Income,Limit,Rating,Cards,Age,Education,Own,Student,Married,Region,Balance 14.891,3606,283,2,34,11,No,No,Yes,South,333 106.025,6645,483,3,82,15,Yes,Yes,Yes,West,903...

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Income,Limit,Rating,Cards,Age,Education,Own,Student,Married,Region,Balance 14.891,3606,283,2,34,11,No,No,Yes,South,333 106.025,6645,483,3,82,15,Yes,Yes,Yes,West,903 104.593,7075,514,4,71,11,No,No,No,West,580 148.924,9504,681,3,36,11,Yes,No,No,West,964 55.882,4897,357,2,68,16,No,No,Yes,South,331 80.18,8047,569,4,77,10,No,No,No,South,1151 20.996,3388,259,2,37,12,Yes,No,No,East,203 71.408,7114,512,2,87,9,No,No,No,West,872 15.125,3300,266,5,66,13,Yes,No,No,South,279 71.061,6819,491,3,41,19,Yes,Yes,Yes,East,1350 63.095,8117,589,4,30,14,No,No,Yes,South,1407 15.045,1311,138,3,64,16,No,No,No,South,0 80.616,5308,394,1,57,7,Yes,No,Yes,West,204 43.682,6922,511,1,49,9,No,No,Yes,South,1081 19.144,3291,269,2,75,13,Yes,No,No,East,148 20.089,2525,200,3,57,15,Yes,No,Yes,East,0 53.598,3714,286,3,73,17,Yes,No,Yes,East,0 36.496,4378,339,3,69,15,Yes,No,Yes,West,368 49.57,6384,448,1,28,9,Yes,No,Yes,West,891 42.079,6626,479,2,44,9,No,No,No,West,1048 17.7,2860,235,4,63,16,Yes,No,No,West,89 37.348,6378,458,1,72,17,Yes,No,No,South,968 20.103,2631,213,3,61,10,No,No,Yes,East,0 64.027,5179,398,5,48,8,No,No,Yes,East,411 10.742,1757,156,3,57,15,Yes,No,No,South,0 14.09,4323,326,5,25,16,Yes,No,Yes,East,671 42.471,3625,289,6,44,12,Yes,Yes,No,South,654 32.793,4534,333,2,44,16,No,No,No,East,467 186.634,13414,949,2,41,14,Yes,No,Yes,East,1809 26.813,5611,411,4,55,16,Yes,No,No,South,915 34.142,5666,413,4,47,5,Yes,No,Yes,South,863 28.941,2733,210,5,43,16,No,No,Yes,West,0 134.181,7838,563,2,48,13,Yes,No,No,South,526 31.367,1829,162,4,30,10,No,No,Yes,South,0 20.15,2646,199,2,25,14,Yes,No,Yes,West,0 23.35,2558,220,3,49,12,Yes,Yes,No,South,419 62.413,6457,455,2,71,11,Yes,No,Yes,South,762 30.007,6481,462,2,69,9,Yes,No,Yes,South,1093 11.795,3899,300,4,25,10,Yes,No,No,South,531 13.647,3461,264,4,47,14,No,No,Yes,South,344 34.95,3327,253,3,54,14,Yes,No,No,East,50 113.659,7659,538,2,66,15,No,Yes,Yes,East,1155 44.158,4763,351,2,66,13,Yes,No,Yes,West,385 36.929,6257,445,1,24,14,Yes,No,Yes,West,976 31.861,6375,469,3,25,16,Yes,No,Yes,South,1120 77.38,7569,564,3,50,12,Yes,No,Yes,South,997 19.531,5043,376,2,64,16,Yes,Yes,Yes,West,1241 44.646,4431,320,2,49,15,No,Yes,Yes,South,797 44.522,2252,205,6,72,15,No,No,Yes,West,0 43.479,4569,354,4,49,13,No,Yes,Yes,East,902 36.362,5183,376,3,49,15,No,No,Yes,East,654 39.705,3969,301,2,27,20,No,No,Yes,East,211 