Hello, I am

Neha Patidar

Software Engineer

Who am I ?

I am a Software Developer at Metacube Software Pvt. Ltd.

Technologies that I konw are Salesforce, Java, Python, C, HTML, CSS, Git & Docker.
I spent my free time in coding, dancing, painting & playing basketball.

Personal Info

  • DoB : 2nd August 1999
  • Email : nehapatidar89260@gmail.com
  • Mother's Name : SUMITRA PATIDAR
  • Father's Name : Late MANGLESHWAR PATIDAR
  • Permanent Address : Village- TUMDAWADA, District- MANDSAUR (M.P.)

My Expertise

Salesforce

Certified salesforce professional


GitHub

Have proper knowledge of Git and GitHub


Development

Have experience in software development


My Resume

Experience

Oct 2021 - Present

Software Engineer

Metacube Software Pvt Ltd
Jaipur, Rajasthan, India


Feb 2021 - Sept 2021

Trainee Software Engineer

Metacube Software Pvt Ltd
Jaipur, Rajasthan, India


Sept 2019 - Oct 2019

Internship :Data Annotation

Edupro E Solution India Private Limited
Kolkata, West Bengal

Education

B.Tech CSE

2017 - 2021

Rajasthan College of Engineering for Women, Jaipur (RJ)


XIIth PCM

2017

United Alpha Senior Secondary School, Neemuch (MP)


Xth MPBSE

2015

Vivekanand Saraswati Higher Secondary School, Sitamau (MP)

Technical Skills

Salesforce
Java
Python
Git
SQL
HTML5 & CSS3
JavaScript
Docker
C++

Languages Proficiency

English
Hindi
Malwi

My Achievements

  • Won 1st prize at RTU Hackathon 2020

  • Won 1st prize in Basketball tournament in ZEAL 2019 at RCEW

  • Runnerup in Basketball tournament in ZEAL 2020 at RCEW

  • Runnerup in Rangoli competition in Techvyom at RCEW

  • Runnerup in Dance competition in Orientation at RCEW

  • Participated in Code & Build Hackathon 2020 at JECRC University

My Projects

House Power Consumption Prediction

Technology: Jupyter Notebook

This project is based on machine learning that predict the power consumption of our house for next week.

Problem Description

The household power consumption dataset that describes electricity usage for a single house over four years.
How to explore and understand the dataset using a suite of line plots for the series data and histogram for the data distributions.
How to use the new understanding of the problem to consider different framings of the prediction problem, ways the data may be prepared, and modeling methods that may be used.

Read more