Condo Mark

Condo Mark

1598836574

JavaScript ES6: Var, Let, and Const || ES6 Variable Declarations Explained

Learn the 2 new keywords that ES6 brings for declaring variables: let & const, when and why to use them instead of the old var keyword.

Documented Version: https://itnext.io/understanding-var-l…

#javascript #es6 #webdevelopment #frontend

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JavaScript ES6: Var, Let, and Const || ES6 Variable Declarations Explained
Lina  Biyinzika

Lina Biyinzika

1678051620

A Practical Guide of Unsupervised Learning Algorithms

In this article, learn about Machine Learning Tutorial: A Practical  Guide of Unsupervised Learning Algorithms. Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, recommender systems, and much more.

Machine learning (ML) can be classified into three main categories; supervised, unsupervised, and reinforcement learning. In supervised learning, the model is trained on labeled data. While in unsupervised learning, unlabeled data is provided to the model to predict the outcomes. Reinforcement learning is feedback learning in which the agent collects a reward for each correct action and gets a penalty for a wrong decision. The goal of the learning agent is to get maximum reward points and deduce the error.

What is Unsupervised Learning?

In unsupervised learning, the model learns from unlabeled data without proper supervision.

Unsupervised learning uses machine learning techniques to cluster unlabeled data based on similarities and differences. The unsupervised algorithms discover hidden patterns in data without human supervision. Unsupervised learning aims to arrange the raw data into new features or groups together with similar patterns of data.

For instance, to predict the churn rate, we provide unlabeled data to our model for prediction. There is no information given that customers have churned or not. The model will analyze the data and find hidden patterns to categorize into two clusters: churned and non-churned customers.

Unsupervised Learning Approaches

Unsupervised algorithms can be used for three tasks—clustering, dimensionality reduction, and association. Below, we will highlight some commonly used clustering and association algorithms.

Clustering Techniques

Clustering, or cluster analysis, is a popular data mining technique for unsupervised learning. The clustering approach works to group non-labeled data based on similarities and differences. Unlike supervised learning, clustering algorithms discover natural groupings in data. 

A good clustering method produces high-quality clusters having high intra-class similarity (similar data within a cluster) and less intra-class similarity (cluster data is dissimilar to other clusters). 

It can be defined as the grouping of data points into various clusters containing similar data points. The same objects remain in the group that has fewer similarities with other groups. Here, we will discuss two popular clustering techniques: K-Means clustering and DBScan Clustering.

K-Means Clustering

K-Means is the simplest unsupervised technique used to solve clustering problems. It groups the unlabeled data into various clusters. The K value defines the number of clusters you need to tell the system how many to create.

K-Means is a centroid-based algorithm in which each cluster is associated with the centroid. The goal is to minimize the sum of the distances between the data points and their corresponding clusters.

It is an iterative approach that breaks down the unlabeled data into different clusters so that each data point belongs to a group with similar characteristics.

K-means clustering performs two tasks:

  1. Using an iterative process to create the best value of K.
  2. Each data point is assigned to its closest k-center. The data point that is closer to the particular k-center makes a cluster.

 

An illustration of K-means clustering. Image source

DBScan Clustering

“DBScan” stands for “Density-based spatial clustering of applications with noise.” There are three main words in DBscan: density, clustering, and noise. Therefore, this algorithm uses the notion of density-based clustering to form clusters and detect the noise.

Clusters are usually dense regions that are separated by lower density regions. Unlike the k-means algorithm, which works only on well-separated clusters, DBscan has a wider scope and can create clusters within the cluster. It discovers clusters of various shapes and sizes from a large set of data, which consists of noise and outliers.

There are two parameters in the DBScan algorithm:

minPts: The threshold, or the minimum number of points grouped together for a region considered as a dense region.

eps(ε): The distance measure used to locate the points in the neighborhood. 

dbscan-clustering

 

An illustration of density-based clustering. Image Source 

Association Rule Mining

An association rule mining is a popular data mining technique. It finds interesting correlations in large numbers of data items. This rule shows how frequently items occur in a transaction.

Market Basket Analysis is a typical example of an association rule mining that finds relationships between items in the grocery store. It enables retailers to identify and analyze the associations between items that people frequently buy.

Important terminology used in association rules:

Support: It tells us about the combination of items bought frequently or frequently bought items.

Confidence: It tells us how often the items A and B occur together, given the number of times A occurs.

