Tejkumar Chintakayala

Dedicated Finance and IT professional with history of meeting
company goals utilizing consistent and organized practices.
Skilled in working under pressure and adapting to new situations
and challenges to best enhance the organizational brand.

To seek challenging assignments and responsibility, with an opportunity for growth and career advancement as successful achievements

• Best Employee employee award for the quarter of 2019
• Runner Up in “PHILANTHROPHY” conducted in “BIZOMANIA” fest at ABBS.

Projects done on Python:

Project 1:

Data Science project on MVR:

Title: Predicting Boston House prices using python and Multivariable Regression

Description: In this project, I developed and evaluated the performance and the predictive power of a model trained and tested on data collected from houses in Boston’s.

Data analysis, exploration, handling null values to estimate the pricing of houses using various factors.

Method used: Multivariable Regression.

Libraries used: Pandas, Numpy, Matplotlib, sklearn, Seaborn, Statsmodel etc.

Project 2:

Title: Sending Automatic Message to whatsapp when the target share price is reached

Description: Fetching the share price from NSE and once the share price reaches the target price an automatic mail has to be sent to whats app

Used Beautiful Soup module to fetch data from NSE site

Automatic message will be sent to whatsapp number when target price is reached for a
particular share price with delay of 10 seconds
Used different modules like requests, pywhatkit, Beautiful soup, datetime.....
Created and implemented user defined functions

Project 3:

Title: Password Generator using Python
Description: By using PyCharm created password generator tool that helps to generate password automatically based on user requirement
Used different Modules like Random, tkinter, json...
Implemented Error handling exceptions in python
Updated completed knowledge and implantation of Class, Object and def function in Python

Have knowledge on Mathematical Models:

1. Linear Regression

2. Multivariable Regression

3. Gradient Descent Algorithm

4. Naïve Bayes Classifier

Data Science with Python Training from NUCOT