I need help with the following please

Please implement using Python, Pandas, and dynamic programming algorithm

The first 30 rows of the dataset

Date	RegionID	RegionName	SizeRank	Zhvi	MoM	QoQ	YoY	5Year	10Year	PeakMonth	PeakQuarter	PeakZHVI	PctFallFromPeak	LastTimeAtCurrZHVI
5/31/19	102001	United States	0	226800	-0.001321	-0.0008811	0.05390335	0.06274586	0.03015913	2019-03	2019-Q1	227200	-0.0017606	2019-02
5/31/19	9	California	1	547700	-0.0020044	-0.0047247	0.01746238	0.06217506	0.048065	2019-01	2019-Q1	550500	-0.0050863	2018-10
5/31/19	54	Texas	2	196100	-0.0030503	-0.0015275	0.05942734	0.07578106	0.04092943	2019-03	2019-Q1	197000	-0.0045685	2019-02
5/31/19	43	New York	3	300400	0.00334001	0.01727057	0.07094474	0.04504882	0.01323088	2007-05	2007-Q2	305200	-0.0157274	2006-06
5/31/19	14	Florida	4	233200	-0.0021395	-0.0051195	0.04714863	0.09168198	0.04162567	2006-08	2006-Q3	258900	-0.0992661	2005-11
5/31/19	21	Illinois	5	181100	-0.0011031	0.00110558	0.03663423	0.04561069	0.005052	2007-04	2007-Q2	203900	-0.1118195	2005-01
5/31/19	47	Pennsylvania	6	173100	-0.0028802	-0.0063146	0.03343284	0.03879891	0.0136185	2019-02	2019-Q1	174200	-0.0063146	2018-12
5/31/19	44	Ohio	7	139100	-0.0028674	-0.0050072	0.05378788	0.0523051	0.02190591	2019-03	2019-Q1	139900	-0.0057184	2019-01
5/31/19	30	Michigan	8	152000	-0.0006575	0.00197759	0.07344633	0.08368866	0.04296877	2019-04	2019-Q2	152100	-0.0006575	2019-03
5/31/19	16	Georgia	9	188500	-0.001589	0	0.08708189	0.07880842	0.02801046	2019-03	2019-Q1	188900	-0.0021175	2019-02
5/31/19	36	North Carolina	10	185000	0.00108225	0.00488865	0.0762071	0.05702374	0.02277269	2019-05	2019-Q2	185000	0	2019-05
5/31/19	40	New Jersey	11	327400	-0.0030451	-0.0006105	0.03542062	0.04007649	0.00550842	2006-08	2006-Q3	375900	-0.1290237	2005-04
5/31/19	56	Virginia	12	258800	-0.0007722	0	0.03395925	0.0328313	0.01400381	2007-04	2007-Q2	265600	-0.0256024	2005-11
5/31/19	59	Washington	13	387100	-0.0015476	-0.0025767	0.05190217	0.09216911	0.03917281	2019-03	2019-Q1	388400	-0.0033471	2019-02
5/31/19	26	Massachusetts	14	407900	-0.0036639	-0.0060916	0.03554202	0.05533721	0.03332137	2019-03	2019-Q1	410600	-0.0065757	2019-01
5/31/19	22	Indiana	15	144500	-0.0006916	0.00486787	0.08810241	0.06513694	0.02654102	2019-04	2019-Q2	144600	-0.0006916	2019-04
5/31/19	8	Arizona	16	253300	-0.000789	0.00118577	0.05983264	0.07202514	0.0406834	2006-06	2006-Q2	269400	-0.0597624	2005-11
5/31/19	53	Tennessee	17	167300	0	0.00119689	0.06424936	0.07140956	0.03421969	2019-03	2019-Q1	167400	-0.0005974	2019-03
5/31/19	32	Missouri	18	161100	-0.0024768	-0.0024768	0.0591716	0.05104469	0.01927588	2019-03	2019-Q1	161700	-0.0037106	2019-01
5/31/19	27	Maryland	19	289900	-0.0037801	-0.0058299	0.02510608	0.03532938	0.00955569	2006-12	2006-Q4	334800	-0.1341099	2005-06
5/31/19	60	Wisconsin	20	187800	0	0.00374132	0.06342016	0.05035174	0.02063872	2019-05	2019-Q2	187800	0	2019-05
5/31/19	31	Minnesota	21	236700	0.00084567	0.00339127	0.05340454	0.06372581	0.03262538	2019-05	2019-Q2	236700	0	2019-05
5/31/19	10	Colorado	22	380200	-0.0020997	0.00026309	0.0496963	0.09338724	0.05724463	2019-03	2019-Q1	381100	-0.0023616	2019-03
5/31/19	4	Alabama	23	131200	0.00076278	0.00076278	0.0420969	0.03606554	0.02117276	2019-05	2019-Q2	131200	0	2019-05
5/31/19	51	South Carolina	24	167000	0.00059916	0.003003	0.05830165	0.05762431	0.02233585	2019-05	2019-Q2	167000	0	2019-05
5/31/19	25	Louisiana	25	145900	-0.0006849	-0.0088315	0.0062069	0.03456897	0.01485257	2018-11	2018-Q4	147700	-0.0121869	2018-08
5/31/19	24	Kentucky	26	145400	0.00068823	0.00137741	0.05438724	0.05078462	0.0247173	2019-05	2019-Q2	145400	0	2019-05
5/31/19	46	Oregon	27	345800	-0.0020202	-0.0046056	0.03875038	0.08629993	0.04171159	2019-02	2019-Q1	347400	-0.0046056	2019-01
5/31/19	45	Oklahoma	28	124000	0	0.0008071	0.03852596	0.05517057	0.02603059	2019-05	2019-Q2	124000	0	2019-05

