Interactive Power BI dashboard analyzing Indian startup funding trends — explore top investors, funding types, industry insights, and city-wise distributions.

This project predicts startup profitability using Logistic Regression and Random Forest, analysing financial (funding amount, funding rounds, revenue), market (market share), and operational (startup age, employee count) factors. It evaluates AUC, accuracy, precision, recall, and F1-score, addressing underfitting, overfitting, and feature selection

Interactive Power BI dashboard analyzing Indian startup funding trends — explore top investors, funding types, industry insights, and city-wise distributions.

This project predicts startup profitability using Logistic Regression and Random Forest, analysing financial (funding amount, funding rounds, revenue), market (market share), and operational (startup age, employee count) factors. It evaluates AUC, accuracy, precision, recall, and F1-score, addressing underfitting, overfitting, and feature selection

“3rd Semester Design Thinking Case Study on Mahina, a sustainable menstrual startup.”

A data mining project that analyzes startup traction data and applies various machine learning models (Logistic Regression, Decision Trees, Random Forest, XGBoost) to predict whether a startup will succeed or fail based on engagement, web presence, and social media activity.

🚀 Predict startup success using machine learning by analyzing real-world data and key indicators for informed decision-making.

📈 Predict startup profits accurately to guide investment decisions and improve business strategies using data-driven insights.