├── P01-DataCleaning.ipynb
├── P02-DataCleaning.ipynb
├── P03-Correlation.ipynb
├── P04-ANOVA.ipynb
├── P05-SimpleLinearRegression.ipynb
├── P06-MultipleLinearRegression.ipynb
├── P07-PolynomialRegression.ipynb
├── P08-Model Evaluation.ipynb
├── P09-LogisticRegression.ipynb
└── README.md


/P03-Correlation.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Practical - Correlation\n",
   8 |     "This practical session will demonstrate how to handle missing data. We assume everyone to have adequate understanding of Python programming language. For those who would like to refresh Python skill, we would like to recommend our <b>\"Programming for Data Science Series\"</b> where we covered almost all aspects of Python programming in data science domain.\n",
   9 |     "Refer below URL for full playlist of almost 10 hours video lesson in Burmese Language.\n",
  10 |     "URL : https://www.youtube.com/watch?v=jOZNjVVZIVs&list=PLD_eiqVVLZDi9GZZJDC8Zx4-3Np8LHs52"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 2,
  16 |    "metadata": {},
  17 |    "outputs": [
  18 |     {
  19 |      "data": {
  20 |       "text/html": [
  21 |        "<div>\n",
  22 |        "<style scoped>\n",
  23 |        "    .dataframe tbody tr th:only-of-type {\n",
  24 |        "        vertical-align: middle;\n",
  25 |        "    }\n",
  26 |        "\n",
  27 |        "    .dataframe tbody tr th {\n",
  28 |        "        vertical-align: top;\n",
  29 |        "    }\n",
  30 |        "\n",
  31 |        "    .dataframe thead th {\n",
  32 |        "        text-align: right;\n",
  33 |        "    }\n",
  34 |        "</style>\n",
  35 |        "<table border=\"1\" class=\"dataframe\">\n",
  36 |        "  <thead>\n",
  37 |        "    <tr style=\"text-align: right;\">\n",
  38 |        "      <th></th>\n",
  39 |        "      <th>Day_No</th>\n",
  40 |        "      <th>Temp</th>\n",
  41 |        "      <th>Cold_Drink</th>\n",
  42 |        "      <th>Hot_Drink</th>\n",
  43 |        "      <th>Snacks</th>\n",
  44 |        "    </tr>\n",
  45 |        "  </thead>\n",
  46 |        "  <tbody>\n",
  47 |        "    <tr>\n",
  48 |        "      <th>0</th>\n",
  49 |        "      <td>1</td>\n",
  50 |        "      <td>35</td>\n",
  51 |        "      <td>120</td>\n",
  52 |        "      <td>175</td>\n",
  53 |        "      <td>45</td>\n",
  54 |        "    </tr>\n",
  55 |        "    <tr>\n",
  56 |        "      <th>1</th>\n",
  57 |        "      <td>2</td>\n",
  58 |        "      <td>35</td>\n",
  59 |        "      <td>122</td>\n",
  60 |        "      <td>170</td>\n",
  61 |        "      <td>50</td>\n",
  62 |        "    </tr>\n",
  63 |        "    <tr>\n",
  64 |        "      <th>2</th>\n",
  65 |        "      <td>3</td>\n",
  66 |        "      <td>36</td>\n",
  67 |        "      <td>125</td>\n",
  68 |        "      <td>172</td>\n",
  69 |        "      <td>51</td>\n",
  70 |        "    </tr>\n",
  71 |        "    <tr>\n",
  72 |        "      <th>3</th>\n",
  73 |        "      <td>4</td>\n",
  74 |        "      <td>36</td>\n",
  75 |        "      <td>130</td>\n",
  76 |        "      <td>170</td>\n",
  77 |        "      <td>52</td>\n",
  78 |        "    </tr>\n",
  79 |        "    <tr>\n",
  80 |        "      <th>4</th>\n",
  81 |        "      <td>5</td>\n",
  82 |        "      <td>35</td>\n",
  83 |        "      <td>128</td>\n",
  84 |        "      <td>177</td>\n",
  85 |        "      <td>45</td>\n",
  86 |        "    </tr>\n",
  87 |        "  </tbody>\n",
  88 |        "</table>\n",
  89 |        "</div>"
  90 |       ],
  91 |       "text/plain": [
  92 |        "   Day_No  Temp  Cold_Drink  Hot_Drink  Snacks\n",
  93 |        "0       1    35         120        175      45\n",
  94 |        "1       2    35         122        170      50\n",
  95 |        "2       3    36         125        172      51\n",
  96 |        "3       4    36         130        170      52\n",
  97 |        "4       5    35         128        177      45"
  98 |       ]
  99 |      },
 100 |      "execution_count": 2,
 101 |      "metadata": {},
 102 |      "output_type": "execute_result"
 103 |     }
 104 |    ],
 105 |    "source": [
 106 |     "import pandas as pd\n",
 107 |     "data = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/correlation.csv')\n",
 108 |     "data.head()"
 109 |    ]
 110 |   },
 111 |   {
 112 |    "cell_type": "code",
 113 |    "execution_count": 3,
 114 |    "metadata": {},
 115 |    "outputs": [
 116 |     {
 117 |      "data": {
 118 |       "text/html": [
 119 |        "<div>\n",
 120 |        "<style scoped>\n",
 121 |        "    .dataframe tbody tr th:only-of-type {\n",
 122 |        "        vertical-align: middle;\n",
 123 |        "    }\n",
 124 |        "\n",
 125 |        "    .dataframe tbody tr th {\n",
 126 |        "        vertical-align: top;\n",
 127 |        "    }\n",
 128 |        "\n",
 129 |        "    .dataframe thead th {\n",
 130 |        "        text-align: right;\n",
 131 |        "    }\n",
 132 |        "</style>\n",
 133 |        "<table border=\"1\" class=\"dataframe\">\n",
 134 |        "  <thead>\n",
 135 |        "    <tr style=\"text-align: right;\">\n",
 136 |        "      <th></th>\n",
 137 |        "      <th>Day_No</th>\n",
 138 |        "      <th>Temp</th>\n",
 139 |        "      <th>Cold_Drink</th>\n",
 140 |        "      <th>Hot_Drink</th>\n",
 141 |        "      <th>Snacks</th>\n",
 142 |        "    </tr>\n",
 143 |        "  </thead>\n",
 144 |        "  <tbody>\n",
 145 |        "    <tr>\n",
 146 |        "      <th>count</th>\n",
 147 |        "      <td>120.000000</td>\n",
 148 |        "      <td>120.000000</td>\n",
 149 |        "      <td>120.000000</td>\n",
 150 |        "      <td>120.000000</td>\n",
 151 |        "      <td>120.000000</td>\n",
 152 |        "    </tr>\n",
 153 |        "    <tr>\n",
 154 |        "      <th>mean</th>\n",
 155 |        "      <td>60.500000</td>\n",
 156 |        "      <td>38.533333</td>\n",
 157 |        "      <td>141.750000</td>\n",
 158 |        "      <td>128.766667</td>\n",
 159 |        "      <td>50.066667</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>std</th>\n",
 163 |        "      <td>34.785054</td>\n",
 164 |        "      <td>2.315216</td>\n",
 165 |        "      <td>13.111833</td>\n",
 166 |        "      <td>19.615948</td>\n",
 167 |        "      <td>2.946963</td>\n",
 168 |        "    </tr>\n",
 169 |        "    <tr>\n",
 170 |        "      <th>min</th>\n",
 171 |        "      <td>1.000000</td>\n",
 172 |        "      <td>34.000000</td>\n",
 173 |        "      <td>115.000000</td>\n",
 174 |        "      <td>109.000000</td>\n",
 175 |        "      <td>45.000000</td>\n",
 176 |        "    </tr>\n",
 177 |        "    <tr>\n",
 178 |        "      <th>25%</th>\n",
 179 |        "      <td>30.750000</td>\n",
 180 |        "      <td>36.000000</td>\n",
 181 |        "      <td>130.000000</td>\n",
 182 |        "      <td>114.750000</td>\n",
 183 |        "      <td>48.000000</td>\n",
 184 |        "    </tr>\n",
 185 |        "    <tr>\n",
 186 |        "      <th>50%</th>\n",
 187 |        "      <td>60.500000</td>\n",
 188 |        "      <td>39.000000</td>\n",
 189 |        "      <td>145.000000</td>\n",
 190 |        "      <td>120.000000</td>\n",
 191 |        "      <td>51.000000</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>75%</th>\n",
 195 |        "      <td>90.250000</td>\n",
 196 |        "      <td>40.000000</td>\n",
 197 |        "      <td>151.000000</td>\n",
 198 |        "      <td>139.250000</td>\n",
 199 |        "      <td>52.000000</td>\n",
 200 |        "    </tr>\n",
 201 |        "    <tr>\n",
 202 |        "      <th>max</th>\n",
 203 |        "      <td>120.000000</td>\n",
 204 |        "      <td>42.000000</td>\n",
 205 |        "      <td>162.000000</td>\n",
 206 |        "      <td>177.000000</td>\n",
 207 |        "      <td>56.000000</td>\n",
 208 |        "    </tr>\n",
 209 |        "  </tbody>\n",
 210 |        "</table>\n",
 211 |        "</div>"
 212 |       ],
 213 |       "text/plain": [
 214 |        "           Day_No        Temp  Cold_Drink   Hot_Drink      Snacks\n",
 215 |        "count  120.000000  120.000000  120.000000  120.000000  120.000000\n",
 216 |        "mean    60.500000   38.533333  141.750000  128.766667   50.066667\n",
 217 |        "std     34.785054    2.315216   13.111833   19.615948    2.946963\n",
 218 |        "min      1.000000   34.000000  115.000000  109.000000   45.000000\n",
 219 |        "25%     30.750000   36.000000  130.000000  114.750000   48.000000\n",
 220 |        "50%     60.500000   39.000000  145.000000  120.000000   51.000000\n",
 221 |        "75%     90.250000   40.000000  151.000000  139.250000   52.000000\n",
 222 |        "max    120.000000   42.000000  162.000000  177.000000   56.000000"
 223 |       ]
 224 |      },
 225 |      "execution_count": 3,
 226 |      "metadata": {},
 227 |      "output_type": "execute_result"
 228 |     }
 229 |    ],
 230 |    "source": [
 231 |     "data.describe()"
 232 |    ]
 233 |   },
 234 |   {
 235 |    "cell_type": "markdown",
 236 |    "metadata": {},
 237 |    "source": [
 238 |     "There are few methods to calculate correlation statistics using python:\n",
 239 |     "* Pearson\n",
 240 |     "* Kendall\n",
 241 |     "* Spearman\n",
 242 |     "<p>It is good to take note that result of the calculated values may differ but the meaning behind the data, i.e. how it is correlated/whether correlated or not, is the same.</p>"
 243 |    ]
 244 |   },
 245 |   {
 246 |    "cell_type": "code",
 247 |    "execution_count": 4,
 248 |    "metadata": {},
 249 |    "outputs": [
 250 |     {
 251 |      "data": {
 252 |       "text/html": [
 253 |        "<div>\n",
 254 |        "<style scoped>\n",
 255 |        "    .dataframe tbody tr th:only-of-type {\n",
 256 |        "        vertical-align: middle;\n",
 257 |        "    }\n",
 258 |        "\n",
 259 |        "    .dataframe tbody tr th {\n",
 260 |        "        vertical-align: top;\n",
 261 |        "    }\n",
 262 |        "\n",
 263 |        "    .dataframe thead th {\n",
 264 |        "        text-align: right;\n",
 265 |        "    }\n",
 266 |        "</style>\n",
 267 |        "<table border=\"1\" class=\"dataframe\">\n",
 268 |        "  <thead>\n",
 269 |        "    <tr style=\"text-align: right;\">\n",
 270 |        "      <th></th>\n",
 271 |        "      <th>Day_No</th>\n",
 272 |        "      <th>Temp</th>\n",
 273 |        "      <th>Cold_Drink</th>\n",
 274 |        "      <th>Hot_Drink</th>\n",
 275 |        "      <th>Snacks</th>\n",
 276 |        "    </tr>\n",
 277 |        "  </thead>\n",
 278 |        "  <tbody>\n",
 279 |        "    <tr>\n",
 280 |        "      <th>Day_No</th>\n",
 281 |        "      <td>1.000000</td>\n",
 282 |        "      <td>0.269834</td>\n",
 283 |        "      <td>0.378274</td>\n",
 284 |        "      <td>-0.468887</td>\n",
 285 |        "      <td>0.045251</td>\n",
 286 |        "    </tr>\n",
 287 |        "    <tr>\n",
 288 |        "      <th>Temp</th>\n",
 289 |        "      <td>0.269834</td>\n",
 290 |        "      <td>1.000000</td>\n",
 291 |        "      <td>0.890255</td>\n",
 292 |        "      <td>-0.734043</td>\n",
 293 |        "      <td>-0.021266</td>\n",
 294 |        "    </tr>\n",
 295 |        "    <tr>\n",
 296 |        "      <th>Cold_Drink</th>\n",
 297 |        "      <td>0.378274</td>\n",
 298 |        "      <td>0.890255</td>\n",
 299 |        "      <td>1.000000</td>\n",
 300 |        "      <td>-0.756659</td>\n",
 301 |        "      <td>0.042191</td>\n",
 302 |        "    </tr>\n",
 303 |        "    <tr>\n",
 304 |        "      <th>Hot_Drink</th>\n",
 305 |        "      <td>-0.468887</td>\n",
 306 |        "      <td>-0.734043</td>\n",
 307 |        "      <td>-0.756659</td>\n",
 308 |        "      <td>1.000000</td>\n",
 309 |        "      <td>-0.100323</td>\n",
 310 |        "    </tr>\n",
 311 |        "    <tr>\n",
 312 |        "      <th>Snacks</th>\n",
 313 |        "      <td>0.045251</td>\n",
 314 |        "      <td>-0.021266</td>\n",
 315 |        "      <td>0.042191</td>\n",
 316 |        "      <td>-0.100323</td>\n",
 317 |        "      <td>1.000000</td>\n",
 318 |        "    </tr>\n",
 319 |        "  </tbody>\n",
 320 |        "</table>\n",
 321 |        "</div>"
 322 |       ],
 323 |       "text/plain": [
 324 |        "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
 325 |        "Day_No      1.000000  0.269834    0.378274  -0.468887  0.045251\n",
 326 |        "Temp        0.269834  1.000000    0.890255  -0.734043 -0.021266\n",
 327 |        "Cold_Drink  0.378274  0.890255    1.000000  -0.756659  0.042191\n",
 328 |        "Hot_Drink  -0.468887 -0.734043   -0.756659   1.000000 -0.100323\n",
 329 |        "Snacks      0.045251 -0.021266    0.042191  -0.100323  1.000000"
 330 |       ]
 331 |      },
 332 |      "execution_count": 4,
 333 |      "metadata": {},
 334 |      "output_type": "execute_result"
 335 |     }
 336 |    ],
 337 |    "source": [
 338 |     "data.corr(method='pearson', min_periods=1)"
 339 |    ]
 340 |   },
 341 |   {
 342 |    "cell_type": "code",
 343 |    "execution_count": 5,
 344 |    "metadata": {},
 345 |    "outputs": [
 346 |     {
 347 |      "data": {
 348 |       "text/html": [
 349 |        "<div>\n",
 350 |        "<style scoped>\n",
 351 |        "    .dataframe tbody tr th:only-of-type {\n",
 352 |        "        vertical-align: middle;\n",
 353 |        "    }\n",
 354 |        "\n",
 355 |        "    .dataframe tbody tr th {\n",
