Goal: - The aim is to predict who will be infected by Zeta Disease.  Collected the data form WHO datasets.  The data contains 9 columns with 800 people samples.  Used univariate analysis to check the individual feature’s distribution.  The entire features are continuous variables we do segmented analysis to check how they are related to target variable.  Train the model by model building and predict the probability and test the model.  Since the dataset is small we use hyper parameter tuning for best fitting. Here is the link of the project https://github.com/RamanjaneyareddyTV/Zeta-Disease-Prediction.

Goal: - In this project, we will understand how to interpret a DNA structure and how machine learning algorithms can be used to build a prediction model on DNA sequence data. Build a classification model that is trained on the human DNA sequence and can predict a gene family based on the DNA sequence of the coding sequence. To test the model, we will use the DNA sequence of humans.  Collected the data form the database using SQL Quarries.  For data handling of DNA data used BIOPYTHON library.  By using ordinal encoding, one-hat encoding to manipulate the data.  DNA sequence as a “language”, known as k-mer counting. Here is the link of the project https://github.com/RamanjaneyareddyTV/DNA-Sequencing-Classification.

Goal: - Create a dashboard for heart disease  Import the data form the local drive.  Transform the data and edit the data using power query editor.  Create the visualization and convert them to dashboard. Here is the link of the project https://github.com/RamanjaneyareddyTV/Power_BIdashboards/tree/master/Heart Disease Analysis.