44.205,5441,394,1,32,12,No,No,Yes,South,607 16.304,5466,413,4,66,10,No,No,Yes,West,957 15.333,1499,138,2,47,9,Yes,No,Yes,West,0 32.916,1786,154,2,60,8,Yes,No,Yes,West,0 57.1,4742,372,7,79,18,Yes,No,Yes,West,379 76.273,4779,367,4,65,14,Yes,No,Yes,South,133 10.354,3480,281,2,70,17,No,No,Yes,South,333 51.872,5294,390,4,81,17,Yes,No,No,South,531 35.51,5198,364,2,35,20,Yes,No,No,West,631 21.238,3089,254,3,59,10,Yes,No,No,South,108 30.682,1671,160,2,77,7,Yes,No,No,South,0 14.132,2998,251,4,75,17,No,No,No,South,133 32.164,2937,223,2,79,15,Yes,No,Yes,East,0 12,4160,320,4,28,14,Yes,No,Yes,South,602 113.829,9704,694,4,38,13,Yes,No,Yes,West,1388 11.187,5099,380,4,69,16,Yes,No,No,East,889 27.847,5619,418,2,78,15,Yes,No,Yes,South,822 49.502,6819,505,4,55,14,No,No,Yes,South,1084 24.889,3954,318,4,75,12,No,No,Yes,South,357 58.781,7402,538,2,81,12,Yes,No,Yes,West,1103 22.939,4923,355,1,47,18,Yes,No,Yes,West,663 23.989,4523,338,4,31,15,No,No,No,South,601 16.103,5390,418,4,45,10,Yes,No,Yes,South,945 33.017,3180,224,2,28,16,No,No,Yes,East,29 30.622,3293,251,1,68,16,No,Yes,No,South,532 20.936,3254,253,1,30,15,Yes,No,No,West,145 110.968,6662,468,3,45,11,Yes,No,Yes,South,391 15.354,2101,171,2,65,14,No,No,No,West,0 27.369,3449,288,3,40,9,Yes,No,Yes,South,162 53.48,4263,317,1,83,15,No,No,No,South,99 23.672,4433,344,3,63,11,No,No,No,South,503 19.225,1433,122,3,38,14,Yes,No,No,South,0 43.54,2906,232,4,69,11,No,No,No,South,0 152.298,12066,828,4,41,12,Yes,No,Yes,West,1779 55.367,6340,448,1,33,15,No,No,Yes,South,815 11.741,2271,182,4,59,12,Yes,No,No,West,0 15.56,4307,352,4,57,8,No,No,Yes,East,579 59.53,7518,543,3,52,9,Yes,No,No,East,1176 20.191,5767,431,4,42,16,No,No,Yes,East,1023 48.498,6040,456,3,47,16,No,No,Yes,South,812 30.733,2832,249,4,51,13,No,No,No,South,0 16.479,5435,388,2,26,16,No,No,No,East,937 38.009,3075,245,3,45,15,Yes,No,No,East,0 14.084,855,120,5,46,17,Yes,No,Yes,East,0 14.312,5382,367,1,59,17,No,Yes,No,West,1380 26.067,3388,266,4,74,17,Yes,No,Yes,East,155 36.295,2963,241,2,68,14,Yes,Yes,No,East,375 83.851,8494,607,5,47,18,No,No,No,South,1311 21.153,3736,256,1,41,11,No,No,No,South,298 17.976,2433,190,3,70,16,Yes,Yes,No,South,431 68.713,7582,531,2,56,16,No,Yes,No,South,1587 146.183,9540,682,6,66,15,No,No,No,South,1050 15.846,4768,365,4,53,12,Yes,No,No,South,745 12.031,3182,259,2,58,18,Yes,No,Yes,South,210 16.819,1337,115,2,74,15,No,No,Yes,West,0 39.11,3189,263,3,72,12,No,No,No,West,0 107.986,6033,449,4,64,14,No,No,Yes,South,227 13.561,3261,279,5,37,19,No,No,Yes,West,297 34.537,3271,250,3,57,17,Yes,No,Yes,West,47 28.575,2959,231,2,60,11,Yes,No,No,East,0 