 356 |        "        vertical-align: top;\n",
 357 |        "    }\n",
 358 |        "\n",
 359 |        "    .dataframe thead th {\n",
 360 |        "        text-align: right;\n",
 361 |        "    }\n",
 362 |        "</style>\n",
 363 |        "<table border=\"1\" class=\"dataframe\">\n",
 364 |        "  <thead>\n",
 365 |        "    <tr style=\"text-align: right;\">\n",
 366 |        "      <th></th>\n",
 367 |        "      <th>Day_No</th>\n",
 368 |        "      <th>Temp</th>\n",
 369 |        "      <th>Cold_Drink</th>\n",
 370 |        "      <th>Hot_Drink</th>\n",
 371 |        "      <th>Snacks</th>\n",
 372 |        "    </tr>\n",
 373 |        "  </thead>\n",
 374 |        "  <tbody>\n",
 375 |        "    <tr>\n",
 376 |        "      <th>Day_No</th>\n",
 377 |        "      <td>1.000000</td>\n",
 378 |        "      <td>0.189172</td>\n",
 379 |        "      <td>0.275782</td>\n",
 380 |        "      <td>-0.341609</td>\n",
 381 |        "      <td>0.009087</td>\n",
 382 |        "    </tr>\n",
 383 |        "    <tr>\n",
 384 |        "      <th>Temp</th>\n",
 385 |        "      <td>0.189172</td>\n",
 386 |        "      <td>1.000000</td>\n",
 387 |        "      <td>0.736854</td>\n",
 388 |        "      <td>-0.551354</td>\n",
 389 |        "      <td>-0.033551</td>\n",
 390 |        "    </tr>\n",
 391 |        "    <tr>\n",
 392 |        "      <th>Cold_Drink</th>\n",
 393 |        "      <td>0.275782</td>\n",
 394 |        "      <td>0.736854</td>\n",
 395 |        "      <td>1.000000</td>\n",
 396 |        "      <td>-0.511530</td>\n",
 397 |        "      <td>0.025593</td>\n",
 398 |        "    </tr>\n",
 399 |        "    <tr>\n",
 400 |        "      <th>Hot_Drink</th>\n",
 401 |        "      <td>-0.341609</td>\n",
 402 |        "      <td>-0.551354</td>\n",
 403 |        "      <td>-0.511530</td>\n",
 404 |        "      <td>1.000000</td>\n",
 405 |        "      <td>-0.036256</td>\n",
 406 |        "    </tr>\n",
 407 |        "    <tr>\n",
 408 |        "      <th>Snacks</th>\n",
 409 |        "      <td>0.009087</td>\n",
 410 |        "      <td>-0.033551</td>\n",
 411 |        "      <td>0.025593</td>\n",
 412 |        "      <td>-0.036256</td>\n",
 413 |        "      <td>1.000000</td>\n",
 414 |        "    </tr>\n",
 415 |        "  </tbody>\n",
 416 |        "</table>\n",
 417 |        "</div>"
 418 |       ],
 419 |       "text/plain": [
 420 |        "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
 421 |        "Day_No      1.000000  0.189172    0.275782  -0.341609  0.009087\n",
 422 |        "Temp        0.189172  1.000000    0.736854  -0.551354 -0.033551\n",
 423 |        "Cold_Drink  0.275782  0.736854    1.000000  -0.511530  0.025593\n",
 424 |        "Hot_Drink  -0.341609 -0.551354   -0.511530   1.000000 -0.036256\n",
 425 |        "Snacks      0.009087 -0.033551    0.025593  -0.036256  1.000000"
 426 |       ]
 427 |      },
 428 |      "execution_count": 5,
 429 |      "metadata": {},
 430 |      "output_type": "execute_result"
 431 |     }
 432 |    ],
 433 |    "source": [
 434 |     "data.corr(method='kendall', min_periods=1)"
 435 |    ]
 436 |   },
 437 |   {
 438 |    "cell_type": "code",
 439 |    "execution_count": 6,
 440 |    "metadata": {},
 441 |    "outputs": [
 442 |     {
 443 |      "data": {
 444 |       "text/html": [
 445 |        "<div>\n",
 446 |        "<style scoped>\n",
 447 |        "    .dataframe tbody tr th:only-of-type {\n",
 448 |        "        vertical-align: middle;\n",
 449 |        "    }\n",
 450 |        "\n",
 451 |        "    .dataframe tbody tr th {\n",
 452 |        "        vertical-align: top;\n",
 453 |        "    }\n",
 454 |        "\n",
 455 |        "    .dataframe thead th {\n",
 456 |        "        text-align: right;\n",
 457 |        "    }\n",
 458 |        "</style>\n",
 459 |        "<table border=\"1\" class=\"dataframe\">\n",
 460 |        "  <thead>\n",
 461 |        "    <tr style=\"text-align: right;\">\n",
 462 |        "      <th></th>\n",
 463 |        "      <th>Day_No</th>\n",
 464 |        "      <th>Temp</th>\n",
 465 |        "      <th>Cold_Drink</th>\n",
 466 |        "      <th>Hot_Drink</th>\n",
 467 |        "      <th>Snacks</th>\n",
 468 |        "    </tr>\n",
 469 |        "  </thead>\n",
 470 |        "  <tbody>\n",
 471 |        "    <tr>\n",
 472 |        "      <th>Day_No</th>\n",
 473 |        "      <td>1.000000</td>\n",
 474 |        "      <td>0.264297</td>\n",
 475 |        "      <td>0.380530</td>\n",
 476 |        "      <td>-0.457385</td>\n",
 477 |        "      <td>0.009190</td>\n",
 478 |        "    </tr>\n",
 479 |        "    <tr>\n",
 480 |        "      <th>Temp</th>\n",
 481 |        "      <td>0.264297</td>\n",
 482 |        "      <td>1.000000</td>\n",
 483 |        "      <td>0.870657</td>\n",
 484 |        "      <td>-0.690304</td>\n",
 485 |        "      <td>-0.043479</td>\n",
 486 |        "    </tr>\n",
 487 |        "    <tr>\n",
 488 |        "      <th>Cold_Drink</th>\n",
 489 |        "      <td>0.380530</td>\n",
 490 |        "      <td>0.870657</td>\n",
 491 |        "      <td>1.000000</td>\n",
 492 |        "      <td>-0.686986</td>\n",
 493 |        "      <td>0.029932</td>\n",
 494 |        "    </tr>\n",
 495 |        "    <tr>\n",
 496 |        "      <th>Hot_Drink</th>\n",
 497 |        "      <td>-0.457385</td>\n",
 498 |        "      <td>-0.690304</td>\n",
 499 |        "      <td>-0.686986</td>\n",
 500 |        "      <td>1.000000</td>\n",
 501 |        "      <td>-0.045372</td>\n",
 502 |        "    </tr>\n",
 503 |        "    <tr>\n",
 504 |        "      <th>Snacks</th>\n",
 505 |        "      <td>0.009190</td>\n",
 506 |        "      <td>-0.043479</td>\n",
 507 |        "      <td>0.029932</td>\n",
 508 |        "      <td>-0.045372</td>\n",
 509 |        "      <td>1.000000</td>\n",
 510 |        "    </tr>\n",
 511 |        "  </tbody>\n",
 512 |        "</table>\n",
 513 |        "</div>"
 514 |       ],
 515 |       "text/plain": [
 516 |        "              Day_No      Temp  Cold_Drink  Hot_Drink    Snacks\n",
 517 |        "Day_No      1.000000  0.264297    0.380530  -0.457385  0.009190\n",
 518 |        "Temp        0.264297  1.000000    0.870657  -0.690304 -0.043479\n",
 519 |        "Cold_Drink  0.380530  0.870657    1.000000  -0.686986  0.029932\n",
 520 |        "Hot_Drink  -0.457385 -0.690304   -0.686986   1.000000 -0.045372\n",
 521 |        "Snacks      0.009190 -0.043479    0.029932  -0.045372  1.000000"
 522 |       ]
 523 |      },
 524 |      "execution_count": 6,
 525 |      "metadata": {},
 526 |      "output_type": "execute_result"
 527 |     }
 528 |    ],
 529 |    "source": [
 530 |     "data.corr(method='spearman', min_periods=1)"
 531 |    ]
 532 |   },
 533 |   {
 534 |    "cell_type": "markdown",
 535 |    "metadata": {},
 536 |    "source": [
 537 |     "<b>Note: We can find correlation between two variables only as shown below.</b>"
 538 |    ]
 539 |   },
 540 |   {
 541 |    "cell_type": "code",
 542 |    "execution_count": 7,
 543 |    "metadata": {},
 544 |    "outputs": [
 545 |     {
 546 |      "data": {
 547 |       "text/html": [
 548 |        "<div>\n",
 549 |        "<style scoped>\n",
 550 |        "    .dataframe tbody tr th:only-of-type {\n",
 551 |        "        vertical-align: middle;\n",
 552 |        "    }\n",
 553 |        "\n",
 554 |        "    .dataframe tbody tr th {\n",
 555 |        "        vertical-align: top;\n",
 556 |        "    }\n",
 557 |        "\n",
 558 |        "    .dataframe thead th {\n",
 559 |        "        text-align: right;\n",
 560 |        "    }\n",
 561 |        "</style>\n",
 562 |        "<table border=\"1\" class=\"dataframe\">\n",
 563 |        "  <thead>\n",
 564 |        "    <tr style=\"text-align: right;\">\n",
 565 |        "      <th></th>\n",
 566 |        "      <th>Temp</th>\n",
 567 |        "      <th>Cold_Drink</th>\n",
 568 |        "    </tr>\n",
 569 |        "  </thead>\n",
 570 |        "  <tbody>\n",
 571 |        "    <tr>\n",
 572 |        "      <th>Temp</th>\n",
 573 |        "      <td>1.000000</td>\n",
 574 |        "      <td>0.890255</td>\n",
 575 |        "    </tr>\n",
 576 |        "    <tr>\n",
 577 |        "      <th>Cold_Drink</th>\n",
 578 |        "      <td>0.890255</td>\n",
 579 |        "      <td>1.000000</td>\n",
 580 |        "    </tr>\n",
 581 |        "  </tbody>\n",
 582 |        "</table>\n",
 583 |        "</div>"
 584 |       ],
 585 |       "text/plain": [
 586 |        "                Temp  Cold_Drink\n",
 587 |        "Temp        1.000000    0.890255\n",
 588 |        "Cold_Drink  0.890255    1.000000"
 589 |       ]
 590 |      },
 591 |      "execution_count": 7,
 592 |      "metadata": {},
 593 |      "output_type": "execute_result"
 594 |     }
 595 |    ],
 596 |    "source": [
 597 |     "data[['Temp', 'Cold_Drink']].corr()"
 598 |    ]
 599 |   },
 600 |   {
 601 |    "cell_type": "markdown",
 602 |    "metadata": {},
 603 |    "source": [
 604 |     "Since the correlation value between Temperature and Cold_Drink gave us positive and somewhere close to one, we can conclude that these two variables has positive correlation."
 605 |    ]
 606 |   },
 607 |   {
 608 |    "cell_type": "code",
 609 |    "execution_count": 8,
 610 |    "metadata": {},
 611 |    "outputs": [
 612 |     {
 613 |      "data": {
 614 |       "text/html": [
 615 |        "<div>\n",
 616 |        "<style scoped>\n",
 617 |        "    .dataframe tbody tr th:only-of-type {\n",
 618 |        "        vertical-align: middle;\n",
 619 |        "    }\n",
 620 |        "\n",
 621 |        "    .dataframe tbody tr th {\n",
 622 |        "        vertical-align: top;\n",
 623 |        "    }\n",
 624 |        "\n",
 625 |        "    .dataframe thead th {\n",
 626 |        "        text-align: right;\n",
 627 |        "    }\n",
 628 |        "</style>\n",
 629 |        "<table border=\"1\" class=\"dataframe\">\n",
 630 |        "  <thead>\n",
 631 |        "    <tr style=\"text-align: right;\">\n",
 632 |        "      <th></th>\n",
 633 |        "      <th>Temp</th>\n",
 634 |        "      <th>Hot_Drink</th>\n",
 635 |        "    </tr>\n",
 636 |        "  </thead>\n",
 637 |        "  <tbody>\n",
 638 |        "    <tr>\n",
 639 |        "      <th>Temp</th>\n",
 640 |        "      <td>1.000000</td>\n",
 641 |        "      <td>-0.734043</td>\n",
 642 |        "    </tr>\n",
 643 |        "    <tr>\n",
 644 |        "      <th>Hot_Drink</th>\n",
 645 |        "      <td>-0.734043</td>\n",
 646 |        "      <td>1.000000</td>\n",
 647 |        "    </tr>\n",
 648 |        "  </tbody>\n",
 649 |        "</table>\n",
 650 |        "</div>"
 651 |       ],
 652 |       "text/plain": [
 653 |        "               Temp  Hot_Drink\n",
 654 |        "Temp       1.000000  -0.734043\n",
 655 |        "Hot_Drink -0.734043   1.000000"
 656 |       ]
 657 |      },
 658 |      "execution_count": 8,
 659 |      "metadata": {},
 660 |      "output_type": "execute_result"
 661 |     }
 662 |    ],
 663 |    "source": [
 664 |     "data[['Temp', 'Hot_Drink']].corr()"
 665 |    ]
 666 |   },
 667 |   {
 668 |    "cell_type": "markdown",
 669 |    "metadata": {},
 670 |    "source": [
 671 |     "Since the correlation value between Temperature and Hot_Drink gave us negative and somewhere close to minus one, we can conclude that these two variables has negative correlation."
 672 |    ]
 673 |   },
 674 |   {
 675 |    "cell_type": "code",
 676 |    "execution_count": 9,
 677 |    "metadata": {},
 678 |    "outputs": [
 679 |     {
 680 |      "data": {
 681 |       "text/html": [
 682 |        "<div>\n",
 683 |        "<style scoped>\n",
 684 |        "    .dataframe tbody tr th:only-of-type {\n",
 685 |        "        vertical-align: middle;\n",
 686 |        "    }\n",
 687 |        "\n",
 688 |        "    .dataframe tbody tr th {\n",
 689 |        "        vertical-align: top;\n",
 690 |        "    }\n",
 691 |        "\n",
 692 |        "    .dataframe thead th {\n",
 693 |        "        text-align: right;\n",
 694 |        "    }\n",
 695 |        "</style>\n",
 696 |        "<table border=\"1\" class=\"dataframe\">\n",
 697 |        "  <thead>\n",
 698 |        "    <tr style=\"text-align: right;\">\n",
 699 |        "      <th></th>\n",
 700 |        "      <th>Temp</th>\n",
 701 |        "      <th>Snacks</th>\n",
 702 |        "    </tr>\n",
 703 |        "  </thead>\n",
 704 |        "  <tbody>\n",
 705 |        "    <tr>\n",
 706 |        "      <th>Temp</th>\n",
 707 |        "      <td>1.000000</td>\n",
 708 |        "      <td>-0.021266</td>\n",
 709 |        "    </tr>\n",
 710 |        "    <tr>\n",
 711 |        "      <th>Snacks</th>\n",
 712 |        "      <td>-0.021266</td>\n",
 713 |        "      <td>1.000000</td>\n",
 714 |        "    </tr>\n",
 715 |        "  </tbody>\n",
 716 |        "</table>\n",
 717 |        "</div>"
 718 |       ],
 719 |       "text/plain": [
 720 |        "            Temp    Snacks\n",
 721 |        "Temp    1.000000 -0.021266\n",
 722 |        "Snacks -0.021266  1.000000"
 723 |       ]
 724 |      },
 725 |      "execution_count": 9,
 726 |      "metadata": {},
 727 |      "output_type": "execute_result"
 728 |     }
 729 |    ],
 730 |    "source": [
 731 |     "data[['Temp', 'Snacks']].corr()"
 732 |    ]
 733 |   },
 734 |   {
 735 |    "cell_type": "markdown",
 736 |    "metadata": {},
 737 |    "source": [
 738 |     "Since the correlation value between Temperature and Snacks is far from one and minus one, we can conclude that these two variables has no correlation."
