Punita Ojha's repositories
creating-customer-segmentation
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 cheaper 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. You’ve been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
10-Easy-Steps
A Repository Created for #Hacktoberfest to Help Beginners Get Started with Code
Angular-Fun-App
Created with StackBlitz ⚡️
Angular8MeanstackAngularMaterial
Angular 8 MEAN Stack Tutorial – Build CRUD web application with Angular Material 7 - Learn to create an Angular single page web application from scratch.
blockchain
📖Code for Blockchain Demo
chinese-checkers
A game which was invented in germany, initially called Stern Halma.
cloud_haiku
Community-made poetry about infrastructure
Finding-Donors-For-Charity-ML
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 were 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 best identify potential donors and reduce overhead cost of sending mail. Our goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.
Hacktoberfest-2020
:octocat: Simply add your details to readme and stand a chance to earn a free tee! ✅
ivy
Unified AI
parse-server
API server module for Node/Express
parse-server-example
Example server using Express and the parse-server module.
Teach-A-Quadcopter-To-Fly
The Quadcopter or Quadrotor Helicopter is becoming an increasingly popular aircraft for both personal and professional use. Its maneuverability lends itself to many applications, from last-mile delivery to cinematography, from acrobatics to search-and-rescue. Most quadcopters have 4 motors to provide thrust, although some other models with 6 or 8 motors are also sometimes referred to as quadcopters. Multiple points of thrust with the center of gravity in the middle improves stability and enables a variety of flying behaviors. But it also comes at a price–the high complexity of controlling such an aircraft makes it almost impossible to manually control each individual motor's thrust. So, most commercial quadcopters try to simplify the flying controls by accepting a single thrust magnitude and yaw/pitch/roll controls, making it much more intuitive and fun. The next step in this evolution is to enable quadcopters to autonomously achieve desired control behaviors such as takeoff and landing. You could design these controls with a classic approach (say, by implementing PID controllers). Or, you can use reinforcement learning to build agents that can learn these behaviors on their own. This is what you are going to do in this project!
theatre-management-system
A fun project pursuing from the perspective of learning IONIC + Angular
Titanic-Prediction-Using-Machine-Learning
In this project, we will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger's features, such as sex and age. We will start with a simple algorithm and increase its complexity until we are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce us to some of the concepts of machine learning.