1) Why there is a difference in the formula of variance for population and sample? ans : It is because the variance calculated for the whole population and variance calculated for just the sample are diffferent. it is to get a better and unbiased estimate considering the size of the popultion to the sample taken.  …

1)Calculate all 4 business moments using pen and paper for the below data set? ans : 1) measure of central tendency(mean) = 1.4 2) measure of dispersion(variance) = 1.84 3) skewness = 0.568 , therefore positively skewed. 4) kurtosis = -0.976 *calculations attached in document*   2)What is the significance of expected…

1.Perform Gradient Descent in Python with any loss function. ans: i performed gradient descent in python using the loss function of sum of mean squared error. i used salary_data as the data to train the model. first i defined the function Y = lamda x*m + b. also defined plot_line to draw Y i a grapkeeping the leraning…

1. Pros and cons of SVM. Pros Cons - It works really well with a clear margin of separation - It doesn’t perform well when we have large data set because the required training time is higher. - It is effective in high dimensional spaces. - It also doesn’t perform very well, when the data set has more noise…

1. Apply knn to the “Surface defects in stainless steel plates” and identify the differences. - labeled the dependent variables to a single dimentioanal array. - droped TypeOfSteel_A400 column because its just the distinction from TypeOfSteel_A400 and TypeOfSteel_A300. this helps avoiding co_linearity among…

1. What is a Neural Network? ans: A Neural Network is a network or circuit of artificial neurons which works together to understand or solve multiple situations by mimicing the functions of an actaul brain.it takes input and, in conjunction with information from other nodes, develop output without programmed rules. Essentially,…

1. How does similarity is calculated if data is categorical in nature? ans: similarity in categorical data is not as direct when itcomes to numerical data. the categorical data is first tranformed to numerical values and both are standardised to make sense of the similarity and dissimilarity between/among those features.…

The data seemed to have a lot of missing data with no column names.  - first had to asses the data to find out how many null values it had. - '?' were placed instead of empty values. such values had to be removed and replaced with null value. - Then each column was given name to ease the analysis process. - removed…

Implement Clustering and then predict the class of car from “Car dataset”. ans: I chose the " CarPrice_Assignment " dataset as there was no dataset with the exact name  as “Car dataset”. - The data was read and checked for any missing data or anomalies. - Dropped 'car ID' and 'CarName'…