I live by the line "infusing life into my code". I am good at initiating conversations and I have affluent public speaking skills. I am currently a Microsoft Learn Student Ambassador (Beta). I like to participate in hackathons, technical workshops and seminars, as well as organise them. I like to learn new technologies. I recently learned to make a website using MERN stack. I also completed a data science professional certificate from IBM by coursera. I am practicing competitive coding these days and focusing on data structure and algorithms. I like to do Yoga, write poetries, and play guitar.

The link to my LinkedIn account is as follows: https://www.linkedin.com/in/pranavgurditta/

Project description
Content Zilla is a platform where poets and content writers can showcase their talent by posting poems and content write-ups.
It provides an opportunity for creative people to read each other's content as well as contact the poet/writer for hiring purposes.

Project description
The project performs sentiment-based analysis on the basis of video comments provided by the YouTube Data API for a given topic and suggests the top 5 video links.
The video recommendation system can be seen live at https://video-recommendation.azurewebsites.net/

Project description You could see a live demo of the project here:
https://www.youtube.com/watch?v=T1cBttVZ-7c

A multilingual voice-based chatbot developed for the Kotak Mahindra Bank‘s problem statement in Smart India Hackathon, 2019.
It can handle banking activities like querying account balance, filling forms via voice in regional languages like Gujarati, Marathi, Tamil, etc.

Problem statement: Suppose a company wants to know which boroughs of Canada have the same type/cuisines of restaurants so that it can know the number of types of stores of raw food it should open in the whole of Canada. For example, if a borough in Canada is a part of cluster that offers Chinese and Japanese food the company would open a store in that area and all the areas in that cluster that offer raw food material of Chinese and Japanese dishes.

Benefits of solving this: This would help the restaurants also as it would bring down the transportation costs of raw food items and hence provide cheaper food to the customers. The company can gain a lot of profit by becoming the nearest seller as restaurants would need these items everyday. The company can also filter which raw materials to send to a store on a daily basis.

The link to the project is as follows: https://github.com/pranavgurditta/Capstone_project_Clustering_boroughs_based_on_cuisines/blob/master/CAPSTONE_PROJECT_FINAL_FILE.ipynb

Project description: Create an application in R using R-Shiny and do text-based sentiment analysis of the IPL team's tweets and use it to check whether or not a sponsor should invest in the IPL team or not.

The problem is to make an application in R which does sentiment analysis using tweets from R following a menu-based approach to let the user choose to do text-based sentiment analysis or check the popularity of IPL teams or do a comparison of chances of winning of two IPL teams.

The application is used in R to do the following tasks:

Text-based sentiment analysis: To check whether the word entered has been used in a positive or negative sense in a tweet.
IPL team tweet analysis: To know whether the tweets of the IPL team are positive or negative and hence utilize this data to show advertisements to the user based on the situation of the team that he is following on twitter.
IPL team comparison: To compare the chances of winning of two teams and also as to know which team is better for investing money for advertisements and brand promotions as brands generally give advertisements to the winning team.