Jun 2021 – Nov 2023 · 2 yrs 5 mos · Mumbai, Maharashtra, India

• Build private car customers renewal propensity model, increasing retention rate by 4%. Delivered additional GWP of 125+ Cr (in 2.5yrs)
• Build ML model for Private car risk based customer level pricing model in order to increase revenue and maximize the retention of desirable customers on the basis of renewal and claim propensity
• Delivered GWP impact of 16lac extra revenue at one location with ₹1300 extra premium on every policy ( in 4 months). And increased retention by 5% for Bangalore and 10% for other 5 locations
• Created call volume forecasting model and optimized call center executive staffing using Amazon DeepAR forecasting algorithm. Impacting 30% increase in SLA
• Implemented and learned time series models like SARIMAX, Prophet and auto time series models like AutoTS, DeepAR and FEDOT
• Experience in end to end Model Development – right from Data Engineering (using PostgreSQL in DBeaver Data Lake), Cleansing, EDA, Feature Engineering and Modelling

Aug 2020 – Sep 2020 · 1 mo

• Predicted Stock Market Price of 20 years of ONGC stock record using keras stacked Long Short Term Memory (LSTM) neural network layer
• Preprocessed the dataset by sorting according to date, used MinMaxScaler for scaling and considered closing price as target variable
• Applied Dense, LSTM, Dropout keras layers, used Adam optimizer, mean squared error as loss function with 50 epoch and 60 batch size
• Achieved Root Mean Square Error (RMSE) equal to 9.41 and evaluated the model with R squared score metrics and got 0.95

Aug 2020 – Sep 2020 · 1 mo

• Calculated top 10 features of mobile price Kaggle dataset using correlation matrix, obtained 89.5% and 95% accuracy using DTC and SVM
• Predicted 3 flower species from 4 features of Iris Dataset using K-means clustering and Random Forest Classifier with an accuracy of 97%
• Applied FFNN on MNIST data with sparse categorical crossentropy loss function ,ReLU ,softmax activation function with accuracy of 92%
• Generated Spam Mail Classifier by NLTK text processing on ~5000 Kaggle dataset, with an accuracy of 98% using Logistic Regression

Jun 2020 – Aug 2020 · 2 mos · Kharagpur, West Bengal, India

• Generated Forward Model and calculated Apparent Resistivity and Phase using Resistivity and Thickness of 1D - MT Data in MATLAB
• Formulated Inversion Model using SVD technique to invert Jacobian Matrix containing Resistivity and Thickness values of 1D-MT Data
• Obtained Root Mean Square Error for resistivity in the range of 0.005 - 0.8 and thickness in the range of 0.001 - 0.04 on noise free data
• Generated Synthetic data from given true model using forward model and added 5% of random noise on synthetic data using MS Excel
• Calculated Root Mean Square Error for resistivity in the range of 0.78 - 1.15 and for thickness in the range of 1.59 - 1.96 on noisy data

May 2020 – Jul 2020 · 2 mos · Patna, Bihar, India

• Implemented various Python libraries like Pandas, Numpy, Matplotlib and TKinter package for GUI Programmimg
• Calculated Normalized Difference Vegetation Index (NDVI) using the Google Earth Engine API in Python over India (used LANDSAT image)

May 2019 – Jun 2019 · 1 mo · Bengaluru Area, India

• Detected existence of hydrocarbon reservoir using vertical to horizontal spectral ratio analysis applied on processed microtremor data
• Worked on 3 component data of 4 locations, each having 36,00,000 data sets and 60-minute duration with a sampling frequency of 100Hz.
• Applied Fast Fourier Transform and vertical to horizontal spectral ratio analysis by splitting the microtremor data in hamming windows
• Formulated code for the conversion of time-domain data to frequency domain data and produced windows of the data using MATLAB
• Obtained MATLAB VHSR plot identical to the GEOPSY software VHSR plot and generated average vertical upon horizontal ratio plot.
• Analysed the plot and concluded high anomaly peaks in the VHSR plot can provide theoretical evidence of new hydrocarbon reservoir

May 2018 – Jun 2018 · 1 mo

• Designed and developed virtual drum by converting mechanical force into Digital Signal using Piezo Electric Sensors and Arduino UNO
• Developed a line following robot using IR Sensor and Soil moisture detector sensor using YL 69 moisture sensor and 8051 IC