Unsupervised Learning - Kmeans Week 11 Challenge

Objective: Implement Clustering and then predict the class of car from “Car dataset” # Data cleaning of a Automobile dataset #importing packages import pandas as pd import numpy as np import seaborn as sns import matplotlib.mlab as mlab import matplotlib import matplotlib.pyplot as plt #read the data df = pd.read_csv('D:/MLnAI/Challenges/Project1/auto.csv')…

Objective: Data cleaning on Automobile 1985 dataset and perform descriptive analytics # Data cleaning of a Automobile dataset #importing packages import pandas as pd import numpy as np import seaborn as sns import matplotlib.mlab as mlab import matplotlib import matplotlib.pyplot as plt #read the data df = pd.read_csv('D:/MLnAI/Challenges/Project1/auto.csv')…

Objective: Brief introduction to K-means algorithm and similarity feature determination. Implementation of K-means algorithm in python for automobile dataset. a) K-means algorithm and similarity feature determination K-means algorithm is a simple iterative machine learning algorithm used in unsupervised learning. The basic…

Objectives: Pros and cons of K-Nearest Neighbours Application of K-NN algorithm to predict the surface defects in stainless steel plates. a) Pros and cons of K-Nearest Neighbours K-Nearest Neighbours (K-NN) is a supervised machine learning algorithm and it can be used for classification as well regression. It is non -parametric…

Objective: Brief explaination on Support Vector Machine (SVM). Pros and cons. SVM application to “Surface defects in stainless steel plates” dataset and its evaluation. a) Support Vector Machine (SVM) The SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. SVMs comes under…

Objective : Numerical simulation of backward facing step using openFOAM. Geometry:                                                                                                   …

Objective: Simulation of 1D subsonic-supersonic nozzle flow simulation using the Maccormack’s technique for Non-conservative and conservative forms of governing equations. Introduction: Maccormack’s technique is an explicit finite differencing method and a variant of the Lax-Wendroff approach. Maccormack’s…

Objectives: Matlab code to solve the 2D heat conduction equation by using the point iterative techniques under steady and transient state conditions. Solve using iterative methods such as Jacobi, Gauss seidel and Successive Over Relaxation (S O R) method. Implement implicit and explicit schemes for transient condition.…

Objectives: Derive the 4th order approximations of the second order derivative using:          1. Central difference (CD)          2. Skewed right sided difference (S-RSD)          3. Skewed left sided difference (S-LSD) Write a Matlab code to evaluate…