Gohil Parthkumar

To work in the way to maintain work and team spirit within an organization. Work in the environment full of competition and work under pressure with a good outcome.

• Presented on Research Paper in International Conference on Advent Trends in Computer Technology (ICATCT-2020)
• Webinar on “Innovative Project Development” held at R.N.G. Patel Institute of Technology.
• Webinar on “Machine Learning with Python” held at R.N.G. Patel Institute of Technology.
• Webinar on “Multimedia Security” held at R.N.G. Patel Institute of Technology.
• Webinar on “Block chain Road-Map for Application Development” held at R.N.G. Patel Institute of Technology.
• Webinar on “Software Testing” held at R.N.G. Patel Institute of Technology.
• Webinar on “JDBC in Java” held at R.N.G. Patel Institute of Technology.

• IOT based Line of Control Monitoring System, Filed Design IPR titled, IPR Design Current status in under Technical Examination., 351315-001
• IOT based Line of Control Monitoring System, International Conference on Advent Trends in Computer Technology, 08/27/2020 - 08/28/2020, Jaipur Engineering College and Research Centre, Jaipur, International Journal on Operating System Development &Treads, vol 7, No 3(2020), SSIP (student standard innovation policy) grant

• ThatsEnd PVT. LTD, 05/2021, 04/2022, Surat, The internship program was on “PHP Traditional Project” started from 25th May to 12th Jun with ThatsEnd Pvt. Ltd, Surat. Learnt about introduction to Web developing, Website hosting and Database fetching and using and implementing tool., PHP Traditional Project (use to CSS/HTML/PHP), PHP Strom/Wamp-server/ Bootstrap/
• ThatsEnd PVT. LTD, 01/01/2022, 04/01/2022, Surat, Front-End developer, Worked with WordPress and Laravel.

Time Series Forecasting model in Python, by using the FBProphet module

• The forecasting model should be able to predict New York City’s Electricity
• Consumption (see below) by using Facebook’s Prophet model. Prophet is a
• procedure/model for forecasting time series data based on an additive model where nonlinear trends are fit with yearly, weekly, and daily seasonality.
• You should test your forecasting model in three (3) distinct datasets. On Daily, Monthly, and Yearly Mean electric consumption.

Explanatory Data Analysis (EDA)

• Perform an explanatory data analysis (EDA) on Customer Churn data within the Telecommunication
• industry. Although there will be no need to build a model based on the data provided, you are asked to
• look for issues in the data and find correlation among the various variables in order to improve/lower
• customer churn predictions