chesh27 Goto Github PK
Name: Cheshta Dhingra
Type: User
Bio: Curious, motivated analyst with a passion for data science and a strong interest in medical technology and equitable access to healthcare
Location: San Francisco, CA
Cheshta Dhingra's Projects
autocomplete
Lexicographically sort and truncate 5 results from a text field search
exploratory-data-analysis
Homework 1 from Modern Data Mining class. Use of dplyr, ggplot2, & simple linear regression.
health-insurance-claims-eda
Detailed EDA on Health Insurance Claims dataset; visualization using Seaborn library.
logistic-regression-classification
Use of BIC, AIC, ROC, Bayes linear boundary
multiple-regression-model-selection-penalized-regression
Use of elasticNet for penalized regression
predicting-imdb-ratings-using-movie-characteristics
The goals of this analysis are: 1. Identify the factors predicting a movie’s IMDb score. 2. Propose a classification rule to predict if a movie was rated above or below the average rating for our sample.
predicting-readmission-probability-for-diabetes-inpatients
The goals of this analysis are: 1. Identify the factors predicting whether or not a diabetic patient will be readmitted within 30 days. 2. Propose a classification rule to predict if a patient will be readmitted within 30 days.
robond-deeplearning-project
RoboND Term 1 Deep Learning Project, Follow-Me
robond-deeprl-project
The goal of the project is to create a DQN agent and define reward functions to teach a robotic arm to carry out two primary objectives: Have any part of the robot arm touch the object of interest, with at least a 90% accuracy and have only the gripper base of the robot arm touch the object, with at least a 80% accuracy.
robond-home-service-robot
Home service robot autonomously maps an environment and navigates to pickup and deliver objects
robond-inference-project
Train a deployable robotic inference model to classify images into 3 classes
robond-kinematics-project
Pick and Place Kuka Arm
robond-localization-project
Two different mobile robot models are described and evaluated for performance in a simulated environment. Both simulations are run using Gazebo and RViz. The robots use Adaptive Monte Carlo Localization and the move-base plug in for navigation to successfully arrive at a goal that was either pre-determined or user-defined in real time.
robond-perception-exercises
Perception Exercises for Robotics
robond-perception-project
3D Perception project for the Robotics Nanodegree Program
robond-rover-project
Project repository for the Unity rover search and sample return project
robond-slam
Use of graphSLAM to map two worlds
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