • Model Tuning
  • Metric Performance
  • Statistical Analysis
  • Cross Validation

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

Titanic survival exploration

  • Data Analysis
  • Data Preprocessing

In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. I explored a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, I implemented several conditional predictions.