We are skipping the first two rows and only using the columns: 'RegionName' as 'InvestmentName', 'Zhvi' as 'InvestmentCost', and '10Year' as 'EstimatedReturnOnInvestment'.

The investment amount is $1000000 (1 Million U.S. dollars).

This is my code of what I have. I think it is right but I am lost on the next step.

Copyable code:

import pandas as pd

def loadInvestments(file_name) -> list:

data = pd.read_csv(file_name)[1:].reset_index(drop=True)

data = pd.DataFrame({'InvestmentName':data['RegionName'],

'InvestmentCost':data['Zhvi'], 'EstimatedReturnOnInvestment':data['10Year']})

return data.values.tolist()

def optimizeInvestments(A, amount):

roi = 0

investments = []

return(roi, investments)

print(optimizeInvestments(loadInvestments('State_Zhvi_Summary_AllHomes.csv'), 1000000))

Portfolio Optimization In this assignment, you will be using dynamic programming to select a set of investment options that maximize your return on investment. You will be given a file of investment options along with the estimated return on investment for each one. You also have a given amount of money to invest. The specific file of investment options you will be given will vary, but you will always have the following three pieces of information you can obtain from the file: InvestmentName, Investment Cost, EstimatedReturnOnInvestment For example: A, 10,60 B, 20,100 C, 30, 120 Start by putting all your code in a file called portfolio.py. Create a function called loadinvestments that takes in the investmentFilename and returns a list of possible investment options: name, cost, and estimated return. You will be given a file of actual investment options along with specific directions for that file on how to obtain the three pieces of information necessary for this assignment. Then make a function called optimizelnvestments that takes the list of possible investments along with the amount of money available to spend. This function should return both the optimal return on investment amount as well as the actual investments selected to achieve this optimal return. Implement this function using dynamic programming. For dynamic programming, one needs a recursive breakdown of the problem. We have a number of possible investments and an amount we are able to spend. At each step we can decide to either include investment x or exclude it. If we include it, then we are left with a smaller problem of finding the optimal return with all the possible investments without investment x with a smaller amount we are able to spend (because we purchased investment x). If we exclude it, then we are left with a smaller problem of finding the optimal return with all the possible investments without investment x but with the same amount of money to spend as we had before. A DAC solution could be attempted, but it ends up having too much repeated work. Therefore, we will implement our solution with dynamic programming. Note that this is a 2-D problem (number of investments and amount of money to spend). Thus, we will need a 2-D table. The second step is filling in the base cases. Note that when we don't select any investments, no matter how much money we have to spend (top row), then we get no return on investment. This is a natural base case. The third step is to identify the goal location in the table. This will be the position where we are allowed to include all investments with our full allocation of funds to spend. This will be the lower-right corner. The last step in computing any optimal table is to determine the order in which to fill in the table, keeping in mind that one always needs all the results for the recursive subproblems available to fill in the next location. After the optimal table is computed, a traceback table needs to be added to the implementation so we can find the actual investments that produce the optimal. Recall that traceback tables store the winning" options at each step and are then used at the end to trace back through these winning options. Your code should return the optimal return on investment number as well as a list of the investment names used to obtain this optimal number. Note that this is a similar problem to the 0/1 Knapsack problem, and you should use that idea to help guide you through your implementation. 