 739 |    ]
 740 |   },
 741 |   {
 742 |    "cell_type": "code",
 743 |    "execution_count": 10,
 744 |    "metadata": {},
 745 |    "outputs": [
 746 |     {
 747 |      "data": {
 748 |       "text/html": [
 749 |        "<style  type=\"text/css\" >\n",
 750 |        "    #T_2e494026_0479_11eb_9341_689423f042d4row0_col0 {\n",
 751 |        "            background-color:  #b40426;\n",
 752 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row0_col1 {\n",
 753 |        "            background-color:  #efcfbf;\n",
 754 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row0_col2 {\n",
 755 |        "            background-color:  #f6bea4;\n",
 756 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row0_col3 {\n",
 757 |        "            background-color:  #6e90f2;\n",
 758 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row0_col4 {\n",
 759 |        "            background-color:  #6384eb;\n",
 760 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row1_col0 {\n",
 761 |        "            background-color:  #dddcdc;\n",
 762 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row1_col1 {\n",
 763 |        "            background-color:  #b40426;\n",
 764 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row1_col2 {\n",
 765 |        "            background-color:  #ca3b37;\n",
 766 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row1_col3 {\n",
 767 |        "            background-color:  #3e51c5;\n",
 768 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row1_col4 {\n",
 769 |        "            background-color:  #506bda;\n",
 770 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row2_col0 {\n",
 771 |        "            background-color:  #eed0c0;\n",
 772 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row2_col1 {\n",
 773 |        "            background-color:  #cb3e38;\n",
 774 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row2_col2 {\n",
 775 |        "            background-color:  #b40426;\n",
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 777 |        "            background-color:  #3b4cc0;\n",
 778 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row2_col4 {\n",
 779 |        "            background-color:  #6384eb;\n",
 780 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row3_col0 {\n",
 781 |        "            background-color:  #3b4cc0;\n",
 782 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row3_col1 {\n",
 783 |        "            background-color:  #3b4cc0;\n",
 784 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row3_col2 {\n",
 785 |        "            background-color:  #3b4cc0;\n",
 786 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row3_col3 {\n",
 787 |        "            background-color:  #b40426;\n",
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 790 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row4_col0 {\n",
 791 |        "            background-color:  #afcafc;\n",
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 793 |        "            background-color:  #c4d5f3;\n",
 794 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row4_col2 {\n",
 795 |        "            background-color:  #d1dae9;\n",
 796 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row4_col3 {\n",
 797 |        "            background-color:  #b7cff9;\n",
 798 |        "        }    #T_2e494026_0479_11eb_9341_689423f042d4row4_col4 {\n",
 799 |        "            background-color:  #b40426;\n",
 800 |        "        }</style>  \n",
 801 |        "<table id=\"T_2e494026_0479_11eb_9341_689423f042d4\" > \n",
 802 |        "<thead>    <tr> \n",
 803 |        "        <th class=\"blank level0\" ></th> \n",
 804 |        "        <th class=\"col_heading level0 col0\" >Day_No</th> \n",
 805 |        "        <th class=\"col_heading level0 col1\" >Temp</th> \n",
 806 |        "        <th class=\"col_heading level0 col2\" >Cold_Drink</th> \n",
 807 |        "        <th class=\"col_heading level0 col3\" >Hot_Drink</th> \n",
 808 |        "        <th class=\"col_heading level0 col4\" >Snacks</th> \n",
 809 |        "    </tr></thead> \n",
 810 |        "<tbody>    <tr> \n",
 811 |        "        <th id=\"T_2e494026_0479_11eb_9341_689423f042d4level0_row0\" class=\"row_heading level0 row0\" >Day_No</th> \n",
 812 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row0_col0\" class=\"data row0 col0\" >1</td> \n",
 813 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row0_col1\" class=\"data row0 col1\" >0.269834</td> \n",
 814 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row0_col2\" class=\"data row0 col2\" >0.378274</td> \n",
 815 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row0_col3\" class=\"data row0 col3\" >-0.468887</td> \n",
 816 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row0_col4\" class=\"data row0 col4\" >0.0452506</td> \n",
 817 |        "    </tr>    <tr> \n",
 818 |        "        <th id=\"T_2e494026_0479_11eb_9341_689423f042d4level0_row1\" class=\"row_heading level0 row1\" >Temp</th> \n",
 819 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row1_col0\" class=\"data row1 col0\" >0.269834</td> \n",
 820 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row1_col1\" class=\"data row1 col1\" >1</td> \n",
 821 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row1_col2\" class=\"data row1 col2\" >0.890255</td> \n",
 822 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row1_col3\" class=\"data row1 col3\" >-0.734043</td> \n",
 823 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row1_col4\" class=\"data row1 col4\" >-0.0212665</td> \n",
 824 |        "    </tr>    <tr> \n",
 825 |        "        <th id=\"T_2e494026_0479_11eb_9341_689423f042d4level0_row2\" class=\"row_heading level0 row2\" >Cold_Drink</th> \n",
 826 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row2_col0\" class=\"data row2 col0\" >0.378274</td> \n",
 827 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row2_col1\" class=\"data row2 col1\" >0.890255</td> \n",
 828 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row2_col2\" class=\"data row2 col2\" >1</td> \n",
 829 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row2_col3\" class=\"data row2 col3\" >-0.756659</td> \n",
 830 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row2_col4\" class=\"data row2 col4\" >0.0421907</td> \n",
 831 |        "    </tr>    <tr> \n",
 832 |        "        <th id=\"T_2e494026_0479_11eb_9341_689423f042d4level0_row3\" class=\"row_heading level0 row3\" >Hot_Drink</th> \n",
 833 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row3_col0\" class=\"data row3 col0\" >-0.468887</td> \n",
 834 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row3_col1\" class=\"data row3 col1\" >-0.734043</td> \n",
 835 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row3_col2\" class=\"data row3 col2\" >-0.756659</td> \n",
 836 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row3_col3\" class=\"data row3 col3\" >1</td> \n",
 837 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row3_col4\" class=\"data row3 col4\" >-0.100323</td> \n",
 838 |        "    </tr>    <tr> \n",
 839 |        "        <th id=\"T_2e494026_0479_11eb_9341_689423f042d4level0_row4\" class=\"row_heading level0 row4\" >Snacks</th> \n",
 840 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row4_col0\" class=\"data row4 col0\" >0.0452506</td> \n",
 841 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row4_col1\" class=\"data row4 col1\" >-0.0212665</td> \n",
 842 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row4_col2\" class=\"data row4 col2\" >0.0421907</td> \n",
 843 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row4_col3\" class=\"data row4 col3\" >-0.100323</td> \n",
 844 |        "        <td id=\"T_2e494026_0479_11eb_9341_689423f042d4row4_col4\" class=\"data row4 col4\" >1</td> \n",
 845 |        "    </tr></tbody> \n",
 846 |        "</table> "
 847 |       ],
 848 |       "text/plain": [
 849 |        "<pandas.io.formats.style.Styler at 0x9bc47b8>"
 850 |       ]
 851 |      },
 852 |      "execution_count": 10,
 853 |      "metadata": {},
 854 |      "output_type": "execute_result"
 855 |     }
 856 |    ],
 857 |    "source": [
 858 |     "import matplotlib.pyplot as plt\n",
 859 |     "%matplotlib inline\n",
 860 |     "\n",
 861 |     "data.corr().style.background_gradient(cmap='coolwarm')"
 862 |    ]
 863 |   },
 864 |   {
 865 |    "cell_type": "code",
 866 |    "execution_count": 11,
 867 |    "metadata": {},
 868 |    "outputs": [
 869 |     {
 870 |      "data": {
 871 |       "text/html": [
 872 |        "<style  type=\"text/css\" >\n",
 873 |        "    #T_596530d0_0479_11eb_90c0_689423f042d4row0_col0 {\n",
 874 |        "            background-color:  #b40426;\n",
 875 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row0_col1 {\n",
 876 |        "            background-color:  #efcfbf;\n",
 877 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row0_col2 {\n",
 878 |        "            background-color:  #f6bea4;\n",
 879 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row0_col3 {\n",
 880 |        "            background-color:  #6e90f2;\n",
 881 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row0_col4 {\n",
 882 |        "            background-color:  #6384eb;\n",
 883 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row1_col0 {\n",
 884 |        "            background-color:  #dddcdc;\n",
 885 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row1_col1 {\n",
 886 |        "            background-color:  #b40426;\n",
 887 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row1_col2 {\n",
 888 |        "            background-color:  #ca3b37;\n",
 889 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row1_col3 {\n",
 890 |        "            background-color:  #3e51c5;\n",
 891 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row1_col4 {\n",
 892 |        "            background-color:  #506bda;\n",
 893 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row2_col0 {\n",
 894 |        "            background-color:  #eed0c0;\n",
 895 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row2_col1 {\n",
 896 |        "            background-color:  #cb3e38;\n",
 897 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row2_col2 {\n",
 898 |        "            background-color:  #b40426;\n",
 899 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row2_col3 {\n",
 900 |        "            background-color:  #3b4cc0;\n",
 901 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row2_col4 {\n",
 902 |        "            background-color:  #6384eb;\n",
 903 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row3_col0 {\n",
 904 |        "            background-color:  #3b4cc0;\n",
 905 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row3_col1 {\n",
 906 |        "            background-color:  #3b4cc0;\n",
 907 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row3_col2 {\n",
 908 |        "            background-color:  #3b4cc0;\n",
 909 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row3_col3 {\n",
 910 |        "            background-color:  #b40426;\n",
 911 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row3_col4 {\n",
 912 |        "            background-color:  #3b4cc0;\n",
 913 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row4_col0 {\n",
 914 |        "            background-color:  #afcafc;\n",
 915 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row4_col1 {\n",
 916 |        "            background-color:  #c4d5f3;\n",
 917 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row4_col2 {\n",
 918 |        "            background-color:  #d1dae9;\n",
 919 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row4_col3 {\n",
 920 |        "            background-color:  #b7cff9;\n",
 921 |        "        }    #T_596530d0_0479_11eb_90c0_689423f042d4row4_col4 {\n",
 922 |        "            background-color:  #b40426;\n",
 923 |        "        }</style>  \n",
 924 |        "<table id=\"T_596530d0_0479_11eb_90c0_689423f042d4\" > \n",
 925 |        "<thead>    <tr> \n",
 926 |        "        <th class=\"blank level0\" ></th> \n",
 927 |        "        <th class=\"col_heading level0 col0\" >Day_No</th> \n",
 928 |        "        <th class=\"col_heading level0 col1\" >Temp</th> \n",
 929 |        "        <th class=\"col_heading level0 col2\" >Cold_Drink</th> \n",
 930 |        "        <th class=\"col_heading level0 col3\" >Hot_Drink</th> \n",
 931 |        "        <th class=\"col_heading level0 col4\" >Snacks</th> \n",
 932 |        "    </tr></thead> \n",
 933 |        "<tbody>    <tr> \n",
 934 |        "        <th id=\"T_596530d0_0479_11eb_90c0_689423f042d4level0_row0\" class=\"row_heading level0 row0\" >Day_No</th> \n",
 935 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row0_col0\" class=\"data row0 col0\" >1</td> \n",
 936 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row0_col1\" class=\"data row0 col1\" >0.27</td> \n",
 937 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row0_col2\" class=\"data row0 col2\" >0.38</td> \n",
 938 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row0_col3\" class=\"data row0 col3\" >-0.47</td> \n",
 939 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row0_col4\" class=\"data row0 col4\" >0.045</td> \n",
 940 |        "    </tr>    <tr> \n",
 941 |        "        <th id=\"T_596530d0_0479_11eb_90c0_689423f042d4level0_row1\" class=\"row_heading level0 row1\" >Temp</th> \n",
 942 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row1_col0\" class=\"data row1 col0\" >0.27</td> \n",
 943 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row1_col1\" class=\"data row1 col1\" >1</td> \n",
 944 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row1_col2\" class=\"data row1 col2\" >0.89</td> \n",
 945 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row1_col3\" class=\"data row1 col3\" >-0.73</td> \n",
 946 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row1_col4\" class=\"data row1 col4\" >-0.021</td> \n",
 947 |        "    </tr>    <tr> \n",
 948 |        "        <th id=\"T_596530d0_0479_11eb_90c0_689423f042d4level0_row2\" class=\"row_heading level0 row2\" >Cold_Drink</th> \n",
 949 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row2_col0\" class=\"data row2 col0\" >0.38</td> \n",
 950 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row2_col1\" class=\"data row2 col1\" >0.89</td> \n",
 951 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row2_col2\" class=\"data row2 col2\" >1</td> \n",
