Pinned Repositories
ChatApp
A real-time Chat App using socket.io, react js and node js
COVID-19_tracker_reactjs
COVID-19 Tracker
CryptoDash_Tracker
Academic Project
ecommerce-mern-web-app
A dynamic e-commerce Progressive web application
GeoFence
An App to make Todo list, change sound profile automatically based in the location added and also ping on the added location.
Linking
Cross-platform mobile Application using react native, nodejs and mongodb atlas
ManInTheMiddleAttack
MovieLens_data_analysis_using_pig_pyspark
In this system, we have used here the MovieLens 20M dataset. MovieLens, that is a movie suggesting service, provided this dataset (ml-20m), which describes 5-star rating and free-text tagging behaviour. Over 27278 movies, it has 20000263 ratings and 465564 tag applications. 138493 users produced this data, between January 9, 1995, and March 31, 2015. On October 17, 2016, this dataset was created. In this dataset, users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided. All the included genres are action, adventure, animation, children's, comedy, crime, documentary, drama, fantasy, film-Noir, horror, musical, mystery, romance, sci-Fi, thriller, war, western, and (no genres listed). As a result of the movieLens dataset analysis, users may find or obtain data on the most popular films based on the number of users, reviews, and insights into films based on ratings. The user may also find out the average rating of films based on a variety of factors such as genre, occupation, and age group. They would be able to have a better knowledge of the films by learning about the most popular genre for each age group. Additionally, filmmakers may be interested in learning about the yearly trends in film production that might aid them in making critical decisions. As a result, we've conducted a number of studies employing movie Lens datasets in order to give consumers and experts with accurate movie information.
Project-Viewer-Redux-Firebase
Add, update, delete and view projects per each user based on users
WebSearchEngine
The assignment is creating a Web search engine that incorporates concepts from three to five different classes. Individual or group projects can be developed, however group work is encouraged. In the case of group work, each group member will receive an individual grade. Students are encouraged to submit their own suggestions for things that should be included in the Web search engine. Finding patterns using regular expressions, converting HTML to text, ranking web pages using sorting, heaps, or other data structures, finding keywords using string matching, using inverted index, analysing frequencies using hash tables or search trees, using large dictionaries/datasets, sorting techniques, search trees, spellchecking keywords or HTML files, and so on are just a few examples. Some concepts were discussed in class, while others were included in the assignments (as optional work). It's worth noting that a graphical user interface for your search engine isn't needed, even if you want to use one.
naisarg53's Repositories
naisarg53/ecommerce-mern-web-app
A dynamic e-commerce Progressive web application
naisarg53/COVID-19_tracker_reactjs
COVID-19 Tracker
naisarg53/GeoFence
An App to make Todo list, change sound profile automatically based in the location added and also ping on the added location.
naisarg53/WebSearchEngine
The assignment is creating a Web search engine that incorporates concepts from three to five different classes. Individual or group projects can be developed, however group work is encouraged. In the case of group work, each group member will receive an individual grade. Students are encouraged to submit their own suggestions for things that should be included in the Web search engine. Finding patterns using regular expressions, converting HTML to text, ranking web pages using sorting, heaps, or other data structures, finding keywords using string matching, using inverted index, analysing frequencies using hash tables or search trees, using large dictionaries/datasets, sorting techniques, search trees, spellchecking keywords or HTML files, and so on are just a few examples. Some concepts were discussed in class, while others were included in the assignments (as optional work). It's worth noting that a graphical user interface for your search engine isn't needed, even if you want to use one.
naisarg53/ChatApp
A real-time Chat App using socket.io, react js and node js
naisarg53/CryptoDash_Tracker
Academic Project
naisarg53/Linking
Cross-platform mobile Application using react native, nodejs and mongodb atlas
naisarg53/ManInTheMiddleAttack
naisarg53/MovieLens_data_analysis_using_pig_pyspark
In this system, we have used here the MovieLens 20M dataset. MovieLens, that is a movie suggesting service, provided this dataset (ml-20m), which describes 5-star rating and free-text tagging behaviour. Over 27278 movies, it has 20000263 ratings and 465564 tag applications. 138493 users produced this data, between January 9, 1995, and March 31, 2015. On October 17, 2016, this dataset was created. In this dataset, users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided. All the included genres are action, adventure, animation, children's, comedy, crime, documentary, drama, fantasy, film-Noir, horror, musical, mystery, romance, sci-Fi, thriller, war, western, and (no genres listed). As a result of the movieLens dataset analysis, users may find or obtain data on the most popular films based on the number of users, reviews, and insights into films based on ratings. The user may also find out the average rating of films based on a variety of factors such as genre, occupation, and age group. They would be able to have a better knowledge of the films by learning about the most popular genre for each age group. Additionally, filmmakers may be interested in learning about the yearly trends in film production that might aid them in making critical decisions. As a result, we've conducted a number of studies employing movie Lens datasets in order to give consumers and experts with accurate movie information.
naisarg53/Project-Viewer-Redux-Firebase
Add, update, delete and view projects per each user based on users
naisarg53/TODOAPP
Add, Update, Delete daily task
naisarg53/weatherApp
A weather app using react native to display temperature, humidity, weather and description.
naisarg53/WhatsApp_Clone_ChatApp
WhatsApp Clone Chat App using Android studio