Data science, machine learning

JAINAM

SHAH

SKILLS

Machine Learning, Data Analysis,

Data Science, Deep Learning, SAS,

OpenCV, Python

C, C++

SQL

MS Office, MS Excel

SDLC

HTML/CSS

INTERNSHIPS

Ineuron Internship – Food

Recommendation System

Studity – Web Development

TRAINING & CERTIFICATONS

Ineuron Machine Learning Course

Deep Learning

Data Analytics Bootcamp by Rajeev

Ratan (Udemy)

Data Science and Business by Rajeev

Ratan (Udemy)

WORKSHOPS ATTENDED

Attended Robosapiens Workshop for

IOT in Nirma University

Attended Android App Development

Workshop at Nirma University

Mobile:940-***-****

Email:**********@*****.***

LinkedIn: https://www.linkedin.com/in/jainam-shah-24a1a9143 EDUCATION

Silver Oak College of Engineering and Technology, B.E.(CGPA – 8.2) August 2015 – June 2019

L P Savani High School, HSC

June 2014 – May 2015

MGSK Higher Secondary School, SSC

July 2012 – March 2013

WORK EXPERIENCE

Project Manager, Jemistry Info

Solutions, Surat December 2019 – July 2020

Responsibilities:

Team Lead in development of 3 websites for Clients Team Lead for ERP Solutions Project

Client Communication

Interns Supervisor.

Project Manager, Cyber Suraksha

Setu, Surat January 2020 – July 2020

Responsibilities:

Coordination with Surat Police for conducting Programs For Cyber awareness of Cyber Bullying and Cyber Fraud.

PROJECTS

1. Insurance Fraud Detection – Custom Machine Learning Approach Developed entire pipeline with my team from beginning of file validation, raw data validation to model selection using python. Algorithms – K Means, SVM, XGBoost

2. Web Application for Zomato Restaurants Rating Prediction – Developed a web application using Flask for predicting rating of Restaurants in Zomato using given input feature.

Deployment – Heroku

Algorithms – RandomForest and Extra Tree Regressor 3. Streamlit Web Application for Breast Cancer Detection – Developed a Web Application using streamlit for Breast Cancer covering EDA, Data Visualization and Model Selection for Classification Approach.

Algorithms – Different classification algorithms of Machine Learning KNN SVM, LR, naive_bayes, decision tree

4. Flight fare Prediction web app – Developed a web application using Flask, after performing necessary data cleaning and transformation of categorical data. Using the input features city and type of airways one can predict the FlightFair.

Deployment – Heroku

Algorithms – XGBoost Regressor

5. Song Recommendation – Using popularity recommendation and item similarity recommendation, created a song recommendation engine using the given data set.

Algorithms – Item similarity recommendation, recommenders library 6. Sonar, Rock vs Mine Prediction – Developed a system that can predict whether object is rock or mine based on sonar data. Algorithm–Logistic_Regress

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