Thank GOD for Sklearn

Every subject has some fundamentals and without these fundamentals, the subject becomes tough to understand. Sklearn is one such fundamental library of python which played a key role in making technologies like Machine learning and Data Science catapult many years into the future and making it a reality today. Sklearn is also known as scikit-learn.

This project was started by David Cournapeau as a Google Summer of Code project which later became a part of Matthieu Brucher’s thesis. Further many authors contributed and made sklearn what it is today. sklearn specializes in classification, regression, clustering and dimensionality reduction.

As mentioned above there are 4 primary ways to use sklearn:-

  1. Classification: — In general classification means “The action or process of classifying something”
  • In terms of machine learning, it means identifying the categories present in the data set.

sklearn contains 3 classification models: -

1)Support vector machines (SVMs)

2)Nearest neighbours

3)Random forest

2. Regression: — It means finding a relationship between the input and output of the given data. An example would be linear regression.

  • Regression contains these 3 algorithms: -

1-SVMs

2-Ridge regression

3-Lasso

3.Clustering- When we are provided with a data set there is some data which are alike and clustering helps us bring all the alike data set together. There are mainly 3 clustering methods: -

1-K-means

2-Spectral clustering

3-Mean-shift

4.Dimensionality reduction: -In an analysis of a data set there can be n number of variables and these variables can decrease the efficiency and increase the time for analysis. So sklearn has features to reduce the non-required variables: -

1-Principal component analysis (PCA)

2-Feature selection

3-Non-negative matrix factorization

These were the main or top 4 uses of sklearn but it has many more features like model selection and pre-processing.

In my last blog, I went into detail about Data Pre-processing sklearn plays a major role in Data pre-processing

For further information, you can refer to the scikit-learn website or get in contact with me through mail-aishwar99govil@gmail.com or you can contact me on instagram-aishwargovil

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