Lift: The lift indicates the strength of a rule over the random occurrence of A and B. For instance, A->B, the life value is 5. It means that if you buy A, the occurrence of buying B is five times.

The Apriori algorithm is a well-known association rule based technique.

Apriori algorithm 

The Apriori algorithm was proposed by R. Agarwal and R. Srikant in 1994 to find the frequent items in the dataset. The algorithm’s name is based on the fact that it uses prior knowledge of frequently occurring things. 

The Apriori algorithm finds frequently occurring items with minimum support. 

It consists of two steps:

  • Generation of candidate itemsets.
  • Removing items that are infrequent and don’t fulfill the criteria of minimum support.

Practical Implementation of Unsupervised Algorithms 

In this tutorial, you will learn about the implementation of various unsupervised algorithms in Python. Scikit-learn is a powerful Python library widely used for various unsupervised learning tasks. It is an open-source library that provides numerous robust algorithms, which include classification, dimensionality reduction, clustering techniques, and association rules.

Let’s begin!

1. K-Means algorithm 

Now let’s dive deep into the implementation of the K-Means algorithm in Python. We’ll break down each code snippet so that you can understand it easily.

Import libraries

First of all, we will import the required libraries and get access to the functions.

#Let's import the required libraries
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

Loading the dataset 

The dataset is taken from the kaggle website. You can easily download it from the given link. To load the dataset, we use the pd.read_csv() function. head() returns the first five rows of the dataset.

my_data = pd.read_csv('Customers_Mall.csv.') my_data.head() dataset-columns

The dataset contains five columns: customer ID, gender, age, annual income in (K$), and spending score from 1-100. 

Data Preprocessing 

The info() function is used to get quick information about the dataset. It shows the number of entries, columns, total non-null values, memory usage, and datatypes. 

my_data.info()

dataset-description

 

To check the missing values in the dataset, we use isnull().sum(), which returns the total number of null values.

 

#Check missing values 
my_data.isnull().sum()

dataset-null-values

 

The box plot or whisker plot is used to detect outliers in the dataset. It also shows a statistical five number summary, which includes the minimum, first quartile, median (2nd quartile), third quartile, and maximum.

my_data.boxplot(figsize=(8,4)) dataset-boxplot-detect-outliers

Using Box Plot, we’ve detected an outlier in the annual income column. Now we will try to remove it before training our model. 

#let's remove outlier from data
med =61
my_data["Annual Income (k$)"] = np.where(my_data["Annual Income (k$)"] >
 120,med,my_data['Annual Income (k$)'])


The outlier in the annual income column has been removed now to confirm we used the box plot again.

my_data.boxplot(figsize=(8,5)) outlier-removed

Data Visualization

A histogram is used to illustrate the important features of the distribution of data. The hist() function is used to show the distribution of data in each numerical column.

my_data.hist(figsize=(6,6)) 

The correlation heatmap is used to find the potential relationships between variables in the data and to display the strength of those relationships. To display the heatmap, we have used the seaborn plotting library.

plt.figure(figsize=(10,6)) sns.heatmap(my_data.corr(), annot=True, cmap='icefire').set_title('seaborn') plt.show() dataset-histogram

Choosing the Best K Value

The iloc() function is used to select a particular cell of the data. It enables us to select a value that belongs to a specific row or column. Here, we’ve chosen the annual income and spending score columns.

 

X_val = my_data.iloc[:, 3:].values X_val

 

# Loading Kmeans Library

from sklearn.cluster import KMeans

Now we will select the best value for K using the Elbow’s method. It is used to determine the optimal number of clusters in K-means clustering.

my_val = []

for i in range(1,11):

    kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 123)

    kmeans.fit(X_val)

    my_val.append(kmeans.inertia_)

The sklearn.cluster.KMeans() is used to choose the number of clusters along with the initialization of other parameters. To display the result, just call the variable.

my_val dataset-iloc-function #Visualization of clusters using elbow’s method plt.plot(range(1,11),my_val) plt.xlabel('The No of clusters') plt.ylabel('Outcome') plt.title('The Elbow Method') plt.show() clusters-elbow-method

Through Elbow’s Method, when the graph looks like an arm, then the elbow on the arm is the best value of K. In this case, we’ve taken K=3, which is the optimal value for K.

