Angular #1 Installation

Why is it called Angular?

Because it's written between < >! Get it? (Okay, bad joke, but now you won’t forget it!)

Angular is a powerful framework built with TypeScript for developing client-side applications. It’s like a superhero for web developers—making code cleaner, reusable, and testable!

---

Angular Architecture (Frontend + Backend)

Frontend (UI)

This is what users see in their browsers! It includes:

• HTML & CSS (Templates & Styles)
• TypeScript (Super-powered JavaScript)
• Angular (The magic framework!)

Backend (Data Processing)

This is where the data magic happens!

• Data + API (Application Programming Interface)
• Business Logic (The brain behind the app)

In short, Angular makes life easier!

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Angular Versions: What’s the Deal?

• The first version was AngularJS.
• Later versions dropped the "JS" and are called Angular (Vx) (Latest: Angular 14.0).

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AngularJS vs Angular – What’s Different?

Feature	AngularJS	Angular
Architecture	MVC Model	Components & Directives
Language	JavaScript	TypeScript
Mobile Friendly?	Nope	Yes!
Expression Syntax	{{ }}	() []
Dependency Injection	Not used	Hierarchical DI
Routing	@routeProvider	@Route Configuration
Code Structure	Messy 😵‍💫	Well-structured ✅

---

Let’s Build Our First Angular Project! 🚀

Step 1: Install Node.js

• Go to nodejs.org
• Download the latest stable version (Windows/Mac/Linux)
• Install it like a pro! (Run as Administrator)
• Check if it's installed by running:

  node --version
  

Step 2: Install Angular CLI (Command Line Interface)

CLI is your best friend for Angular projects!

npm install -g @angular/cli

Check if it's installed:

ng version

Step 3: Create Your First Angular Project

Run this command:

ng new hello-universe

hello-universe is your project name! It will ask a few questions:

• Would you like to add Angular routing? (Say ‘N’)
• Which stylesheet format to use? (Choose CSS)
• Then, it will take some time to set up all the files. Grab a coffee! ☕

Step 4: Install Visual Studio Code (VS Code)

• Download it from Visual Studio Code
• Install it as Administrator
• Open your project by running:

  cd hello-universe
  code .
  

Now, your VS Code will open with your first Angular project! 🎉

Step 5: Run Your First Angular App! 🚀

Run this command inside your project folder:

ng serve

Wait for the magic words: Compiled Successfully!

Now, open your browser and go to:

localhost:4200

Great My friend, You have done it!

Boom! Your first Angular app is live! 🎉🎉🎉

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🎯 What’s Next?

• Learn about Components, Modules & Directives
• Play around with Routing and Services
• Build something cool & show off your Angular skills! 😎

Happy Coding! 🚀

#0 Intro to Data Analysis

 Two Words

Data & Analysis

Data : oil, Electricity,  everywhere, every time, even now.

Analysis : Answer how and Why ?

Analysis :

Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the investigation

Why Is Data Analysis Important?

Informed decision-making: 

Reduce costs| Increase Profits

Target Customers Better

DA is a Process

Identify-Collect-Clean-Analyze-Interpret or Infer- Act

(ICCAIA)

Types of DA

1.Descriptive: What ?-Data to Valuable Insights

2.Exploratory: How  ?-To generate Hypothesis to solve specific 3.Problems

4.Diagnostic : Why ? - To tackle and to cure 

5.Predictive  : What if ? - Future Projection

6.Prescriptive : IF-?.Then-This : 

Top DA Methods

1. Cluster Analysis: Similar elements - Grouping Data 

2. Cohort Analysis : Historical data to examine and compare -Google Analytics:

3. Regression Analysis - y=mx+c

4. Factor Analysis : Dimension Reduction

5. Neural Networks : Mimic Human Brain to find solutions

6. Data Mining: Identify patterns, Dependencies, realations, Trends

7. Text Analysis : Actional Insights with Relevant Data

8. TimeSeries Analysis : Data Collected over a specified period of time.

9. Decison Trees: To support smart and strategic Decisions

10. Conjoint Analysis : How individuals value different attributes of a product or Service

Top 17 Data Analysis Techniques:

Collaborate your needs

Establish your questions

Data democratization

Think of data governance 

Clean your data

Set your KPIs

Omit useless data

Build a data management roadmap

Integrate technology

Answer your questions

Visualize your data

Interpretation of data

Consider autonomous technology

Build a narrative

Share the load

Data Analysis tools

Refine your process constantly 

Quality Criteria For Data Analysis 

Internal Validity

External Validity

Reliability

Objectivity

Application of DA:

Academics: Universities and academic institutions can perform data analysis to measure student performance and gather insights on how certain behaviors can further improve education.

