I have attached the submission. I tried to attach screenshots for all tasks kindly check below.

I have attached the submission. I have attached the screenshots and relevant files to all tasks  for 4th task i have attached ipynb file Also, 16th task has been done differently  

I have attached the project submission file.

I have attached the project submission file. I have also attached the individual screenshots of the results what i got after running the code. DTC stands for  Decision Tree Classifier RT stands for Random Forest Classifier LR stands for  Logistic Regression SVM stands for Support Vector machine…

1)  To put it simply (n−1) is a smaller number than (n). When you divide by a smaller number you get a larger number. Therefore when you divide by (n−1) the sample variance will work out to be a larger number. Let’s think about what a larger vs. smaller sample variance means. If the sample variance…

2) weight of probability distribution is different in the above case which makes the expected value 1.4 instead of mean i.e; 3 3) it depends on what analysis we are doing it is neither good nor bad we can't judge it. 4) a) 0.841 b) 0.0268 c) question not clear d) 0 5) It is a standard normal distribution curve with colour…

1) python file attached  2) L1 and L2 are two loss functions in machine learning which are used to minimize the error. L1 Loss function stands for Least Absolute Deviations. Also known as LAD. L2 Loss function stands for Least Square Errors. The differences between L1 and L2 regularization: L1 regularization…

1)  A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates…

1) Pros: 1)It works really well with a clear margin of separation. 2)It is effective in high dimensional spaces. 3)It is effective in cases where the number of dimensions is greater than the number of samples. 4)It uses a subset of training points in the decision function (called support vectors), so it is also memory…

2) pros:- No Training Period- KNN modeling does not include training period as the data itself is a model which will be the reference for future prediction and because of this it is very time efficient in term of improvising for a random modeling on the available data. Easy Implementation- KNN is very easy to implement…

1) we can use label encoders or onehot encoders for converting the categorical data into numeric  or hamming distance for categorical variables for measuring distance  we can use cosθ for similarity which lies between 0 and 1 where 0 for more similarity and  more the θ lesser the similarity…

1)studied the data and its data types 2)Replaced nan with appropriate mean, median and mode values 3)Identified key parameters 4)Replaced the categorical values and dropped the dummies 5)Standardized the data 6)plot the sctters and correlation matrix File is attached below