import jsonfrom difflib import get_close_matches data = json.load(open("dictionary.json")) def meaning(w): #data = json.load(open("dictionary.json")) w = w.lower() if w in data: return data[w] elif len(get_close_matches(w, data.keys())) > 0: return data[get_close_matches(w, data.keys())[0]] else: return "The word doesn't…

#1Create new schema as ecommercecreate database ecommerces;use ecommerces; #3 Run SQL command to see the structure of tableselect * from users_data;desc users_data; #4 Run SQL command to select first 100 rows of the databaseselect * from users_data limit 100; #5 How many distinct values exist in table for field country…

#importing librariesimport pandas as pdimport numpy as npdata=pd.read_csv('AXISBANK.csv')

use

1.Why there is a difference in the formula of variance for population and sample?-> Population variance Sample variance 1.σ2=∑(xi−μ)2/N 1. s2=∑(xi−¯x)2./(n-1) where N is the size of the population The xi tends to be closer to consisting of x1,x2,…xN and μμ is the their…

#importing libarariesimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns

2)What is the significance of expected value when simple mean (Sum of all observations/number of observations) is already in place?->1.The expected value used to find average for random variable and simple mean find the average for given sample. 2.significance of expected value called as predicted average outcome for…

import numpy as npdef gradient_descent(x,y): #values of m and b m_curr = b_curr = 0 #Initializing number of steps iterations=1000 #Number of data points(n) n=len(x) #Learning rate learning_rate = 0.001 for i in range(iterations): y_p = m_curr * x + b_curr cost= (1/n) * sum([val**2 for val in (y - y_p)]) md = -(2/n) * sum(x…

1.Pros and cons of SVM ->Advantages: 1.It has a high level of accuracy. 2.It works very weell with limited datasets. 3.Kernel SVM contains a non-linear transformation function to convert the complicated non-linearly separable data into linearly separable data 4.It is effective on datasets that have multiple features.…

2.What are the pros and cons of knn? ->Advantages: 1.No training period,knn is called lazy learner.There is no training dataset and learns from it only at the time of making real time prediction. 2.KNN is very easy to implement:KNN is very easy to implement as the only thing to be calculated is the distance between…

1.How does similarity is calculated if data is categorical in nature?->The simplest way to find similarity between two categorical attributes is to assign a similarity of 1 if the values are identical and a similarity of 0 if the values are not identical.

1.What is a Neural Network?->Neural Network models the relationship between a set of input signals and an output signal using a model derived from our understanding of how a biological brain responds to stimuli from sensory inputs just as a brain uses a network of interconnected cells called neurons to create a massive…

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as sns

#import librariesimport pandas as pdimport numpy as np#from sklearn import datasetsimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.cluster import KMeans

Project1.xlxs