46.007,6637,491,4,42,14,No,No,Yes,South,1046 69.251,6386,474,4,30,12,Yes,No,Yes,West,768 16.482,3326,268,4,41,15,No,No,No,South,271 40.442,4828,369,5,81,8,Yes,No,No,East,510 35.177,2117,186,3,62,16,Yes,No,No,South,0 91.362,9113,626,1,47,17,No,No,Yes,West,1341 27.039,2161,173,3,40,17,Yes,No,No,South,0 23.012,1410,137,3,81,16,No,No,No,South,0 27.241,1402,128,2,67,15,Yes,No,Yes,West,0 148.08,8157,599,2,83,13,No,No,Yes,South,454 62.602,7056,481,1,84,11,Yes,No,No,South,904 11.808,1300,117,3,77,14,Yes,No,No,East,0 29.564,2529,192,1,30,12,Yes,No,Yes,South,0 27.578,2531,195,1,34,15,Yes,No,Yes,South,0 26.427,5533,433,5,50,15,Yes,Yes,Yes,West,1404 57.202,3411,259,3,72,11,Yes,No,No,South,0 123.299,8376,610,2,89,17,No,Yes,No,East,1259 18.145,3461,279,3,56,15,No,No,Yes,East,255 23.793,3821,281,4,56,12,Yes,Yes,Yes,East,868 10.726,1568,162,5,46,19,No,No,Yes,West,0 23.283,5443,407,4,49,13,No,No,Yes,East,912 21.455,5829,427,4,80,12,Yes,No,Yes,East,1018 34.664,5835,452,3,77,15,Yes,No,Yes,East,835 44.473,3500,257,3,81,16,Yes,No,No,East,8 54.663,4116,314,2,70,8,Yes,No,No,East,75 36.355,3613,278,4,35,9,No,No,Yes,West,187 21.374,2073,175,2,74,11,Yes,No,Yes,South,0 107.841,10384,728,3,87,7,No,No,No,East,1597 39.831,6045,459,3,32,12,Yes,Yes,Yes,East,1425 91.876,6754,483,2,33,10,No,No,Yes,South,605 103.893,7416,549,3,84,17,No,No,No,West,669 19.636,4896,387,3,64,10,Yes,No,No,East,710 17.392,2748,228,3,32,14,No,No,Yes,South,68 19.529,4673,341,2,51,14,No,No,No,West,642 17.055,5110,371,3,55,15,Yes,No,Yes,South,805 23.857,1501,150,3,56,16,No,No,Yes,South,0 15.184,2420,192,2,69,11,Yes,No,Yes,South,0 13.444,886,121,5,44,10,No,No,Yes,West,0 63.931,5728,435,3,28,14,Yes,No,Yes,East,581 35.864,4831,353,3,66,13,Yes,No,Yes,South,534 41.419,2120,184,4,24,11,Yes,Yes,No,South,156 92.112,4612,344,3,32,17,No,No,No,South,0 55.056,3155,235,2,31,16,No,No,Yes,East,0 19.537,1362,143,4,34,9,Yes,No,Yes,West,0 31.811,4284,338,5,75,13,Yes,No,Yes,South,429 56.256,5521,406,2,72,16,Yes,Yes,Yes,South,1020 42.357,5550,406,2,83,12,Yes,No,Yes,West,653 53.319,3000,235,3,53,13,No,No,No,West,0 12.238,4865,381,5,67,11,Yes,No,No,South,836 31.353,1705,160,3,81,14,No,No,Yes,South,0 63.809,7530,515,1,56,12,No,No,Yes,South,1086 13.676,2330,203,5,80,16,Yes,No,No,East,0 76.782,5977,429,4,44,12,No,No,Yes,West,548 25.383,4527,367,4,46,11,No,No,Yes
credit-lcwgp0hi.csv assignment02-lsakxmf1.ipynb ziptest-cyxevsvj.csv ziptrain-csg231dc.csv assignment-screenshot-ejjsm352-oblt0hd2.docx
Answered 8 days AfterOct 18, 2021

Answer To: Income,Limit,Rating,Cards,Age,Education,Own,Student,Married,Region,Balance...

