Supervised Learning - Classification Week 8 Challenge

Implement Clustering and then predict the class of car from “Car dataset”? Importing Libraries: Reading the data and separating the target variables: Performing Standardization and One hot encoding and combining the data: Checking the converted data: Clustering using Kmeans: Tuning the model: Defining the best clusters:…

Data Cleaning on Automobile 1985 dataset and perform descriptive analytics:-  No. of data points: 205 Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal Attribute Information: | Attribute | Attribute Range -- symboling: -3, -2, -1, 0, 1, 2, 3-- normalized-losses: continuous from 65 to 256-- make:…

1. How does similarity is calculated if data is categorical in nature   To measure similarity between categorical data in K-means, we can use a distance metric such as Jaccard distance or Hamming distance. Jaccard distance measures the dissimilarity between two binary vectors, where each element in the vector represents…

1.What is a Neural Network? -- A neural network is a type of artificial intelligence model that is inspired by the structure and function of the human brain. It is a computational system that is composed of interconnected nodes or neurons that work together to process and analyze complex data. The neurons are organized…

KNN: K-Nearest Neighbors is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. It classifies new cases based on feature similarity measures. KNN algorithm k in the KNN algorithm is a parameter that refers to the number of nearest neighbors to include in the majority…

  Pros and cons of SVM Support Vector Machines (SVMs) are a type of machine learning algorithm that can be used for classification or regression tasks. Here are some pros and cons of using SVM: Pros: 1. Effective in high-dimensional spaces: SVMs are particularly useful in situations where the number of features is…

Q1: Perform Gradient Descent in Python with any loss function. A1:  Gradient descent is an optimization technique that can find the minimum of an objective function. It is a greedy technique that finds the optimal solution by taking a step in the direction of the maximum rate of decrease of the…

1)Calculate all 4 business moments using pen and paper for the below data set? First business moment The first business moment is the measure of central tendency Mean or expected value, μ=∑x.P(x) For the data, we calculate the data as follows: μ=∑x.P(x)=1.4 Second business moment The second business moment…

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Dear Team,  I have completed project 1. Please find the attachment below.

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1) Why there is a difference in the formula of variance for population and sample Ans:-Two Quantities that we are considering here .   PopulationVariance−σ²  SampleVariance−s²  Sample Variance of “n” samples from the Population is given by the below formula  …

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Dear Team,          I have completed Project 2. Please, Find the attachment below.

Dear Team,     i have completed SQL Project 1. Please,find the attachment below.

Dear Sir,       I have completed project1. please find the attachment below.