BALAKRISHNAREDDY BASWA

PROJECT DESCRIPTION: PERSONALIED CANCER DIAGNOSIS

We built a model on home price prediction using a linear regression algorithm.

Responsibilities:

Applied logistic regression algorithm for classifying the population with and without cancer.

We have applied multiple algorithms like K-NN regression, Linear SVM, Naive bayes and logistic regression.

We used train,cross-validation and test log loss for all the above models and suggested logistic regression model.

PROJECT DESCRIPTION: LOAN PROCESSING SYSTEM

Make a decision whether to process the customer loan Application or not. We built a model for the predictions we used Logistic regression and linear regression algorithms.

Responsibilities:

Worked on the Acquisition model which gives a credit score to a customer, based on the score of the customer the bank takes the decision whether to process the loan to the customer or not.

Applied Linear and logistic regression algorithm to classify the customers.

Performed Roll Rate Analysis to define the target variable. The Roll Rate Analysis tells us when the customer will become the default.

Did the Vintage analysis to know how much data to be considered in order to implement the classification algorithm.

Took the data from three data repositories i.e.: Application data, CIBIL data, and Performance Data.

We used the weight of evidence for the fine classing and coarse classing.

We used Information value to select the important variable.

We used the Confusion matrix, adjusted R square, Mean Square Error, and ROC curves as performance metrics.

● Worked as Control Room Engineer for GE 2X9E of 120MW and ALSTOM ST- 150MW combined cycle power plant at Genting Lanco private India Limited of 390 MW from September 2017 to October 2018.

● Working as Control Room Engineer for GE 2X9FA-240MW and ST 2XHarbin ST-120MW a combined cycle power plant at Genting Lanco private India Limited of 742 MW from September 2015 to September 2017.

● Worked as a Control Room Engineer of 228MW ALstom 13E2-150MW and ST-80MW