Uttam answered on Oct 27 2021
115 Votes
{
"cells": [
{
"cell_type": "markdown",
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"colab_type": "text",
"id": "BQu32icXDMSj"
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"source": [
"# Assignment 2 (15 points) \n",
" \n",
"***\n",
"\n",
"### General Instructions\n",
" + You may need additional libraries besides the Python standard library to solve some questions. Import only necessary libraries. \n",
" + If more than one library exist for a same purpose, choose the one you wish as long as it does the task properly. \n",
" + If we want you to use a specific library, then we will state it clearly. \n",
" + Use the exact variable names asked in the questions. When no clear instructions given, feel free to do it the way you would like to.\n",
" + After each question, add the needed number of new cells and place your answers inside the cells. \n",
" + Use text cells for explanations. Use explanation and plain text as much as possible. \n",
" + Do not remove or modify the original cells provided by the instructor.\n",
" + In the following cell you will find some extra options to make your code more readable, including output colors RED, OKBLUE, or output text styles like BOLD or UNDERLINE that. Do not hesitate to use them. As an example, one may output text in red as follows: \n",
" ```python\n",
" print(bcolors.RED + \"your text\" + bcolors.ENDC)\n",
" ```\n",
" + Comment your code whenever needed using # sign at the beginning of the row.\n",
" + In some questions some of the details needed for solving the problem are **purposely** omitted to encourage additional self-directed research. This, especially, helps you develop some search skills for coding in Python (which is inevitable due to the inconsistent syntax of Python).\n",
" + Do not hesitate to communicate your questions to the TA's or instructors. \n",
" \n",
" Good luck! "
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "KJ3ey5VeDMSr"
},
"outputs": [],
"source": [
"\n",
"# The following piece of code gives the opportunity to show multiple outputs\n",
"# in one cell:\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"\n",
"# Colorful outputs\n",
"class bcolors:\n",
" RED = '\\033[91m'\n",
" OKBLUE = '\\033[94m'\n",
" BOLD = '\\033[1m'\n",
" UNDERLINE = '\\033[4m'\n",
" ENDC = '\\033[0m'"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fn7Hdf97DMS0"
},
"source": [
"## **Part A** (7 points)\n",
"\n",
"1. ** (1 point) ** Download `Credit.csv` from and upload it into this notebook. Print the first $5$ rows of the data. Using appropriate descriptive statistics or visualization methods describe the variables and possible association amongst them. Interpret the results. \n",
"2. ** (0.5 points)** Keep only `Income`, `Limit`, `Rating`, `Cards`, `Age`, `Education`, and `Balance` as your variables and throw the rest of variables away. Print the dimension of this new dataset. \n",
"3. ** (0.5 points) ** Create a binary variable `Balance_1500` which equals $1$ for the observations with `Balance` $> 1500$, and equals $0$ otherwise.\n",
"4. ** (3 points) ** Model `Balance_1500` by the explanatory variables `Income`, `Limit`, `Rating`, `Cards`, `Age`, `Education` using the following models: \n",
" + logistic regression, \n",
" + linear discriminant, and \n",
" + quadratic discriminant.\n",
"8. ** (0.5 points) ** Find the probability of (`Balance` $> 1500$), for the following values, using all three aforementionned methods:\n",
"\n",
"| Income | Limit | Rating | Cards | Age | Education | \n",
"|--------------|--------------|----------------|--------------|-----------------|---------------|\n",
"| 63 | 8100 | 600 | 4 | 30 | 13 |\n",
"| 186 | 13414 | 950 | 2 | 41 | 13 |\n",
"\n",
"
\n",
"Compare the probabilities and comment.\n",
