Application of Bootstrap samples in Random Forest

i want someone from machine learning background who can solve bootstrap sampling in random forest using python problem goes something like this: Task: 1 Step 1 Creating samples: Randomly create 30 samples from the whole boston data points. Creating each sample: Consider any random 303(60% of 506) data points from whole data set and then replicate any 203 points from the sampled points Ex: For better understanding of this procedure lets check this examples, assume we have 10 data points [1,2,3,4,5,6,7,8,9,10], first we take 6 data points randomly consider we have selected [4, 5, 7, 8, 9, 3] now we will replciate 4 points from [4, 5, 7, 8, 9, 3], consder they are [5, 8, 3,7] so our final sample will be [4, 5, 7, 8, 9, 3, 5, 8, 3,7] we create 30 samples like this Note that as a part of the Bagging when you are taking the random samples make sure each of the sample will have different set of columns Ex: assume we have 10 columns for the first sample we will select [3, 4, 5, 9, 1, 2] and for the second sample [7, 9, 1, 4, 5, 6, 2] and so on... Make sure each sample will have atleast 3 feautres/columns/attributes Step 2 Building High Variance Models on each of the sample and finding train MSE value: Build a DecisionTreeRegressor on each of the sample. Build a regression trees on each of 30 samples. computed the predicted values of each data point(506 data points) in your corpus. predicted house price of ithith data point yipred=130∑30k=1(predicted value of xi with kth model)ypredi=130∑k=130(predicted value of xi with kth model) . Now calculate the MSE=1506∑506i=1(yi−yipred)2MSE=1506∑i=1506(yi−ypredi)2 . Step 3 Calculating the OOB score : Computed the predicted values of each data point(506 data points) in your corpus. Predicted house price of ithith data point yipred=1k∑k= model which was buit on samples not included xi(predicted value of xi with kth model)ypredi=1k∑k= model which was buit on samples not included xi(predicted value of xi with kth model) . Now calculate the OOBScore=1506∑506i=1(yi−yipred)2OOBScore=1506∑i=1506(yi−ypredi)2 . ### Task: 2 Computing CI of OOB Score and Train MSE Repeat Task 1 for 35 times, and for each iteration store the Train MSE and OOB score After this we will have 35 Train MSE values and 35 OOB scores using these 35 values (assume like a sample) find the confidence intravels of MSE and OOB Score you need to report CI of MSE and CI of OOB Score Note: Refer the [login to view URL] to check how to find the confidence intravel ### Task: 3 Given a single query point predict the price of house. Consider xq= [0.18,20.0,5.00,0.0,0.421,5.60,72.2,7.95,7.0,30.0,19.1,372.13,18.60] Predict the house price for this point as mentioned in the step 2 of Task 1.