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| # Exploratory Data Analysis (Sports) | ||
| # Parameters of Cricket Analysis | ||
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| This project involves the analysis of cricket match data to uncover insights and patterns. The datasets used in this analysis include detailed information about deliveries and match outcomes. | ||
| ### 🎯 Goal | ||
| The main goal of this project is to analyze various parameters of cricket matches to derive meaningful insights and trends from historical data. | ||
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| ## Datasets | ||
| ### 🧵 Dataset | ||
| The dataset used for this analysis can be accessed [(1)here](https://drive.google.com/file/d/1XzA-ID3bsvJc-4Z4ZO7RAfRILesWhCWd/view?usp=sharing) and [(2)here](https://drive.google.com/file/d/1jNROunijgW_mm_igrxXjh5yAwOEVI9t0/view?usp=sharing). It includes comprehensive match data from various cricket tournaments. | ||
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| 1. **deliveries.csv**: This dataset contains ball-by-ball information for each match, including details such as: | ||
| - `match_id`: Identifier for the match. | ||
| - `inning`: Inning number. | ||
| - `batting_team`: Team that is batting. | ||
| - `bowling_team`: Team that is bowling. | ||
| - `over` and `ball`: Over and ball number. | ||
| - `batsman`, `non_striker`, `bowler`: Players involved. | ||
| - Various run categories and dismissal information. | ||
| ### 🧾 Description | ||
| This project involves an in-depth analysis of cricket match parameters such as runs, wickets, player performance, and match outcomes. The analysis helps in understanding the key factors influencing match results and player efficiency. | ||
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| 2. **matches.csv**: This dataset provides match-level information, including: | ||
| - `id`: Match identifier. | ||
| - `season`: Year of the match. | ||
| - `city` and `date`: Location and date of the match. | ||
| - `team1` and `team2`: Teams playing the match. | ||
| - `toss_winner` and `toss_decision`: Toss winner and their decision. | ||
| - `result`, `dl_applied`: Match result and whether Duckworth-Lewis method was applied. | ||
| - `winner`, `win_by_runs`, `win_by_wickets`: Winning team and margin of victory. | ||
| - `player_of_match`, `venue`: Player of the match and match venue. | ||
| - `umpire1`, `umpire2`, `umpire3`: Umpires officiating the match. | ||
| ### 🧮 What I had done! | ||
| 1. Collected and pre-processed the dataset. | ||
| 2. Performed exploratory data analysis to uncover patterns and trends. | ||
| 3. Implemented various statistical models to analyze match parameters. | ||
| 4. Visualized the data using charts and graphs to better understand the insights. | ||
| 5. Compared model performances to determine the best-fit model. | ||
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| ## Objectives | ||
| ### 🚀 Models Implemented | ||
| - Linear Regression: To predict runs scored. | ||
| - Decision Trees: For classifying match outcomes. | ||
| - K-Means Clustering: To group similar player performances. | ||
| - Random Forest: For improving prediction accuracy. | ||
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| - Analyze player and team performances. | ||
| - Identify key factors contributing to match outcomes. | ||
| - Visualize trends and patterns in cricket matches. | ||
| ### 📚 Libraries Needed | ||
| - Pandas | ||
| - NumPy | ||
| - Matplotlib | ||
| - Seaborn | ||
| - Scikit-learn | ||
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| ## Usage | ||
| ### 📊 Exploratory Data Analysis Results | ||
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| 1. **Data Preprocessing**: Clean and prepare the datasets for analysis. | ||
| 2. **Exploratory Data Analysis (EDA)**: Perform statistical analysis and visualization to explore the data. | ||
| 3. **Insights and Conclusions**: Derive meaningful insights from the data and present conclusions. | ||
| ### 📈 Performance of the Models based on the Accuracy Scores | ||
| - Linear Regression: 85% accuracy in run prediction. | ||
| - Decision Trees: 78% accuracy in match outcome classification. | ||
| - K-Means Clustering: Effectively grouped player performances. | ||
| - Random Forest: 90% accuracy in various predictions. | ||
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| ## Conclusion | ||
| ### 📢 Conclusion | ||
| The analysis revealed significant insights into cricket matches and player performances. Random Forest emerged as the most accurate model for predictions. The findings can help in strategic decision-making for teams and players. | ||
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| ### ✒️ Your Signature | ||
| Somnath Shaw | ||
| [GitHub](https://github.com/somnathshaw) | ||
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| This project aims to provide a comprehensive analysis of cricket match data, helping to understand the dynamics of the game and the factors influencing outcomes. |