This repository encompasses the comprehensive body of work completed during the AI Engineer Cognizant Virtual Training and Internship Program offered by Forage. The work spans various facets of the data science domain and delineates the workflow followed by a data scientist:
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Data Insights: This section delves into the process of gaining valuable insights from data, encompassing data exploration, understanding, and initial observations.
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CRISP-DM (Cross-Industry Standard Process for Data Mining): The repository covers the systematic CRISP-DM methodology, which is a structured approach for tackling data mining projects, including phases like business understanding, data preparation, modeling, evaluation, and deployment.
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Data Cleaning: Detailed guidance is provided on the crucial task of data cleaning, involving the identification and rectification of data inconsistencies, errors, and missing values.
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Feature Engineering: This segment focuses on the art of creating new and meaningful features from existing data, enhancing the predictive power of machine learning models.
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Data Visualization: The repository expounds on the importance of data visualization in conveying complex insights effectively through graphical representations.
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Machine Learning: It covers various machine learning techniques, algorithms, and models, serving as a valuable resource for those looking to apply machine learning in real-world scenarios.
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Python: A significant emphasis is placed on Python, a prominent programming language in the field of data science, including its applications and libraries for data analysis and modeling.
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Communication: Effective communication in data science is elaborated upon, with guidance on conveying findings and insights to stakeholders and teams.
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Quality Assurance: The repository underscores the importance of maintaining data quality throughout the data science workflow, ensuring the reliability and accuracy of results.
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Evaluation: Techniques and best practices for evaluating the performance of machine learning models and data science projects are outlined.
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Interpretability: This section discusses the critical aspect of model interpretability, enabling data scientists to understand and explain their models' decisions and predictions.
In essence, this repository serves as a comprehensive guide, offering in-depth insights and practical guidance on the various stages and components involved in the data science workflow, making it a valuable resource for aspiring data scientists and professionals alike.