Airline Price Prediction

A dashboard where users can explore the data, filter by various parameters (e.g., airline, route, date). • Tools technologies used: Tableau and Microsoft Power BI. • Here are some of the features of the dashboard: Gain valuable insights to aid in decision-making, pricing strategies, and customer ta

Insurance Premium Prediction

The goal of this project is to build a machine learning model which predicts whether a person needs insurance or not based on his age, bmi, etc. using Linear Regression.

Dry Beans Classification

• Supervised Machine Learning model to classify the dry beans into their classes. • Algorithms used: Logistic Regression, Decision Trees, KNN, SVM, Naive Bayes. • The dataset consisted of features describing the shape of the bean and we predicted it’s type.

Recommendation System

• Implemented collaborative filtering and content-based filtering algorithms to personalize movie recommendations. • Built a recommendation engine based on user similarity metrics, achieving a recommendation accuracy of 0.85 on a test dataset.

Online Payment Fraud Detection

• A machine learning model for classifying fraudulent and non-fraudulent payments. • Algorithms techniques used: Ensemble Learning, Data Imbalance, SMOTE, Cross Validation. • The dataset was highly imbalanced which required oversampling and using this created a model to detect fraud transactions.

Handwriiten notes and PDF Comparison

• Employed advanced methodologies like NLP for comparing and analyzing handwritten notes and PDF documents. • With the help of Google Lens scanned handwritten notes which can be compared with the original answer in PDF.