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Python File Handling

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Python OOPs Concept

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Python Iterators

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Python Generators

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Python MySQL

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Python MongoDB

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Python 2

What is Python 2

Python 3

Anaconda in Python 3 Anaconda python 3 installation for windows 10 List Comprehension in Python3

How to

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Python Sort List Sort Dictionary in Python Python sort() function Python Bubble Sort

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Standard Scaler in SKLearn

  • The sci kit learns in python is a library thatch is used in machine learning which is used to work on data modeling.
  • It is only focused on the data modeling, it does not focus on manipulation and loading of the data.
  • The classification, clustering through constancy interface, and regression are the modeling.

Uses of SK learn

  • The main use of scikit learn in python is that it is open source.
  • The benefit is:
    1. Open source
    2. Versatile used
    3. Free
    4. Easy to use
    5. Properly documented

Open source:

  • The SK learn is a library that is open source and publicly available.
  • Anyone can use this library and can modify the code.

Versatile used:

  • The SK learn is a library that is used for many things like can identify user actions.
  • Customer behaviour is also predicted which shows the versatile nature.
  • SK learn library is user-friendly and comes with many tools.

Free:

  • The SK learn is a library, which people use for free and any license for running is not necessary

Easy to use:

  • The SK learn is a python library that people can access the library at any time.
  • It is mostly used by research organizations for their operation since SK learn is easy to use.

Properly documented:

  • The SK learn is a python library in which documentation is used properly.
  • It contains API documentation which is accessible from the website provided.

Advantages of SK learn

  • It is user-friendly and can do many things like can identify user actions.
  • It is free to use.
  • It is easy to use.
  • It provides API documentation for the user.
  • Here the documentation is done properly.
  • The contributor updated the SK learn library done by the international community.
  • It is spread for the BSD license which means it can used  without restriction.

Disadvantages of SK learn

  • It is not much of use for in-depth learning.

Let us consider an example to see the working of the SK learn library:

from sklearn.cluster import kmeans_plusplus
from sklearn.datasets import make_blobs
import matplotlib.pyplot as pl
/ / here sample means it is used to generate sample data
sample = 5000
component = 4
/ / kmeans_plusplus  which is used to seed calculate from k
a, b_true = make_blobs( n_samples = sample,centers = component, cluster_std = 0.60, random_state = 0 )
a = a[ : , : : -1]
centers_init, indices = kmeans_plusplus(a, n_clusters = 4, random_state = 0)
/ / plot figure means it is used for seed plotting on the screen
plot.figure(1)
colors =["red", "blue", "green", "yellow"]
 
for k, col in enumerate(colors):
cluster_data = b_true == k
plot.scatter( a [cluster_data, 0], a [cluster_data, 1], c=col, marker=".", s=10)
 
plot.scatter(centers_init[:, 0], centers_init[:, 1], c="b", s=50)
/ / plot tile is used to give the title for the graph on the screen
plot.title("K-Mean Clustering")
plot.xticks([])
plot.yticks([])
plot.show()

output:

What is SK Learn in Python

Installing the SK learn:

  • SK learn is used to model the data.To install the SK learn library we need to install scipy and numpy.
  • Command to install the numpy is:
    pip install numpy
  • After execution of the above command, we can see the numpy is installed.
  • After numpy is installed we need to install scipy. We can install scipy using the following command.
    pip install scipy
  • After execution of the above command, the scipy is installed. After both libraries are installed. We can install scikit library using the following command:
    pip install scikit-learn
  • If the SK learn is already installed then no need to install them again.
  • We can update the SK learn using the following command:
    pip install -u scikit-learn
    SK learn features:
  • We know that SK learn is used to model the data but not to manipulate the data.
  • The following are the features of SK learn:
    Supervised learning:
  • It is a predictive model, in which data comes with good quality which we want to predict.
  • It is divided into two types:
    1. Classification:
      • if the problem output has been categorized into “white”, and”black” then the problem is considered to be a classification problem.it is a predictive model in which the set data is categorized into classes.
    2. Regression:
      • If the problem output is continuous output then the problem is considered to be a regression problem. Examples are “kilometers”.

   Dimension reduction:

  • In mathematics the dimension has measured the distance and size of the object.
  • We can reduce the number of input features from the set using dimension reduction which is predictive modelling.

Cross-validation:

  • The cross-validation is used to make sure that the model is accurate since predictive modeling is done by supervised learning.
  • It is used to predict data of the predictive modeling.
    Open source:
  • The program code is freely available or anyone can access it since it is open source.
  • The user can not only access but also can modify the code since is made for public use.

Feature selection:

  • The SK learn is used to select the features from the set.
  • The predictor object that is used by the made is used to tunning down.
  • The supervise model is used to recognize the attribute.

Ensemble methods:

  • The machine learning technique is used to combine many models which is turned intothe predictive model, this model is called the ensemble model.
  • In other words we call consider it as the combination of prediction of models.
    Clustering:
  • The problem in which we want to find the inherent is called the clustering problem.
  • Grouping the customers by their behaviour which is permanent.
    Unsupervised learning:
  • It does not provide any guidance and does not provide any superior. here the labels are not present for data.
  • For a variable x there is no output respectively since there is no superior.
  • Here the model is needed to allowed to work on own data.