Successfully developed a machine learning model which can accurately predict whether a firm will become bankrupt or not, depending on various features such as net value growth rate, borrowing dependency, cash/total assets, etc.

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

Data was downloaded through Kaggle

Successfully established a machine learning model which can accurately predict whether an employee of a given company will leave it in the impending future or not, based on several employee details and employment metrics.

Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

Explored a dataset of planes while learning PySpark commands.

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

Successfully created a machine learning model which can accurately predict the fare of a taxi trip based on several features such as trip duration, tip amount, etc.

An SQL-based exploration of COVID-19 data and vaccination progress using the Covid-Deaths dataset for insights into global pandemic trends.

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis, NLP processing and ML, achieving the functionality of a Content based movie recommender system

Prediction of happy Customers based on Happiness Survey Data

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.