Q no 1 b) Has Search, Issue and Delete button has no idea to implement.

Sorry, For some cell fully commented. Because they are crash the jupyter notebook. 2) Dot py file I added,It has full program with output when run it. 

The .csv file is too big. So it import from the file very Slowly. But the questions are answered. The Q no 2 is attached the screenshots Sql-Project-1.1, Sql-Project-1.2.

Q no 2 import .csv files it shows (row import failed with error for 3 .csv files) ScreenShot attached SQL-Project-2-Q2.png.Q no 16 is I can't give correct answer, Its very difficult to answer

Tableau Public Server Link :- Project 1.1 DS | Tableau Public

a) in sales_data workbook sales_data sheet. b) in sales_data workbook sales_data sheet starts with C2827 cell. c) in sales_data workbook C sheet. d) in sales_data workbook before help tab. e) in sales_data workbook E sheet. f) in sales_data workbook C sheet. g) in sales_data workbook sales_data…

Features of Dashboard in Features of Dashboard sheet Separate Worksheet Required for in Q2.1,Q2.2 and Q2.3 sheets Other Deliverables in Q3_1, SaveasPDF and Spellcheck Macro   Link:- https://drive.google.com/file/d/1slAx8toMmPadhnEJEHFuaqKMLnn1lIjH/view?usp=sharing

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 Problem statement Methodology Analysis (Data sheets…

Table Of Contents   Aim Introduction Problem Statement Methodology Analysis (Data sheets pertaining to it) Insights Recommendations Conclusion   Aim:                 The aim of this project is to analysis data in detail on employees details to improve the management of the company.   Introduction:                 The main…

Q1. Why there is a difference in the formula of variance for population and sample This has to do with a concept called Degrees of freedom (statistics). It basically means the number of variables that are free to vary. Because , The first (n-1) samples are independent of each other given the mean, but the last one is not…

Note: I do Q1 in Excel, but answers vary from my calculation so I didn't attached it. 1)Calculate all 4 business moments using pen and paper for the below data set 1)  mean µ= ∑(x1) * p(x)             = 0*0.35+1*0.25+ 2*0.15 + 3*0.15 + 4*0.10    …

Q1.Perform Gradient Descent in Python with any loss function :    In pyhton we use MSE as a loss function in a given dataset. X = [1,3,5,7,9] Y = [1,2,3,4,5]   Loss Function:(1n)∑(y(i)-(mx(i)+b))2">  J= (1n)∑(y(i)-(mx(i)+b))2">(1/n)∑(y(i)−(mx(i)+b))^2 Where, x(i) are the…

Q1. Pros of Support Vector Machine: It works comparably well when there is an understandable margin of dissociation between classes. It is more productive in high-dimensional spaces. It is effective in instances where the number of dimensions is larger than the number of specimens. Regularization : SVMs can be regularized,…

Q1. Answer in Week_8_Challenges.py attached below. Q2. K-Nearest Neighbours. Pros:  KNN is known for its simplicity, comprehensibility and scalability. Learning and implementation are extremely simple and intuitive. The calculation time is less. Predictive power is very high, which makes it effective and efficient. It…

Q1. Neural networks, also known as artifical neural networks, (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Q2.Deep…

Q1. Similarity in categorical data is not as direct when it comes to numerical data, the numerial data. The categorical data is first transformed to numerical values and both are standardised to make sense of the similarity and dissimilarity between/among those features.   Q2. Answer in Week_11_Challenges.py

Answer in Project_1.py

Answer in Project_2.py To categorize the car class, we have a model with five clusters.   New data = ["58", "120"] New data for prediction and it predict as Cluster No : 4 "In the Screenshot has yellow circle, It was the predicted one in Project_2.png"