Human Resources: Organizations can use data analysis to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data to find out the best resources whose skill set matches the organizational goals

Communication is very Important

Qualitative

Stephen Few - 8 Types of Quantitative Messages out of DA:

1. Time Series : Line Chart

2. Ranking : Bar Chart

3. Part-to-Whole : Pie Chart

4. Deviation: Bar Comparison : Actual Vs Variation

5. Frequency Distribuition : Histogram

6. Correlation: Scatter Plot 

7. Nominal Comparison : bar Chart

8. Geographical : Cartogram

Quantitative

mean, median, mode, Standard deviation, chi-square, eigen vector

Free software for data analysis

Notable free software for data analysis include:

DevInfo – A database system endorsed by the United Nations Development Group for monitoring and analyzing human development.[149]

ELKI – Data mining framework in Java with data mining oriented visualization functions.

KNIME – The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.

Orange – A visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning.

Pandas – Python library for data analysis.

PAW – FORTRAN/C data analysis framework developed at CERN.

R – A programming language and software environment for statistical computing and graphics.[150]

ROOT – C++ data analysis framework developed at CERN.

SciPy – Python library for data analysis.

Julia - A programming language well-suited for numerical analysis and computational science.

Slides for DS Intro

1. Intro to Data Science

Pyspark Tutorial

DATA SCIENCE ETHICS

 DSE definition:

“Data ethics is a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values).”

MongoDB - Introduction

 MongaDB Features

• Full cloud-based application data platform.
• Flexible document schemas.
• JSON..BSON (BJSON)..
• Widely supported and code-native data access.
• Change-friendly design.
• Powerful querying and analytics.
• Easy horizontal scale-out with sharding.
• Simple installation.
• Cost-effective.

Learning to use MongoDB

Step 1:

Download and install "PyMongo":

                pip install mongadb

Step 2:

                    import pymonga

step 3: list database names

                # import library

import pymongo
# creating a client
myc = pymongo.MongoClient("mongodb://localhost:27017/")
# Just printing existing databases
print(myc.list_database_names()) # ['admin', 'config', 'db1', 'local']

Step 4: Create Collections

# import library

import pymongo
# creating a client
c = pymongo.MongoClient("mongodb://localhost:27017/")
# Just printing existing databases
print(c.list_database_names()) # ['admin', 'config', 'db1', 'local']

# choose any demo db as db1
# create collection customers in db1
db = c['db1']
col = db["customers"]
print(db.list_collection_names())

# add Dict data
dict = { "name": "John", "address": "Highway 37" }
x = col.insert_one(dict)
print(x.inserted_id)

#add Dict1 data
dict2 = { "name": "Peter", "address": "Lowstreet 27" }
y = col.insert_one(dict2)
print(y.inserted_id)

"""
['admin', 'config', 'db1', 'local']
['customers']
62bd57445c7e7af908e87efa
62bd57445c7e7af908e87efb

"""

To display with Object_ID

clist = db.list_collection_names()
if "customers" in clist:
  print("The collection exists.")

for x in col.find():
   print(x)

"""
['admin', 'config', 'local']
[]
62bd58d899982b27053eb9cc
62bd58d899982b27053eb9cd
The collection exists.
{'_id': ObjectId('62bd58d899982b27053eb9cc'), 'name': 'John', 'address': 'Highway 37'}
{'_id': ObjectId('62bd58d899982b27053eb9cd'), 'name': 'Peter', 'address': 'Lowstreet 27'}
"""

