India IPO Dataset (2010–2025)

This dataset includes comprehensive IPO records such as company name, issue size, listing date, offer price, listing price, sector classification, subscription rates, and performance indicators where available. The dataset has undergone a structured exploratory data analysis workflow including duplicate removal, date normalization, issue size validation, and categorical standardization of industry sectors. Numeric fields such as offer price and listing gains were validated using range checks and statistical anomaly detection to eliminate unrealistic values. The dataset is exported in a structured format compatible with financial modeling tools, econometric analysis, and machine learning workflows.

Data Quality

The dataset is organized in a normalized tabular structure with consistent column naming and validated data types. Duplicate IPO entries have been removed to preserve accuracy. Issue size, pricing fields, and subscription rates were validated through range checks and outlier detection to ensure realistic financial data representation. Industry and sector classifications were standardized to avoid inconsistent labeling. The dataset is optimized for financial trend analysis and predictive modeling tasks.

Use Cases

This dataset can be used for IPO trend analysis, listing gain modeling, and capital market performance benchmarking. Financial analysts can evaluate subscription behavior and sector-level IPO performance. Investors can analyze historical IPO returns and volatility patterns. Data scientists can build predictive models to forecast listing gains or IPO success probability. The dataset also supports dashboard development for capital market intelligence and research in financial economics.

Limitations

The dataset is community-sourced and may not reflect official exchange filings. It summarizes IPO-level attributes and may not include full prospectus-level financial details. Market conditions and macroeconomic variables are not directly included. The dataset is intended for research and analytical purposes and should not replace official exchange disclosures.

Methodology

The dataset was prepared using a structured validation and normalization workflow including schema standardization, duplicate removal, date formatting consistency checks, and numeric validation of issue size and pricing attributes. Statistical anomaly detection was applied to identify unrealistic listing gains or subscription spikes. Sector classifications were standardized to maintain categorical consistency. The final dataset was exported in a clean Excel/CSV format encoded in UTF-8 for compatibility with Python, R, SQL, and financial analytics platforms.