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
![Test Your Antarctica Water Segmentation Function Create a function to segment the Antarctica ice shelf images using the Color Thresholder app as described in the previous reading. You can use this function to calculate the increase in visible water between the two images. Copy the code from your generated function into the first code box below. We've provided a function name for you below, segmentMeltwater. Copy the code from inside your function (do not copy the first line or the last end, just the code inside) and paste into the first code box below. Use the Run Function button to make sure you've copied the code over correctly and it works before submitting. Use the feedback to help you determine how to adjust your thresholds. Function: ``` function [BW, maskedRGBImage] = segmentMeltwater(RGB) % copy the code inside your function here end ```](https://gotit-pro.com/wp-content/uploads/2023/10/16891987-0fcf-4a3f-9352-c1af3a659f17.png)
![Exercise 4A Write functions to perform the following tasks efficiently. Do not change the function names in the template file, and do not import any packages besides those already indicated in the template. (1) Take x and return 2x^3. (2) Take two inputs x and y and return their product and the absolute value of their difference, in that order. E.g. inputs 3,4 should return 12,1. (3) The geometric mean of n numbers is the nth root of their product. The mid-range is the ordinary (arithmetic) mean of the maximum and minimum elements. Write a function which takes a list of numbers as input and returns True if the geometric mean is less than or equal to the midrange, and False if the geometric mean is greater than the midrange. For example, the input [3,8,9] should return True and [3,6,7,5] should return False. (4) Take an integer n and return a list of the first n terms of the Padovan sequence pn. (This is a variant of the Fibonacci numbers, defined by p[1] = p[2] = p[3] = 1, and p[n] = p[n-2] + p[n-3] for n>=4.) (5) Take a pair r0 and theta representing a point's polar coordinates and return a pair (x,y) representing its Cartesian coordinates. (6) Take a pair (x,y) representing a point's Cartesian coordinates and return a pair (r, theta) representing its polar coordinates, where theta is in the range [0,2π). Check that your function works for (x,y) in all quadrants.](https://gotit-pro.com/wp-content/uploads/2023/10/69b0fd8b-79ad-4a6a-ac52-c635ef316302.jpg)
ATA MINING Homework Clustering andMonte Carlo SimulationYou should return a Python notebook with
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