Before and After deleting 

# import library

import pymongo
# creating a client
c = pymongo.MongoClient("mongodb://localhost:27017/")
db = c['db1']
col = db["customers"]
col.drop()
q1 = { 'address': 'Highway 37'}
q2 = { 'address': 'Highway 38'}
q3 = { 'address': 'Highway 39'}

print(10*'-', 'Insering 3 qs')
col.insert_one(q1)
col.insert_one(q2)
col.insert_one(q3)

print(10*'-', 'Before Delete')
for x in col.find():
   print(x)

q4 = { 'address': 'Highway 39'}
col.delete_many(q4)
print(10*'-', 'After Delete')
for x in col.find():
   print(x)
"""
---------- Insering 3 qs
---------- Before Delete
{'_id': ObjectId('62bd5db330e379663c8851ce'), 'address': 'Highway 37'}
{'_id': ObjectId('62bd5db430e379663c8851cf'), 'address': 'Highway 38'}
{'_id': ObjectId('62bd5db430e379663c8851d0'), 'address': 'Highway 39'}
---------- After Delete
{'_id': ObjectId('62bd5db330e379663c8851ce'), 'address': 'Highway 37'}
{'_id': ObjectId('62bd5db430e379663c8851cf'), 'address': 'Highway 38'}
"""

After deleting another Record

print('After Prinitng')

q5 = { 'address': 'Highway 37'}
col.delete_many(q5)
for x in col.find():
   print(x)
"""
After Prinitng
{'_id': ObjectId('62bd5f3cf32b68f5a744b5ab'), 'address': 'Highway 38'}\
"""

Before and After dropping collections

import pymongo
# creating a client
c = pymongo.MongoClient("mongodb://localhost:27017/")
db = c['db1']
col = db["customers"]

many = [
  { "_id": 1, "name": "AMET", "address": "ECR,  Chennai"},
  { "_id": 2, "name": "ABS", "address": "Anna Nagar, Chennai"},
]
x = col.insert_many(many)

print('Before dropping')
for x in col.find():
       print(x)
col.drop()
print('After dropping')
for x in col.find():
       print(x)
"""
Before dropping
{'_id': 1, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 2, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
After dropping

"""

Sorting Collections

import pymongo

# creating a client
c = pymongo.MongoClient("mongodb://localhost:27017/")
db = c['db1']
col = db["customers"]
col.drop()
many = [
  { "_id": 11, "name": "AMET", "address": "ECR,  Chennai"},
  { "_id": 21, "name": "ABS", "address": "Anna Nagar, Chennai"},
]
x = col.insert_many(many)
print('Before Sorting')
for x in col.find():
       print(x)
doc = col.find().sort("name")

print('After Sorting')
for x in doc:
  print(x)
  
"""
Before Sorting
{'_id': 11, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 21, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
After Sorting
{'_id': 21, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
{'_id': 11, 'name': 'AMET', 'address': 'ECR,  Chennai'}
"""

Before and After Updating

import pymongo

# creating a client
c = pymongo.MongoClient("mongodb://localhost:27017/")
db = c['db1']
col = db["customers"]
col.drop()
many = [
  { "_id": 11, "name": "AMET", "address": "ECR,  Chennai"},
  { "_id": 21, "name": "ABS", "address": "Anna Nagar, Chennai"},
]
x = col.insert_many(many)
q6 = { "address": 'ECR,  Chennai' }
newvalues = { "$set": { "address": "#123, East Coast Road,  Chennai" } }
print('Before Updation')
for x in col.find():
  print(x)
col.update_one(q6, newvalues)
print("After Updation")
#print "customers" after the update:
for x in col.find():
  print(x)
"""
Before Updation
{'_id': 11, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 21, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
After Updation
{'_id': 11, 'name': 'AMET', 'address': '#123, East Coast Road,  Chennai'}
{'_id': 21, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
"""

B4 and After Many Updating using regular expression

import pymongo

client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["mydatabase"]
col = db["customers"]
col.drop()

many = [
  { "_id": 10, "name": "AMET", "address": "ECR,  Chennai"},
  { "_id": 12, "name": "ABS", "address": "Anna Nagar, Chennai"},
  { "_id": 13, "name": "ABS!@", "address": "ECR,  Chennai"},
  { "_id": 14, "name": "Stanford", "address": "Anna Nagar, Chennai"},
]
x = col.insert_many(many)


print('Before Updation')
for x in col.find():
   print(x)

query = { "address": { "$regex": "^A" } }
newvalues = { "$set": { "name": "AMET Updated" } }
x = col.update_many(query, newvalues)
print(x.modified_count, "documents updated.")

