This repository contains six projects for Udacity's Machine Learning Engineer Program.
All of the projects requires Python 2.7 or 3 I have Used python 3.0. The following Python libraries are also required:
P0: Titanic Survival Exploration
In this optional project, I 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 increased its complexity until I was able to accurately predict the outcomes for at least 80% of the passengers in the provided data.
P1: Predicting Boston Housing Prices
The Boston housing market is highly competitive. The primary objective of the project was to find the best selling price for a home in Boston area. The Boston Housing dataset contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. I have used DecisionTreeRegressor algorithm to make model based on a statistical analysis with the tools available.
P2: Finding Donors for CharityML
CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to identify potential donors best and reduce overhead cost of sending email.
P3: Creating Customer Segments
A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week.Initial testing did not discover any significant unsatisfactory results, so they implemented the more affordable option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change, and customers were canceling deliveries — losing the distributor more money than what was being saved. I have used unsupervised algorithms ICA and Gaussian mixer model to find what types of customers they have to help them make better, more informed business decisions in the future.
P4: Train a Smartcab to Drive
In the not-so-distant future, taxicab companies across the United States no longer employ human drivers to operate their fleet of vehicles. Instead, the taxicabs are operated by self-driving agents — known as smart cars — to transport people from one location to another within the cities those companies operate. In major metropolitan areas, such as Chicago, New York City, and San Francisco, an increasing number of people have come to rely on smart cabs to get to where they need to go as safely and efficiently as possible. Although smart cabs have become the transport of choice, concerns have arisen that a self-driving agent might not be as safe or efficient as human drivers, particularly when considering city traffic lights and other vehicles. To alleviate these concerns, I used reinforcement learning techniques to construct a demonstration of a smart cab operating in real-time to prove that both safety and efficiency can be achieved. P5: Dog Breed Classifier
P5: Dog Breed Classifier Build a dog breed classifier using a Convolutional Neural Network and transfer learning
I am the only one who works on these projects.
MIT