POOJA H S REDDY’s Post

Gained a lot of knowledge completing the project as part of the Exploratory Data Analysis using Python course in the PGPDSE-FT program at Great Learning. A new football club named ‘Brussels United FC’ has just been inaugurated & the team is looking to hire players for their roster. Management wants to make such decisions using data based approach. Player data for all teams has been acquired from FIFA. The data contains details for over 18,000 players playing in various football clubs in Europe. It contains information on age, skill rating, wages and player value, etc. in the files named fifa.csv which is data file and fifa_variable_information.cs #TeamWork, #ITP, #NPVandEDA, #MyGreatLearning

Say hello to my data science project! 👋  In a trial to predict new football players rating in FIFA video game, I tackled real-world data using Grid regression technique and BeautifulSoup for webscrabing  . To achieve a high accuracy scores in the range of 99% for each model that each one of them is used to predict the overall rating and the main 6 attributes for football players in FIFA (Pace , Shooting , Passing , Dribbling , Defending and Physical ). I: - Webscrabed FIFA website to have the data of the first 100 player using BeautifulSoup including the overall rating , main attributes and detailed attributes that make the the main ones - Used Ridge Regression to make the models that predict the attributes and the overall rating - Used the models on 2 players outside the first 100 player and it gave me an acceptable results This project improved my skills in Python skills, Webscrabing and data modeling. The project is shown on that video : https://lnkd.in/evVP39zb I will be very pleasured if you have a look on my GitHub account, i had made some another projects that i hope is showing my skills in Data analysing , Data Visualisation , EDA and Power BI. GitHub : https://lnkd.in/efEZG9QC And i am ready to hear your comments , Thank You #regression #datascientist #machinelearning #datascience #dataanalysis #dataanalysts #dataanalyticstraining #data #Webscrabing #opentowork

🏏 Excited to share my latest project's part in cricket analytics! 📊 Here's a quick rundown of what I've been working on: 1️⃣ Data Collection: I started by collecting match data from JSON files, focusing on T20 World Cup match results. 2️⃣ DataFrame Creation: Utilizing the Pandas library in Python, I structured the JSON data into Pandas DataFrames for easier analysis. 3️⃣ Match Summary Analysis: In the match summary DataFrame, I extracted essential details like team names and match IDs, crucial for further analysis. 4️⃣ Data Transformation: I processed batting summary data, including categorizing players as 'out' or 'not out' based on dismissal information. 5️⃣ Matching IDs: To integrate match summaries with batting data, I mapped match IDs to corresponding matches, ensuring cohesive analysis across datasets. 6️⃣ Exporting Results: Finally, I exported the processed data to CSV files, making it accessible for further exploration and visualization. Excited to delve deeper into cricket analytics and uncover insights from this rich dataset! 📈🏏 #CricketAnalytics #DataScience #Python #Pandas #T20WorldCup

I recently completed a 2-month Sports Analytics Program offered by Saiprasad Kagne. The program provided in-depth training in deep learning, Python programming, statistics, and Tableau reporting, which not only enhanced my analytical skills but also polished my dashboarding abilities. As part of the program, I completed a "Team Level Analysis - T20 World Cup 2024" focused on the Afghanistan team (you can check out the Medium link below). The analysis highlighted the team's strengths and areas for improvement through various metrics across the entire tournament. I am grateful for the guidance of Saiprasad Kagne and BallTrack Analyzer, who not only provided valuable insights but also patiently addressed all my queries throughout the course. With my strong interest in cricket and sports in general, I have always been intrigued by the analytical aspects of these fields, and this course offered the perfect foundation to start my journey in cricket and sports analytics. The program covered not only technical knowledge but also included one-on-one sessions to help me understand the significance of different metrics and how to apply and interpret them in real sports scenarios. I am eager to dive deeper into this field! Medium Link: https://lnkd.in/gzS_8DzA BallTrack Analyzer: https://lnkd.in/gzFPDHCv - I used the app to get data points in my second article. Do check this out. #CricketAnalytics #sports

🚀 Excited to share my latest project: IPL Matches Dataset Analysis! 🏏 I've compiled a comprehensive dataset of Indian Premier League (IPL) matches from 2008 to 2024. This dataset includes detailed information on: Match results 🏆 Player performances 🌟 Toss decisions and outcomes 🎲 Venue and umpire details 🏟️ It’s perfect for data enthusiasts, sports analysts, and anyone interested in diving deep into the world of IPL cricket. Whether you want to analyze team performance, predict match outcomes, or explore player stats, this dataset has it all! 🔗 Check it out on GitHub: [https://lnkd.in/gsKGgriH] Feel free to explore, contribute, and build your own analyses or visualizations. Let’s uncover some exciting insights from one of the world’s biggest sporting leagues! 🙌 #IPL #Cricket #DataScience #SportsAnalytics #Python #MachineLearning #DataVisualization

