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1.How does similarity is calculated if data is categorical in nature For categorical data it is better to use the hamming distance. But K-means work only on Euclidean distance. So, it is better to classify the categorical data using one hot encoding and removing one extra column of each feature (Dummy Variable). Thus all…

1. What is a Neural Network? sol: Neural networks are a class of machine learning algorithms used to model complex patterns in datasets using multiple hidden layers and non-linear activation functions. 2.What is deep learning? sol:Deep learning is a type of machine learning, which is a subset of artificial intelligence.deep…

2. Pros and cons of knn Pros: K-NN is pretty intuitive and simple: K-NN algorithm is very simple to understand and equally easy to implement. To classify the new data point K-NN algorithm reads through whole dataset to find out K nearest neighbors. K-NN has no assumptions: K-NN is a non-parametric algorithm which means…

1Q) Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that separates data into different classes in a high-dimensional space. The primary objective of SVM is to maximize the margin between the classes, which is…

1.Q Gradient Descent is an optimization algorithm in machine learning used to minimize a function by iteratively moving towards the minimum value of the function. We use the algorithm when we have to find the least possible values that can satisfy a given cost function. In machine learning, more often than not we try to…

1.Q) 2. Q)   The simple mean (also known as the arithmetic mean) is a measure of central tendency that represents the middle or average value of a set of numbers. It is calculated by adding up all the values in a data set and dividing by the number of values. Expected value, on the other hand, is a theoretical concept…

1.Q) The reason for this difference is that when we are calculating variance for a population, we have all the data available, whereas when we are calculating variance for a sample, we only have a subset of the data. In order to estimate the population variance from a sample, we need to use a slightly different formula…

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Project 1 1. Explain the data. Check It is supervised or Unsupervised. Demonstrate? A: The insurance data, with each row representing a single patient and various features about that patient. The features included in this dataset are: <!-- [if !supportLists]-->·        <!--[endif]-->Age (in years) <!-- [if…

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Project 2 https://drive.google.com/file/d/1YMAU7H9419qsAKQrjL5t_jAHL9S3dicX/view?usp=sharing https://drive.google.com/file/d/1YMAU7H9419qsAKQrjL5t_jAHL9S3dicX/view?usp=share_link  

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Project 2

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SQL Project-2 Q.4 Q.5 Q.13

SQL Project 1

Project-2 EDA on vehicle insurance customer data

Python  Project-1 English dictionary app  Library management app