ASHISH TIWARI
Web Developer | Data Science | Machine Learning | Ex- Celebal Intern
Experience
Data Analysis
FICE
Worked on Customer attrition modeling predicts and understands churn. Criminal Face Detection System, a Python GUI, features account creation, training, detection, and test pages for comprehensive criminal face recognition.
February 2022–June 2022
Data Science
Celebal Technologies
Worked on a variety of projects in different domains of data science, including machine learning, deep learning, computer vision, and natural language processing (NLP). Used Python libraries such as NumPy, Pandas, Seaborn, and Matplotlib for data analysis.
May 2023- July 2023
Subject Matter Expert AI/ML
House of Couton Private Limited
Conducted research and analysis in healthcare, technology, and marketing domains for over 10+ projects. Expertise in data-driven insights. Utilized statistical tools, produced actionable reports, and delivered valuable recommendations.
July 2023- September 2023
Full Stack Developer
DesignScript
As a Full Stack Developer, I played a pivotal role in elevating online presence and customer engagement. I designed, developed, and maintained a dynamic website showcasing diverse projects, facilitating seamless client connections.
November 2023- February 2024
Projects
Whatsapp Chat Analyzer
It helps users understand various aspects of their WhatsApp conversations, such as message frequency, word count, most active participants, popular emojis, and common topics of discussion. Chat Analyzer include: - Message Statistics, Participant Analysis, Word Frequency and Clouds, Emojis and Media Analysis and Conversation Trends
Tech used: - Jupyter Notebook, Streamlit and Python
Dukan Web App
Full-featured eCommerce MERN stack web app with user authentication, product management, shopping cart, payment integration, and responsive design. It has some functionality like Login/Sign out, add to cart, User/admin authentication, Admin dashboard. Some features Backend Error Handling, Search, Filter & Pagination, Payment using Stripe.
Tech used: - React, Express, Mongodb, NodeJs
Book Recommendation
The website consists of two pages. On the home page, a recommender system based on popularity is implemented. The second page utilizes Collaborative Filtering, a widely used approach in Recommender Systems, to suggest books.
Tech used: - HTML, CSS, Jupyter Notebook, Flask, and Python
Tools and Technologies
Python
Node.js
JavaScript
Docker
c++
CSS
AI/ML
