Your data speaks a lot, get to know what it is by using this Machine Learning Library.
There are many libraries to handle the above processes, of those Scikit has its special place with its simplicity and versatility. Let us explore more about Scikit learn Library of Machine learning.
Originally it is called scikits.learn and it started life from google summer of code project by David Cournapeau. It’s name is derived from scipy and tool kit. The current version is 0.23.2. as of today.
The above table shows what Scikit can achieve. It also shows the example algorithms which are available in scikit.
Data classification provides a clear picture of all data and an understanding of how data is impacting the prediction.
Regression is used to establish relation between the variables which helps to estimate the values.
Clustering is useful for exploring data. If there are many cases and no obvious groupings, clustering algorithms can be used to find natural groupings. Clustering can also serve as a useful data-preprocessing step to identify homogeneous groups on which to build supervised models.
Model selection is the task of selecting model from a large set of computational models for the purpose of decision making or optimization.
Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data.
Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn, it is extremely important that we preprocess our data before feeding it into our model.
The sklearn.preprocessing module includes scaling, centering, normalization, binarization methods.
Scikit has Simple and efficient tools for data mining and predictive data analysis. It is accessible to everybody, and reusable in various contexts. It is built on NumPy, SciPy, matplotlib, pandas. It has rich suit of tools which are needed around ML(Dataset loading, Manipulation, Preprocessing pipelines, Metrics, Clustering and lot more). It has Vast amount of collection of ML algorithms with minimal amount of code change. It is well maintained and quite reliable. It also has sample data sets where we can try performing tasks.
Benefits of Scikit
Easy to use(simplicity, reusability)
Let us see an example of how simple scikit is.
For every model one of the important steps is to shuffle and split the data. To achieve this we need the following code (without scikit)
data = data.sample(frac=1).reset_index(drop=True) data_total_len = data[data.columns].size data_train_frac = 0.9 split_index = math.floor(data_total_len*data_train_frac) train_data = data.iloc[:split_index] eval_data = data.iloc[split_index:]
With scikit, the shuffle and split can be achieved using the following code
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(all_X, all_y)
It is a simple single line code to split the data with scikit. The above example shows the simplicity of scikit, similarly there are many such simple yet powerful functions in Scikit which can achieve all of the Data Science computational needs from analyzing to visualizing which can be referred here.
In the real world the fields of medical diagnosis, speech recognition, Image recognition, statistical arbitrage, Learning associations, Classification, regression, extraction, prediction,
Financial services have more need of the techniques.
Sci kit library is a very good tool for Data professionals as it has many Machine learning algorithms which can be used in a simple way.