Yes. The records fall most naturally into **funding round type × time period**, with **city** and **industry vertical** as secondary cuts. All four fields need label cleaning before grouping, because the raw values are inconsistent. **1. Funding round type (the strongest segmentation)** - Two labels dominate: **Private Equity** (1,356 deals) and **Seed Funding** (1,355 deals). - The long tail is mostly variants of **seed/angel**: "Seed/ Angel Funding" (60), "Seed / Angel Funding" (47), "Seed\nFunding" (30), "Seed/Angel Funding" (23), "Angel / Seed Funding" (8), and others. - Smaller groups are **Debt Funding** (25) and the **Series A–F** rounds (Series A 24, B 20, C 14, D 12). - A sensible grouping is: Seed/Angel, Private Equity, Venture Series A+, and Debt/Other. Merging the spelling and whitespace variants first is essential. The "Seed\nFunding" label alone splits off 30 deals through a stray newline. **2. Time period (year of deal date)** - Deals per year are 935 in 2015, 993 in 2016, 687 in 2017, 310 in 2018, 111 in 2019 and 7 in 2020. - Year therefore works as a natural cohort, showing the ecosystem's rise and then the thinning of reported deals. Part of the decline may be reporting coverage rather than real activity. - The date is stored as text, so it has to be parsed first. The card's stated date range is unreliable. **3. City (geographic segment)** - Bangalore (700), Mumbai (567), New Delhi (421) and Gurgaon (287) lead the list of 944 distinct location strings. - Many of those strings are duplicates or multi-city entries. Bangalore and Bengaluru (141) are the same city, as are Gurgaon, Gurugram (50) and "\xa0Gurgaon" (4), and also New Delhi and Delhi (34). - Others are typos ("Ahemadabad"), combined entries ("Pune / US", "Bangalore / SFO") or missing values ("nan" 171, blank 6, "N/A" 3). - Group by a standardised metro (Bengaluru, Delhi-NCR, Mumbai, Other) rather than the raw text. **4. Industry vertical** - Consumer Internet (941) and Technology (478) lead, followed by eCommerce, Healthcare, Finance, Logistics and Education. - eCommerce is split across "eCommerce" (186), "ECommerce" (61) and "E-Commerce" (29), so it needs merging. - 171 deals have a missing vertical, shown as "nan". - The card counts about 903 distinct verticals, so the field needs consolidation into broad sectors before it is useful. **Recommendation:** build a cleaned segmentation of *round-type group × year × metro × broad sector*. Round type and year are the most reliable and stable axes. City and vertical are useful only after the labels are normalised. I did not segment by deal size here, because `Amount in USD` is stored as text and would need cleaning first. The counts above come from the round-type, city, vertical and year breakdown queries I ran.