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Machine Learning

Location:
Bangalore, Karnataka, India
Posted:
November 06, 2019

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Resume:

Btm-Layout *St stage, Madiwala,Bangalore

+91-994*******,

Gmail: adartn@r.postjobfree.com

GitHub: https://github.com/Harisuryatm

LinkedIn: https://www.linkedin.com/in/hari-s-129465a0/ HARISURYA TM

OBJECTIVE

Seeking for a career to utilize my knowledge, personal skills to gain comprehensive understanding of reputed organization so as to take responsibility and contribute significantly.

I educate, refine and drive myself to be a better person

Self-taught myself in Machine Learning after my graduation.

I stay calm when faced with adversity.

I am constantly learning because I never settle. EDUCATION B-TECH, ELECTRONICS AND COMMUNICATION, 2014 - 2018 Amrita school of Engineering, Coimbatore (DEEMED) GPA : 6.50

HIGHER SECONDARY XII, COMPUTER SCIENCE, 2013 - 2014 Saratha higher secondary school, Erode district

PERCENTAGE : 86 %

TRAININGS

AND

PRACTICAL

KNOWLEDGE

ALGORITHMS USING PYTHON, INTRODUCTION TO MACHINE LEARNING, ARTIFICIAL INTELLIGENCE

NPTEL (online) – Python 3

July – 2018

Description : Based on implementing from scratch to better understand the core mathematics of Supervised Algorithms . Hands-on Machine Learning with Python Book

Description : This Book is the best resource and let me to dive into projects and more insights and strategies to achieve (Data pre-prococessing, feature Engineering and feature selection) the model with good accuracy.

Page 2 HariSurya TM

PROJECTS Customer Churn Prediction Model

This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company. This dataset helps to analyse and to build a perfect model with better accuracy. Link : https://github.com/Harisuryatm/Customer-Churn-Prediction Employee Attrition Prediction Model

aim to predict whether an employee of a company will leave or not, using Logistic Regression algorithm. The Dataset used IBM HR Analytics Employee Attrition, which was downloaded from Kaggle. The dataset contains 1470 rows (records) and 35 columns

(features) was split, using 70% for training the algorithm and 30% for testing it, achieving an accuracy of 88%.

Link : https://github.com/Harisuryatm/Employee-Attrition-Prediction-Model- Loan Dataset Analyses.

The Real time Dataset consists of some customer information and their associated loan information from company. Task here is to clean the data, exploratory data analysis on ANDREW NG ‘S MACHINE LEARNING COURSE

Coursera (online)

July – 2019

Description : Gained knowledge about workings of Neural Networks and various optimizers to tune the model. And mostly, how to handle the data so that model’s performance becomes high. This course is the gateway to do my projects basically. SKILLS

LANGUAGE : Python 3, OpenCV

BACKEND : MySQL (RDMS)

IDE : Spyder, PyCharm, Jupyter

DATA VISUALIZATION : Seaborn, Matplotlib, ggplot MACHINE LEARNING : scikit-Learn, TensorFlow, Keras ( Neural Networks, Deep Learning (CNN))

DATA PROCESSING : Numpy, Pandas

data mining, manipulating data sets and building statistical models, proficiency with large datasets.

I hereby confirm that the skills given above is true to the best of my knowledge. Page 3 HariSurya TM

it and infer from the data about the trends in loans taken and products bought. Link : https://github.com/Harisuryatm/Loan_Dataset_Cleansing Classifying whether the person is at risk of Diabetes or Not using Neural Network. (Dataset taken from Kaggle).

Modeled Neural Network with two Hidden Layers to predict whether women is a risk of Diabetes or Not with 80 percent (without feature Engineering) accuracy using keras. Link : https://github.com/Harisuryatm/Project-2_using_DeepNeuralNetwork Hand-written Digits classification (MNIST dataset) Trained a Stochastic Gradient Descent Classifier and compared it with Random Forest using ROC- AUC to predict Handwritten Digits with good accuracy. (Binary as well as Multiclass classification). Link : https://github.com/Harisuryatm/Support-Vector-Machine/blob/master/Voice%20classification%20(SVM).ipynb Housing price prediction. (Dataset taken from Kaggle). Modeled Housing price using Random Forest Regression that shows insights about how to handle missing values,categorical values in the features. Housing Price data set contains 1,460 homes, each with a few dozen features of types: float, integer, and categorical . Task is to predict Sales Price of the particular house for our taste. Link : https://github.com/Harisuryatm/Random-Forest-Algorithm/blob/master/Housing%20dataset%20(RandomForestRegressor).ipynb



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