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Tejaswee P. Python, Flask and Data Platforms

My name is Tejaswee P. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: Python, Automation, Trading Automation, Software Development, Data Scraping, etc.. I hold a degree in . Some of the notable projects I’ve worked on include: Scraping 15000+ companies- using Scrapy, Selenium- LinkedIn Auto Lead Generation script, Selenium: Rotating Proxies and Scraping, Buddha Gyan App, Scrapy project: Extracting companies information, etc.. I am based in Kathmandu, Nepal. I've successfully completed 6 projects while developing at Softaims.

I am a dedicated innovator who constantly explores and integrates emerging technologies to give projects a competitive edge. I possess a forward-thinking mindset, always evaluating new tools and methodologies to optimize development workflows and enhance application capabilities. Staying ahead of the curve is my default setting.

At Softaims, I apply this innovative spirit to solve legacy system challenges and build greenfield solutions that define new industry standards. My commitment is to deliver cutting-edge solutions that are both reliable and groundbreaking.

My professional drive is fueled by a desire to automate, optimize, and create highly efficient processes. I thrive in dynamic environments where my ability to quickly master and deploy new skills directly impacts project delivery and client satisfaction.

Main technologies

  • Python, Flask and Data Platforms

    5 years

  • Python

    3 Years

  • Automation

    3 Years

  • Trading Automation

    3 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Leapfrog Technology

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Experience Highlights

Scraping 15000+ companies- using Scrapy

I once had the task of scraping more than 15000 websites from the 'businessfinder' website which are located at Somerville. I inspected the elements there and I had to go deep inside several layers of the pages to extract information about the companies. I had to extract several things like name, address, phone no, website, information, etc.. After inspecting each element of every layer of pages I quickly got an idea about how to write Scrapy functions to complete this task. I wrote the necessary function and run the crawler. Within 2 hours of the time, my Crawler finished the task and provided me with 15000+ companies details in CSV format.

Selenium- LinkedIn Auto Lead Generation script

This is a project where I wrote a Selenium Script in Python to collect leads using Google search Engine. I had to find out names, email and Linkedin profiles of 200 cardiologists of NewYork. To solve this, I wrote this script which works quickly and smoothly. - I used Chrome Driver for automation - The search string and no. of pages are provided inside the script - Finally desired output is obtained in the form of a CSV file.

Selenium: Rotating Proxies and Scraping

- I once had to scrape data from a site given by a client and when I tried scraping data on that site I found that it had high protections. Just after making requests for a couple of time, it would redirect to the captcha containing page. - To solve this problem I developed this script which works on rotating proxies. This script first gets a list of free https proxy from 'spys' site and it then filters 15 best proxies based on the latency value provided on that site. Whenever there is captcha redirect during scraping, it automatically changes the proxy and works from that proxy. - I can not share the data which I scraped through this script at this time as my client told me that this data shouldn't be shared. But I have slightly modified my script and redirected it to IP checking websites and generated screenshots from there and compiled them here.

Buddha Gyan App

I developed this app - Budhha Gyan about 2 years ago. I translated the teachings of Lord Budhha to my native language- Nepali and gave it a form of App.

Scrapy project: Extracting companies information

- My job was to extract information about the companies which are present in the Indian Chamber of Commerce Hongkong site. There were 37 pages and there were several companies on each page. I had to enter those companies on each page and extract information from there. - After inspecting elements of those pages I quickly wrote a Scrapy function that can do this task. Within 10 minutes of scraping, my crawler provided me a CSV document containing all the required data.

Education

  • Agriculture and Forestry University, Nepal

    in

Languages

  • English
  • Nepali