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georgeberkeleygeorgeberkeley
authoredMay 12, 2022
Merge pull request dlab-berkeley#36 from dlab-berkeley/george
George
2 parents e60bf2e + 9af0516 commit 2c81557

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‎05_Machine Learning Walk Through.ipynb

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‎1_classification.ipynb

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‎2_regression.ipynb

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‎3_clustering.ipynb

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‎4_dimensionality_reduction.ipynb

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‎data/auto-mpg.csv

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‎data/auto-mpg.names

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1. Title: Auto-Mpg Data
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2. Sources:
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(a) Origin: This dataset was taken from the StatLib library which is
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maintained at Carnegie Mellon University. The dataset was
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used in the 1983 American Statistical Association Exposition.
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(c) Date: July 7, 1993
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3. Past Usage:
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- See 2b (above)
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- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
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In Proceedings on the Tenth International Conference of Machine
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Learning, 236-243, University of Massachusetts, Amherst. Morgan
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Kaufmann.
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4. Relevant Information:
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This dataset is a slightly modified version of the dataset provided in
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the StatLib library. In line with the use by Ross Quinlan (1993) in
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predicting the attribute "mpg", 8 of the original instances were removed
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because they had unknown values for the "mpg" attribute. The original
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dataset is available in the file "auto-mpg.data-original".
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"The data concerns city-cycle fuel consumption in miles per gallon,
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to be predicted in terms of 3 multivalued discrete and 5 continuous
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attributes." (Quinlan, 1993)
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5. Number of Instances: 398
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6. Number of Attributes: 9 including the class attribute
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7. Attribute Information:
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1. mpg: continuous
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2. cylinders: multi-valued discrete
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3. displacement: continuous
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4. horsepower: continuous
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5. weight: continuous
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6. acceleration: continuous
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7. model year: multi-valued discrete
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8. origin: multi-valued discrete
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9. car name: string (unique for each instance)
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8. Missing Attribute Values: horsepower has 6 missing values
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‎data/diamonds.csv

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‎data/heart.csv

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‎data/heart_2020_cleaned_sample.csv

