The Startup Growth Analytics project focuses on understanding the dynamics of startup success through data-driven analysis. The main objective of this project is to explore real-world startup funding data to uncover patterns related to industries, locations, and investors, and to develop a machine learning model capable of predicting the funding amount a startup might receive. By studying various factors such as industry vertical, city, investor type, and funding year, this project aims to highlight the key determinants of startup growth and investment potential. The dataset used in this project, startup_funding.csv, contains over 3,000 records of startup funding events across India. The data underwent extensive preprocessing, including cleaning missing values, standardizing text formats, converting non-numeric funding amounts to numerical values, and extracting time-based features like year and month. After preparing the data, exploratory data analysis (EDA) was conducted to visualize trends such as the top startup hubs, leading industries, and yearly investment patterns. The analysis revealed that Bengaluru, Mumbai, and Delhi are the primary centers for startup activity, while sectors like E-Tech, FinTech, and E-commerce dominate the funding landscape. For predictive modeling, a Random Forest Regressor was employed to estimate funding amounts based on categorical and numerical features. The data was label-encoded and split into training and testing sets, followed by model evaluation using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. Although the model achieved moderate accuracy due to the variability of real-world data, it effectively demonstrated the application of regression algorithms to financial prediction problems. In conclusion, the Startup Growth Analytics project successfully integrates data cleaning, visualization, predictive modeling, and deployment into a single analytical pipeline. It provides valuable insights into the startup ecosystem and highlights the potential of data science techniques in understanding business growth and investment trends. The project demonstrates strong analytical, technical, and problem-solving skills while emphasizing the practical use of AI and machine learning for business intelligence.