This project analyzes real-world startup datasets to evaluate funding growth patterns, engineer new KPIs such as Age, Total Funding, and Rounds, and visualize insights through dashboards for better data-driven decision-making.

This project is focused on analysing the growth patterns of startups using real-world startup datasets. The goal of the project is to identify which factors contribute to high growth and success in startups using data-driven insights. The project includes data extraction, cleaning, exploratory data analysis (EDA), feature engineering and building analytical visualizations. Different startup attributes such as total funding raised, number of investment rounds, industry sector, location, age of the startup, investor type and valuation ranking are studied to identify which variables have stronger impact on growth. Power BI / Tableau dashboards are used to visualize patterns like funding trends, sector-wise investment distribution, success indicators and startup maturity cycle. Python libraries like Pandas, NumPy and Matplotlib are used for preprocessing and model insights. This project helps in understanding what makes startups scale faster and supports investors, founders or analysts to take data-based decisions. It bridges analytics with startup business understanding and provides growth recommendation insights based on data.