kmeans = KMeans(n_clusters = 3, init='k-means++') kmeans.fit(X_val) number-of-clusters #To show centroids of clusters  kmeans.cluster_centers_ cluster-centers #Prediction of K-Means clustering  y_kmeans = kmeans.fit_predict(X_val) y_kmeans

fit-predict-function-kmeans

Splitting the dataset into three clusters

The scatter graph is used to plot the classification results of our dataset into three clusters.

plt.scatter(X_val[y_kmeans == 0,0], X_val[y_kmeans == 0,1], c='red',s=100)

plt.scatter(X_val[y_kmeans == 1,0], X_val[y_kmeans == 1,1], c='green',s=100)

plt.scatter(X_val[y_kmeans == 2,0], X_val[y_kmeans == 2,1], c='orange',s=100)

plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='brown')

plt.title('K-Means Unsupervised Learning')

plt.show()


2. Apriori Algorithm

To implement the apriori algorithm, we will utilize “The Bread Basket” dataset. The dataset is available on Kaggle and you can download it from the link. This algorithm suggests products based on the user’s purchase history. Walmart has greatly utilized the algorithm to recommend relevant items to its users.

Let’s implement the Apriori algorithm in Python. 

Import libraries 

To implement the algorithm, we need to import some important libraries.

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

Loading the dataset 

The dataset contains five columns and 20507 entries. The data_time is a prominent column and we can extract many vital insights from it.

my_data= pd.read_csv("bread basket.csv") my_data.head() bread-basket-dataset-apriori

Data Preprocessing 

Convert the data_time into an appropriate format.

my_data['date_time'] = pd.to_datetime(my_data['date_time'])

#Total No of unique customers

my_data['Transaction'].nunique()

unique-customers-apriori

Now we want to extract new columns from the data_time to extract meaningful information from the data.

#Let's extract date

my_data['date'] = my_data['date_time'].dt.date

#Let's extract time

my_data['time'] = my_data['date_time'].dt.time

#Extract month and replacing it with String

my_data['month'] = my_data['date_time'].dt.month

my_data['month'] = my_data['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), 

                                          ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug',

                                          'Sep','Oct','Nov','Dec'))

#Extract hour

my_data[‘hour’] = my_data[‘date_time’].dt.hour

# Replacing hours with text

# Replacing hours with text

hr_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)

hr_obj = (‘1-2′,’7-8′,’8-9′,’9-10′,’10-11′,’11-12′,’12-13′,’13-14′,’14-15’,

               ’15-16′,’16-17′,’17-18′,’18-19′,’19-20′,’20-21′,’21-22′,’22-23′,’23-24′)

my_data[‘hour’] = my_data[‘hour’].replace(hr_num, hr_obj)

# Extracting weekday and replacing it with String 

my_data[‘weekday’] = my_data[‘date_time’].dt.weekday

my_data[‘weekday’] = my_data[‘weekday’].replace((0,1,2,3,4,5,6), 

                                          (‘Mon’,’Tues’,’Wed’,’Thur’,’Fri’,’Sat’,’Sun’))

#Now drop date_time column

my_data.drop(‘date_time’, axis = 1, inplace = True)

After extracting the date, time, month, and hour columns, we dropped the data_time column.

Now to display, we simply use the head() function to see the changes in the dataset.

my_data.head()

dataset-apriori

# cleaning the item column

my_data[‘Item’] = my_data[‘Item’].str.strip()

my_data[‘Item’] = my_data[‘Item’].str.lower()

my_data.head()

clean-dataset

Data Visualization 

To display the top 10 items purchased by customers, we used a barplot() of the seaborn library. 

plt.figure(figsize=(10,5))

sns.barplot(x=my_data.Item.value_counts().head(10).index, y=my_data.Item.value_counts().head(10).values,palette='RdYlGn')

plt.xlabel('No of Items', size = 17)

plt.xticks(rotation=45)

plt.ylabel('Total Items', size = 18)

plt.title('Top 10 Items purchased', color = 'blue', size = 23)

plt.show()


From the graph, coffee is the top item purchased by the customers, followed by bread.