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‎data/heart_preproc.npz

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‎data/spotify_features.csv

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‎data/world_happiness.csv

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rank,country,happiness_score,gdp,social_support,life_expectancy,freedom,generosity,corruption,continent
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1,Finland,7.821,1.892,1.258,0.775,0.736,0.109,0.534,Europe
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2,Denmark,7.636,1.953,1.243,0.777,0.719,0.188,0.532,Europe
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3,Iceland,7.557,1.936,1.32,0.803,0.718,0.27,0.191,Europe
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4,Switzerland,7.512,2.026,1.226,0.822,0.677,0.147,0.461,Europe
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5,Netherlands,7.415,1.945,1.206,0.787,0.651,0.271,0.419,Europe
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6,Luxembourg*,7.404,2.209,1.155,0.79,0.7,0.12,0.388,Europe
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7,Sweden,7.384,1.92,1.204,0.803,0.724,0.218,0.512,Europe
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8,Norway,7.365,1.997,1.239,0.786,0.728,0.217,0.474,Europe
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9,Israel,7.364,1.826,1.221,0.818,0.568,0.155,0.143,Asia
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10,New Zealand,7.2,1.852,1.235,0.752,0.68,0.245,0.483,Oceania
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11,Austria,7.163,1.931,1.165,0.774,0.623,0.193,0.329,Europe
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12,Australia,7.162,1.9,1.203,0.772,0.676,0.258,0.341,Oceania
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13,Ireland,7.041,2.129,1.166,0.779,0.627,0.19,0.408,Europe
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14,Germany,7.034,1.924,1.088,0.776,0.585,0.163,0.358,Europe
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15,Canada,7.025,1.886,1.188,0.783,0.659,0.217,0.368,North America
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16,United States,6.977,1.982,1.182,0.628,0.574,0.22,0.177,North America
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17,United Kingdom,6.943,1.867,1.143,0.75,0.597,0.289,0.329,Europe
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18,Czechia,6.92,1.815,1.26,0.715,0.66,0.158,0.048,Europe
20+
19,Belgium,6.805,1.907,1.106,0.764,0.492,0.049,0.204,Europe
21+
20,France,6.687,1.863,1.219,0.808,0.567,0.07,0.266,Europe
22+
21,Bahrain,6.647,1.854,1.029,0.625,0.693,0.199,0.155,Asia
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22,Slovenia,6.63,1.81,1.249,0.769,0.685,0.118,0.115,Europe
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23,Costa Rica,6.582,1.584,1.054,0.744,0.661,0.089,0.102,North America
25+
24,United Arab Emirates,6.576,1.998,0.98,0.633,0.702,0.204,0.25,Asia
26+
25,Saudi Arabia,6.523,1.87,1.092,0.577,0.651,0.078,0.18,Asia
27+
26,Taiwan Province of China,6.512,1.897,1.095,0.733,0.542,0.075,0.168,Asia
28+
27,Singapore,6.48,2.149,1.127,0.851,0.672,0.163,0.587,Asia
29+
28,Romania,6.477,1.719,1.006,0.655,0.605,0.039,0.006,Europe
30+
29,Spain,6.476,1.808,1.211,0.808,0.505,0.101,0.149,Europe
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30,Uruguay,6.474,1.615,1.18,0.672,0.665,0.103,0.265,South America
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31,Italy,6.467,1.834,1.052,0.801,0.412,0.085,0.059,Europe
33+
32,Kosovo,6.455,1.362,0.949,0.569,0.599,0.309,0.035,Europe
34+
33,Malta,6.447,1.838,1.169,0.789,0.679,0.174,0.166,Europe
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34,Lithuania,6.446,1.804,1.204,0.659,0.496,0.053,0.077,Europe
36+
35,Slovakia,6.391,1.736,1.232,0.707,0.479,0.118,0.025,Europe