" \n",
"9. ** (1.5 points) ** For each method, print the confusion matrix, the accuracy score and the AUC using all observations. Compare these metrics and comment. "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5KzgIsCdn_ux"
},
"source": [
"## **Part B** (8 points)\n",
"\n",
"Donwload `ziptrain.csv` and `ziptest.csv` datasets from **Athena/Content/Data**. Save them and upload them here as **two separate datasets** and name them `ziptrain` and `ziptest`, respectively. Explore the data in order to understand it. \n",
"\n",
" 1. **(1 point)** From `ziptrain` dataset select only the rows corresponding to digits $2$ and $7$ and save them in a new dataset called `binar_train`. Do the same thing in `ziptest` and call it `binar_test`. \n",
" 2. **(1 point)** Project `binar_train` onto the first **two principal components** and make a scatterplot of the data in the new space (two-dimensional space spanned by the frist two PCs). Use a different color (or marker) for each digit. Based on the plot do you think that these two digits can be separated well using only two PCs? Explain.\n",
" 3. **(1 point)** Fit a **logistic regression**, in the new space, to separate digits $2$ and $7$. \n",
" 4. **(1 point)** Evaluate the trainded model on `binar_test` using **accuracy**, and an **appropriate F-measure**. \n",
" 5. **(0.5 points)** Build and print a confusion matrix for your predictions.\n",
"\n",
"For the rest of the questions use the **whole training data**, i.e., `ziptrain` (**not** `binar_train`). \n",
"\n",
" 6. **(1 point)** Project the whole data onto the first $m=2, 3, 4, 5$ principal components (one $m$ at a time).\n",
" 7. **(1.5 points)** For each $m$, and using **$5$-fold cross-validation**, train a **linear discriminant** classifier on `ziptrain`. \n",
" 8. **(1 point)** Based on **cross-validated accuracy**, select the best number of principal components $m$.\n"
]
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IncomeLimitRatingCardsAgeEducationOwnStudentMarriedRegionBalance
014.891360628323411NoNoYesSouth333
1106.025664548338215YesYesYesWest903
2104.593707551447111NoNoNoWest580
3148.924950468133611YesNoNoWest964
455.882489735726816NoNoYesSouth331
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"text/plain": [
" Income Limit Rating Cards Age Education Own Student Married Region \\\n",
"0 14.891 3606 283 2 34 11 No No Yes South \n",
"1 106.025 6645 483 3 82 15 Yes Yes Yes West \n",
"2 104.593 7075 514 4 71 11 No No No West \n",
"3 148.924 9504 681 3 36 11 Yes No No West \n",
"4 55.882 4897 357 2 68 16 No No Yes South \n",
"\n",
" Balance \n",
"0 333 \n",
"1 903 \n",
"2 580 \n",
"3 964 \n",
"4 331 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"################ Part A\n",
"################ 1\n",
"#Download Credit.csv from http://faculty.marshall.usc.edu/gareth-james/ISL/data.html and upload it into this notebook. Print the first 5 rows of the data. Using appropriate descriptive statistics or visualization methods describe the variables and possible association amongst them. Interpret the results.\n",
"import pandas as pd\n",
"df = pd.read_csv('C:\\\\Users\\\\uttam.grade\\\\Downloads\\\\credit-lcwgp0hi.csv')\n",
"df.head() # Print First 5 Rows of the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 400 entries, 0 to 399\n",
"Data columns (total 11 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Income 400 non-null float64\n",
" 1 Limit 400 non-null int64 \n",
" 2 Rating 400 non-null int64 \n",
" 3 Cards 400 non-null int64 \n",
" 4 Age 400 non-null int64 \n",
" 5 Education 400 non-null int64 \n",
" 6 Own 400 non-null object \n",
" 7 Student 400 non-null object \n",
" 8 Married 400 non-null object \n",
" 9 Region 400 non-null object \n",
" 10 Balance 400 non-null int64 \n",