Now, to display the number of orders received each month, the groupby() function is used along with barplot() to visually show the results.

mon_Tran =my_data.groupby('month')['Transaction'].count().reset_index() mon_Tran.loc[:,"mon_order"] =[4,8,12,2,1,7,6,3,5,11,10,9] mon_Tran.sort_values("mon_order",inplace=True) plt.figure(figsize=(12,5)) sns.barplot(data = mon_Tran, x = "month", y = "Transaction") plt.xlabel('Months', size = 14) plt.ylabel('Monthly Orders', size = 14) plt.title('No of orders received each month', color = 'blue', size = 18) plt.show() orders-received-dataset

To show the number of orders received each day, we applied groupby() to the weekday column.

wk_Tran = my_data.groupby('weekday')['Transaction'].count().reset_index()

wk_Tran.loc[:,"wk_ord"] = [4,0,5,6,3,1,2]

wk_Tran.sort_values("wk_ord",inplace=True)

plt.figure(figsize=(11,4))

sns.barplot(data = wk_Tran, x = "weekday", y = "Transaction",palette='RdYlGn')

plt.xlabel('Week Day', size = 14)

plt.ylabel('Per day orders', size = 14)

plt.title('Orders received per day', color = 'blue', size = 18)

plt.show()


 Implementation of the Apriori Algorithm 

We import the mlxtend library to implement the association rules and count the number of items.

from mlxtend.frequent_patterns import association_rules, apriori

tran_str= my_data.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')

tran_str.head(8)

association-rule-mixtend

Now we’ll make a mxn matrix where m=transaction and n=items, and each row represents whether the item was in the transaction or not.

Mar_baskt = tran_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0)

Mar_baskt.head()

market-basket

We want to make a function that returns 0 and 1. 0 means that the item wasn’t present in the transaction, while 1 means the item exists.

def encode(val):

    if val<=0:

        return 0

    if val>=1:

        return 1

#Let's apply the function to the dataset

Basket=Mar_baskt.applymap(encode)

Basket.head()

basket-head

#using apriori algorithm to set min_support 0.01 means 1% freq_items = apriori(Basket, min_support = 0.01,use_colnames = True) freq_items.head()

frequent-items-apriori

Using the association_rules() function to generate the most frequent items from the dataset.

App_rule= association_rules(freq_items, metric = "lift", min_threshold = 1) App_rule.sort_values('confidence', ascending = False, inplace = True) App_rule.head() association-rules-apriori

From the above implementation, the most frequent items are coffee and toast, both having a lift value of 1.47 and a confidence value of 0.70. 

3. Principal Component Analysis 

Principal component analysis (PCA) is one of the most widely used unsupervised learning techniques. It can be used for various tasks, including dimensionality reduction, information compression, exploratory data analysis and Data de-noising.

Let’s use the PCA algorithm!

First we import the required libraries to implement this algorithm.

import numpy as np 

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

from sklearn.decomposition import PCA

from sklearn.datasets import load_digits

Loading the Dataset 

To implement the PCA algorithm the load_digits dataset of Scikit-learn is used which can easily be loaded using the below command. The dataset contains images data which include 1797 entries and 64 columns.

 

#Load the dataset

my_data= load_digits()

#Creating features

X_value = my_data.data

#Creating target

#Let's check the shape of X_value

X_value.shape

 

dataset-X-shape

 

 

#Each image is 8x8 pixels therefore 64px  my_data.images[10] image-pixels #Let's display the image plt.gray()  plt.matshow(my_data.images[34])  plt.show()

image-pixels

Now let’s project data from 64 columns to 16 to show how 16 dimensions classify the data.

X_val = my_data.data 

y_val = my_data.target

my_pca = PCA(16)  

X_projection = my_pca.fit_transform(X_val)

print(X_val.shape)

print(X_projection.shape)

projection-shape

Using colormap we visualize that with only ten dimensions  we can classify the data points. Now we’ll select the optimal number of dimensions (principal components) by which data can be reduced into lower dimensions.

plt.scatter(X_projection[:, 0], X_projection[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("gist_heat",12))

plt.colorbar();

x-projection

pca=PCA().fit(X_val)

plt.plot(np.cumsum(my_pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')

Based on the below graph, only 12 components are required to explain more than 80% of the variance which is still better than computing all the 64 features. Thus, we’ve reduced the large number of dimensions into 12 dimensions to avoid the dimensionality curse. pca=PCA().fit(X_val)

plt.plot(np.cumsum(pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')



#Let's visualize how it looks like

Unsupervised_pca = PCA(12)  

X_pro = Unsupervised_pca.fit_transform(X_val)

print("New Data Shape is =>",X_pro.shape)

#Let's Create a scatter plot

plt.scatter(X_pro[:, 0], X_pro[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("nipy_spectral",10))

plt.colorbar();


principal-component-analysis

Wrapping Up 

beyond machine

In this machine learning tutorial, we’ve implemented the Kmeans, Apriori, and PCA algorithms. These are some of the most widely used algorithms, having numerous industrial applications and solve many real world problems. For instance, K-means clustering is used in astronomy to study stellar and galaxy spectra, solar polarization spectra, and X-ray spectra. And, Apriori is used by retail stores to optimize their product inventory. 