 952 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row2_col3\" class=\"data row2 col3\" >-0.76</td> \n",
 953 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row2_col4\" class=\"data row2 col4\" >0.042</td> \n",
 954 |        "    </tr>    <tr> \n",
 955 |        "        <th id=\"T_596530d0_0479_11eb_90c0_689423f042d4level0_row3\" class=\"row_heading level0 row3\" >Hot_Drink</th> \n",
 956 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row3_col0\" class=\"data row3 col0\" >-0.47</td> \n",
 957 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row3_col1\" class=\"data row3 col1\" >-0.73</td> \n",
 958 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row3_col2\" class=\"data row3 col2\" >-0.76</td> \n",
 959 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row3_col3\" class=\"data row3 col3\" >1</td> \n",
 960 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row3_col4\" class=\"data row3 col4\" >-0.1</td> \n",
 961 |        "    </tr>    <tr> \n",
 962 |        "        <th id=\"T_596530d0_0479_11eb_90c0_689423f042d4level0_row4\" class=\"row_heading level0 row4\" >Snacks</th> \n",
 963 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row4_col0\" class=\"data row4 col0\" >0.045</td> \n",
 964 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row4_col1\" class=\"data row4 col1\" >-0.021</td> \n",
 965 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row4_col2\" class=\"data row4 col2\" >0.042</td> \n",
 966 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row4_col3\" class=\"data row4 col3\" >-0.1</td> \n",
 967 |        "        <td id=\"T_596530d0_0479_11eb_90c0_689423f042d4row4_col4\" class=\"data row4 col4\" >1</td> \n",
 968 |        "    </tr></tbody> \n",
 969 |        "</table> "
 970 |       ],
 971 |       "text/plain": [
 972 |        "<pandas.io.formats.style.Styler at 0x9bc4390>"
 973 |       ]
 974 |      },
 975 |      "execution_count": 11,
 976 |      "metadata": {},
 977 |      "output_type": "execute_result"
 978 |     }
 979 |    ],
 980 |    "source": [
 981 |     "data.corr().style.background_gradient(cmap='coolwarm').set_precision(2)"
 982 |    ]
 983 |   },
 984 |   {
 985 |    "cell_type": "markdown",
 986 |    "metadata": {},
 987 |    "source": [
 988 |     "<b>Note: Scatter plots are very common visualization technique to see correlation between two variables.</b>"
 989 |    ]
 990 |   },
 991 |   {
 992 |    "cell_type": "code",
 993 |    "execution_count": 12,
 994 |    "metadata": {},
 995 |    "outputs": [
 996 |     {
 997 |      "data": {
 998 |       "image/png": "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\n",
 999 |       "text/plain": [
1000 |        "<matplotlib.figure.Figure at 0xb4279e8>"
1001 |       ]
1002 |      },
1003 |      "metadata": {},
1004 |      "output_type": "display_data"
1005 |     }
1006 |    ],
1007 |    "source": [
1008 |     "plt.scatter(data['Temp'], data['Cold_Drink'])\n",
1009 |     "plt.show()"
1010 |    ]
1011 |   },
1012 |   {
1013 |    "cell_type": "code",
1014 |    "execution_count": 13,
1015 |    "metadata": {},
1016 |    "outputs": [
1017 |     {
1018 |      "data": {
1019 |       "image/png": "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\n",
1020 |       "text/plain": [
1021 |        "<matplotlib.figure.Figure at 0xb4f29e8>"
1022 |       ]
1023 |      },
1024 |      "metadata": {},
1025 |      "output_type": "display_data"
1026 |     }
1027 |    ],
1028 |    "source": [
1029 |     "plt.scatter(data['Temp'], data['Hot_Drink'])\n",
1030 |     "plt.show()"
1031 |    ]
1032 |   },
1033 |   {
1034 |    "cell_type": "code",
1035 |    "execution_count": 14,
1036 |    "metadata": {},
1037 |    "outputs": [
1038 |     {
1039 |      "data": {
1040 |       "image/png": 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\n",
1041 |       "text/plain": [
1042 |        "<matplotlib.figure.Figure at 0xb69c898>"
1043 |       ]
1044 |      },
1045 |      "metadata": {},
1046 |      "output_type": "display_data"
1047 |     }
1048 |    ],
1049 |    "source": [
1050 |     "plt.scatter(data['Temp'], data['Snacks'])\n",
1051 |     "plt.show()"
1052 |    ]
1053 |   },
1054 |   {
1055 |    "cell_type": "code",
1056 |    "execution_count": null,
1057 |    "metadata": {},
1058 |    "outputs": [],
1059 |    "source": []
1060 |   }
1061 |  ],
1062 |  "metadata": {
1063 |   "kernelspec": {
1064 |    "display_name": "Python 3",
1065 |    "language": "python",
1066 |    "name": "python3"
1067 |   },
1068 |   "language_info": {
1069 |    "codemirror_mode": {
1070 |     "name": "ipython",
1071 |     "version": 3
1072 |    },
1073 |    "file_extension": ".py",
1074 |    "mimetype": "text/x-python",
1075 |    "name": "python",
1076 |    "nbconvert_exporter": "python",
1077 |    "pygments_lexer": "ipython3",
1078 |    "version": "3.6.4"
1079 |   }
1080 |  },
1081 |  "nbformat": 4,
1082 |  "nbformat_minor": 2
1083 | }
1084 | 


--------------------------------------------------------------------------------
/P04-ANOVA.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Practical - ANOVA\n",
  8 |     "This practical session will demonstrate how to handle missing data. We assume everyone to have adequate understanding of Python programming language. For those who would like to refresh Python skill, we would like to recommend our <b>\"Programming for Data Science Series\"</b> where we covered almost all aspects of Python programming in data science domain.\n",
  9 |     "Refer below URL for full playlist of almost 10 hours video lesson in Burmese Language.\n",
 10 |     "URL : https://www.youtube.com/watch?v=jOZNjVVZIVs&list=PLD_eiqVVLZDi9GZZJDC8Zx4-3Np8LHs52"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "metadata": {},
 17 |    "outputs": [
 18 |     {
 19 |      "data": {
 20 |       "text/html": [
 21 |        "<div>\n",
 22 |        "<style scoped>\n",
 23 |        "    .dataframe tbody tr th:only-of-type {\n",
 24 |        "        vertical-align: middle;\n",
 25 |        "    }\n",
 26 |        "\n",
 27 |        "    .dataframe tbody tr th {\n",
 28 |        "        vertical-align: top;\n",
 29 |        "    }\n",
 30 |        "\n",
 31 |        "    .dataframe thead th {\n",
 32 |        "        text-align: right;\n",
 33 |        "    }\n",
 34 |        "</style>\n",
 35 |        "<table border=\"1\" class=\"dataframe\">\n",
 36 |        "  <thead>\n",
 37 |        "    <tr style=\"text-align: right;\">\n",
 38 |        "      <th></th>\n",
 39 |        "      <th>emp_id</th>\n",
 40 |        "      <th>first_name</th>\n",
 41 |        "      <th>last_name</th>\n",
 42 |        "      <th>gender</th>\n",
 43 |        "      <th>salary</th>\n",
 44 |        "      <th>work_exp</th>\n",
 45 |        "    </tr>\n",
 46 |        "  </thead>\n",
 47 |        "  <tbody>\n",
 48 |        "    <tr>\n",
 49 |        "      <th>0</th>\n",
 50 |        "      <td>1</td>\n",
 51 |        "      <td>Georgi</td>\n",
 52 |        "      <td>Facello</td>\n",
 53 |        "      <td>M</td>\n",
 54 |        "      <td>500000</td>\n",
 55 |        "      <td>Mid</td>\n",
 56 |        "    </tr>\n",
 57 |        "    <tr>\n",
 58 |        "      <th>1</th>\n",
 59 |        "      <td>2</td>\n",
 60 |        "      <td>Bezalel</td>\n",
 61 |        "      <td>Simmel</td>\n",
 62 |        "      <td>F</td>\n",
 63 |        "      <td>120000</td>\n",
 64 |        "      <td>Junior</td>\n",
 65 |        "    </tr>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>2</th>\n",
 68 |        "      <td>3</td>\n",
 69 |        "      <td>Parto</td>\n",
 70 |        "      <td>Bamford</td>\n",
 71 |        "      <td>M</td>\n",
 72 |        "      <td>350000</td>\n",
 73 |        "      <td>Junior</td>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>3</th>\n",
 77 |        "      <td>4</td>\n",
 78 |        "      <td>Chirstian</td>\n",
 79 |        "      <td>Koblick</td>\n",
 80 |        "      <td>M</td>\n",
 81 |        "      <td>400000</td>\n",
 82 |        "      <td>Mid</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>4</th>\n",
 86 |        "      <td>5</td>\n",
 87 |        "      <td>Kyoichi</td>\n",
 88 |        "      <td>Maliniak</td>\n",
 89 |        "      <td>M</td>\n",
 90 |        "      <td>200000</td>\n",
 91 |        "      <td>Junior</td>\n",
 92 |        "    </tr>\n",
 93 |        "  </tbody>\n",
 94 |        "</table>\n",
 95 |        "</div>"
 96 |       ],
 97 |       "text/plain": [
 98 |        "   emp_id first_name last_name gender  salary work_exp\n",
 99 |        "0       1     Georgi   Facello      M  500000      Mid\n",
100 |        "1       2    Bezalel    Simmel      F  120000   Junior\n",
101 |        "2       3      Parto   Bamford      M  350000   Junior\n",
102 |        "3       4  Chirstian   Koblick      M  400000      Mid\n",
103 |        "4       5    Kyoichi  Maliniak      M  200000   Junior"
104 |       ]
105 |      },
106 |      "execution_count": 1,
107 |      "metadata": {},
108 |      "output_type": "execute_result"
109 |     }
110 |    ],
111 |    "source": [
112 |     "import pandas as pd\n",
113 |     "data = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/sample_anova.csv')\n",
114 |     "data.head()"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": 2,
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/html": [
125 |        "<div>\n",
126 |        "<style scoped>\n",
127 |        "    .dataframe tbody tr th:only-of-type {\n",
128 |        "        vertical-align: middle;\n",
129 |        "    }\n",
130 |        "\n",
131 |        "    .dataframe tbody tr th {\n",
132 |        "        vertical-align: top;\n",
133 |        "    }\n",
134 |        "\n",
135 |        "    .dataframe thead th {\n",
136 |        "        text-align: right;\n",
137 |        "    }\n",
138 |        "</style>\n",
139 |        "<table border=\"1\" class=\"dataframe\">\n",
140 |        "  <thead>\n",
141 |        "    <tr style=\"text-align: right;\">\n",
142 |        "      <th></th>\n",
143 |        "      <th>emp_id</th>\n",
144 |        "      <th>first_name</th>\n",
145 |        "      <th>last_name</th>\n",
146 |        "      <th>gender</th>\n",
147 |        "      <th>salary</th>\n",
148 |        "      <th>work_exp</th>\n",
149 |        "    </tr>\n",
150 |        "  </thead>\n",
151 |        "  <tbody>\n",
152 |        "    <tr>\n",
153 |        "      <th>0</th>\n",
154 |        "      <td>1</td>\n",
155 |        "      <td>Georgi</td>\n",
156 |        "      <td>Facello</td>\n",
157 |        "      <td>M</td>\n",
158 |        "      <td>500000</td>\n",
159 |        "      <td>Mid</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>1</th>\n",
163 |        "      <td>2</td>\n",
164 |        "      <td>Bezalel</td>\n",
165 |        "      <td>Simmel</td>\n",
166 |        "      <td>F</td>\n",
167 |        "      <td>120000</td>\n",
168 |        "      <td>Junior</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>2</th>\n",
172 |        "      <td>3</td>\n",
173 |        "      <td>Parto</td>\n",
174 |        "      <td>Bamford</td>\n",
175 |        "      <td>M</td>\n",
176 |        "      <td>350000</td>\n",
177 |        "      <td>Junior</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>3</th>\n",
181 |        "      <td>4</td>\n",
182 |        "      <td>Chirstian</td>\n",
183 |        "      <td>Koblick</td>\n",
184 |        "      <td>M</td>\n",
185 |        "      <td>400000</td>\n",
186 |        "      <td>Mid</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>4</th>\n",
190 |        "      <td>5</td>\n",
191 |        "      <td>Kyoichi</td>\n",
192 |        "      <td>Maliniak</td>\n",
193 |        "      <td>M</td>\n",
194 |        "      <td>200000</td>\n",
195 |        "      <td>Junior</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>5</th>\n",
199 |        "      <td>6</td>\n",
200 |        "      <td>Anneke</td>\n",
201 |        "      <td>Preusig</td>\n",
202 |        "      <td>F</td>\n",
203 |        "      <td>300000</td>\n",
204 |        "      <td>Junior</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>6</th>\n",
208 |        "      <td>7</td>\n",
209 |        "      <td>Tzvetan</td>\n",
210 |        "      <td>Zielinski</td>\n",
211 |        "      <td>F</td>\n",
212 |        "      <td>150000</td>\n",
213 |        "      <td>Junior</td>\n",
214 |        "    </tr>\n",
215 |        "    <tr>\n",
216 |        "      <th>7</th>\n",
217 |        "      <td>8</td>\n",
218 |        "      <td>Saniya</td>\n",
219 |        "      <td>Kalloufi</td>\n",
220 |        "      <td>M</td>\n",
221 |        "      <td>750000</td>\n",
222 |        "      <td>Mid</td>\n",
223 |        "    </tr>\n",
224 |        "    <tr>\n",
225 |        "      <th>8</th>\n",
226 |        "      <td>9</td>\n",
227 |        "      <td>Sumant</td>\n",
228 |        "      <td>Peac</td>\n",
229 |        "      <td>F</td>\n",
230 |        "      <td>750000</td>\n",
231 |        "      <td>Senior</td>\n",
232 |        "    </tr>\n",
233 |        "    <tr>\n",
234 |        "      <th>9</th>\n",
235 |        "      <td>10</td>\n",
236 |        "      <td>Duangkaew</td>\n",
237 |        "      <td>Piveteau</td>\n",
238 |        "      <td>F</td>\n",
239 |        "      <td>200000</td>\n",
240 |        "      <td>Junior</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>10</th>\n",
244 |        "      <td>11</td>\n",
245 |        "      <td>Mary</td>\n",
246 |        "      <td>Sluis</td>\n",
247 |        "      <td>F</td>\n",
248 |        "      <td>400000</td>\n",
249 |        "      <td>Mid</td>\n",
250 |        "    </tr>\n",
251 |        "    <tr>\n",
252 |        "      <th>11</th>\n",
253 |        "      <td>12</td>\n",
254 |        "      <td>Patricio</td>\n",
255 |        "      <td>Bridgland</td>\n",
256 |        "      <td>M</td>\n",
257 |        "      <td>200000</td>\n",
258 |        "      <td>Junior</td>\n",
259 |        "    </tr>\n",
260 |        "    <tr>\n",
261 |        "      <th>12</th>\n",
262 |        "      <td>13</td>\n",
263 |        "      <td>Eberhardt</td>\n",
264 |        "      <td>Terkki</td>\n",
265 |        "      <td>M</td>\n",
266 |        "      <td>300000</td>\n",
267 |        "      <td>Junior</td>\n",
268 |        "    </tr>\n",
269 |        "    <tr>\n",
270 |        "      <th>13</th>\n",
271 |        "      <td>14</td>\n",
272 |        "      <td>Berni</td>\n",
273 |        "      <td>Genin</td>\n",
274 |        "      <td>M</td>\n",
275 |        "      <td>150000</td>\n",
276 |        "      <td>Junior</td>\n",
277 |        "    </tr>\n",
278 |        "    <tr>\n",
279 |        "      <th>14</th>\n",
