Basics of Probability and Statistics Week 1 Challenge

done

I clean the data and given some insights and also done some mean mode for remove null i  used one hot for horsepower_binned  

Q1. The neuron is the smallest unit and the building block of a neural network. A neuron takes a set of inputs, performs some mathematical computations, and gives an output. The inputs and outputs are numbers, either positive or negative. In this example, the neuron takes two inputs. However, there is no limit to the number…

Q1.computing similarity between categorical data instances is not straightforward owing to the fact that there is no explicit notion of ordering between categorical values. To overcome this problem, several data-driven similarity measures have been proposed for categorical data. The behavior of such measures directly depends…

high dimensional data may not perform as well as other techniques. KNN can benefit from feature selection that reduces the dimensionality of the input feature space. this is the mAIN key note   Pros:- As there is no training period thus new data can be added at any time since it wont affect the model. cons:- Does…

ADVT:- SVM works relatively well when there is a clear margin of separation between classes.  more effective in high dimensional spaces becaus kernal tric SVM is effective in cases where the number of dimensions is greater than the number of samples this is used in computer vision for reduced the dimentanality. SVM…

Q1. in ipython file Q2. The key difference between these techniques is that the less important feature’s coefficient to zero thus, removing some feature altogether. So, this works well for feature selection in case we have a huge number of features. L1 regularization penalizes the sum of absolute values of the weights,…

Q1. 1st Business Moment: Measure of Central Tendency  This moment speaks about the center of the data point and indicates where the majority of data points lie. a.) Mean: The average of all the data points in a data set is called the mean. For Population, μ = (Σ Xi) / N  here it is nothaving the same…

  Q1.   Sample Variance Population Variance The formula for sample variance is given as ∑ni=1(xi−μ)2n−1 ∑ i = 1 n ( x i − μ ) 2 n − 1 The formula for population variance is equal to ∑ni=1(xi−μ)2n ∑ i = 1 n ( x i − μ ) 2 n Where xi = ith. Number,…

Q1 Q2 Q3 Q4   Q5     Q.6   Q7. Q.8 Q.9 Q.10   Q11 Q12. 13   14 15 16  

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