Aashish Bansal aashish22bansal

# 👋 Hola Amigos!

My profile reflects my journey and expertise in the intersection of Technology and Finance. With a background in Information Technology from a top-tier institution, I've cultivated skills in Software Development, Data Analysis, and Financial Modeling. Throughout my career, I have worked in renowned organization, consistently delivering innovative solutions and contributing to business growth. My passion for Technology is evident in my involvement in diverse projects spanning Software Development, Machine Learning, and Data Analysis. I thrive in dynamic environments, leveraging my analytical mindset to tackle complex challenges. Continuously learning and adapting to emerging trends, I'm committed to excellence and making meaningful contributions in the tech and finance sectors. My profile embodies my dedication to professional growth and my drive to make a lasting impact.

📌 About Myself

Who I Am	Education	Skills	Special Qualities
Passion for converting complex data into actionable insights.	Bachelor's Degree in Information Technology	Oracle Pl/SQL, Informatica IDMC, Python, Java, Angular	Data strategist committed to solving complex business issues.

Collaboration & Engagement	Data Specialties
Thrive in team environments, collaborating with Business Analysts, Data Scientists, and Software Engineer	Data Engineering, Predictive Analytics, Data Visualization & Machine Learning

📞 Let's Connect!

Connect on LinkedIn.

Tools and technologies ⚙️

Property

Data

Languages

Tools

Database

Domain Knowledge

Major Projects 👷‍♂️

Project Name	Description	Project Link
SMS Spam Detection Using Machine Learning Techniques	Comparative study on the existing algorithms for learning to predict if a received message is spam or not Attempt to create a new hybrid model using the rarely used algorithms to create an improved decision- making system for prediction User interface built to use to check if a message (given as input) is spam
User Validation in Online Class	Study on different methods of user authentication while attending an online class Attempt to create a secured system to prevent students from carried out any malicious activity during class to give attendance Secured Facial recognition system to mark attendance to prevent passing credentials for any malicious activity	Patent In-Progress
Fake News Detection Using Machine Learning Techniques	Comparative study on recent algorithms to predict if the given input news is fake or real Algorithms analysed to improve the efficiency of learning Improved Neural Network for reduced loss and improved accuracy High training time due to the increased number of required resources for training the model
Breast Cancer Detection Using Machine Learning Techniques	Comparative study on the recent algorithms to predict if a person is suffering from Breast Cancer Algorithms analysed to improve the efficiency of learning Comparison of training before and after hyperparameters tuning
Testing of E- Commerce Website	Consists of a separate Administrator Panel for the Admins of the company to view and update product information The products are categorized based on the needs to the users and types of users with a Classical Interface Implemented using HTML, CSS, JavaScript, PHP, MySQL which can run on XAMPP Server Testing of the user interface and functionality of the E-commerce website Testing conducted with the help of Black Box and White Box Testing methods Probable Errors and bugs fixed after thorough analysis
Analysis of COVID-19 Data	Data provided was the 2019 COVID-19 data including the list of Indian and International cases divided into the category of total cases, infected cases, recovered cases, and deceased cases Story creation with the data for all the states to analyse the increase on a daily basis for the states Implemented using Tableau
Fake News Prediction	Prediction if the given news is fake or not based on the key words in the text and vectors created after removing all the stop words and statistical analysis
Network Intrusion Detection System	Worked on Machine Learning Algorithms which can be used to be predict whether the Request coming is an attack Comparative Study of several available Algorithms Novel Approach for the Deep Learning model to improve learning efficiency	Publication In-Progress
Skin Cancer Detection using Transfer Learning and Ensemble Modelling	Improved Machine Learning Model to predict if the person is suffering from Skin Cancer Improved Accuracy Results of the Algorithms based on the Current Project Approved for publishing in SoCPaR 2022, LNNS 417 proceedings
Apple Store Management System	The major component is the Database normalized to reduce redundancy in data to the minimum level possible The interface is simple for both administrators to update product information and for the users to view product information Implemented using HTML, CSS, JavaScript, PHP, MySQL which can run on XAMPP Server
Summer Internship Project: Twitter Sentiment Analysis (2020)	Deep Learning Model predicts whether the tweet given by the used is Positive, Negative or Neutral based on the training using Bi-LSTM
View all projects