280 |        "      <td>15</td>\n",
281 |        "      <td>Guoxiang</td>\n",
282 |        "      <td>Nooteboom</td>\n",
283 |        "      <td>M</td>\n",
284 |        "      <td>300000</td>\n",
285 |        "      <td>Mid</td>\n",
286 |        "    </tr>\n",
287 |        "    <tr>\n",
288 |        "      <th>15</th>\n",
289 |        "      <td>16</td>\n",
290 |        "      <td>Kazuhito</td>\n",
291 |        "      <td>Cappelletti</td>\n",
292 |        "      <td>M</td>\n",
293 |        "      <td>150000</td>\n",
294 |        "      <td>Junior</td>\n",
295 |        "    </tr>\n",
296 |        "    <tr>\n",
297 |        "      <th>16</th>\n",
298 |        "      <td>17</td>\n",
299 |        "      <td>Cristinel</td>\n",
300 |        "      <td>Bouloucos</td>\n",
301 |        "      <td>F</td>\n",
302 |        "      <td>750000</td>\n",
303 |        "      <td>Senior</td>\n",
304 |        "    </tr>\n",
305 |        "    <tr>\n",
306 |        "      <th>17</th>\n",
307 |        "      <td>18</td>\n",
308 |        "      <td>Kazuhide</td>\n",
309 |        "      <td>Peha</td>\n",
310 |        "      <td>F</td>\n",
311 |        "      <td>200000</td>\n",
312 |        "      <td>Junior</td>\n",
313 |        "    </tr>\n",
314 |        "    <tr>\n",
315 |        "      <th>18</th>\n",
316 |        "      <td>19</td>\n",
317 |        "      <td>Lillian</td>\n",
318 |        "      <td>Haddadi</td>\n",
319 |        "      <td>M</td>\n",
320 |        "      <td>400000</td>\n",
321 |        "      <td>Junior</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>19</th>\n",
325 |        "      <td>20</td>\n",
326 |        "      <td>Mayuko</td>\n",
327 |        "      <td>Warwick</td>\n",
328 |        "      <td>M</td>\n",
329 |        "      <td>200000</td>\n",
330 |        "      <td>Mid</td>\n",
331 |        "    </tr>\n",
332 |        "  </tbody>\n",
333 |        "</table>\n",
334 |        "</div>"
335 |       ],
336 |       "text/plain": [
337 |        "    emp_id first_name    last_name gender  salary work_exp\n",
338 |        "0        1     Georgi      Facello      M  500000      Mid\n",
339 |        "1        2    Bezalel       Simmel      F  120000   Junior\n",
340 |        "2        3      Parto      Bamford      M  350000   Junior\n",
341 |        "3        4  Chirstian      Koblick      M  400000      Mid\n",
342 |        "4        5    Kyoichi     Maliniak      M  200000   Junior\n",
343 |        "5        6     Anneke      Preusig      F  300000   Junior\n",
344 |        "6        7    Tzvetan    Zielinski      F  150000   Junior\n",
345 |        "7        8     Saniya     Kalloufi      M  750000      Mid\n",
346 |        "8        9     Sumant         Peac      F  750000   Senior\n",
347 |        "9       10  Duangkaew     Piveteau      F  200000   Junior\n",
348 |        "10      11       Mary        Sluis      F  400000      Mid\n",
349 |        "11      12   Patricio    Bridgland      M  200000   Junior\n",
350 |        "12      13  Eberhardt       Terkki      M  300000   Junior\n",
351 |        "13      14      Berni        Genin      M  150000   Junior\n",
352 |        "14      15   Guoxiang    Nooteboom      M  300000      Mid\n",
353 |        "15      16   Kazuhito  Cappelletti      M  150000   Junior\n",
354 |        "16      17  Cristinel    Bouloucos      F  750000   Senior\n",
355 |        "17      18   Kazuhide         Peha      F  200000   Junior\n",
356 |        "18      19    Lillian      Haddadi      M  400000   Junior\n",
357 |        "19      20     Mayuko      Warwick      M  200000      Mid"
358 |       ]
359 |      },
360 |      "execution_count": 2,
361 |      "metadata": {},
362 |      "output_type": "execute_result"
363 |     }
364 |    ],
365 |    "source": [
366 |     "data.head(20)"
367 |    ]
368 |   },
369 |   {
370 |    "cell_type": "markdown",
371 |    "metadata": {},
372 |    "source": [
373 |     "<b>First, we will create a dataframe consists with features we would like to perform ANOVA, i.e. salary & work_exp in this case</b>"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": 3,
379 |    "metadata": {},
380 |    "outputs": [
381 |     {
382 |      "data": {
383 |       "text/html": [
384 |        "<div>\n",
385 |        "<style scoped>\n",
386 |        "    .dataframe tbody tr th:only-of-type {\n",
387 |        "        vertical-align: middle;\n",
388 |        "    }\n",
389 |        "\n",
390 |        "    .dataframe tbody tr th {\n",
391 |        "        vertical-align: top;\n",
392 |        "    }\n",
393 |        "\n",
394 |        "    .dataframe thead th {\n",
395 |        "        text-align: right;\n",
396 |        "    }\n",
397 |        "</style>\n",
398 |        "<table border=\"1\" class=\"dataframe\">\n",
399 |        "  <thead>\n",
400 |        "    <tr style=\"text-align: right;\">\n",
401 |        "      <th></th>\n",
402 |        "      <th>salary</th>\n",
403 |        "      <th>work_exp</th>\n",
404 |        "    </tr>\n",
405 |        "  </thead>\n",
406 |        "  <tbody>\n",
407 |        "    <tr>\n",
408 |        "      <th>0</th>\n",
409 |        "      <td>500000</td>\n",
410 |        "      <td>Mid</td>\n",
411 |        "    </tr>\n",
412 |        "    <tr>\n",
413 |        "      <th>1</th>\n",
414 |        "      <td>120000</td>\n",
415 |        "      <td>Junior</td>\n",
416 |        "    </tr>\n",
417 |        "    <tr>\n",
418 |        "      <th>2</th>\n",
419 |        "      <td>350000</td>\n",
420 |        "      <td>Junior</td>\n",
421 |        "    </tr>\n",
422 |        "    <tr>\n",
423 |        "      <th>3</th>\n",
424 |        "      <td>400000</td>\n",
425 |        "      <td>Mid</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>4</th>\n",
429 |        "      <td>200000</td>\n",
430 |        "      <td>Junior</td>\n",
431 |        "    </tr>\n",
432 |        "  </tbody>\n",
433 |        "</table>\n",
434 |        "</div>"
435 |       ],
436 |       "text/plain": [
437 |        "   salary work_exp\n",
438 |        "0  500000      Mid\n",
439 |        "1  120000   Junior\n",
440 |        "2  350000   Junior\n",
441 |        "3  400000      Mid\n",
442 |        "4  200000   Junior"
443 |       ]
444 |      },
445 |      "execution_count": 3,
446 |      "metadata": {},
447 |      "output_type": "execute_result"
448 |     }
449 |    ],
450 |    "source": [
451 |     "df = data[['salary', 'work_exp']]\n",
452 |     "df.head()"
453 |    ]
454 |   },
455 |   {
456 |    "cell_type": "markdown",
457 |    "metadata": {},
458 |    "source": [
459 |     "<b>Following step, we will import the required library and perform oneway ANOVA</b>"
460 |    ]
461 |   },
462 |   {
463 |    "cell_type": "code",
464 |    "execution_count": 4,
465 |    "metadata": {},
466 |    "outputs": [
467 |     {
468 |      "data": {
469 |       "text/plain": [
470 |        "(43.767065333017584, 2.84255584357536e-14)"
471 |       ]
472 |      },
473 |      "execution_count": 4,
474 |      "metadata": {},
475 |      "output_type": "execute_result"
476 |     }
477 |    ],
478 |    "source": [
479 |     "from scipy import stats\n",
480 |     "\n",
481 |     "F, p = stats.f_oneway(df[df.work_exp == 'Junior'].salary,\n",
482 |     "                      df[df.work_exp == 'Mid'].salary,\n",
483 |     "                      df[df.work_exp == 'Senior'].salary)\n",
484 |     "F, p"
485 |    ]
486 |   },
487 |   {
488 |    "cell_type": "markdown",
489 |    "metadata": {},
490 |    "source": [
491 |     "Now we will perform few things to understand the concept better:\n",
492 |     "* Create 3 bins based on salary and name them as Low, Medium, High\n",
493 |     "* Notice that these bins are created according to the salary value inside dataset\n",
494 |     "* Now we will perform oneway ANOVA based on newly created bins<br/>"
495 |    ]
496 |   },
497 |   {
498 |    "cell_type": "code",
499 |    "execution_count": 5,
500 |    "metadata": {},
501 |    "outputs": [
502 |     {
503 |      "data": {
504 |       "text/plain": [
505 |        "array([120000., 330000., 540000., 750000.])"
506 |       ]
507 |      },
508 |      "execution_count": 5,
509 |      "metadata": {},
510 |      "output_type": "execute_result"
511 |     }
512 |    ],
513 |    "source": [
514 |     "import numpy as np\n",
515 |     "bins = np.linspace(min(df['salary']), max(df['salary']), 4)\n",
516 |     "bins"
517 |    ]
518 |   },
519 |   {
520 |    "cell_type": "code",
521 |    "execution_count": 6,
522 |    "metadata": {},
523 |    "outputs": [
524 |     {
525 |      "data": {
526 |       "text/html": [
527 |        "<div>\n",
528 |        "<style scoped>\n",
529 |        "    .dataframe tbody tr th:only-of-type {\n",
530 |        "        vertical-align: middle;\n",
531 |        "    }\n",
532 |        "\n",
533 |        "    .dataframe tbody tr th {\n",
534 |        "        vertical-align: top;\n",
535 |        "    }\n",
536 |        "\n",
537 |        "    .dataframe thead th {\n",
538 |        "        text-align: right;\n",
539 |        "    }\n",
540 |        "</style>\n",
541 |        "<table border=\"1\" class=\"dataframe\">\n",
542 |        "  <thead>\n",
543 |        "    <tr style=\"text-align: right;\">\n",
544 |        "      <th></th>\n",
545 |        "      <th>emp_id</th>\n",
546 |        "      <th>first_name</th>\n",
547 |        "      <th>last_name</th>\n",
548 |        "      <th>gender</th>\n",
549 |        "      <th>salary</th>\n",
550 |        "      <th>work_exp</th>\n",
551 |        "      <th>new_salary_group</th>\n",
552 |        "    </tr>\n",
553 |        "  </thead>\n",
554 |        "  <tbody>\n",
555 |        "    <tr>\n",
556 |        "      <th>0</th>\n",
557 |        "      <td>1</td>\n",
558 |        "      <td>Georgi</td>\n",
559 |        "      <td>Facello</td>\n",
560 |        "      <td>M</td>\n",
561 |        "      <td>500000</td>\n",
562 |        "      <td>Mid</td>\n",
563 |        "      <td>Medium</td>\n",
564 |        "    </tr>\n",
565 |        "    <tr>\n",
566 |        "      <th>1</th>\n",
567 |        "      <td>2</td>\n",
568 |        "      <td>Bezalel</td>\n",
569 |        "      <td>Simmel</td>\n",
570 |        "      <td>F</td>\n",
571 |        "      <td>120000</td>\n",
572 |        "      <td>Junior</td>\n",
573 |        "      <td>Low</td>\n",
574 |        "    </tr>\n",
575 |        "    <tr>\n",
576 |        "      <th>2</th>\n",
577 |        "      <td>3</td>\n",
578 |        "      <td>Parto</td>\n",
579 |        "      <td>Bamford</td>\n",
580 |        "      <td>M</td>\n",
581 |        "      <td>350000</td>\n",
582 |        "      <td>Junior</td>\n",
583 |        "      <td>Medium</td>\n",
584 |        "    </tr>\n",
585 |        "    <tr>\n",
586 |        "      <th>3</th>\n",
587 |        "      <td>4</td>\n",
588 |        "      <td>Chirstian</td>\n",
589 |        "      <td>Koblick</td>\n",
590 |        "      <td>M</td>\n",
591 |        "      <td>400000</td>\n",
592 |        "      <td>Mid</td>\n",
593 |        "      <td>Medium</td>\n",
594 |        "    </tr>\n",
595 |        "    <tr>\n",
596 |        "      <th>4</th>\n",
597 |        "      <td>5</td>\n",
598 |        "      <td>Kyoichi</td>\n",
599 |        "      <td>Maliniak</td>\n",
600 |        "      <td>M</td>\n",
601 |        "      <td>200000</td>\n",
602 |        "      <td>Junior</td>\n",
603 |        "      <td>Low</td>\n",
604 |        "    </tr>\n",
605 |        "    <tr>\n",
606 |        "      <th>5</th>\n",
607 |        "      <td>6</td>\n",
608 |        "      <td>Anneke</td>\n",
609 |        "      <td>Preusig</td>\n",
610 |        "      <td>F</td>\n",
611 |        "      <td>300000</td>\n",
612 |        "      <td>Junior</td>\n",
613 |        "      <td>Low</td>\n",
614 |        "    </tr>\n",
615 |        "    <tr>\n",
616 |        "      <th>6</th>\n",
617 |        "      <td>7</td>\n",
618 |        "      <td>Tzvetan</td>\n",
619 |        "      <td>Zielinski</td>\n",
620 |        "      <td>F</td>\n",
621 |        "      <td>150000</td>\n",
622 |        "      <td>Junior</td>\n",
623 |        "      <td>Low</td>\n",
624 |        "    </tr>\n",
625 |        "    <tr>\n",
626 |        "      <th>7</th>\n",
627 |        "      <td>8</td>\n",
628 |        "      <td>Saniya</td>\n",
629 |        "      <td>Kalloufi</td>\n",
630 |        "      <td>M</td>\n",
631 |        "      <td>750000</td>\n",
632 |        "      <td>Mid</td>\n",
633 |        "      <td>High</td>\n",
634 |        "    </tr>\n",
635 |        "    <tr>\n",
636 |        "      <th>8</th>\n",
637 |        "      <td>9</td>\n",
638 |        "      <td>Sumant</td>\n",
639 |        "      <td>Peac</td>\n",
640 |        "      <td>F</td>\n",
641 |        "      <td>750000</td>\n",
642 |        "      <td>Senior</td>\n",
643 |        "      <td>High</td>\n",
644 |        "    </tr>\n",
645 |        "    <tr>\n",
646 |        "      <th>9</th>\n",
647 |        "      <td>10</td>\n",
648 |        "      <td>Duangkaew</td>\n",
649 |        "      <td>Piveteau</td>\n",
650 |        "      <td>F</td>\n",
651 |        "      <td>200000</td>\n",
652 |        "      <td>Junior</td>\n",
653 |        "      <td>Low</td>\n",
654 |        "    </tr>\n",
655 |        "  </tbody>\n",
656 |        "</table>\n",
657 |        "</div>"
658 |       ],
659 |       "text/plain": [
660 |        "   emp_id first_name  last_name gender  salary work_exp new_salary_group\n",
661 |        "0       1     Georgi    Facello      M  500000      Mid           Medium\n",
662 |        "1       2    Bezalel     Simmel      F  120000   Junior              Low\n",
663 |        "2       3      Parto    Bamford      M  350000   Junior           Medium\n",
664 |        "3       4  Chirstian    Koblick      M  400000      Mid           Medium\n",
665 |        "4       5    Kyoichi   Maliniak      M  200000   Junior              Low\n",
666 |        "5       6     Anneke    Preusig      F  300000   Junior              Low\n",
667 |        "6       7    Tzvetan  Zielinski      F  150000   Junior              Low\n",
668 |        "7       8     Saniya   Kalloufi      M  750000      Mid             High\n",
669 |        "8       9     Sumant       Peac      F  750000   Senior             High\n",
670 |        "9      10  Duangkaew   Piveteau      F  200000   Junior              Low"
671 |       ]
672 |      },
673 |      "execution_count": 6,
674 |      "metadata": {},
675 |      "output_type": "execute_result"
676 |     }
677 |    ],
678 |    "source": [
679 |     "bin_names = ['Low', 'Medium', 'High']\n",
680 |     "data['new_salary_group'] = pd.cut(data['salary'], bins, labels=bin_names, include_lowest=True)\n",
