Search Results for "statistical analysis"
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11 projects for "statistical analysis" with 2 filters applied:
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MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
MEDIUM_NoteBook is an open-source repository that contains a collection of Jupyter notebooks and code examples originally developed to accompany technical articles published on Medium. The project provides practical demonstrations of machine learning algorithms, data analysis workflows, and visualization techniques. Each notebook typically focuses on explaining a specific concept through step-by-step examples that combine explanatory text, code, and visual outputs. The repository covers a wide variety of data science topics such as predictive modeling, data preprocessing, statistical analysis, and feature engineering. ... -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and generating trading signals based on quantitative models. ... -
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Python Programming Hub
Learn Python and Machine Learning from scratch
Python Programming Hub repository by Tanu-N-Prabhu is an educational resource designed to help programmers learn Python programming and data science concepts through practical examples and notebooks. The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. ... -
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AutoViz
Automatically Visualize any dataset, any size
...The system also includes built-in tools for evaluating data quality and identifying potential issues such as missing values or unusual distributions. By automating the visualization process, AutoViz allows users to rapidly explore datasets before applying machine learning models or statistical analysis. -
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QuantResearch
Quantitative analysis, strategies and backtests
QuantResearch is a large educational repository dedicated to quantitative finance, algorithmic trading, and financial machine learning research. The project contains numerous notebooks and research materials demonstrating quantitative analysis techniques used in financial markets. These include implementations of factor models, statistical arbitrage strategies, portfolio optimization methods, and reinforcement learning approaches to trading. The repository also explores financial modeling topics such as vector autoregression, Gaussian mixture models, and option pricing techniques. ... -
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Complete Machine Learning Package
A comprehensive machine learning repository containing 30+ notebooks
Complete Machine Learning Package repository is a comprehensive educational collection of machine learning notebooks designed to teach core data science and AI concepts through practical coding examples. The project includes more than thirty notebooks that cover a wide range of topics including data analysis, statistical modeling, neural networks, and deep learning. Each notebook introduces theoretical ideas and then demonstrates how to implement them using Python libraries commonly used in data science, such as NumPy, pandas, scikit-learn, and TensorFlow. The repository also includes examples related to natural language processing, computer vision, and data visualization, giving learners exposure to several subfields of machine learning. ... -
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Machine Learning Git Codebook
For extensive instructor led learning
...Many lessons emphasize hands-on exercises where learners analyze datasets, implement algorithms, and evaluate results through visualizations and statistical metrics. -
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Machine Learning Mindmap
A mindmap summarising Machine Learning concepts
...The project organizes a wide range of machine learning topics into an interconnected diagram that helps learners understand how concepts relate to one another across the broader field of artificial intelligence. The mind map covers fundamental areas such as data preprocessing, statistical analysis, supervised learning, unsupervised learning, reinforcement learning, and deep learning architectures. By arranging these concepts visually, the repository allows students and practitioners to quickly explore the relationships between algorithms, techniques, and modeling approaches used in modern machine learning workflows. ... -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s... -
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
DS-Take-Home is a repository that provides practical solutions to a series of real-world data science challenges inspired by the book A Collection of Data Science Take-Home Challenges. The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing... -
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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation...
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