1 2 3 import pandas as pd def loadInvestments(file_name) -> list: data = pd.read_csv(file_name)[1:].reset_index(drop=True) data = pd.DataFrame({ 'InvestmentName':data['RegionName'], 'Investment Cost':data['Zhvi'], 'EstimatedReturnOnInvestment':data['10Year']}) 4 5 6 7 return data.values.tolist() 8 9 10 11 def optimizeInvestments(A, amount): roi = 0 investments = [] 12 13 14 return(roi, investments) 15 16 17 18 print(optimizeInvestments(loadInvestments('State_Zhvi_Summary_All Homes.csv'), 1000000)) 19 20 21 # RegionID, Zhvi, 10Year # $1,000,000 Investment # Skip rows 1 and 2 22 Portfolio Optimization In this assignment, you will be using dynamic programming to select a set of investment options that maximize your return on investment. You will be given a file of investment options along with the estimated return on investment for each one. You also have a given amount of money to invest. The specific file of investment options you will be given will vary, but you will always have the following three pieces of information you can obtain from the file: InvestmentName, Investment Cost, EstimatedReturnOnInvestment For example: A, 10,60 B, 20,100 C, 30, 120 Start by putting all your code in a file called portfolio.py. Create a function called loadinvestments that takes in the investmentFilename and returns a list of possible investment options: name, cost, and estimated return. You will be given a file of actual investment options along with specific directions for that file on how to obtain the three pieces of information necessary for this assignment. Then make a function called optimizelnvestments that takes the list of possible investments along with the amount of money available to spend. This function should return both the optimal return on investment amount as well as the actual investments selected to achieve this optimal return. Implement this function using dynamic programming. For dynamic programming, one needs a recursive breakdown of the problem. We have a number of possible investments and an amount we are able to spend. At each step we can decide to either include investment x or exclude it. If we include it, then we are left with a smaller problem of finding the optimal return with all the possible investments without investment x with a smaller amount we are able to spend (because we purchased investment x). If we exclude it, then we are left with a smaller problem of finding the optimal return with all the possible investments without investment x but with the same amount of money to spend as we had before. A DAC solution could be attempted, but it ends up having too much repeated work. Therefore, we will implement our solution with dynamic programming. Note that this is a 2-D problem (number of investments and amount of money to spend). Thus, we will need a 2-D table. The second step is filling in the base cases. Note that when we don't select any investments, no matter how much money we have to spend (top row), then we get no return on investment. This is a natural base case. The third step is to identify the goal location in the table. This will be the position where we are allowed to include all investments with our full allocation of funds to spend. This will be the lower-right corner. The last step in computing any optimal table is to determine the order in which to fill in the table, keeping in mind that one always needs all the results for the recursive subproblems available to fill in the next location. After the optimal table is computed, a traceback table needs to be added to the implementation so we can find the actual investments that produce the optimal. Recall that traceback tables store the winning" options at each step and are then used at the end to trace back through these winning options. Your code should return the optimal return on investment number as well as a list of the investment names used to obtain this optimal number. Note that this is a similar problem to the 0/1 Knapsack problem, and you should use that idea to help guide you through your implementation. 1 2 3 import pandas as pd def loadInvestments(file_name) -> list: data = pd.read_csv(file_name)[1:].reset_index(drop=True) data = pd.DataFrame({ 'InvestmentName':data['RegionName'], 'Investment Cost':data['Zhvi'], 'EstimatedReturnOnInvestment':data['10Year']}) 4 5 6 7 return data.values.tolist() 8 9 10 11 def optimizeInvestments(A, amount): roi = 0 investments = [] 12 13 14 return(roi, investments) 15 16 17 18 print(optimizeInvestments(loadInvestments('State_Zhvi_Summary_All Homes.csv'), 1000000)) 19 20 21 # RegionID, Zhvi, 10Year # $1,000,000 Investment # Skip rows 1 and 2 22