Skip to content

Latest commit

 

History

History
11 lines (7 loc) · 3.24 KB

README.md

File metadata and controls

11 lines (7 loc) · 3.24 KB
Raw

README.md

This repository showcases my projects that I have done as a partial fulfillment for the Udacity's Machine-Learning Nano Degree program.

  1. titanic_survival_exploration: In this project, I have created decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. I started with a simple algorithm and increase its complexity until I was able to accurately predict the outcomes for at least 80% of the passengers in the provided data.

  2. boston_housing: The Boston housing market is highly competitive, and I want to be the best real estate agent in the area. To compete with my peers, I decided to leverage a few basic machine learning concepts to assist me and a client with finding the best selling price for their home. Luckily, I’ve come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. My task was to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for my clients' homes.

  3. customer_segments: In this project, I have applied supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause. I first explore the data to learn how the census data is recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format. I then evaluated several supervised learners of your choice on the data, and consider which is best suited for the solution. Afterwards, I optimized the model that I've selected and present it as my solution to CharityML. Finally, I have explored the chosen model and its predictions under the hood, to see just how well it is performing when considering the data it's given.

  4. smartcab: In this project I have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.I have first investigated the environment the agent operates in by constructing a very basic driving implementation. Once my agent is successful at operating within the environment, then I identified each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, I have implemented a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, I have improved upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results.

  5. dog-project: Welcome to the Convolutional Neural Networks (CNN) project! In this project, I learned how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, my algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.