Unsupervised Learning - Kmeans Week 11 Challenge

a. Do proper formatting of the data with using borders, font size and appropriate alignment of the content.   b. Make summary of Products Sales by providing   c. Make worksheet from the given Data for categorical representation of table based on fields Status, ProductLine and Sales using Pivot Table.…

2.1.Data Summary of All Female Client’s for Income and Loan Amount using New Functions   3.1.   3.3.Check Spelling mistakes in the workbook using VBA Code Sub Highlight_Misspelled_Words()Dim cell As RangeFor Each cell In ActiveSheet.UsedRangeIf Not Application.CheckSpelling(Word:=cell.Text) Then cell.Interior.ColorIndex…

Attached the Tableau Workbook

Attached the jupyter notebook file. 

1.How does similarity is calculated if data is categorical in nature Similarity is the basic building block for techniques such as Recommendation engines, clustering, classification andanomaly detection. Similarity is the measure of how alike two data objects are. Similarity in the data mining context is usually described…

1. What is a Neural Network? *An artificial neural network (ANN) is a system of hardware and/or software patterned after the operation of neurons in thehuman brain. *Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset ofmachine learning and are at the heart…

1. Apply knn to the “Surface defects in stainless steel plates” and identify the differences -Attached jupyter notebook file for this solution.   2. What are the pros and cons of knn? Pros: It is a simple Machine Learning algorithm and easy to understand. kNN can be used for classification and regression.…

1.Pros and cons of SVM Pros of SVM: *Thrives in High Dimension : When data has high dimension (think 1000+ to infinity features) a Support Vector Machine withthe right settings (right kernel choice etc.) can be the way to go and produce really accurate results. *Kernel Flexibility : Support Vector Machines are all about…

1.Perform Gradient Descent in Python with any loss function import numpy as np import pandas as pd import matplotlib.pyplot as pt #importing input data  data = pd.read_csv('data.csv') X = data.iloc[:,0]Y = data.iloc[:,1] print(X) #building a mean square error loss or cost function modelm = 0c = 0L = 0.0001 #learing…

1)Calculate all 4 business moments using pen and paper for the below data set? x p(x) p*p(x) X-mean (X-mean)2*x ((X-mean)/variance))^3 ((X-mean)/variance))^4 0 0.35 0 -1.4 0 -0.012918411373136  0.003031182931448 1 0.25 0.25 -0.4 0.16  -0.00030130405535 2.01994697639187E-05 2 0.15 0.3 0.6 0.72 0.001016901186807…

1.Why there is a difference in the formula of variance for population and sample? *N is a greater value than N-1. *When divided N-1 a smaller number we will get a larger number for variance. This number near to the possible variance value for the population. *It is always good to have a large variance for the population.…

Project 2

Solution

Solutions Attached the pdf for excel table and pivot chart Attached jupyter notebook and sql file

Solutions

solution

Solution