"dtypes: float64(1), int64(6), object(4)\n",
"memory usage: 34.5+ KB\n"
]
}
],
"source": [
"df.info() "
]
},
{
"cell_type": "code",
"execution_count": 3,
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"
IncomeLimitRatingCardsAgeEducationBalance
count400.000000400.000000400.000000400.000000400.000000400.000000400.000000
mean45.2188854735.600000354.9400002.95750055.66750013.450000520.015000
std35.2442732308.198848154.7241431.37127517.2498073.125207459.758877
min10.354000855.00000093.0000001.00000023.0000005.0000000.000000
25%21.0072503088.000000247.2500002.00000041.75000011.00000068.750000
50%33.1155004622.500000344.0000003.00000056.00000014.000000459.500000
75%57.4707505872.750000437.2500004.00000070.00000016.000000863.000000
max186.63400013913.000000982.0000009.00000098.00000020.0000001999.000000
\n",
"
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],
"text/plain": [
" Income Limit Rating Cards Age \\\n",
"count 400.000000 400.000000 400.000000 400.000000 400.000000 \n",
"mean 45.218885 4735.600000 354.940000 2.957500 55.667500 \n",
"std 35.244273 2308.198848 154.724143 1.371275 17.249807 \n",
"min 10.354000 855.000000 93.000000 1.000000 23.000000 \n",
"25% 21.007250 3088.000000 247.250000 2.000000 41.750000 \n",
"50% 33.115500 4622.500000 344.000000 3.000000 56.000000 \n",
"75% 57.470750 5872.750000 437.250000 4.000000 70.000000 \n",
"max 186.634000 13913.000000 982.000000 9.000000 98.000000 \n",
"\n",
" Education Balance \n",
"count 400.000000 400.000000 \n",
"mean 13.450000 520.015000 \n",
"std 3.125207 459.758877 \n",
"min 5.000000 0.000000 \n",
"25% 11.000000 68.750000 \n",
"50% 14.000000 459.500000 \n",
"75% 16.000000 863.000000 \n",
"max 20.000000 1999.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()# descriptive statistics"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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IncomeLimitRatingCardsAgeEducationBalance
Income1.0000000.7920880.791378-0.0182730.175338-0.0276920.463656
Limit0.7920881.0000000.9968800.0102310.100888-0.0235490.861697
Rating0.7913780.9968801.0000000.0532390.103165-0.0301360.863625
Cards-0.0182730.0102310.0532391.0000000.042948-0.0510840.086456
Age0.1753380.1008880.1031650.0429481.0000000.0036190.001835
Education-0.027692-0.023549-0.030136-0.0510840.0036191.000000-0.008062
Balance0.4636560.8616970.8636250.0864560.001835-0.0080621.000000
\n",
"
"
],
"text/plain": [
" Income Limit Rating Cards Age Education \\\n",
"Income 1.000000 0.792088 0.791378 -0.018273 0.175338 -0.027692 \n",
"Limit 0.792088 1.000000 0.996880 0.010231 0.100888 -0.023549 \n",
"Rating 0.791378 0.996880 1.000000 0.053239 0.103165 -0.030136 \n",
"Cards -0.018273 0.010231 0.053239 1.000000 0.042948 -0.051084 \n",
"Age 0.175338 0.100888 0.103165 0.042948 1.000000 0.003619 \n",
"Education -0.027692 -0.023549 -0.030136 -0.051084 0.003619 1.000000 \n",
"Balance 0.463656 0.861697 0.863625 0.086456 0.001835 -0.008062 \n",
"\n",
" Balance \n",
"Income 0.463656 \n",
"Limit 0.861697 \n",
"Rating 0.863625 \n",
"Cards 0.086456 \n",
"Age 0.001835 \n",
"Education -0.008062 \n",
"Balance 1.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Income','Limit','Rating', 'Cards', \"Age\", 'Education', 'Balance']].corr() # Correlation Or Association"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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 IncomeLimitRatingCardsAgeEducationBalance
Income1.0000000.7920880.791378-0.0182730.175338-0.0276920.463656
Limit0.7920881.0000000.9968800.0102310.100888-0.0235490.861697
Rating0.7913780.9968801.0000000.0532390.103165-0.0301360.863625
Cards-0.0182730.0102310.0532391.0000000.042948-0.0510840.086456
Age0.1753380.1008880.1031650.0429481.0000000.0036190.001835
Education-0.027692-0.023549-0.030136-0.0510840.0036191.000000-0.008062
Balance