Data Science and ML in Cricket

 Objective of the data science in cricket  - Increate team performance  - Maximiser winning chances  Here's a simplified version: --- The IPL has expanded cricket, increasing the number of matches and the amount of data collected. Modern cricket data analysis involves tracking various factors like player positions, ball movements, shot types, delivery angle, spin, speed, and trajectory, which makes data cleaning and preprocessing more complex. **Dynamic Modeling** In cricket, numerous variables must be tracked, including player actions, ball attributes, and potential outcomes. The complexity of modeling depends on the type of predictive questions asked. Predictive models become especially challenging when analyzing hypothetical scenarios, like how a batsman’s shot might change with different ball angles or speeds. **Predictive Analytics Complexity** Cricket decision-making often relies on queries like "how often does a batsman play a specific shot against a certain b...
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Algorithms in Data Science

 Here’s a list of commonly used algorithms in data science, categorized by their type and application:

 

 Supervised Learning Algorithms

 

1. Regression Algorithms:

   - Linear Regression

   - Polynomial Regression

   - Ridge Regression

   - Lasso Regression

   - Elastic Net Regression

 

2. Classification Algorithms:

   - Logistic Regression

   - Support Vector Machines (SVM)

   - Decision Trees

   - Random Forest

   - k-Nearest Neighbors (k-NN)

   - Naive Bayes

   - Gradient Boosting Machines (GBM)

   - XGBoost

   - LightGBM

   - CatBoost

 

 Unsupervised Learning Algorithms

 

1. Clustering Algorithms:

   - k-Means Clustering

   - Hierarchical Clustering

   - DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

   - Gaussian Mixture Models (GMM)

 

2. Dimensionality Reduction Algorithms:

   - Principal Component Analysis (PCA)

   - t-Distributed Stochastic Neighbor Embedding (t-SNE)

   - Linear Discriminant Analysis (LDA)

   - Singular Value Decomposition (SVD)

   - Independent Component Analysis (ICA)

   - UMAP (Uniform Manifold Approximation and Projection)

 

 Reinforcement Learning Algorithms

 

1. Model-Free Algorithms:

   - Q-Learning

   - Deep Q-Network (DQN)

   - SARSA (State-Action-Reward-State-Action)

   - Policy Gradient Methods

   - Actor-Critic Methods (A3C, DDPG, PPO)

 

2. Model-Based Algorithms:

   - Dynamic Programming

   - Monte Carlo Tree Search (MCTS)

 

 Ensemble Learning Algorithms

 

1. Bagging:

   - Bootstrap Aggregating (Bagging)

   - Random Forest

 

2. Boosting:

   - AdaBoost

   - Gradient Boosting

   - XGBoost

   - LightGBM

   - CatBoost

 

 Neural Network Algorithms

 

1. Feedforward Neural Networks (FNN)

2. Convolutional Neural Networks (CNN)

3. Recurrent Neural Networks (RNN)

   - Long Short-Term Memory (LSTM)

   - Gated Recurrent Unit (GRU)

4. Generative Adversarial Networks (GAN)

5. Autoencoders

 

 Natural Language Processing (NLP) Algorithms

 

1. Text Classification and Clustering:

   - Bag of Words (BoW)

   - Term Frequency-Inverse Document Frequency (TF-IDF)

   - Word2Vec

   - GloVe

   - FastText

   - BERT (Bidirectional Encoder Representations from Transformers)

   - GPT (Generative Pre-trained Transformer)

 

2. Sequence Modeling:

   - Hidden Markov Models (HMM)

   - Conditional Random Fields (CRF)

 

 Time Series Analysis Algorithms

 

1. ARIMA (AutoRegressive Integrated Moving Average)

2. SARIMA (Seasonal ARIMA)

3. Exponential Smoothing

4. Prophet

 

 Anomaly Detection Algorithms

 

1. Statistical Methods:

   - Z-Score

   - Grubbs' Test

2. Machine Learning Methods:

   - Isolation Forest

   - One-Class SVM

   - Autoencoders for Anomaly Detection

 

 Association Rule Learning Algorithms

 

1. Apriori Algorithm

2. Eclat Algorithm

3. FP-Growth Algorithm

 

 Other Useful Algorithms

 

1. Graph Algorithms:

   - PageRank

   - Community Detection Algorithms (Louvain, Girvan-Newman)

2. Optimization Algorithms:

   - Genetic Algorithms

   - Simulated Annealing

   - Particle Swarm Optimization

 

This is not an exhaustive list, but it covers many of the key algorithms used in data science. Each of these algorithms has specific use cases, advantages, and limitations.

 


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