In this article, we explore Clustering algorithms, implement them from scratch with Python and learn how to use Sci-Kit Learn implementation.
In this article, we focus on machine learning algorithm performance and its improvement. We explore terms such as biasand variance, and how to balance them in order to achieve better performance. We learn about overfitting and underfitting, ways to avoid them and improve machine learning efficiency with regularization techniques such as Lasso_and _Ridge.
Data that we use in this article is the famous Boston Housing Dataset. This dataset is composed 14 features and contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It is a small dataset with only 506 samples.
For the purpose of this article, make sure that you have installed the following _Python _libraries:
Once installed make sure that you have imported all the necessary modules that are used in this tutorial.
import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error from sklearn.linear_model import Lasso, Ridge, ElasticNet, SGDRegressor, LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline from sklearn.base import clone
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
The OS module is a python module that provides the interface for interacting with the underlying operating system that Python is running.