print('After many Updation')
for x in col.find():
   print(x)
"""
Before Updation
{'_id': 10, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 12, 'name': 'ABS', 'address': 'Anna Nagar, Chennai'}
{'_id': 13, 'name': 'ABS!@', 'address': 'ECR,  Chennai'}
{'_id': 14, 'name': 'Stanford', 'address': 'Anna Nagar, Chennai'}
2 documents updated.
After many Updation
{'_id': 10, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 12, 'name': 'AMET Updated', 'address': 'Anna Nagar, Chennai'}
{'_id': 13, 'name': 'ABS!@', 'address': 'ECR,  Chennai'}
{'_id': 14, 'name': 'AMET Updated', 'address': 'Anna Nagar, Chennai'}
"""

After using limit to display 

# limit parameter to limit two records
output = col.find().limit(2)
print('After setting Limit(2)')
for x in output:
    print(x)
"""
After setting Limit(2)
{'_id': 10, 'name': 'AMET', 'address': 'ECR,  Chennai'}
{'_id': 12, 'name': 'AMET Updated', 'address': 'Anna Nagar, Chennai'}
"""

Bon Appetite and Happy MongoDB Coding....Learning!!!

Recommendations System - Course for +2 Students

 Recommendation System using pandas and Sklearn

Student Dataset1.csv

id,gender,stream,subject,marks,course,specialization
1,m,science,physics,78,btech,civil
2,f,commerce,maths,56,bsc,maths
3,m,humanities,history,79,ba,history
4,f,commerce,economics,88,bcom,hons
5,m,science,chemistry,79,bsc,biology
6,m,science,maths,98,btech,mech
7,f,commerce,physics,56,bsc,maths
8,m,humanities,history,79,ba,history
9,f,commerce,commerce,76,bcom,hons
10,m,science,physics,89,bsc,biology

Code snippet (Thanks to  Siddharth Sachdeva — August 25, 2021, Analytics Vidya)

#S1 importing necessary libraries.
import pandas as pd
data = pd.read_csv(r"dataset1.csv")
#s2
descriptions =data['gender'] +' '+ data['subject'] + ' ' + data['stream'] +' '+ data['marks'].apply(str)
#Printing the first description
print(descriptions[0])
# s3 similarity matrix
#import sklearn.TfidfVector
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
def create_similarity_matrix(new_description, overall_descriptions):
        overall_descriptions.append(new_description)
        # Define a tfidf [Term Frequency Inverse document frequency]  vectorizer and remove all stopwords.
        tfidf = TfidfVectorizer(stop_words="english")
        tfidf_matrix = tfidf.fit_transform(overall_descriptions)
        tfidf_matrix.shape
        cosine_sim = linear_kernel(tfidf_matrix,tfidf_matrix)
        return cosine_sim
#s5 Sim Matrix reco
def get_recommendations(new_description,overall_descriptions):

        cosine_sim = create_similarity_matrix(new_description,overall_descriptions)
        sim_scores = list(enumerate(cosine_sim[-1]))
        sim_scores = sorted(sim_scores,key =lambda x:x[1],reverse= True )
        sim_scores = sim_scores[1:10]
        indices = [i[0]for i in sim_scores]
        return data.iloc[indices]
# s6 Recommendations
new_description = pd.Series('m physics science 78')
recommendation= get_recommendations(new_description,descriptions)
print(recommendation[0:3])
"""
   id gender    stream    subject  marks course specialization
0   1      m   science    physics     78  btech          civil
6   7      f  commerce    physics     56    bsc          maths
4   5      m   science  chemistry     79    bsc        biology
"""

Simple not refined. But throw some light on basics of Recommendation System.