Dreaming of becoming a data scientist or data analyst even without a university and a college degree? Do you need the knowledge of data science and analysis for promotions in your current role?

Are you interested in securing your dream job in data science and analysis and looking for a way to get started, we can help you? With over 10 years of experience in data science and data analysis, we will teach you the rubrics, guiding you with one-on-one lessons from the fundamentals until you become a pro.

Our courses are affordable and easy to understand with numerous exercises and assignments you can learn from. At the completion of our courses, you’ll be readily equipped with technical and practical skills to take on any data science and data analysis role in companies, collaborate effectively among teams and help businesses meet and exceed their objectives by extracting actionable insights from data.

Original article sourced at: https://thedatascientist.com

#machine-learning 

Dexter  Goodwin

Dexter Goodwin

1623916080

A Simple Explanation Of JavaScript Variables: Const, Let, Var

The variable is a fundamental concept that any developer should know.

In JavaScript, constlet, and var are the statements you can declarate variable.

I’m going to describe each variable type around the declaration, initialization, value access, and assignment. Each of the 3 types (constlet, and var) create variables that behave differently exactly in these 4 steps.

This post isn’t quite beginner friendly, but rather useful to solidify your knowledge of variables and their behavior.

Let’s get started.

1. Variable identifier

First, let’s understand what a variable is.

In simple terms, a variable is a placeholder (or a box) for a value. A value in JavaScript can be either a primitive value or an object.

The variable has a name, which stricter is called identifier. Examples of identifiers are myNumbernamelistitem.

The syntax of an identifier is pretty simple:

An identifier can contain letters, digits 0..9, and special symbols $_. An identifier cannot start with a digit 0..9.

Examples of valid identifiers are myNumbermy_numberlist1$item_nameab$_.

#javascript #variable #const #let #var

Eldora  Bradtke

Eldora Bradtke

1593862020

JavaScript Variables Explained. Var, Let and Const with examples.

Before the arrival of ES6 or EcmaScript 6 released in 2015, as a Javascript developer, you could only declare variables using the var keyword.
Now things has changed and two new keywords have been introduced to meet the developers needs who felt the old one way solution very tight for the modern web app requirements.
In this article I will try to explain you in the simplest way possible, the differences between var, let and const type variables.
I think the best way to do this is to try to extrapolate the essence of their nature by describing them in few words followed by examples which gives you the confirmation and consolidation of what you learned in the theory.
Like many other programming languages, JavaScript has variables. I talk you through var, let and const, and how they differ from each other with the use of some simple examples.

#javascript #var #let #const #programming

Eldora  Bradtke

Eldora Bradtke

1589938080

JavaScript Variables: var and let and const

There are three ways to create variables in a JavaScript application: using var, using let, or using const. This will not be a post trying to convince you which one you should use, or arguing about what is best. It’s just good to know about the differences and what it means when you use the different options. But hopefully by the end of all this you’ll be comfortable with the three options and can make a decision for your team that will suit your needs. To get the most out of this post, it is best if you understand variable scope, which we covered in this post previously.

#javascript #var #let #const

Tanya  Shields

Tanya Shields

1591952760

Declaring A Winner Between JavaScript's var, let And const

When ECMAScript 6 (also known as ECMAScript 2015) was released a collection of new APIs, programming patterns and language changes became a standard. Since ES6 started gaining browser and nodejs support developers are wondering if they should stop using the traditional var to declare variables.
ES6 introduced two new ways to declare variables, let and const.

var - has function level scoping and can change the value reference
let - has block level scoping and can change the value reference
const - has block level scoping but cannot change the value reference
Both provide better block scoping that var. const differs from let because the immediate value cannot be changed once it is declared.
Variables declared using var are function scoped, which has led to confusion to many developers as they start using in JavaScript.

#javascript #var #let #const #programming