https://github.com/harmanveer-2546/statistics-for-machine-learning

Statistical tools help you clean and organize your data. You can identify outliers, manage missing values, and ensure your data is in a format that the ML algorithms can understand.
https://github.com/harmanveer-2546/statistics-for-machine-learning

inline matplotlib matplotlib-styles numpy pandas probability python seaborn statistics

Last synced: 6 days ago
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Statistical tools help you clean and organize your data. You can identify outliers, manage missing values, and ensure your data is in a format that the ML algorithms can understand.

• Host: GitHub
• URL: https://github.com/harmanveer-2546/statistics-for-machine-learning
• Owner: harmanveer-2546
• Created: 2024-06-09T09:32:48.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-09T09:37:15.000Z (5 months ago)
• Last Synced: 2024-06-09T10:44:42.140Z (5 months ago)
• Topics: inline, matplotlib, matplotlib-styles, numpy, pandas, probability, python, seaborn, statistics
• Homepage:
• Size: 8.87 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## What Is Statistics?

Statistics is a branch of mathematics that collects, analyzes, interprets, and visualizes empirical data. Descriptive statistics and inferential statistics are the two major areas of statistics. Descriptive statistics are for describing the properties of sample and population data (what has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and make predictions (what can you expect).

## 📊 Statistics for Machine Learning

Machine learning (ML) has become a transformative force across industries, but it's true potential lies in the data it feeds on. Just like a delicious meal relies on fresh ingredients, a powerful ML model depends on clean, well-understood data. This is where statistics comes in – it's the secret sauce that unlocks the hidden patterns and insights within your data.

## Why Statistics is the Backbone of ML

Statistics provides the foundation for every stage of the ML lifecycle:

* Data Preparation: Statistical tools help you clean and organize your data. You can identify outliers, manage missing values, and ensure your data is in a format that the ML algorithms can understand.

* Feature Engineering: Statistics empowers you to create new features from existing ones, uncovering hidden relationships and improving the model's ability to learn.

* Model Selection and Evaluation: Statistical techniques help you choose the right ML algorithm for the job and assess its performance. You can use metrics like accuracy, precision, and recall to gauge how well your model is learning.

* Interpretation and Insights: Statistical analysis helps you interpret the results of your ML model. You can understand which factors have the most significant impact on the model's predictions and gain valuable insights from the data.

In essence, statistics equips you to not just build ML models, but to build them well.