Yes. The records fall naturally into segments on four dimensions, but each needs cleaning first because the raw labels are inconsistent. **1. Funding round type (`InvestmentnType`), the strongest segmentation** - Two buckets dominate. Private Equity has about 1,356 deals and Seed Funding about 1,355, and they account for most of the 3,044 rows (step-0). - A "seed/angel" family is split across many spellings: "Seed/ Angel Funding" (60), "Seed / Angel Funding" (47), "Seed/Angel Funding" (23), "Angel / Seed Funding" (8), "Seed / Angle Funding" (3), plus "Seed Round" and "Seed". Collapsed, this is a distinct early-stage segment. - "Seed\nFunding" (30 rows) has an embedded newline. Once normalised, Seed Funding rises to 1,385 (step-3 vs step-4). - A small tail covers the later and other rounds: Debt Funding (25), Series A (24), B (20), C (14), D (12), and a few E, F and H, crowd funding and corporate rounds. - A sensible grouping is: **Seed/Angel/Pre-Series A**, **Priced venture rounds (Series A–H)**, **Private Equity**, **Debt**, and **Other**. **2. Time (`Date dd/mm/yyyy`)** - Deals per year fall steadily after a 2016 peak: 2015 (935), 2016 (993), 2017 (687), 2018 (310), 2019 (111) and 2020 (7) (step-2). - Year is a natural cohort or period segment. The decline suggests the dataset's coverage thins out in later years, so it may not reflect market activity alone. **3. Geography (`City Location`)** - Deals are concentrated in a few hubs: Bangalore (700), Mumbai (567), New Delhi (421), Gurgaon (287), then Pune, Hyderabad, Chennai and Noida (step-1). - The raw labels need merging before use: - Bangalore and Bengaluru. - Gurgaon and Gurugram, including the versions with a non-breaking space. - New Delhi and Delhi. - Typos such as "Ahemadabad". - Multi-city values such as "Pune / US" and "Bangalore / SFO". - About 171 rows have no city (`nan`), and a few more are blank or "N/A". - A good grouping is **metro hub** (Bangalore, Mumbai, Delhi-NCR, which combines New Delhi, Gurgaon, Noida and Delhi), **other Tier-1 and Tier-2 Indian cities**, and **international or unknown**. **4. Industry vertical (`Industry Vertical`)** - The top categories are Consumer Internet (941), Technology (478), eCommerce (186), Healthcare (70) and Finance (62) (step-2). - The column has roughly 903 distinct values, so it is a long tail and not usable as-is. - eCommerce is also spelled "ECommerce" and "E-Commerce", and 171 rows are `nan`. - It needs a rolled-up taxonomy of about 10–15 groups, such as Consumer Internet, Technology/SaaS, eCommerce, Health, Fintech, Logistics and Education. **Recommendation:** Segment primarily by **normalised round type × year**, then by **city hub** and **rolled-up vertical**. Cleaning the labels comes first: trim whitespace and non-breaking spaces, unify case and spelling variants, and treat `nan` as "Unknown". Without it, the same segment appears as several different groups. I did not check the investor names (`Investors Name`) or the deal amounts. Amount is stored as text, so segmenting by deal size (for example micro, small and large tickets) would first need that column parsed and cleaned.