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Showing posts from January, 2020

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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Data Mining

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Data Mining  Introduction  Data mining finds valuable information hidden in large volumes of data. Data mining is the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. The computer is responsible for finding the patterns by identifying the underlying rules and features in the data. Databases used in Data Mining  Flat Files. Relational Databases. DataWarehouse. Transactional Databases. Multimedia Databases. Spatial Databases. Time Series Databases. World Wide Web(WWW) <script data-ad-client="ca-pub-6042744672336815" async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script> Copy code snippet
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Data Analysis

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Get your Database Simplified . Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion and supporting decision-making. Tools  R Programming R is the leading analytics tool in the industry and widely used for statistics and data modeling  Tableau Public:  SAS:  Apache Spark  Excel  RapidMiner: KNIME  QlikView
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Popular posts from this blog

How AI playing role in Automation Testing

  Automated testing, enhanced by AI, has revolutionized how software quality is ensured. Here’s a breakdown of how AI is used in automated testing: 1. Test Case Generation:    - AI-Based Test Design: AI can generate test cases automatically by analyzing the code or application behavior. It can create scenarios that might not be immediately obvious to human testers, covering edge cases and complex user interactions. 2. Test Execution and Optimization:    - Smart Test Execution: AI algorithms can prioritize test cases based on code changes, usage patterns, or historical data. This ensures that the most critical tests are run first, optimizing test execution time and resources.    - Test Suite Optimization: AI can help in selecting the most relevant tests to run, reducing redundancy and focusing on tests that are more likely to find new bugs. 3. Defect Prediction and Analysis:    - Predictive Analytics: AI models can analyze ...
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How AI is transforming software engineering industry

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Data science has become an integral part of the software industry, driving innovation and efficiency across various domains. Here are some key areas where data science plays a pivotal role in the software industry:        1.  Product Development and Enhancement    User Behavior Analysis : Data science helps in understanding user behavior through data analytics, enabling software companies to tailor their products to meet user needs more effectively.    Feature Optimization : By analyzing data on feature usage, companies can determine which features are most valued by users and prioritize their development efforts accordingly.     2.  Predictive Analytics and Decision Making    Forecasting Trends : Data science enables companies to predict market trends, user demand, and potential challenges, allowing for proactive decision making.    Risk Management : Predictive mo...
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AI in bugs prediction

  Predicting bugs in software testing requires a dataset that includes various attributes related to the software development and testing process. The dataset should capture information about past software projects, their development characteristics, and their testing outcomes. Here are some key types of data and specific attributes that would be beneficial:     1. Historical Bug Data:    - Bug Reports: Detailed descriptions of bugs found in past projects.    - Bug Severity: Information on the severity of each bug (e.g., critical, major, minor).    - Bug Status: Status of each bug (e.g., open, closed, in progress).    - Bug Resolution Time: Time taken to resolve each bug.     2. Code and Commit Data:    - Code Metrics: Lines of code (LOC), cyclomatic complexity, code churn (changes in code), and other code quality metrics.    - Commit History: Details of code commits including author...
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