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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phone: +91-830******* EMAIL: ******.**********@*****.*** HARSHA SAI MANOHAR
IIT Jodhpur
PROFILE
Business-Minded Data Scientist with a passion for delivering valuable insights through analytical functions and data- driven methods. Committed to helping companies to advance by developing strategic plans based on predictive modelling and findings. Skilled in machine learning, statistics, programming and problem-solving. PROJECTS
Audio pre-processing: clips were chopped/padded up to 16khz for easy processing. Features: Three types of features(mel, mfcc, raw input) were extracted from audio data for modelling. Model Building: Built various CNNs (i.e vgg, senet, resnet, densenet) on the three different inputs. Stochastic gradient descent optimizer is used to reduce the loss for the all CNNs Built ensemble model by stacking all the trained CNNs to boost the prediction score by at least 6% Defining and Understanding the business problem and built a Data Quality Report for given data-set. Cleaning data, Deleted all the columns where only 10% of information was given. Outlier Treatment, Variable Profiling to impute missing values and to remove insignificant variables. Built logistic regression model using backward selection algorithm and validated it. Predicted churn rate for each customer gives the customers who are likely to churn. EDUCATION
Bachelor of Technology
High Secondary School
SKILLS
CERTIFICATIONS
Pursing Deep Learning Specialization in coursera.
Certified Data Scientist by IBM at Jigsaw Academy. Data Analyst certified by Dataquest.io
REFERENCES
References available upon request
Speech Recognition Using CNNs Mar 2018 – Apr 2019
IIT Jodhpur
Telecom Churn Analysis Aug 2018 – Dec 2018
Jigsaw Academy
Indian Institute of Technology Jul 2014 – May 2019 Jodhpur
Sri Chaitanya Jr. Kalasala Jul 2012 – May 2014
Vijayawada
Data Analysis Expert Predictive Modeling Expert
Machine Learning Expert Data Visualization Skillful Python Skillful R Programming Skillful
Deep Learning Specialization Jan 2019 – May 2019
Coursera
Full Stack Data science Jul 2018 – Dec 2018
jigsaw Academy
Data Analyst Jan 2018 – Mar 2018
Dataquest