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ATA MINING Homework Clustering andMonte Carlo SimulationYou should return a Python notebook with

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ATA MINING Homework: Clustering andMonte Carlo SimulationYou should return a Python notebook with your code and the answers to the questions. You are provided with a dataset that includes information about various movies such as budget, IMBD score, duration etc.: A- . Provide a good clustering for the data and a description of them (i.e., what type of movies do they include) Hint: Examine up to 20 clusters.b. As you will observe, the various features of the data are in different scales. In these cases, normalizing the data helps into obtaining better models – either supervised or unsupervised. Use the sklearn.preprocessing.normalize function to normalize the data and perform the clustering again. What do you observe? c. Visualize the data on a 2D scatter plot, where each point corresponds to a movie and movies that belong to the same cluster have the same color. What do you observe with regards to their visual separability? budgetgrossruntimescorevotes800000052287414898.12991746000000701363691037.8264740150000001.8E+081106.923690918500000851602481378.4540152900000018564613906.93663660000001.39E+081208.131758525000000127299171017.4102879600000085512281207.8146768900000040471663966.8605651500000040456565967.512969888000001.75E+08976.5794651600000059000001167.2104860600000082000001006.8122282500000011100000997.31016781500000086209291207.2540001700000067348441175.931798100000007433663985.4248812500000038747385946.95332727000003500000935.91163535000000162957741104.636020200000013167232826255171100000091258000966.62312047000008025872895.62125315000000427240171306.832954250000001.1E+081197.366366040697761986.647068071534871307.8869910241598721175.3113042500000079817937945.94299719000004600000997.11286225000000392467341046.4574062500000049042224875.754851020038221207.41456224500000172180231257.547497138000005229398211976249501.15E+081135.958370071774311146.5462360000005844868977.3361911000000037499651916.341278300000019472057865.92831025000000143027791176.623076047483002806.941004400000028265231127.1250321000000047112201053.8440145000001261000866.71663618000000413828411036.3285291900000040996665915.72066911000005450815824.375690385007261076.593440262855441065.910239111000609939837253671800000096977391075.6114846400000400840411078569881400000038600000747.2365398500000162094591065.52485160000005849647847.33360902015882917.240395000000591366886.8160771200000048846631005.5587805839031997.1114156000000286075241147.63660403006531498.116571650000023470001117116070154483841106.11041204662137855.612803014481606935.31002220000000253142891086.17570150000001953732985.3534012000000177689001255.61581908988731915.554190387023101136.2123070278200001045.26440013431806936.41158106797218985.8458518000000366116101126.2996502514705510364943043579163835.2354431750007137502846.65257071624879936.922554870000048659121096.32297051866461106.438660318530801197.2125381200000015945534885.69040110000014360001077.83945610000000206037151096.714502013245219907.19178083629691146.9131861800000039000001176.14666500000012947763896.211458250000001579260944.833970176853071026.6100220255878041055.8157034000000016418251216.1659605274807.8790010000000214582291046.25584020695211078959066375651005.440451200000077909311045.9552320000007369373985.7474414000000621342251036.11356985000002776461367.25364076349091055.637200595845686621250916172875.758431800000013051141155.736070123039041065.7556408033397825.655490320256835.7462003751699994.47491057941841047.4107470323245571185.953458000000118343021045475204834601176264933000004941117935.9390903151936.740720214831167.34922046361691036.612641500000012308521964.9504940000000498515911165.9837401476356966.69689013418091896.327051300000084754661005.64573024440761014.5680049409391208.12091402510433945.2171703940542113817849170000002315683973.143391600000038000001115.722870604849845.9102320000000229055001056.4307902278264915.5300604601256964.5142202147228963.32003600000099476311005.93561025451429073885010090429984.925490278623955.9183409302111085.62659032726001337.44028400000046796501045.9180802995527965.43400112132885.82243048457241024128405099316935.4101604781448975.6142618000000119579431086.92720037455987.8461203136701975.5327017204501005.9208001131399905.421570441863967.6184006029824904.51164078068882.989502037811557.326940277405865.73230239073855.315401409801025.29782500000040072501275.5134002750741934.69731700000027932141015.51358012540401015.868402750001005.49070142050211065.723000681337914.712650537681907.8233008540346765.317601400000018034150976.31288019437511005.363234013763388020974.31509100000006947787834.47920544472907.21623026693661024.151508736107769905633581075.56520444746886.2639040798951026.11032014926895.37950750000966.6162507250001057.82991019027061017.3662204743287885.7553015000001006.21672200000013011211115.944804163931056.682101777378936.56040925952847202205838001006.227501814101005.1310


5 reviews for ATA MINING Homework Clustering andMonte Carlo SimulationYou should return a Python notebook with

  1. Lynn Prytula

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