Supervised Learning - Prediction Week 3 Challenge

 Solution is given below

Q1) Question Data cleaning on Automobile 1985 dataset and performing descriptive analytics   ans ) solution is available below in jupiter notebook

Q1) How does the similarity is calculated if data is categorical in nature and) 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.    q2) solution…

Q1) What is Neural Network? Neural networks, also known as artificial 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…

q1) Pros and Cons of KNN 1. No Training Period: KNN is called Lazy Learner (Instance based learning). It does not learn anything in the training period. It does not derive any discriminative function from the training data. In other words, there is no training period for it. It stores the training dataset…

question 1¶   Pros and Cons of SVM   Pros: It works really well with a clear margin of separation. It is effective in high dimensional spaces. Cons: It doesn't perform well when we have large data set because the required training time is higher.   question 2 in jupiter notebook

Question 1 explained in jupiter book below   question 2 Difference between MSE and MAE The mean absolute error (MAE) measures how close the predictions are to the outcomes. It is considered as an average of the all absolute errors. The mean squared error measures the average of the squares of the errors, which means…

# libraries import cv2 import numpy as np # gaussian filter def gaussian_filter(image,filter_size,sigma): # used direct library of gaussian from open cv filtered_image = cv2.GaussianBlur(image,(filter_size,filter_size), sigma) # given the values sigma has to be decided to define SD. return filtered_image def save_image(image_name,…

The first question solution is in the excel file attached below.   2) Expected value : It is termed as the average value or mean that is expected to obtained when multipled by the values of the x with their probablity measures. This value is expected to be obtained when we conduct an experiment whose value are represented…

 1) Why there is a difference in the formula of variance for population and sample ? The difference between the formula of variance in population and sampling is because : The variance is calculated in five steps First, mean is calculated Then deviations from the mean deviations are squared Squared deviations are…

import cv2 # importing the important libraries import numpy as np # importing numpy libraries as np # creating helper functions for further use # a read image function def read_image(image_path): image = cv2.imread(image_path) # Cv2 reads in BGR format return image # a display image function def display_image(window_name,…

import json # to print the words from the app def output(): f = open('words.txt', 'r+') # opening the file in read mode print('file opened') lines = f.readlines() # reading all the lines of the file in list called lines for line in lines: print(line, end = ' ')# iterate through the listone item at a time #print(len(line))…