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Normalizing data involves transforming it to a standard scale without distorting differences in the ranges of values.
1. Min-Max Scaling (Normalization)
This technique scales the data to a fixed range, usually 0 to 1 or -1 to 1.
2. Z-Score Standardization (Standardization)
This technique transforms the data to have a mean of 0 and a standard deviation of 1.
3. Robust Scaler
This technique uses the median and the interquartile range (IQR) for scaling, making it robust to outliers.
4. Max Abs Scaler
This technique scales the data by its maximum absolute value, preserving sparsity in data (i.e., useful for sparse data like text data represented as TF-IDF).
5. Decimal Scaling
This technique involves moving the decimal point of values of the feature. The number of decimal points moved depends on the maximum absolute value of the feature.
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Choosing the Right Normalization Method:Min-Max Scaling: Useful when you want data within a specific range (e.g., 0 to 1). It is sensitive to outliers.
Z-Score Standardization: Preferred when the data has a Gaussian (normal) distribution. It is less sensitive to outliers than Min-Max Scaling.
Robust Scaler: Best when dealing with data that has many outliers.
Max Abs Scaler: Suitable for data that is sparse or has large variations in scale.
The normal distribution is crucial in data science and data analysis for several reasons:
Central Limit Theorem (CLT):
Statistical Inference:
Simplification and Approximation:
Prediction and Error Analysis:
Natural Phenomena:
Probabilistic Interpretations:
Parameter Estimation:
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Why normal distribution is important in data science/data analysis?
In summary, normal distribution is important in data science because it is a fundamental concept that is used in many statistical analyses, including hypothesis testing, regression analysis, and confidence intervals.
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How to normalize data?
There are several ways to normalize data, but one of the most common methods is min-max normalization.
Q1. Why normal distribution is important in data science/ data analysis?
The normal distribution, also known as the Gaussian distribution or bell curve, is essential in data science and data analysis for several reasons
Common Occurrence in Nature:
Central Limit Theorem (CLT):
Statistical Inference:
Parameter Estimation:
Z-Scores and Percentiles:
Machine Learning Algorithms:
Quality Control and Six Sigma:
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Q2. How to normalize the data?
There are several ways to normalize data, but one of the most common methods is min-max normalization.
learn distributions of data done
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