The IPL EDA (Exploratory Data Analysis) was conducted, revealing valuable insights. The analysis focused on various aspects such as player performance, team statistics, and match outcomes. Key findings include trends in run-scoring, top performers, and team dynamics. The EDA offers actionable insights for teams and fans to make data-driven decision
🔍 Step 1: Data Scraping 🌐 Scraped IPL data from the website using Beautiful Soup library, extracting valuable information for analysis.
🧹 Step 2: Data Cleaning 🧼 Utilized the powerful pandas library to clean the dataset, removing duplicates, handling missing values, and ensuring data consistency.
📊 Step 3: Visualization 📈 Leveraged Plotly libraries to create visually appealing charts and graphs, uncovering patterns, trends, and key insights in the IPL data.
🚀 Step 4: Deployment 🌟 Deployed the IPL EDA project using Streamlit, providing an interactive and user-friendly interface for easy exploration and sharing of findings.
🔑 Key Takeaways 📚 This project showcases the power of web scraping, data cleaning, visualization, and deployment using popular Python libraries. It enables efficient analysis and empowers IPL enthusiasts with valuable insights.
Dataset resource: I scraped the dataset from this website link:https://www.iplt20.com/matches/results/2008 and Kaggle
Website link: https://eda-ipl.streamlit.app/
Website Interface