681 |     "data.head(10)"
682 |    ]
683 |   },
684 |   {
685 |    "cell_type": "markdown",
686 |    "metadata": {},
687 |    "source": [
688 |     "<b>Since lowest salary will fall into employee type \"Low\" for newly created bin, the f-test score will be high</b>"
689 |    ]
690 |   },
691 |   {
692 |    "cell_type": "code",
693 |    "execution_count": 7,
694 |    "metadata": {},
695 |    "outputs": [
696 |     {
697 |      "data": {
698 |       "text/plain": [
699 |        "(579.7277387424342, 1.1214878408179476e-54)"
700 |       ]
701 |      },
702 |      "execution_count": 7,
703 |      "metadata": {},
704 |      "output_type": "execute_result"
705 |     }
706 |    ],
707 |    "source": [
708 |     "F, p = stats.f_oneway(data[data.new_salary_group == 'Low'].salary,\n",
709 |     "                      data[data.new_salary_group == 'Medium'].salary,\n",
710 |     "                      data[data.new_salary_group == 'High'].salary)\n",
711 |     "F, p"
712 |    ]
713 |   },
714 |   {
715 |    "cell_type": "code",
716 |    "execution_count": null,
717 |    "metadata": {},
718 |    "outputs": [],
719 |    "source": []
720 |   }
721 |  ],
722 |  "metadata": {
723 |   "kernelspec": {
724 |    "display_name": "Python 3",
725 |    "language": "python",
726 |    "name": "python3"
727 |   },
728 |   "language_info": {
729 |    "codemirror_mode": {
730 |     "name": "ipython",
731 |     "version": 3
732 |    },
733 |    "file_extension": ".py",
734 |    "mimetype": "text/x-python",
735 |    "name": "python",
736 |    "nbconvert_exporter": "python",
737 |    "pygments_lexer": "ipython3",
738 |    "version": "3.9.1"
739 |   }
740 |  },
741 |  "nbformat": 4,
742 |  "nbformat_minor": 4
743 | }
744 | 


--------------------------------------------------------------------------------
/P06-MultipleLinearRegression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "%matplotlib inline"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 3,
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "text/html": [
 23 |        "<div>\n",
 24 |        "<style scoped>\n",
 25 |        "    .dataframe tbody tr th:only-of-type {\n",
 26 |        "        vertical-align: middle;\n",
 27 |        "    }\n",
 28 |        "\n",
 29 |        "    .dataframe tbody tr th {\n",
 30 |        "        vertical-align: top;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe thead th {\n",
 34 |        "        text-align: right;\n",
 35 |        "    }\n",
 36 |        "</style>\n",
 37 |        "<table border=\"1\" class=\"dataframe\">\n",
 38 |        "  <thead>\n",
 39 |        "    <tr style=\"text-align: right;\">\n",
 40 |        "      <th></th>\n",
 41 |        "      <th>Country</th>\n",
 42 |        "      <th>Year</th>\n",
 43 |        "      <th>Status</th>\n",
 44 |        "      <th>Life expectancy</th>\n",
 45 |        "      <th>Adult Mortality</th>\n",
 46 |        "      <th>infant deaths</th>\n",
 47 |        "      <th>Alcohol</th>\n",
 48 |        "      <th>percentage expenditure</th>\n",
 49 |        "      <th>Hepatitis B</th>\n",
 50 |        "      <th>Measles</th>\n",
 51 |        "      <th>...</th>\n",
 52 |        "      <th>Polio</th>\n",
 53 |        "      <th>Total expenditure</th>\n",
 54 |        "      <th>Diphtheria</th>\n",
 55 |        "      <th>HIV/AIDS</th>\n",
 56 |        "      <th>GDP</th>\n",
 57 |        "      <th>Population</th>\n",
 58 |        "      <th>thinness 1-19 years</th>\n",
 59 |        "      <th>thinness 5-9 years</th>\n",
 60 |        "      <th>Income composition of resources</th>\n",
 61 |        "      <th>Schooling</th>\n",
 62 |        "    </tr>\n",
 63 |        "  </thead>\n",
 64 |        "  <tbody>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>0</th>\n",
 67 |        "      <td>Afghanistan</td>\n",
 68 |        "      <td>2015</td>\n",
 69 |        "      <td>Developing</td>\n",
 70 |        "      <td>65.0</td>\n",
 71 |        "      <td>263.0</td>\n",
 72 |        "      <td>62</td>\n",
 73 |        "      <td>0.01</td>\n",
 74 |        "      <td>71.279624</td>\n",
 75 |        "      <td>65.0</td>\n",
 76 |        "      <td>1154</td>\n",
 77 |        "      <td>...</td>\n",
 78 |        "      <td>6.0</td>\n",
 79 |        "      <td>8.16</td>\n",
 80 |        "      <td>65.0</td>\n",
 81 |        "      <td>0.1</td>\n",
 82 |        "      <td>584.259210</td>\n",
 83 |        "      <td>33736494.0</td>\n",
 84 |        "      <td>17.2</td>\n",
 85 |        "      <td>17.3</td>\n",
 86 |        "      <td>0.479</td>\n",
 87 |        "      <td>10.1</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>1</th>\n",
 91 |        "      <td>Afghanistan</td>\n",
 92 |        "      <td>2014</td>\n",
 93 |        "      <td>Developing</td>\n",
 94 |        "      <td>59.9</td>\n",
 95 |        "      <td>271.0</td>\n",
 96 |        "      <td>64</td>\n",
 97 |        "      <td>0.01</td>\n",
 98 |        "      <td>73.523582</td>\n",
 99 |        "      <td>62.0</td>\n",
100 |        "      <td>492</td>\n",
101 |        "      <td>...</td>\n",
102 |        "      <td>58.0</td>\n",
103 |        "      <td>8.18</td>\n",
104 |        "      <td>62.0</td>\n",
105 |        "      <td>0.1</td>\n",
106 |        "      <td>612.696514</td>\n",
107 |        "      <td>327582.0</td>\n",
108 |        "      <td>17.5</td>\n",
109 |        "      <td>17.5</td>\n",
110 |        "      <td>0.476</td>\n",
111 |        "      <td>10.0</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>2</th>\n",
115 |        "      <td>Afghanistan</td>\n",
116 |        "      <td>2013</td>\n",
117 |        "      <td>Developing</td>\n",
118 |        "      <td>59.9</td>\n",
119 |        "      <td>268.0</td>\n",
120 |        "      <td>66</td>\n",
121 |        "      <td>0.01</td>\n",
122 |        "      <td>73.219243</td>\n",
123 |        "      <td>64.0</td>\n",
124 |        "      <td>430</td>\n",
125 |        "      <td>...</td>\n",
126 |        "      <td>62.0</td>\n",
127 |        "      <td>8.13</td>\n",
128 |        "      <td>64.0</td>\n",
129 |        "      <td>0.1</td>\n",
130 |        "      <td>631.744976</td>\n",
131 |        "      <td>31731688.0</td>\n",
132 |        "      <td>17.7</td>\n",
133 |        "      <td>17.7</td>\n",
134 |        "      <td>0.470</td>\n",
135 |        "      <td>9.9</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>3</th>\n",
139 |        "      <td>Afghanistan</td>\n",
140 |        "      <td>2012</td>\n",
141 |        "      <td>Developing</td>\n",
142 |        "      <td>59.5</td>\n",
143 |        "      <td>272.0</td>\n",
144 |        "      <td>69</td>\n",
145 |        "      <td>0.01</td>\n",
146 |        "      <td>78.184215</td>\n",
147 |        "      <td>67.0</td>\n",
148 |        "      <td>2787</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>67.0</td>\n",
151 |        "      <td>8.52</td>\n",
152 |        "      <td>67.0</td>\n",
153 |        "      <td>0.1</td>\n",
154 |        "      <td>669.959000</td>\n",
155 |        "      <td>3696958.0</td>\n",
156 |        "      <td>17.9</td>\n",
157 |        "      <td>18.0</td>\n",
158 |        "      <td>0.463</td>\n",
159 |        "      <td>9.8</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>4</th>\n",
163 |        "      <td>Afghanistan</td>\n",
164 |        "      <td>2011</td>\n",
165 |        "      <td>Developing</td>\n",
166 |        "      <td>59.2</td>\n",
167 |        "      <td>275.0</td>\n",
168 |        "      <td>71</td>\n",
169 |        "      <td>0.01</td>\n",
170 |        "      <td>7.097109</td>\n",
171 |        "      <td>68.0</td>\n",
172 |        "      <td>3013</td>\n",
173 |        "      <td>...</td>\n",
174 |        "      <td>68.0</td>\n",
175 |        "      <td>7.87</td>\n",
176 |        "      <td>68.0</td>\n",
177 |        "      <td>0.1</td>\n",
178 |        "      <td>63.537231</td>\n",
179 |        "      <td>2978599.0</td>\n",
180 |        "      <td>18.2</td>\n",
181 |        "      <td>18.2</td>\n",
182 |        "      <td>0.454</td>\n",
183 |        "      <td>9.5</td>\n",
184 |        "    </tr>\n",
185 |        "  </tbody>\n",
186 |        "</table>\n",
187 |        "<p>5 rows × 22 columns</p>\n",
188 |        "</div>"
189 |       ],
190 |       "text/plain": [
191 |        "       Country  Year      Status  Life expectancy  Adult Mortality  \\\n",
192 |        "0  Afghanistan  2015  Developing             65.0            263.0   \n",
193 |        "1  Afghanistan  2014  Developing             59.9            271.0   \n",
194 |        "2  Afghanistan  2013  Developing             59.9            268.0   \n",
195 |        "3  Afghanistan  2012  Developing             59.5            272.0   \n",
196 |        "4  Afghanistan  2011  Developing             59.2            275.0   \n",
197 |        "\n",
198 |        "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles  \\\n",
199 |        "0             62     0.01               71.279624         65.0     1154   \n",
200 |        "1             64     0.01               73.523582         62.0      492   \n",
201 |        "2             66     0.01               73.219243         64.0      430   \n",
202 |        "3             69     0.01               78.184215         67.0     2787   \n",
203 |        "4             71     0.01                7.097109         68.0     3013   \n",
204 |        "\n",
205 |        "     ...      Polio  Total expenditure  Diphtheria  HIV/AIDS         GDP  \\\n",
206 |        "0    ...        6.0               8.16        65.0       0.1  584.259210   \n",
207 |        "1    ...       58.0               8.18        62.0       0.1  612.696514   \n",
208 |        "2    ...       62.0               8.13        64.0       0.1  631.744976   \n",
209 |        "3    ...       67.0               8.52        67.0       0.1  669.959000   \n",
210 |        "4    ...       68.0               7.87        68.0       0.1   63.537231   \n",
211 |        "\n",
212 |        "   Population  thinness 1-19 years  thinness 5-9 years  \\\n",
213 |        "0  33736494.0                 17.2                17.3   \n",
214 |        "1    327582.0                 17.5                17.5   \n",
215 |        "2  31731688.0                 17.7                17.7   \n",
216 |        "3   3696958.0                 17.9                18.0   \n",
217 |        "4   2978599.0                 18.2                18.2   \n",
218 |        "\n",
219 |        "   Income composition of resources  Schooling  \n",
220 |        "0                            0.479       10.1  \n",
221 |        "1                            0.476       10.0  \n",
222 |        "2                            0.470        9.9  \n",
223 |        "3                            0.463        9.8  \n",
224 |        "4                            0.454        9.5  \n",
225 |        "\n",
226 |        "[5 rows x 22 columns]"
227 |       ]
228 |      },
229 |      "execution_count": 3,
230 |      "metadata": {},
231 |      "output_type": "execute_result"
232 |     }
233 |    ],
234 |    "source": [
235 |     "df = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/regression-one.csv')\n",
236 |     "df.head()"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": 4,
242 |    "metadata": {},
243 |    "outputs": [
244 |     {
245 |      "data": {
246 |       "text/html": [
247 |        "<div>\n",
248 |        "<style scoped>\n",
249 |        "    .dataframe tbody tr th:only-of-type {\n",
250 |        "        vertical-align: middle;\n",
251 |        "    }\n",
252 |        "\n",
253 |        "    .dataframe tbody tr th {\n",
254 |        "        vertical-align: top;\n",
255 |        "    }\n",
256 |        "\n",
257 |        "    .dataframe thead th {\n",
258 |        "        text-align: right;\n",
259 |        "    }\n",
260 |        "</style>\n",
261 |        "<table border=\"1\" class=\"dataframe\">\n",
262 |        "  <thead>\n",
263 |        "    <tr style=\"text-align: right;\">\n",
264 |        "      <th></th>\n",
265 |        "      <th>Country</th>\n",
266 |        "      <th>Year</th>\n",
267 |        "      <th>Status</th>\n",
268 |        "      <th>life-expect</th>\n",
269 |        "      <th>Adult Mortality</th>\n",
270 |        "      <th>infant deaths</th>\n",
271 |        "      <th>Alcohol</th>\n",
272 |        "      <th>percentage expenditure</th>\n",
273 |        "      <th>Hepatitis B</th>\n",
274 |        "      <th>Measles</th>\n",
275 |        "      <th>...</th>\n",
276 |        "      <th>Polio</th>\n",
277 |        "      <th>Total expenditure</th>\n",
278 |        "      <th>Diphtheria</th>\n",
279 |        "      <th>HIV/AIDS</th>\n",
280 |        "      <th>GDP</th>\n",
281 |        "      <th>Population</th>\n",
282 |        "      <th>thinness 1-19 years</th>\n",
283 |        "      <th>thinness 5-9 years</th>\n",
284 |        "      <th>Income composition of resources</th>\n",
285 |        "      <th>Schooling</th>\n",
286 |        "    </tr>\n",
287 |        "  </thead>\n",
288 |        "  <tbody>\n",
289 |        "    <tr>\n",
290 |        "      <th>0</th>\n",
291 |        "      <td>Afghanistan</td>\n",
292 |        "      <td>2015</td>\n",
293 |        "      <td>Developing</td>\n",
294 |        "      <td>65.0</td>\n",
295 |        "      <td>263.0</td>\n",
296 |        "      <td>62</td>\n",
297 |        "      <td>0.01</td>\n",
298 |        "      <td>71.279624</td>\n",
299 |        "      <td>65.0</td>\n",
300 |        "      <td>1154</td>\n",
301 |        "      <td>...</td>\n",
302 |        "      <td>6.0</td>\n",
303 |        "      <td>8.16</td>\n",
304 |        "      <td>65.0</td>\n",
305 |        "      <td>0.1</td>\n",
306 |        "      <td>584.259210</td>\n",
307 |        "      <td>33736494.0</td>\n",
308 |        "      <td>17.2</td>\n",
309 |        "      <td>17.3</td>\n",
310 |        "      <td>0.479</td>\n",
311 |        "      <td>10.1</td>\n",
312 |        "    </tr>\n",
313 |        "    <tr>\n",
314 |        "      <th>1</th>\n",
315 |        "      <td>Afghanistan</td>\n",
316 |        "      <td>2014</td>\n",
317 |        "      <td>Developing</td>\n",
318 |        "      <td>59.9</td>\n",
319 |        "      <td>271.0</td>\n",
320 |        "      <td>64</td>\n",
321 |        "      <td>0.01</td>\n",
322 |        "      <td>73.523582</td>\n",
323 |        "      <td>62.0</td>\n",
324 |        "      <td>492</td>\n",
325 |        "      <td>...</td>\n",
326 |        "      <td>58.0</td>\n",
327 |        "      <td>8.18</td>\n",
328 |        "      <td>62.0</td>\n",
