• PREDICTING CUSTOMER CHURN: Worked on business practices customer churn rate prediction using Python, concluding Higher churn

Rates For Month on Month customers as compared to one- or two-Year customers. Developed Churn model for Telecom sector using Random

forest to provide specific conditions in which customers have left the company. This enabled the client to target current customers who satisfy those

conditions And thus, reducing churn significantly.

• HOUSING MARKET PRICE PREDICTION: Compare the model performance among (i) LASSO, (ii) random forests regression, (iii)

XGBoost, and (iv) stacking (v) Kernel Ridge regression, to predict House Prices, the goal of this competition is to predict the price of residential homes in Ames, Iowa using the provided 79 explanatory variables. This one is a regression problem, with XGBoost + Lasso giving better prediction results.

• AUTOMOTIVE FAULT DETECTION & ANALYSIS using Machine Learning, NLTK Natural Language Tool Kit, using word tokenization, stemmed word, Lemmatize word. Inputting CSV Data to TABLEAU to generate Cluster Analysis Report.
• Team Lead for 12 to 15 engineers for above mentioned major projects Fault Detection & Analysis, HOUSING MARKET PRICE PREDICTION,

& ALGORATHIMS.

• Used TABLEAU to create Dashboard report, Cluster & Time Series Analysis, on CSV input Data files.

• Currently working on Image Parsing, Text Image Generator, Neural networks, Mask RCNN, pooling/network layers conceptual project.