P#32 Reg expression

To find a pattern in a statement

match = re.search('love', 'i love you all')

if match:
    print('found love')
else:
    print('love Not found')

# found love

match = re.search('love', 'i leave you all')

if match:
    print('found love')
else:
    print('love Not found')

# love Not found

 ## . = any char but \n
match = re.search(r'..ve', 'i love you all') # found,
print(match)

To match email

 to check up Google Account
str = 'hello I am so& so i have account you@google.com'
match = re.search(r'\w+@\w+', str)
if match:
    print(match.group()) #you@google

match = re.search(r'[\w.-]+@[\w.-]+', str)
if match:
    print(match.group())  ## 'you@google.com'

To Validate IFSC code in banks

import re
def isValidIFSCode(str):
    # Regex to check valid IFSC Code.
    regex = "^[A-Z]{4}0[A-Z0-9]{6}$"  # 4 Alphabets,0, 
    # Compile the ReGex
    p = re.compile(regex)
    # If the string is empty
    # return false
    if (str == None):
        return False
    # Return if the string
    # matched the ReGex
    if (re.search(p, str)):
        return True
    else:
        return False
# Driver code
# Test Case 1:
str1 = "SBIN0125620"
print(isValidIFSCode(str1))

# Test Case 2:
str2 = "SBIN0125"
print(isValidIFSCode(str2))

# Test Case 3:
str3 = "1234SBIN012"
print(isValidIFSCode(str3))

# Test Case 4:
str4 = "SBIN7125620"
print(isValidIFSCode(str4))

# This code is contributed by avanitrachhadiya2155

"""
True
False
False
False
"""

Please Note:

# regex = "^[A-Z]{4}0[A-Z0-9]{6}$";
# Where: 
# ^ represents the starting of the string.
# [A-Z]{4} represents the first four characters should be upper case alphabets.
# 0 represents the fifth character should be 0.
# [A-Z0-9]{6} represents the next six characters usually numeric, but can also be alphabetic.
# $ represents the ending of the string.

To test Pin Code

import re

def isValidPinCode(pinCode):
    # Regex to check valid pin code
    # of India.
    regex = "^[1-9]{1}[0-9]{2}\\s{0,1}[0-9]{3}$";

    # Compile the ReGex
    p = re.compile(regex);

    # If the pin code is empty
    # return false
    if (pinCode == ''):
        return False;

    # Pattern class contains matcher() method
    # to find matching between given pin code
    # and regular expression.
    m = re.match(p, pinCode);

    # Return True if the pin code
    # matched the ReGex else False
    if m is None:
        return False
    else:
        return True



# check Driver code
if __name__ == "__main__":
    # Test case 1
    num1 = "625001";
    print(num1, ": ", isValidPinCode(num1));

    # Test case 2:
    num2 = "625 001";
    print(num2, ": ", isValidPinCode(num2));

    # Test case 3:
    num3 = "0625 001";
    print(num3, ": ", isValidPinCode(num3));

    # Test case 4:
    num4 = "6250001";
    print(num4, ": ", isValidPinCode(num4));

Result

"""
625001 :  True
625 001 :  True
0625 001 :  False
6250001 :  False
"""

To check PANCARD in India

# To Valiate pan Card

import re

def checkPanCard():
        import re


        def isValidPanCardNo(panCardNo):

            # PAN Card number
            regex = "[A-Z]{5}[0-9]{4}[A-Z]{1}"

            # Compile the ReGex
            p = re.compile(regex)

            # If the PAN Card number
            # is empty return false
            if(panCardNo == None):
                return False

            # Return if the PAN Card number
            # matched the ReGex
            if(re.search(p, panCardNo) and
            len(panCardNo) == 10):
                return True
            else:
                return False

        # Driver Code.


        # Test Case 1:
        str1 = "AFGHA2318J"
        print(isValidPanCardNo(str1),' ',str1)

        # Test Case 2:
        str2 = "23AFGHN18J"
        print(isValidPanCardNo(str2),str2)

        # Test Case 3:
        str3 = "AFGHA2318JM"
        print(isValidPanCardNo(str3),str3)

        # Test Case 4:
        str4 = "AFGHA23184"
        print(isValidPanCardNo(str4),str4)

        # Test Case 5:
        str5 = "AFGHA 23184"
        print(isValidPanCardNo(str5),str5)

checkPanCard()
"""
True   AFGHA2318J
False 23AFGHN18J
False AFGHA2318JM
False AFGHA23184
False AFGHA 23184
"""