329 |        "      <td>0.1</td>\n",
330 |        "      <td>612.696514</td>\n",
331 |        "      <td>327582.0</td>\n",
332 |        "      <td>17.5</td>\n",
333 |        "      <td>17.5</td>\n",
334 |        "      <td>0.476</td>\n",
335 |        "      <td>10.0</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>2</th>\n",
339 |        "      <td>Afghanistan</td>\n",
340 |        "      <td>2013</td>\n",
341 |        "      <td>Developing</td>\n",
342 |        "      <td>59.9</td>\n",
343 |        "      <td>268.0</td>\n",
344 |        "      <td>66</td>\n",
345 |        "      <td>0.01</td>\n",
346 |        "      <td>73.219243</td>\n",
347 |        "      <td>64.0</td>\n",
348 |        "      <td>430</td>\n",
349 |        "      <td>...</td>\n",
350 |        "      <td>62.0</td>\n",
351 |        "      <td>8.13</td>\n",
352 |        "      <td>64.0</td>\n",
353 |        "      <td>0.1</td>\n",
354 |        "      <td>631.744976</td>\n",
355 |        "      <td>31731688.0</td>\n",
356 |        "      <td>17.7</td>\n",
357 |        "      <td>17.7</td>\n",
358 |        "      <td>0.470</td>\n",
359 |        "      <td>9.9</td>\n",
360 |        "    </tr>\n",
361 |        "    <tr>\n",
362 |        "      <th>3</th>\n",
363 |        "      <td>Afghanistan</td>\n",
364 |        "      <td>2012</td>\n",
365 |        "      <td>Developing</td>\n",
366 |        "      <td>59.5</td>\n",
367 |        "      <td>272.0</td>\n",
368 |        "      <td>69</td>\n",
369 |        "      <td>0.01</td>\n",
370 |        "      <td>78.184215</td>\n",
371 |        "      <td>67.0</td>\n",
372 |        "      <td>2787</td>\n",
373 |        "      <td>...</td>\n",
374 |        "      <td>67.0</td>\n",
375 |        "      <td>8.52</td>\n",
376 |        "      <td>67.0</td>\n",
377 |        "      <td>0.1</td>\n",
378 |        "      <td>669.959000</td>\n",
379 |        "      <td>3696958.0</td>\n",
380 |        "      <td>17.9</td>\n",
381 |        "      <td>18.0</td>\n",
382 |        "      <td>0.463</td>\n",
383 |        "      <td>9.8</td>\n",
384 |        "    </tr>\n",
385 |        "    <tr>\n",
386 |        "      <th>4</th>\n",
387 |        "      <td>Afghanistan</td>\n",
388 |        "      <td>2011</td>\n",
389 |        "      <td>Developing</td>\n",
390 |        "      <td>59.2</td>\n",
391 |        "      <td>275.0</td>\n",
392 |        "      <td>71</td>\n",
393 |        "      <td>0.01</td>\n",
394 |        "      <td>7.097109</td>\n",
395 |        "      <td>68.0</td>\n",
396 |        "      <td>3013</td>\n",
397 |        "      <td>...</td>\n",
398 |        "      <td>68.0</td>\n",
399 |        "      <td>7.87</td>\n",
400 |        "      <td>68.0</td>\n",
401 |        "      <td>0.1</td>\n",
402 |        "      <td>63.537231</td>\n",
403 |        "      <td>2978599.0</td>\n",
404 |        "      <td>18.2</td>\n",
405 |        "      <td>18.2</td>\n",
406 |        "      <td>0.454</td>\n",
407 |        "      <td>9.5</td>\n",
408 |        "    </tr>\n",
409 |        "  </tbody>\n",
410 |        "</table>\n",
411 |        "<p>5 rows × 22 columns</p>\n",
412 |        "</div>"
413 |       ],
414 |       "text/plain": [
415 |        "       Country  Year      Status  life-expect  Adult Mortality  infant deaths  \\\n",
416 |        "0  Afghanistan  2015  Developing         65.0            263.0             62   \n",
417 |        "1  Afghanistan  2014  Developing         59.9            271.0             64   \n",
418 |        "2  Afghanistan  2013  Developing         59.9            268.0             66   \n",
419 |        "3  Afghanistan  2012  Developing         59.5            272.0             69   \n",
420 |        "4  Afghanistan  2011  Developing         59.2            275.0             71   \n",
421 |        "\n",
422 |        "   Alcohol  percentage expenditure  Hepatitis B  Measles    ...      Polio  \\\n",
423 |        "0     0.01               71.279624         65.0     1154    ...        6.0   \n",
424 |        "1     0.01               73.523582         62.0      492    ...       58.0   \n",
425 |        "2     0.01               73.219243         64.0      430    ...       62.0   \n",
426 |        "3     0.01               78.184215         67.0     2787    ...       67.0   \n",
427 |        "4     0.01                7.097109         68.0     3013    ...       68.0   \n",
428 |        "\n",
429 |        "   Total expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n",
430 |        "0               8.16        65.0       0.1  584.259210  33736494.0   \n",
431 |        "1               8.18        62.0       0.1  612.696514    327582.0   \n",
432 |        "2               8.13        64.0       0.1  631.744976  31731688.0   \n",
433 |        "3               8.52        67.0       0.1  669.959000   3696958.0   \n",
434 |        "4               7.87        68.0       0.1   63.537231   2978599.0   \n",
435 |        "\n",
436 |        "   thinness 1-19 years  thinness 5-9 years  Income composition of resources  \\\n",
437 |        "0                 17.2                17.3                            0.479   \n",
438 |        "1                 17.5                17.5                            0.476   \n",
439 |        "2                 17.7                17.7                            0.470   \n",
440 |        "3                 17.9                18.0                            0.463   \n",
441 |        "4                 18.2                18.2                            0.454   \n",
442 |        "\n",
443 |        "   Schooling  \n",
444 |        "0       10.1  \n",
445 |        "1       10.0  \n",
446 |        "2        9.9  \n",
447 |        "3        9.8  \n",
448 |        "4        9.5  \n",
449 |        "\n",
450 |        "[5 rows x 22 columns]"
451 |       ]
452 |      },
453 |      "execution_count": 4,
454 |      "metadata": {},
455 |      "output_type": "execute_result"
456 |     }
457 |    ],
458 |    "source": [
459 |     "df.rename(columns= {'Life expectancy': 'life-expect'}, inplace = True)\n",
460 |     "df.head()"
461 |    ]
462 |   },
463 |   {
464 |    "cell_type": "code",
465 |    "execution_count": 5,
466 |    "metadata": {},
467 |    "outputs": [
468 |     {
469 |      "data": {
470 |       "text/plain": [
471 |        "(1649, 22)"
472 |       ]
473 |      },
474 |      "execution_count": 5,
475 |      "metadata": {},
476 |      "output_type": "execute_result"
477 |     }
478 |    ],
479 |    "source": [
480 |     "df = df.dropna()\n",
481 |     "df.shape"
482 |    ]
483 |   },
484 |   {
485 |    "cell_type": "code",
486 |    "execution_count": 7,
487 |    "metadata": {},
488 |    "outputs": [
489 |     {
490 |      "data": {
491 |       "text/html": [
492 |        "<div>\n",
493 |        "<style scoped>\n",
494 |        "    .dataframe tbody tr th:only-of-type {\n",
495 |        "        vertical-align: middle;\n",
496 |        "    }\n",
497 |        "\n",
498 |        "    .dataframe tbody tr th {\n",
499 |        "        vertical-align: top;\n",
500 |        "    }\n",
501 |        "\n",
502 |        "    .dataframe thead th {\n",
503 |        "        text-align: right;\n",
504 |        "    }\n",
505 |        "</style>\n",
506 |        "<table border=\"1\" class=\"dataframe\">\n",
507 |        "  <thead>\n",
508 |        "    <tr style=\"text-align: right;\">\n",
509 |        "      <th></th>\n",
510 |        "      <th>Country</th>\n",
511 |        "      <th>Year</th>\n",
512 |        "      <th>Status</th>\n",
513 |        "      <th>life-expect</th>\n",
514 |        "      <th>Adult Mortality</th>\n",
515 |        "      <th>infant deaths</th>\n",
516 |        "      <th>Alcohol</th>\n",
517 |        "      <th>percentage expenditure</th>\n",
518 |        "      <th>Hepatitis B</th>\n",
519 |        "      <th>Measles</th>\n",
520 |        "      <th>...</th>\n",
521 |        "      <th>Polio</th>\n",
522 |        "      <th>Total expenditure</th>\n",
523 |        "      <th>Diphtheria</th>\n",
524 |        "      <th>HIV/AIDS</th>\n",
525 |        "      <th>GDP</th>\n",
526 |        "      <th>Population</th>\n",
527 |        "      <th>thinness 1-19 years</th>\n",
528 |        "      <th>thinness 5-9 years</th>\n",
529 |        "      <th>Income composition of resources</th>\n",
530 |        "      <th>Schooling</th>\n",
531 |        "    </tr>\n",
532 |        "  </thead>\n",
533 |        "  <tbody>\n",
534 |        "  </tbody>\n",
535 |        "</table>\n",
536 |        "<p>0 rows × 22 columns</p>\n",
537 |        "</div>"
538 |       ],
539 |       "text/plain": [
540 |        "Empty DataFrame\n",
541 |        "Columns: [Country, Year, Status, life-expect, Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles, BMI, under-five deaths, Polio, Total expenditure, Diphtheria, HIV/AIDS, GDP, Population, thinness 1-19 years, thinness 5-9 years, Income composition of resources, Schooling]\n",
542 |        "Index: []\n",
543 |        "\n",
544 |        "[0 rows x 22 columns]"
545 |       ]
546 |      },
547 |      "execution_count": 7,
548 |      "metadata": {},
549 |      "output_type": "execute_result"
550 |     }
551 |    ],
552 |    "source": [
553 |     "df[df.isnull().any(axis=1)]"
554 |    ]
555 |   },
556 |   {
557 |    "cell_type": "code",
558 |    "execution_count": 8,
559 |    "metadata": {},
560 |    "outputs": [],
561 |    "source": [
562 |     "X = df[['GDP', 'Alcohol', 'BMI', 'Schooling']]\n",
563 |     "Y = df['life-expect']"
564 |    ]
565 |   },
566 |   {
567 |    "cell_type": "code",
568 |    "execution_count": 11,
569 |    "metadata": {},
570 |    "outputs": [
571 |     {
572 |      "data": {
573 |       "text/plain": [
574 |        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
575 |       ]
576 |      },
577 |      "execution_count": 11,
578 |      "metadata": {},
579 |      "output_type": "execute_result"
580 |     }
581 |    ],
582 |    "source": [
583 |     "from sklearn.linear_model import LinearRegression\n",
584 |     "lm = LinearRegression()\n",
585 |     "lm.fit(X, Y)"
586 |    ]
587 |   },
588 |   {
589 |    "cell_type": "markdown",
590 |    "metadata": {},
591 |    "source": [
592 |     "lm.intercept_"
593 |    ]
594 |   },
595 |   {
596 |    "cell_type": "code",
597 |    "execution_count": 15,
598 |    "metadata": {},
599 |    "outputs": [
600 |     {
601 |      "data": {
602 |       "text/plain": [
603 |        "array([ 1.15265737e-04, -2.52732304e-01,  8.91904815e-02,  1.94339619e+00])"
604 |       ]
605 |      },
606 |      "execution_count": 15,
607 |      "metadata": {},
608 |      "output_type": "execute_result"
609 |     }
610 |    ],
611 |    "source": [
612 |     "lm.coef_"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "code",
617 |    "execution_count": 16,
618 |    "metadata": {},
619 |    "outputs": [],
620 |    "source": [
621 |     "Y_hat = lm.predict(X)"
622 |    ]
623 |   },
624 |   {
625 |    "cell_type": "code",
626 |    "execution_count": 17,
627 |    "metadata": {},
628 |    "outputs": [
629 |     {
630 |      "data": {
631 |       "text/html": [
632 |        "<div>\n",
633 |        "<style scoped>\n",
634 |        "    .dataframe tbody tr th:only-of-type {\n",
635 |        "        vertical-align: middle;\n",
636 |        "    }\n",
637 |        "\n",
638 |        "    .dataframe tbody tr th {\n",
639 |        "        vertical-align: top;\n",
640 |        "    }\n",
641 |        "\n",
642 |        "    .dataframe thead th {\n",
643 |        "        text-align: right;\n",
644 |        "    }\n",
645 |        "</style>\n",
646 |        "<table border=\"1\" class=\"dataframe\">\n",
647 |        "  <thead>\n",
648 |        "    <tr style=\"text-align: right;\">\n",
649 |        "      <th></th>\n",
650 |        "      <th>Actual</th>\n",
651 |        "      <th>Predicted</th>\n",
652 |        "    </tr>\n",
653 |        "  </thead>\n",
654 |        "  <tbody>\n",
655 |        "    <tr>\n",
656 |        "      <th>0</th>\n",
657 |        "      <td>65.0</td>\n",
658 |        "      <td>64.248614</td>\n",
659 |        "    </tr>\n",
660 |        "    <tr>\n",
661 |        "      <th>1</th>\n",
662 |        "      <td>59.9</td>\n",
663 |        "      <td>64.012957</td>\n",
664 |        "    </tr>\n",
665 |        "    <tr>\n",
666 |        "      <th>2</th>\n",
667 |        "      <td>59.9</td>\n",
668 |        "      <td>63.776218</td>\n",
669 |        "    </tr>\n",
670 |        "    <tr>\n",
671 |        "      <th>3</th>\n",
672 |        "      <td>59.5</td>\n",
673 |        "      <td>63.541688</td>\n",
674 |        "    </tr>\n",
675 |        "    <tr>\n",
676 |        "      <th>4</th>\n",
677 |        "      <td>59.2</td>\n",
678 |        "      <td>62.853093</td>\n",
679 |        "    </tr>\n",
680 |        "    <tr>\n",
681 |        "      <th>5</th>\n",
682 |        "      <td>58.8</td>\n",
683 |        "      <td>62.281935</td>\n",
684 |        "    </tr>\n",
685 |        "    <tr>\n",
686 |        "      <th>6</th>\n",
687 |        "      <td>58.6</td>\n",
688 |        "      <td>61.641938</td>\n",
689 |        "    </tr>\n",
690 |        "    <tr>\n",
691 |        "      <th>7</th>\n",
692 |        "      <td>58.1</td>\n",
693 |        "      <td>61.195248</td>\n",
694 |        "    </tr>\n",
695 |        "    <tr>\n",
696 |        "      <th>8</th>\n",
697 |        "      <td>57.5</td>\n",
698 |        "      <td>60.569755</td>\n",
699 |        "    </tr>\n",
700 |        "    <tr>\n",
701 |        "      <th>9</th>\n",
702 |        "      <td>57.3</td>\n",
703 |        "      <td>59.928401</td>\n",
704 |        "    </tr>\n",
705 |        "    <tr>\n",
706 |        "      <th>10</th>\n",
707 |        "      <td>57.3</td>\n",
708 |        "      <td>59.469152</td>\n",
709 |        "    </tr>\n",
710 |        "    <tr>\n",
711 |        "      <th>11</th>\n",
712 |        "      <td>57.0</td>\n",
713 |        "      <td>57.318084</td>\n",
714 |        "    </tr>\n",
715 |        "    <tr>\n",
716 |        "      <th>12</th>\n",
717 |        "      <td>56.7</td>\n",
718 |        "      <td>56.699564</td>\n",
719 |        "    </tr>\n",
720 |        "    <tr>\n",
721 |        "      <th>13</th>\n",
722 |        "      <td>56.2</td>\n",
723 |        "      <td>56.079614</td>\n",
724 |        "    </tr>\n",
725 |        "    <tr>\n",
726 |        "      <th>14</th>\n",
727 |        "      <td>55.3</td>\n",
728 |        "      <td>55.452810</td>\n",
729 |        "    </tr>\n",
730 |        "    <tr>\n",
731 |        "      <th>15</th>\n",
732 |        "      <td>54.8</td>\n",
733 |        "      <td>54.639437</td>\n",
734 |        "    </tr>\n",
