ADITYA (RID : 474kwbrsbmh)

designation   PYTHON, DJANGO, REACT JS DEVELOPER

location   Location : Ahmedabad, India

experience   Experience : 6 Year

rate   Rate: $18 / Hourly

Availability   Availability : Immediate

Work From   Work From : Offsite

designation   Category : Information Technology & Services

Last Active: 30+ Days ago
Shortlisted : 0
Total Views : 9
Key Skills
Python Django React JS Java Script CSS HTML Data science
Discription

Previous Company [Data Scientist, Django] 2015–2021

Provided comprehensive analysis and recommend solutions to address complex business problems and issues using data from internal and external sources and applied advanced analytical methods to assess factors impacting growth and profitability across product and service offerings. Applied statistical and algebraic techniques to interpret key points from gathered data. Scaled analytical capabilities across all business areas, evolving analytics to influence the bank's strategic planning and executives' decision· making. Built, tested, and deployed scalable, highly available Models and Converted Models into Django/Flask applications.

 

Previous company -2 [Python Full Stack Developer] 05/2021–12/2021

Developing Python scripts, Created Flask application, Scrapping scripts, Object detection Models

 

Zyelon Computech [Python, Django, React js Developer] 01/2022–Present

Building Web applications, and APIs using Django, React JS.


SKILLS

JavaScript  

HTML/CSS 

React JS  Django

Data Science

Python

Data Visualization

Projects Malware Detection

There is no doubt that the Android market these days is the most common mobile operating system, with a share of approximately 85% just in 2017, This huge market share has proposed countless Android applications, and the development of such Android apps is still evolving. The problem is that applications are published with no adequate Android malware analysis or security processes. This has strongly supported the spread of malware samples in the Android system and critically affected the reputation of the market regarding its robustness, safety and security, correctness, and correctness of end-user privacy. I was Supposed to classify Malwares and Benign App on a Given Data are Detection There are 3 Dataset Benign Dataset contains MDS id, AP! calls and Intent Permissions Malicious Data Contains MDS ids, API calls, Intent Permissions Family Data Contains MDS, Family names of Malware Import Datasets and Merge using common Variable MDS ID Cleaned the Data Feature Engineering for labeling Malware and Benign APPS Performed NLP(I used Lemmatization and Count vectorizer) to Extract features from Permission and Intent(As Permissions and Intent were in Text ) Split Data for ML into 70:30 Performed Random Forest/XG Boost (Both have given almost same Accuracy) to classify Malware and Benign App


Movie recommendation Application

Movie recommendation systems provide a mechanism to assist users in classifying users with similar interests. This makes recommender systems essentially a central part of websites and e-commerce applications. I was supposed to build a Movie Recommendation system for Apps intellect(a client who gave this Project)

Credit Data: Contains Movie Title, Cast, Crew, IMDB id( Cast & Crew was in string format) Movie Metadata: Contains IMDB id, Original Title, Genre(Was in String Format) Pre Process both datasets one by one by doing these steps below: Convert to Those String values to NumPy array by using ast. Literal EvalQ function (Df['cast'].map Oambda x: ast. literal evil (x))). Fetch Actor Names, Director, and genre from Cast/Crew/Genre variable by iterating through and appending to a list by using lambda. Then Combined all those text values Perform Count vectorizer extract features Create a matrix of the number of rows X number of rows using the cosine matrix to fetch correlations and stored them in a variable. Then Created Function will take a Text value and pass it to enumerate through the index and look for a better correlation score based on the input to get similar movies to be recommended. Made a web app by deploying That model using Flask which can recommend movies by taking input such as Actor's name, Movie Title, Genre, and Director

Name. Compiled Everything as Django Application

E-Commerce WebApp                     

Build A E-Commerce Web Application for a Freelance Client.

This E-Commerce Web application has features of AutoComplete Search, Add to cart, and Checkout Features.

Speech-AI Web App                    

Build A Speech-AI Web Application for a Freelance Client. Build with React js and Django API. Available on www. preuzor.com

Archive-System Web App                    

Build an Archive system Web Application for a Presidency University. Build with Django. Which is still under development, and not available on the public domain.


 

 





 
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