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Punita Ojha's Projects

hacktoberfest-2020 icon hacktoberfest-2020

:octocat: Simply add your details to readme and stand a chance to earn a free tee! βœ…

hbd icon hbd

Coder's way of wishing Happy Birthday!

music-app icon music-app

This is a just a simple music app built using Flask.

my_idea_book icon my_idea_book

This is the collection of some of the problems I encounter in my surroundings and desire to provide a solution . The objective is basically to hunt for the problems around and plan for the solutions . The goal is to make life easy for humans .

plotly.py icon plotly.py

The interactive graphing library for Python (includes Plotly Express) :sparkles:

susper.com icon susper.com

Susper Decentralised Search Engine http://susper.com

teach-a-quadcopter-to-fly icon 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!

test-folder icon test-folder

this is just punita's test folder made for playing purpose .

titanic-prediction-using-machine-learning icon 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.

website icon website

PySlackers website for invites and learning resources

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