pandas projects by students and freshers

pandas builders and their projects

• MethaneGuard - Physics-Informed AI for Methane Leak Detection & Prioritization

  AI that finds methane plumes in satellite imagery, measures each leak in kg/hr, and ranks them so inspection crews fix the worst first.

  PREETANSH DEVPURANirma University

• IPL Match Momentum Analysis (2020–2024): What Truly Decides Victory?

  Toss advantage creates perception, middle overs create control, and death overs create opportunities for comebacks.

  Shaswat HotaVeer Surendra Sai University of Technology, Burla

• Sales ETL Pipeline

  Built an end-to-end ETL pipeline to clean and analyze retail sales data using Python and SQL

  Devi karnambigaiE.G.S pillay engineering college, nagapattinam

• IPL Analytics Dashboard -> Advanced Cricket Intelligence & Performance Visualization

  Built a premium analytics dashboard for the Indian Premier League (IPL) that uncovers toss impact, match-winning phases, batting dominance, bowling efficiency,

  V C Premchand YadavVit-ap University

• IPL Crunch '26: Data-Driven Analysis of IPL Trends and Team Performance

  Analyzed IPL match data using Python to uncover trends in toss impact, venue scoring, powerplay dominance, recovery ability, and team performance.

  Himanshi JainMody University of Science and Technology

• IPL SixSense Analytics

  Analysed 289K IPL deliveries to bust the toss myth, decode phase-wise winning patterns, and rank all-time top performers — with a live Streamlit dashboard.

  Harini NagarajanAmrita Vishwa Vidyapeetham

• IPL Crunch '26: End-to-End Analytics Pipeline & Hypothesis Testing Engine

  Turning IPL data into winning insights through analytics, storytelling, and evidence.

  Kshitiz NegiDit University

• IPL Match Insights Using Python and Data Visualization

  Toss-winning teams secured 53.1% of the matches, highlighting a significant advantage in match outcomes.

  Khushboo AroraChandigarh Engineering College-cgc, Landran, Mohali

• Wobble IPL Analytics

  “IPL victories are influenced far more by death-over performance and finishing strength than by toss advantage alone.”

  Sri VigneshKpr Institute of Engineering and Technology

• IPL Match Intelligence: Data-Driven Analysis of Winning Patterns in T20 Cricket

  Analyzed 200K+ IPL ball-by-ball records to uncover how death-over performance, batting consistency, and venue conditions influence match victories.

  SwayamGandhi Engineering College

• IPL Data Analysis 2015–2019: Python, Pandas & 70K+ Ball-by-Ball Records

  5 seasons. 70,208 balls. One finding — the toss barely matters, the middle overs do.

  Shriyans RajDelhi School of Excellence

• IPL Crunch '26 - What Actually Wins IPL Matches?

  Analyzed whole data of IPL ball-by-ball to reveal that toss decision, death over runs and powerplay wickets decide match outcomes - not just the toss itself.

  MADDIRALA SAI SIDDHARTHAOsmania University

• IPL Match-Winning Insights Analysis

  Death-over scoring showed the strongest correlation with IPL victories, while toss advantage had minimal impact.

  Sushant SinghNational Institute of Electronics and Information Technology - Agartala

• IPL Crunch ’26 Analytics Dashboard

  Processed 289K+ IPL deliveries across 1200+ matches to uncover tactical match-winning insights using Python analytics.

  ISHU MITTALGraphic Era Hill University

• IPL 2026 Ball-by-Ball Match Intelligence & Performance Analytics

  Analyzed 289K+ deliveries to surface toss impact, phase dominance & player rankings across IPL seasons

  Ishita RathiProf. Ram Meghe Institute of Technology and Research

• IPL Data Analysis

  The project successfully transformed raw IPL data into an interactive analytics dashboard that uncovers batting trends, bowling performance, match-winning patte

  animesh JainIIIT Bhagalpur

• IPL DATA ANALYSIS (2008-2026)

  Toss Winner has no advantage in Match Winning [51.49% Win & 49.5% Loss].

  Mehul BalsaraLaxmi Institute of Technology, Sarigam

• IPL Match Insights Dashboard

  Built an IPL analytics dashboard that answers 3 match-winning questions using 5 seasons of ball-by-ball data.

  Suresh MajhiSrusti Academy of Management and Technology

• IPL Data Analysis

  Data has been cleaned and all questions has been answered

  Antra SinghChhattisgarh Swami Vivekanad Technical Universty

• IPL Intelligence Engine 2026: Decoding Winning Patterns Through Data Analytics

  Analyzed 289K+ IPL deliveries to uncover winning strategies, death-over impact, player dominance, and tactical match insights using data analytics.

  mahendra DNitte meenakshi institute of technology banglore

• IPL Match Winning Analysis using Python & Data Visualization

  Analyzed IPL match data to identify winning patterns using Python, Pandas, and Matplotlib. Explored toss impact, scoring phases, top players, and match-winning

  Aavinash MAnna University

• IPL Crunch ’26 — IPL Match Data Analytics Using Python

  Analyzed 1200+ IPL matches using Python and data visualization.

  MOHAMED SHAJITH SRMK College of Engineering and Technology

• IPL CRUNCH ’26 DATA ANALYTICS PROJECT

  Analyzed 1000+ IPL matches and discovered that death-over performance impacts victories more than toss advantage.

  Sri harsha KunkuAnna University

• IPL Crunch

  Uncovered match-winning patterns, player performance trends, toss impact, phase dominance, and hidden insights through comprehensive IPL data analytics.

  Eswar Balaji KareAmrita Vishwa Vidyapeetham

• Decoding IPL Match Outcomes through Data Analytics

  Toss Impact: Winning the toss had almost no effect on overall match outcomes. Match Phases: Strong performance in the death overs showed the highest connection

  RACHAGOLLA PRANAVRajiv Gandhi Institute of Petroleum Technology

• IPL Data Analytics 2008-2026 — Analysed 293,308 Deliveries to Find What Really Wins Matches

  Death overs score highest runs per ball — brutal hitting in overs 16-20 decides every IPL match

  Pradeep Marinedikl_university

• Startup Growth Analytics: Exploring Patterns of Success in Indian Startups

  Objective: Analyze real-world Indian startup funding data to uncover factors that drive startup growth and early success. Dataset Used: Indian Startups Funding Dataset (2015-2024) - includes information on startup name, industry, city, funding amount, investors, and investment type. Tools & Techni

  karan shelkeSanjivani College of Engineering Kopargaon

• IPL CRUNCH '26: A Ball-by-Ball Data Analytics Report

  V Sachien NataraajanMIT Manipal

• Smart City Data Challenge: Solving Urban Chaos with Data

  Adarsh BalCenturion University of Technology and Management

• IPL Data Analytics Challenge

  Ayush KumarDr. B.c. Roy Engineering College, Durgapur

• IPL MATCH INTELLIGENCE SYSTEM

  PALLAVIIndian Institute of Technology Roorkee