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menter session problem    

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-- Question 1-- create schema ecommerce;use ecommerce;-- Question 2select * from users_datalimit 2;select count(identifierHash) from users_data;select count( distinct identifierHash ) from users_data; -- 3. Run SQL command to see the structure of tabledescribe users_data;desc users_data; -- 4 Run SQL command to select…

answer -4  Answer - 5  answer 13  (5,13)  answer in excel worksheet      -- Question 1 create schema alumni; --- Question 2 use alumni; desc college_a_sj; show tables; -- Question 3-- Run SQL command to see the structure of six tables desc college_a_hs;desc college_a_se;desc college_a_sj;describe…

https://docs.google.com/spreadsheets/d/1rWts0xZen7JD4CTduDzVjyo-FqKIu9vT/edit?usp=sharing&ouid=111257044226613665451&rtpof=true&sd=true           micro record   then  Sub formating()'' formating Macro' ' Range("A1:G5").Select Selection.Borders(xlDiagonalDown).LineStyle = xlNone…

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Question1 . The difference in the formula of variance for population and sample is due to the concept of degrees of freedom. The sample variance uses (n-1) in the denominator to provide an unbiased estimate of the population variance.     Answer 2. Stratified sampling involves dividing the population into subgroups…

5)Explain the curve  on the right side     Answer 2 The expected value is a measure of the center of a probability distribution, while the simple mean is a specific calculation for observed data. The expected value is particularly useful when dealing with probability distributions, providing a theoretical…

 ANSwer . 2 Difference between L1 & L2 Gradient descent method: L1 regularization adds the absolute values of the coefficients to the cost function, promoting sparsity. L2 regularization adds the squared values of the coefficients, preventing large weights and encouraging small, distributed weights. ANSwer.3 Different…

Answer 1 Pros:   Effective in High-Dimensional Spaces: SVMs work well in high-dimensional spaces, making them suitable for tasks like image classification.   Versatile Kernel Options: SVMs allow the use of different kernel functions, such as linear, polynomial, and radial basis function (RBF), providing flexibility in…

Answer 1.  A neural network is a computational modelling spired by the way biological neural networks in the human brain work. It consists of interconnected nodes, or artificial neurons, organized in layers. Information is processed through these layers by adjusting the strengths of connections (weights) between…

The Dataset is given below. Kindly use the same. Deliverables of this project: Detailed Documentations (attach a word file and also paste in the text box) Power BI Report / .PBIX file Present the project like a Case Study using Power Point The Detailed Documentation of the Project should contain. Aim Introduction…

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Answer 1 .   Hamming Distance: It measures the number of positions at which the corresponding symbols are different.   Jaccard Similarity: It calculates the size of the intersection divided by the size of the union of two sets. This is often used for comparing sets of categorical data.   Dice Similarity Coefficient: Similar…

Answer 2।     K-Nearest Neighbors (KNN) is a simple and intuitive algorithm, but like any algorithm, it comes with its own set of advantages and disadvantages. Here are the pros and cons of KNN:   Pros: Simple to Understand and Implement:   KNN is straightforward and easy to understand, making it a good choice for beginners.…

Project 1

Project 2 

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble…

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.cluster import KMeans from scipy.cluster.hierarchy import dendrogram, linkage   # Load the data from the CSV file file_path = 'path_to_your_directory/country_data.csv'…

project

# Import necessary libraries import pandas as pd from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt   # Load the dataset # Assuming you have a CSV file named 'car_dataset.csv' with the mentioned columns dataset_path = 'path_to_your_dataset.csv' df =…

proeject 1  of deep learning