Write a report showing all the steps of MapReduce model

1) Using the MapReduce model, obtain the matrix multiplication of A and B. Both matrices are given below. You should use at least two mappers and two reducers. The number of mappers and reducers is not needed to be equal. You may use any number of mappers (two or more) and any number of reducers (two or more). Also, write a program using any programming language to implement the MapReduce matrix multiplication.
A            B
6    9        8    1    20    7
15    4        10    3    18    13
2    11
12    5
Write a report showing all the steps of MapReduce model including the following sections:
1.    How the data (i.e., values of both matrices) is partitioned and stored in memory.
2.    Number of mappers and reducers used for your MapReduce.
3.    The algorithm used for Mapper.
4.    The algorithm used for Reducer.
5.    A diagram showing how different key-value pairs are shuffled and sorted from mappers to reducers.
6.    The complete source code and the programming language used.
7.    The result (i.e., matrix C=A*B) obtained from multiplying matrices A and B. Also include a figure showing the output of the program.
8.    Finally, compare the result obtained from MapReduce and the matrix multiplication of A*B.
Important notes:
i.    You cannot use any libraries for the MapReduce model.
ii.    Your source codes should have enough comments for readability. It is your responsibility to make sure that your codes are understandable for evaluation.
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Q2) Write a program (with any programming language) to train and build a decision tree model using the following labeled dataset so that the model can predict whether in the future the tennis court will be occupied or not.
Day    Outlook    Temperature    Humidity    Wind    Occupied
D1    Sunny    Hot    High    Weak    No
D2    Sunny    Hot    High    Strong    No
D3    Overcast    Hot    High    Weak    Yes
D4    Rain    Mild    High    Weak    Yes
D5    Rain    Cool    Normal    Weak    Yes
D6    Rain    Cool    Normal    Strong    No
D7    Overcast    Cool    Normal    Strong    Yes
D8    Sunny    Mild    High    Weak    No
D9    Sunny    Cool    Normal    Weak    Yes
D10    Rain    Mild    Normal    Weak    Yes
D11    Sunny    Mild    Normal    Strong    Yes
D12    Overcast    Mild    High    Strong    Yes
D13    Overcast    Hot    Normal    Weak    Yes
D14    Rain    Mild    High    Strong    No
Then, write a program to build only the first level of the decision tree (that is to find the first branch of the tree) using MapReduce. You must use multiple mappers and reducers assuming your dataset is stored in chunks at multiple nodes as follows:
Node 1
Day    Outlook    Temperature    Humidity    Wind    Occupied
D1    Sunny    Hot    High    Weak    No
D2    Sunny    Hot    High    Strong    No
D3    Overcast    Hot    High    Weak    Yes
D4    Rain    Mild    High    Weak    Yes
D5    Rain    Cool    Normal    Weak    Yes
Node 2
Day    Outlook    Temperature    Humidity    Wind    Occupied
D6    Rain    Cool    Normal    Strong    No
D7    Overcast    Cool    Normal    Strong    Yes
D8    Sunny    Mild    High    Weak    No
D9    Sunny    Cool    Normal    Weak    Yes
D10    Rain    Mild    Normal    Weak    Yes
Node 3
Day    Outlook    Temperature    Humidity    Wind    Occupied
D11    Sunny    Mild    Normal    Strong    Yes
D12    Overcast    Mild    High    Strong    Yes
D13    Overcast    Hot    Normal    Weak    Yes
D14    Rain    Mild    High    Strong    No
Write a report showing all the steps of the Decision tree and MapReduce Models including the following sections:
1.    Structure of the complete decision tree.
2.    The source codes of the decision tree model.
3.    A figure showing the output of the decision tree program.
4.    Steps to build the first level (branch) of the decision tree using MapReduce
5.    The algorithm used for Mapper.
6.    The algorithm used for Reducer.
7.    A diagram showing how different key-value pairs are shuffled and sorted from mappers to reducers.
8.    The complete source code for building the first branch of the decision tree using MapReduce model.
9.    A figure showing the output of the MapReduce program.
Important notes:
i.    You cannot use any libraries for building the Decision Tree and MapReduce models.
ii.    Your source codes should have enough comments for readability. It is your responsibility to make sure that your codes are understandable for evaluation.