🚀 Data Preprocessing for T20 World Cup Cricket Analysis 🏏 I recently worked on preprocessing the data for T20 World Cup Cricket, and I’m excited to share the journey and key steps involved! Here’s a quick overview: 1. Processing Match Results • Loaded JSON data containing match summaries. • Renamed ‘scorecard’ to ‘match_id’ for better linkage with other tables. • Created a dictionary mapping match ids to team names for easy reference. 2. Processing Batting Summary • Extracted and combined batting summaries from multiple records. • Added a new column to indicate whether the player was ‘out’ or ‘not_out’. • Mapped the match id using our previously created dictionary. • Cleaned unwanted characters from the batsman names to ensure consistency. 3. Processing Bowling Summary • Similar steps were followed to extract and combine bowling summaries. • Mapped match ids and ensured data consistency for further analysis. 4. Processing Players Information • Loaded and transformed player information data. • Cleaned up unwanted characters from player names to maintain data quality. Outcome: • Created clean and structured CSV files ready for detailed analysis and visualization. • Ensured that each record is linked properly via unique match ids, making future data manipulation and insight generation seamless. https://lnkd.in/eEE4v845 #DataScience #Cricket #T20WorldCup #DataPreprocessing #Python #Pandas #DataAnalysis #SportsAnalytics

🏆 For me, UEFA EURO 2024 final match was played, and the tournament concluded in the quarter-finals. ⚽📈 This content highlights several key aspects emphasizing the importance of data analytics in football. The heat map and radar graph clearly illustrate the team's effectiveness and performance on the field. The player performance map details individual contributions and player movements on the pitch. The match results and tournament progress chart summarize the overall team success. These analyses provide a data-driven approach to deeply understand team strategies and player performances. 🐍 These visuals were created using python and Opta Sports Data Ltd data, leveraging libraries such as pandas, matplotlib, mplsoccer, cmasher and seaborn. #euro2024 #footballanalytics #data #spain #germany #socceranalytics #datavisualization

🚀 IPL Data Insights: Analyzing Player Salaries, Games, and Performance! 🚀 I am thrilled to share an IPL dataset (2010-2019), where I explored key factors such as player salaries, games played, and points scored to uncover valuable insights. Using NumPy and matplotlib for data manipulation and visualization, I focused on identifying trends and patterns that provide actionable insights for cricket analysts and enthusiasts alike. 🔑 Key Findings: 📊 Analyzed salary distribution across players and seasons, identifying the best value players who performed above expectations based on their salaries. 🎯 Investigated game performance metrics to highlight players' consistency over time, leading to a better understanding of who contributed the most per season. 📈 Created visual comparisons between players' salaries and their game participation, shedding light on the correlation between player pay and game outcomes. Leveraging data analysis tools helped me break down complex patterns and deliver powerful cricket insights! 🙏 A Special Thanks to My guide, KODI PRAKASH SENAPATI Sir! 🙏 for his guidance, support. His encouragement, and expertise have been invaluable in shaping my skills and knowledge, especially in the field of data analysis and machine learning #DataAnalysis #IPL #SportsAnalytics #Cricket #DataScience #Python #NumPy #DataVisualization #Insights #PerformanceAnalysis

Hello connection, Another day another project, Here is my latest data analytics project, where I explored the FIFA dataset. In that detailed analysis, I derived major insights pertaining to player attributes, team strengths, and market values. Using Python and its visualization packages, I was able to identify trends that put forward the development of football strategies and players over the years. This project improved not only my analytical skills but also enhanced my knowledge related to the beautiful game. Looking forward to how all these insights would be applied in real-world scenarios so that I might contribute to data-driven decision-making in sports management.

Unleashing the Power of Data in Cricket! 🏏📊 Excited to share my small project where I leveraged Python to dive deep into IPL (Indian Premier League) data analysis. We have a dataset loaded with comprehensive information about cricket players and their performances. Our goal is to familiarize you with techniques for filtering, querying, and sorting this rich dataset using Pandas. #DataAnalytics #Python #Pandas #CricketAnalytics #DataVisualization #DataScience