Introduction
Normalisation and standardisation are two common preprocessing techniques included in any Data Scientist Course. These techniques are used in data science to scale and transform features before feeding them into machine learning algorithms. Normalisation and standardisation play a vital role in preparing data for analysis and modelling.
Importance of Normalisation and Standardisation
The following sections describe the role of normalisation and standardisation in rendering data suitable for analysis. Unless data is correctly prepossessed using these techniques, the result obtained for analysis can be skewed and incorrect.
- Scaling Features: In many machine learning algorithms, the scale of features can impact the model’s performance significantly. Features with larger scales might dominate those with smaller scales, leading to biased model training. Normalisation and standardisation ensure that all features are on a similar scale, preventing this issue.
- Improved Convergence: Algorithms like gradient descent converge faster when features are scaled. Normalisation and standardisation facilitate smoother optimisation by ensuring that the gradients are more consistent across features, speeding up the convergence process.
- Distance-Based Algorithms: Many machine learning algorithms rely on distance calculations (for example, K-nearest neighbours, clustering algorithms). Standardising or normalising features ensures that the distance calculations are not dominated by features with larger scales, leading to more meaningful comparisons between data points.
- Regularisation Techniques: Regularisation methods—such as, L1 and L2 regularisation commonly included in a Data Scientist Course—penalise large coefficients in regression models. Standardising or normalising features ensures that all features are penalised equally, preventing the model from being unfairly biased towards certain features.
- Interpretability: When interpreting model coefficients or feature importance, it is easier to compare them when features are on the same scale. Normalisation and standardisation make the interpretation of model parameters more straightforward and meaningful.
- Outlier Robustness: Standardisation is less sensitive to outliers compared to normalisation. By centering the data around the mean and scaling by the standard deviation, standardisation reduces the impact of outliers on the overall distribution of the data. Handling outliers from various perspectives is extremely important in research-oriented data analytics. Thus, a research-oriented course, such as a specialised Data Science Course in Mumbai, Pune, or Chennai would treat standardisation from this view point.
- Model Performance: Ultimately, normalisation and standardisation can lead to improved model performance, as they help algorithms better understand the underlying patterns in the data and make more accurate predictions.
Basic Normalisation and Standardisation Equations
The common basic equations for normalisation and standardisation that will be taught in any Data Scientist Course are the following:
Normalisation: Normalisation typically refers to scaling each feature to a range between 0 and 1. It is useful when the features have different scales. One of the most common normalisation techniques is Min-Max scaling, which transforms each feature to the range [0,1][0,1] using the formula:
where 𝑋 is the original feature value, Xmin is the minimum value of the feature, and Xmax is the maximum value of the feature.
Standardisation: Standardisation (also called S-score normalisation) transforms the data to have a mean of 0 and a standard deviation of 1. It is particularly useful when the features are normally distributed. The formula for standardisation is:
where X is the original feature value, 𝜇 is the mean of the feature, and 𝜎 is the standard deviation of the feature.
Normalisation scales the data to a fixed range (usually [0,1][0,1]), while standardisation rescales the data so that it has a mean of 0 and a standard deviation of 1. The choice between normalisation and standardisation depends on the specific characteristics of the data and the requirements of the machine learning algorithm being used.
Conclusion
Overall, normalisation and standardisation are essential techniques in the data preprocessing pipeline, ensuring that the data is appropriately prepared for analysis and modelling, leading to more reliable and accurate results. Although advanced methods of normalisation and standardisation are usually used in research studies and by statisticians, the basic methods are essential first steps in any data analysis process and are often mandatory topics in a Data Science Course in Mumbai or a data analysis course curriculum
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