# Import the modules requiredimport jsonfrom difflib import get_close_matches # Loading data from json file# in python dictionarydata = json.load(open("dictionary.json")) def translate(w): # converts to lower case w = w.lower() if w in data: return data[w] # for getting close matches of word elif len(get_close_matches(w,…

 import pandas as pd   In [92]:             df = pd.DataFrame(columns=['customer_id','Gender','Age','Driving license present','Region code','previously insured','vehicle Age','vehicle Damage'])     In [93]:           df     Out[93]:…

DROP SCHEMA IF EXISTS ecommerce;CREATE SCHEMA ecommerce;USE ecommerce; CREATE TABLE user_data ( user_id INT PRIMARY KEY, First_name VARCHAR(50) NOT NULL, Last_name VARCHAR(50) NOT NULL, dob DATE NOT NULL, gender VARCHAR(1) NOT NULL, primary_address INT, primary_phone INT, primary_email INT, primary_payment INT ); CREATE…

# Question 1 We are taking only a small set of data from population as a sample. In calculating a variance of sample to get a higher variance, we need to use (n-1) which is a lower number results in higher variance. The higher variance would be slightly  near to variance of population. # Question 2 stratified sampling:…

# Question 1 # Question 2  It is true that simple mean is sufficient enough for having an idea about  the data by computing with its formula ( sum of all observations/number of observations). But, by foregoing this computation  we assume that the possibility of each event happening is equal and hence we…

# Question 2  L1 and L2 are two loss functions in machine learning which are used to minimize the error. L1 Loss function stands for least absolute Deviations. Also known as LAD. L2 Loss function stands for least Square Errors. Also known as LS. L1 Loss Function:  L1 Loss Function is used to minimize the error…

CREATE DATABASE alumni;use alumni; create table COLLEGE_A_HS(ROLLNO INT,LAST_UPDATE date,NAME_ VARCHAR(50),FATHERNAME VARCHAR(50),MOTHERNAME VARCHAR(50),BRANCH VARCHAR(120),BATCH VARCHAR(50),DEGREE VARCHAR(40),PRESENT VARCHAR(50),ORGANIZATION VARCHAR(130),DESIGNATION VARCHAR(50), LOCATION VARCHAR(80));SELECT * FROM COLLEGE_A_HS;…

# Question 1 Pros and cons of SVM Advantages: * SVM works relatively well when there is a clear margin of separation between classes. * SVM is more effective in high dimensional spaces. * SVM is effective in cases where the  number of dimensions is greater than the number of samples. * SVM is relatively memory efficient.…

# Question 1 Applying KNN to surface defects in stainless steel plates "dataset KNN: K-Nearest Neighbors is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. It classifies new cases based on feature similarity measures.  KNN algorithm  K in the KNN…

# Question 1 Neural Network: Neural Networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in teh fields of AI and Machine Learning and deep learning.  Neural Networks, also known as artificial neural networks (ANNs) or simulated neural networks…

# Question 1   Similarly in categorial data is not as direct when itcomes to numerical data. The categorial data is first transformed to numerical values and both  are standardised to make sense of the similarity and dissimilarity between  among those features.    # Question 2   There were…

Question 1  Objective: In this project we are going to clean the dataset of 1985 automobile dataset. About the dataset: This is a raw dataset about cars in 1985. This dataset set consists of three types of entities: a the specification of an auto in terms of various characteristics  b its assigned insurance risk…

# importing packages import pandas as pd import numpy as npimport seaborn as sns import matplotlib.mlab as mlabimport matplotlibimport matplotlib.pyplot as plt # read the data df = pd.read_csv('downloads:/project2/auto.csv')print(df.head())print(df.info())print(df.describe())print(df.describe(include = "all")) # Adding…

<div>import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns</div>         import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns     In [3]:           df = pd.read_csv('heart.csv')…

          import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import KMeans import plotly.express as exp     In [3]:           df = pd.read_csv('Country-data.csv')     In [4]:  …

#Main Menu ​ # Add a new word # Find the meaning # Update a word # Exit ​ import json #We use json to open the file that contains the data.json stands for JavaScript object notation. ​ print("Welcome to the directory \nPlease select your choice \n1.Add a Word \n2.Find the meaning \n3.Update the word\n4.Exit") ​ f = open('word.txt','w')…

<div>import pandas as pd import numpy as np import matplotlib.pyplot as plt</div>         import pandas as pd import numpy as np import matplotlib.pyplot as plt     In [2]:           ​ df1 = pd.read_csv("customer_details.csv") ​ df2 = pd.read_csv("customer_policy_details.csv")…

CREATE DATABASE ecommerce;use ecommerce;DESC users_data;select * from users_data;select * from users_data LIMIT 100;select count(DISTINCT country) country, count(Distinct language) Language from users_data;select gender, max(socialNbFollowers)Max_Followers from users_data Group by gender;select count(hasprofilepicture)…

CREATE DATABASE ALUMNI;use alumni;Desc college_a_hs;Desc college_a_se;Desc college_a_sj;Desc college_b_hs;Desc college_b_se;Desc college_b_sj; CREATE VIEW college_A_HS_V AS (SELECT * FROM college_a_hs WHERE ROLLNO IS NOT NULL AND LASTUPDATE IS NOT NULL AND NAME IS NOT NULL AND FATHERNAME IS NOT NULL AND MOTHERNAME IS NOT…

  import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import KMeans import plotly.express as exp     In [3]:           df = pd.read_csv('Country-data.csv')     In [4]:          …

  import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns     In [3]:           df = pd.read_csv('heart.csv')     In [4]:           df     Out[4]:   age sex cp trtbps chol fbs restecg…

   from project_lib import Project project = Project(project_id='YOUR_PROJECT_ID', project_access_token='YOUR_PROJECT_TOKEN') pc = project.project_context If you do not see the cell above, follow these steps to enable the notebook to access the dataset from the project's resources: Click on More -> Insert…

Aim: This project aims to analyze data in detail on vaccinated people and fully vaccinated by year and country. Introduction: The main objective of this project is to analyze the data on covid 19 vaccinations. Through the analysis of data, we can find out some important insights. Problem statement: * we can find out how…

Aim The aim of this project is to collect and analyze the data in detail of the Pickup point of the Airport and City Request id in Uber Request Data.   Introduction Uber provides the 2016 data. By using this data find out the demand and supply Analysis gap of the cab. The main objective of this project is to analyze the…