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36,Estonia,6.341,1.793,1.232,0.728,0.689,0.123,0.333,Europe
38+
37,Panama,6.309,1.715,1.107,0.709,0.592,0.049,0.051,North America
39+
38,Brazil,6.293,1.462,1.044,0.615,0.546,0.131,0.134,South America
40+
39,Guatemala*,6.262,1.274,0.831,0.522,0.662,0.112,0.115,North America
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40,Kazakhstan,6.234,1.668,1.22,0.611,0.584,0.134,0.157,Asia
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41,Cyprus,6.221,1.815,0.909,0.819,0.448,0.123,0.062,Europe
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42,Latvia,6.18,1.732,1.221,0.637,0.502,0.075,0.09,Europe
44+
43,Serbia,6.178,1.55,1.086,0.658,0.546,0.219,0.088,Europe
45+
44,Chile,6.172,1.651,1.08,0.748,0.46,0.124,0.069,South America
46+
45,Nicaragua,6.165,1.105,1.029,0.617,0.617,0.168,0.212,North America
47+
46,Mexico,6.128,1.552,0.886,0.623,0.621,0.092,0.115,North America
48+
47,Croatia,6.125,1.705,1.183,0.709,0.535,0.109,0.0,Europe
49+
48,Poland,6.123,1.758,1.174,0.712,0.523,0.124,0.14,Europe
50+
49,El Salvador,6.12,1.265,0.768,0.607,0.666,0.089,0.212,North America
51+
50,Kuwait*,6.106,1.904,0.983,0.747,0.617,0.087,0.147,Asia
52+
51,Hungary,6.086,1.748,1.233,0.668,0.485,0.078,0.064,Europe
53+
52,Mauritius,6.071,1.591,1.116,0.568,0.589,0.131,0.107,Africa
54+
53,Uzbekistan,6.063,1.219,1.092,0.6,0.716,0.283,0.24,Asia
55+
54,Japan,6.039,1.835,1.089,0.866,0.537,0.007,0.218,Asia
56+
55,Honduras,6.022,1.111,0.885,0.555,0.582,0.202,0.076,North America
57+
56,Portugal,6.016,1.76,1.078,0.777,0.655,0.016,0.039,Europe
58+
57,Argentina,5.967,1.592,1.102,0.662,0.555,0.081,0.085,South America
59+
58,Greece,5.948,1.703,0.98,0.774,0.249,0.015,0.108,Europe
60+
59,South Korea,5.935,1.851,0.886,0.841,0.414,0.111,0.176,Asia
61+
60,Philippines,5.904,1.268,0.912,0.514,0.678,0.107,0.142,Asia
62+
61,Thailand,5.891,1.535,1.096,0.697,0.617,0.321,0.026,Asia
63+
62,Moldova,5.857,1.417,1.008,0.597,0.561,0.102,0.028,Europe
64+
63,Jamaica,5.85,1.296,1.045,0.646,0.567,0.08,0.053,North America
65+
64,Kyrgyzstan,5.828,1.069,1.109,0.638,0.693,0.208,0.025,Asia
66+
65,Belarus*,5.821,1.562,1.157,0.629,0.342,0.04,0.282,Europe
67+
66,Colombia,5.781,1.452,0.929,0.72,0.545,0.087,0.077,South America
68+
67,Bosnia and Herzegovina,5.768,1.468,1.068,0.665,0.448,0.244,0.006,Europe
69+
68,Mongolia,5.761,1.393,1.197,0.467,0.398,0.247,0.059,Asia
70+
69,Dominican Republic,5.737,1.538,1.003,0.577,0.606,0.084,0.179,North America
71+
70,Malaysia,5.711,1.689,0.938,0.62,0.654,0.213,0.126,Asia
72+
71,Bolivia,5.6,1.256,0.88,0.555,0.627,0.112,0.064,South America
73+
72,China,5.585,1.508,0.958,0.705,0.656,0.099,0.142,Asia
74+
73,Paraguay,5.578,1.409,1.13,0.624,0.629,0.171,0.059,South America
75+
74,Peru,5.559,1.397,0.865,0.735,0.545,0.09,0.037,South America
76+
75,Montenegro,5.547,1.573,1.023,0.659,0.46,0.135,0.077,Europe
77+
76,Ecuador,5.533,1.352,0.879,0.708,0.565,0.08,0.083,South America
78+
77,Vietnam,5.485,1.252,0.932,0.611,0.707,0.143,0.105,Asia
79+
78,Turkmenistan*,5.474,1.484,1.319,0.516,0.649,0.314,0.032,Asia
80+
79,North Cyprus*,5.467,1.815,0.888,0.819,0.523,0.13,0.213,Europe
81+
80,Russia,5.459,1.685,1.095,0.586,0.401,0.117,0.08,Europe
82+
81,Hong Kong S.A.R. of China,5.425,1.957,0.954,0.942,0.4,0.147,0.383,Asia
83+
82,Armenia,5.399,1.434,0.82,0.668,0.558,0.054,0.21,Europe
84+
83,Tajikistan,5.377,0.966,1.005,0.518,0.572,0.118,0.304,Asia
85+
84,Nepal,5.377,0.984,0.784,0.499,0.519,0.237,0.13,Asia
86+
85,Bulgaria,5.371,1.625,1.163,0.64,0.563,0.123,0.021,Europe
87+
86,Libya*,5.33,1.476,0.943,0.606,0.477,0.106,0.179,Africa
88+
87,Indonesia,5.24,1.382,0.883,0.539,0.62,0.468,0.047,Asia
89+
88,Ivory Coast,5.235,1.094,0.442,0.322,0.451,0.149,0.124,Africa
90+
89,North Macedonia,5.199,1.505,0.863,0.637,0.488,0.215,0.031,Europe
91+
90,Albania,5.199,1.439,0.646,0.719,0.511,0.138,0.028,Europe
92+
