Merge pull request #726 from SOMNATH0904/main

Parameters of Cricket Analysis

Parameters of Cricket Analysis/Dataset/README.md

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+# Exploratory Data Analysis (Sports)
+
+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.
+
+## Datasets
+
+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.
+
+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.
+
+## Objectives
+
+- Analyze player and team performances.
+- Identify key factors contributing to match outcomes.
+- Visualize trends and patterns in cricket matches.
+
+## Usage
+
+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.
+
+## Conclusion
+
+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.

Parameters of Cricket Analysis/Models/README.md

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+# Parameters of Cricket Analysis
+
+### 🎯 Goal
+The main goal of this project is to analyze various parameters of cricket matches to derive meaningful insights and trends from historical data.
+
+### 🧵 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.
+
+### 🧾 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.
+
+### 🧮 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.
+
+### 🚀 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.
+
+### 📚 Libraries Needed
+- Pandas
+- NumPy
+- Matplotlib
+- Seaborn
+- Scikit-learn
+
+### 📊 Exploratory Data Analysis Results
+![EDA Results](https://github.com/SOMNATH0904/ML-Crate/blob/6b5436149d5f10024898965b0246cf6f71c60232/Parameters%20of%20Cricket%20Analysis/Images/Output2.png)
+
+### 📈 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.
+
+### 📢 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.
+
+### ✒️ Your Signature
+Somnath Shaw 
+[GitHub](https://github.com/somnathshaw)
+