Detection of COVID-19 induced Pneumonia using Chest X-rays - A Deep Learning Implementation.

An approach to distinguish between NORMAL, PNEUMONIA, and COVID-19 positive patients using Convolutional Neural Networks for Image classification on Chest X-rays.

The links to the pre-processed data sets are provided below:

Download COVID-19 Dataset

Download NORMAL and PNEUMONIA Dataset

  • Exploratory Data Analysis and Data Extraction (Notebook)

*CXR - Chest X-rays

*PA - Posteroanterior (view of X-ray images)

Big Data Wrangling with Google Book Ngrams

(Load, filter, and visualize a large dataset in AWS cloud environment)

  • Work with real-world data using Hadoop, Spark, and the AWS S3 filesystem.

  • Pull data from (public) S3 bucket to HDFS (Hadoop)

  • Analyze and filter data using Spark

  • Perform Data Analysis and visualizations.

Hotel Reviews -

(Natural Language Processing/ Sentiment Analysis)

Download the Dataset

  • Exploratory Data Analysis and Data Wrangling

  • Employ various ML models and comparing their performance (Logistic Regression with PCA and Cross-Validation, KNN, Decision Trees and Random Forests)

US Presidential Elections

(EDA, Data Cleaning, Visualization, Statistical modeling, and inference)

  • EDA and Data cleaning on previous election data.

  • Statistical modeling, Model selection, and Inference

BIXI Montréal, bike-sharing system (Data Analysis and Data Visualization)

  • Data Analysis of the real-world data using SQL queries on MySQL workbench.

  • Data Visualization and Dashboards on Tableau with key insights into revenue and business growth.

* Due to confidentiality reasons and some of these projects being deliverables for the BrainStation Data Science program, the information is not     publicly hosted on Github. Please reach out to me over LinkedIn and I will be happy to provide with more information.

    ©2020 by Sai Krishna Dandamudi.