91,South Africa,5.194,1.425,1.088,0.361,0.442,0.089,0.046,Africa
93+
92,Azerbaijan*,5.173,1.458,1.093,0.56,0.601,0.023,0.341,Europe
94+
93,Gambia*,5.164,0.785,0.621,0.369,0.367,0.388,0.103,Africa
95+
94,Bangladesh,5.155,1.06,0.614,0.581,0.622,0.125,0.187,Asia
96+
95,Laos,5.14,1.239,0.654,0.479,0.679,0.197,0.184,Asia
97+
96,Algeria,5.122,1.363,0.97,0.643,0.146,0.106,0.15,Africa
98+
97,Liberia*,5.122,0.636,0.67,0.309,0.405,0.178,0.08,Africa
99+
98,Ukraine,5.084,1.411,1.081,0.583,0.473,0.188,0.017,Europe
100+
99,Congo,5.075,0.95,0.405,0.355,0.431,0.13,0.146,Africa
101+
100,Morocco,5.06,1.208,0.268,0.565,0.492,0.02,0.102,Africa
102+
101,Mozambique,5.048,0.578,0.66,0.191,0.593,0.185,0.2,Africa
103+
102,Cameroon,5.048,0.968,0.672,0.317,0.397,0.152,0.074,Africa
104+
103,Senegal,5.046,0.933,0.53,0.447,0.494,0.143,0.081,Africa
105+
104,Niger*,5.003,0.57,0.56,0.326,0.571,0.165,0.145,Africa
106+
105,Georgia,4.973,1.467,0.612,0.595,0.508,0.0,0.208,Europe
107+
106,Gabon,4.958,1.459,0.738,0.396,0.343,0.032,0.099,Africa
108+
107,Iraq,4.941,1.289,0.682,0.554,0.328,0.147,0.046,Asia
109+
108,Venezuela,4.925,0.0,0.968,0.578,0.283,0.225,0.082,South America
110+
109,Guinea,4.891,0.848,0.566,0.275,0.334,0.214,0.116,Africa
111+
110,Iran,4.888,1.41,0.741,0.642,0.281,0.241,0.146,Asia
112+
111,Ghana,4.872,1.112,0.595,0.409,0.5,0.23,0.056,Africa
113+
112,Turkey,4.744,1.707,0.865,0.702,0.209,0.087,0.115,Europe
114+
113,Burkina Faso,4.67,0.779,0.565,0.32,0.382,0.186,0.126,Africa
115+
114,Cambodia,4.64,1.019,0.732,0.505,0.74,0.166,0.068,Asia
116+
115,Benin,4.623,0.932,0.064,0.335,0.479,0.127,0.23,Africa
117+
116,Comoros*,4.609,0.899,0.476,0.424,0.185,0.195,0.125,Africa
118+
117,Uganda,4.603,0.777,0.875,0.418,0.402,0.222,0.066,Africa
119+
118,Nigeria,4.552,1.079,0.732,0.3,0.444,0.175,0.038,Africa
120+
119,Kenya,4.543,1.032,0.605,0.401,0.44,0.322,0.082,Africa
121+
120,Tunisia,4.516,1.35,0.596,0.656,0.316,0.029,0.029,Africa
122+
121,Pakistan,4.516,1.049,0.413,0.374,0.448,0.181,0.112,Asia
123+
122,Palestinian Territories*,4.483,1.148,0.957,0.521,0.336,0.073,0.079,Asia
124+
123,Mali,4.479,0.792,0.483,0.311,0.35,0.128,0.042,Africa
125+
124,Namibia,4.459,1.292,0.877,0.354,0.384,0.067,0.071,Africa
126+
125,"Eswatini, Kingdom of*",4.396,1.274,0.786,0.197,0.259,0.038,0.154,Africa
127+
126,Myanmar,4.394,1.038,0.829,0.491,0.513,0.452,0.194,Asia
128+
127,Sri Lanka,4.362,1.415,0.934,0.66,0.529,0.15,0.079,Asia
129+
128,Madagascar*,4.339,0.67,0.645,0.378,0.202,0.143,0.154,Africa
130+
129,Egypt,4.288,1.388,0.732,0.548,0.469,0.041,0.254,Africa
131+
130,Chad*,4.251,0.662,0.506,0.225,0.18,0.182,0.077,Africa
132+
131,Ethiopia,4.241,0.788,0.809,0.457,0.472,0.205,0.136,Africa
133+
132,Yemen*,4.197,0.691,1.043,0.384,0.33,0.09,0.098,Asia
134+
133,Mauritania*,4.153,1.1,0.865,0.45,0.304,0.088,0.138,Africa
135+
134,Jordan,4.152,1.324,0.724,0.675,0.476,0.058,0.2,Asia
136+
135,Togo,4.112,0.771,0.322,0.36,0.292,0.174,0.132,Africa
137+
136,India,3.777,1.167,0.376,0.471,0.647,0.198,0.123,Asia
138+
137,Zambia,3.76,0.93,0.577,0.306,0.525,0.203,0.083,Africa
139+
138,Malawi,3.75,0.648,0.279,0.388,0.477,0.14,0.157,Africa
140+
139,Tanzania,3.702,0.848,0.597,0.425,0.578,0.248,0.27,Africa
141+
140,Sierra Leone,3.574,0.686,0.416,0.273,0.387,0.202,0.055,Africa
142+
141,Lesotho*,3.512,0.839,0.848,0.0,0.419,0.076,0.018,Africa
143+
142,Botswana*,3.471,1.503,0.815,0.28,0.571,0.012,0.102,Africa
144+
143,Rwanda*,3.268,0.785,0.133,0.462,0.621,0.187,0.544,Africa
145+
144,Zimbabwe,2.995,0.947,0.69,0.27,0.329,0.106,0.105,Africa
146+
145,Lebanon,2.955,1.392,0.498,0.631,0.103,0.082,0.034,Asia
147+
146,Afghanistan,2.404,0.758,0.0,0.289,0.0,0.089,0.005,Asia

‎images/KNN.png

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‎images/linear_regression_hyperplane.jpeg

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‎telco_churn.csv

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