735 |        "    <tr>\n",
736 |        "      <th>16</th>\n",
737 |        "      <td>77.8</td>\n",
738 |        "      <td>74.914449</td>\n",
739 |        "    </tr>\n",
740 |        "    <tr>\n",
741 |        "      <th>17</th>\n",
742 |        "      <td>77.5</td>\n",
743 |        "      <td>74.937484</td>\n",
744 |        "    </tr>\n",
745 |        "    <tr>\n",
746 |        "      <th>18</th>\n",
747 |        "      <td>77.2</td>\n",
748 |        "      <td>74.793305</td>\n",
749 |        "    </tr>\n",
750 |        "    <tr>\n",
751 |        "      <th>19</th>\n",
752 |        "      <td>76.9</td>\n",
753 |        "      <td>74.615572</td>\n",
754 |        "    </tr>\n",
755 |        "  </tbody>\n",
756 |        "</table>\n",
757 |        "</div>"
758 |       ],
759 |       "text/plain": [
760 |        "    Actual  Predicted\n",
761 |        "0     65.0  64.248614\n",
762 |        "1     59.9  64.012957\n",
763 |        "2     59.9  63.776218\n",
764 |        "3     59.5  63.541688\n",
765 |        "4     59.2  62.853093\n",
766 |        "5     58.8  62.281935\n",
767 |        "6     58.6  61.641938\n",
768 |        "7     58.1  61.195248\n",
769 |        "8     57.5  60.569755\n",
770 |        "9     57.3  59.928401\n",
771 |        "10    57.3  59.469152\n",
772 |        "11    57.0  57.318084\n",
773 |        "12    56.7  56.699564\n",
774 |        "13    56.2  56.079614\n",
775 |        "14    55.3  55.452810\n",
776 |        "15    54.8  54.639437\n",
777 |        "16    77.8  74.914449\n",
778 |        "17    77.5  74.937484\n",
779 |        "18    77.2  74.793305\n",
780 |        "19    76.9  74.615572"
781 |       ]
782 |      },
783 |      "execution_count": 17,
784 |      "metadata": {},
785 |      "output_type": "execute_result"
786 |     }
787 |    ],
788 |    "source": [
789 |     "data = pd.DataFrame({'Actual':df['life-expect'], 'Predicted': Y_hat})\n",
790 |     "data.head(20)"
791 |    ]
792 |   },
793 |   {
794 |    "cell_type": "code",
795 |    "execution_count": 18,
796 |    "metadata": {},
797 |    "outputs": [
798 |     {
799 |      "data": {
800 |       "image/png": 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\n",
801 |       "text/plain": [
802 |        "<matplotlib.figure.Figure at 0xba9db00>"
803 |       ]
804 |      },
805 |      "metadata": {},
806 |      "output_type": "display_data"
807 |     }
808 |    ],
809 |    "source": [
810 |     "import seaborn as sns\n",
811 |     "\n",
812 |     "axs = sns.distplot(df['life-expect'], hist=False, color=\"r\", label=\"Actual Value\")\n",
813 |     "sns.distplot(Y_hat, hist=False, color=\"b\", label=\"Fitted Values\", ax=axs)\n",
814 |     "\n",
815 |     "plt.title('Acutal vs Fitted Values for LE')\n",
816 |     "plt.xlabel('Age Range')\n",
817 |     "plt.show()"
818 |    ]
819 |   },
820 |   {
821 |    "cell_type": "code",
822 |    "execution_count": null,
823 |    "metadata": {},
824 |    "outputs": [],
825 |    "source": []
826 |   }
827 |  ],
828 |  "metadata": {
829 |   "kernelspec": {
830 |    "display_name": "Python 3",
831 |    "language": "python",
832 |    "name": "python3"
833 |   },
834 |   "language_info": {
835 |    "codemirror_mode": {
836 |     "name": "ipython",
837 |     "version": 3
838 |    },
839 |    "file_extension": ".py",
840 |    "mimetype": "text/x-python",
841 |    "name": "python",
842 |    "nbconvert_exporter": "python",
843 |    "pygments_lexer": "ipython3",
844 |    "version": "3.6.4"
845 |   }
846 |  },
847 |  "nbformat": 4,
848 |  "nbformat_minor": 2
849 | }
850 | 


--------------------------------------------------------------------------------
/P08-Model Evaluation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 5,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "%matplotlib inline"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 6,
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "text/html": [
 23 |        "<div>\n",
 24 |        "<style scoped>\n",
 25 |        "    .dataframe tbody tr th:only-of-type {\n",
 26 |        "        vertical-align: middle;\n",
 27 |        "    }\n",
 28 |        "\n",
 29 |        "    .dataframe tbody tr th {\n",
 30 |        "        vertical-align: top;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe thead th {\n",
 34 |        "        text-align: right;\n",
 35 |        "    }\n",
 36 |        "</style>\n",
 37 |        "<table border=\"1\" class=\"dataframe\">\n",
 38 |        "  <thead>\n",
 39 |        "    <tr style=\"text-align: right;\">\n",
 40 |        "      <th></th>\n",
 41 |        "      <th>Country</th>\n",
 42 |        "      <th>Year</th>\n",
 43 |        "      <th>Status</th>\n",
 44 |        "      <th>Life expectancy</th>\n",
 45 |        "      <th>Adult Mortality</th>\n",
 46 |        "      <th>infant deaths</th>\n",
 47 |        "      <th>Alcohol</th>\n",
 48 |        "      <th>percentage expenditure</th>\n",
 49 |        "      <th>Hepatitis B</th>\n",
 50 |        "      <th>Measles</th>\n",
 51 |        "      <th>...</th>\n",
 52 |        "      <th>Polio</th>\n",
 53 |        "      <th>Total expenditure</th>\n",
 54 |        "      <th>Diphtheria</th>\n",
 55 |        "      <th>HIV/AIDS</th>\n",
 56 |        "      <th>GDP</th>\n",
 57 |        "      <th>Population</th>\n",
 58 |        "      <th>thinness 1-19 years</th>\n",
 59 |        "      <th>thinness 5-9 years</th>\n",
 60 |        "      <th>Income composition of resources</th>\n",
 61 |        "      <th>Schooling</th>\n",
 62 |        "    </tr>\n",
 63 |        "  </thead>\n",
 64 |        "  <tbody>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>0</th>\n",
 67 |        "      <td>Afghanistan</td>\n",
 68 |        "      <td>2015</td>\n",
 69 |        "      <td>Developing</td>\n",
 70 |        "      <td>65.0</td>\n",
 71 |        "      <td>263.0</td>\n",
 72 |        "      <td>62</td>\n",
 73 |        "      <td>0.01</td>\n",
 74 |        "      <td>71.279624</td>\n",
 75 |        "      <td>65.0</td>\n",
 76 |        "      <td>1154</td>\n",
 77 |        "      <td>...</td>\n",
 78 |        "      <td>6.0</td>\n",
 79 |        "      <td>8.16</td>\n",
 80 |        "      <td>65.0</td>\n",
 81 |        "      <td>0.1</td>\n",
 82 |        "      <td>584.259210</td>\n",
 83 |        "      <td>33736494.0</td>\n",
 84 |        "      <td>17.2</td>\n",
 85 |        "      <td>17.3</td>\n",
 86 |        "      <td>0.479</td>\n",
 87 |        "      <td>10.1</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>1</th>\n",
 91 |        "      <td>Afghanistan</td>\n",
 92 |        "      <td>2014</td>\n",
 93 |        "      <td>Developing</td>\n",
 94 |        "      <td>59.9</td>\n",
 95 |        "      <td>271.0</td>\n",
 96 |        "      <td>64</td>\n",
 97 |        "      <td>0.01</td>\n",
 98 |        "      <td>73.523582</td>\n",
 99 |        "      <td>62.0</td>\n",
100 |        "      <td>492</td>\n",
101 |        "      <td>...</td>\n",
102 |        "      <td>58.0</td>\n",
103 |        "      <td>8.18</td>\n",
104 |        "      <td>62.0</td>\n",
105 |        "      <td>0.1</td>\n",
106 |        "      <td>612.696514</td>\n",
107 |        "      <td>327582.0</td>\n",
108 |        "      <td>17.5</td>\n",
109 |        "      <td>17.5</td>\n",
110 |        "      <td>0.476</td>\n",
111 |        "      <td>10.0</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>2</th>\n",
115 |        "      <td>Afghanistan</td>\n",
116 |        "      <td>2013</td>\n",
117 |        "      <td>Developing</td>\n",
118 |        "      <td>59.9</td>\n",
119 |        "      <td>268.0</td>\n",
120 |        "      <td>66</td>\n",
121 |        "      <td>0.01</td>\n",
122 |        "      <td>73.219243</td>\n",
123 |        "      <td>64.0</td>\n",
124 |        "      <td>430</td>\n",
125 |        "      <td>...</td>\n",
126 |        "      <td>62.0</td>\n",
127 |        "      <td>8.13</td>\n",
128 |        "      <td>64.0</td>\n",
129 |        "      <td>0.1</td>\n",
130 |        "      <td>631.744976</td>\n",
131 |        "      <td>31731688.0</td>\n",
132 |        "      <td>17.7</td>\n",
133 |        "      <td>17.7</td>\n",
134 |        "      <td>0.470</td>\n",
135 |        "      <td>9.9</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>3</th>\n",
139 |        "      <td>Afghanistan</td>\n",
140 |        "      <td>2012</td>\n",
141 |        "      <td>Developing</td>\n",
142 |        "      <td>59.5</td>\n",
143 |        "      <td>272.0</td>\n",
144 |        "      <td>69</td>\n",
145 |        "      <td>0.01</td>\n",
146 |        "      <td>78.184215</td>\n",
147 |        "      <td>67.0</td>\n",
148 |        "      <td>2787</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>67.0</td>\n",
151 |        "      <td>8.52</td>\n",
152 |        "      <td>67.0</td>\n",
153 |        "      <td>0.1</td>\n",
154 |        "      <td>669.959000</td>\n",
155 |        "      <td>3696958.0</td>\n",
156 |        "      <td>17.9</td>\n",
157 |        "      <td>18.0</td>\n",
158 |        "      <td>0.463</td>\n",
159 |        "      <td>9.8</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>4</th>\n",
163 |        "      <td>Afghanistan</td>\n",
164 |        "      <td>2011</td>\n",
165 |        "      <td>Developing</td>\n",
166 |        "      <td>59.2</td>\n",
167 |        "      <td>275.0</td>\n",
168 |        "      <td>71</td>\n",
169 |        "      <td>0.01</td>\n",
170 |        "      <td>7.097109</td>\n",
171 |        "      <td>68.0</td>\n",
172 |        "      <td>3013</td>\n",
173 |        "      <td>...</td>\n",
174 |        "      <td>68.0</td>\n",
175 |        "      <td>7.87</td>\n",
176 |        "      <td>68.0</td>\n",
177 |        "      <td>0.1</td>\n",
178 |        "      <td>63.537231</td>\n",
179 |        "      <td>2978599.0</td>\n",
180 |        "      <td>18.2</td>\n",
181 |        "      <td>18.2</td>\n",
182 |        "      <td>0.454</td>\n",
183 |        "      <td>9.5</td>\n",
184 |        "    </tr>\n",
185 |        "  </tbody>\n",
186 |        "</table>\n",
187 |        "<p>5 rows × 22 columns</p>\n",
188 |        "</div>"
189 |       ],
190 |       "text/plain": [
191 |        "       Country  Year      Status  Life expectancy  Adult Mortality  \\\n",
192 |        "0  Afghanistan  2015  Developing             65.0            263.0   \n",
193 |        "1  Afghanistan  2014  Developing             59.9            271.0   \n",
194 |        "2  Afghanistan  2013  Developing             59.9            268.0   \n",
195 |        "3  Afghanistan  2012  Developing             59.5            272.0   \n",
196 |        "4  Afghanistan  2011  Developing             59.2            275.0   \n",
197 |        "\n",
198 |        "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles  ...  \\\n",
199 |        "0             62     0.01               71.279624         65.0     1154  ...   \n",
200 |        "1             64     0.01               73.523582         62.0      492  ...   \n",
201 |        "2             66     0.01               73.219243         64.0      430  ...   \n",
202 |        "3             69     0.01               78.184215         67.0     2787  ...   \n",
203 |        "4             71     0.01                7.097109         68.0     3013  ...   \n",
204 |        "\n",
205 |        "   Polio  Total expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n",
206 |        "0    6.0               8.16        65.0       0.1  584.259210  33736494.0   \n",
207 |        "1   58.0               8.18        62.0       0.1  612.696514    327582.0   \n",
208 |        "2   62.0               8.13        64.0       0.1  631.744976  31731688.0   \n",
209 |        "3   67.0               8.52        67.0       0.1  669.959000   3696958.0   \n",
210 |        "4   68.0               7.87        68.0       0.1   63.537231   2978599.0   \n",
211 |        "\n",
212 |        "   thinness 1-19 years  thinness 5-9 years  Income composition of resources  \\\n",
213 |        "0                 17.2                17.3                            0.479   \n",
214 |        "1                 17.5                17.5                            0.476   \n",
215 |        "2                 17.7                17.7                            0.470   \n",
216 |        "3                 17.9                18.0                            0.463   \n",
217 |        "4                 18.2                18.2                            0.454   \n",
218 |        "\n",
219 |        "   Schooling  \n",
220 |        "0       10.1  \n",
221 |        "1       10.0  \n",
222 |        "2        9.9  \n",
223 |        "3        9.8  \n",
224 |        "4        9.5  \n",
225 |        "\n",
226 |        "[5 rows x 22 columns]"
227 |       ]
228 |      },
229 |      "execution_count": 6,
230 |      "metadata": {},
231 |      "output_type": "execute_result"
232 |     }
233 |    ],
234 |    "source": [
235 |     "df = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/regression-one.csv')\n",
236 |     "df.head()"
237 |    ]
238 |   },
239 |   {
240 |    "cell_type": "code",
241 |    "execution_count": 7,
242 |    "metadata": {},
243 |    "outputs": [
244 |     {
245 |      "data": {
246 |       "text/plain": [
247 |        "(1649, 22)"
248 |       ]
249 |      },
250 |      "execution_count": 7,
251 |      "metadata": {},
252 |      "output_type": "execute_result"
253 |     }
254 |    ],
255 |    "source": [
256 |     "df.rename(columns={'Life expectancy': 'life-expect'}, inplace=True)\n",
257 |     "df = df.dropna()\n",
258 |     "df.shape"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 9,
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "data": {
268 |       "text/html": [
269 |        "<div>\n",
270 |        "<style scoped>\n",
271 |        "    .dataframe tbody tr th:only-of-type {\n",
272 |        "        vertical-align: middle;\n",
273 |        "    }\n",
274 |        "\n",
275 |        "    .dataframe tbody tr th {\n",
276 |        "        vertical-align: top;\n",
277 |        "    }\n",
278 |        "\n",
279 |        "    .dataframe thead th {\n",
280 |        "        text-align: right;\n",
281 |        "    }\n",
282 |        "</style>\n",
283 |        "<table border=\"1\" class=\"dataframe\">\n",
284 |        "  <thead>\n",
285 |        "    <tr style=\"text-align: right;\">\n",
286 |        "      <th></th>\n",
287 |        "      <th>Country</th>\n",
288 |        "      <th>Year</th>\n",
289 |        "      <th>Status</th>\n",
290 |        "      <th>life-expect</th>\n",
291 |        "      <th>Adult Mortality</th>\n",
292 |        "      <th>infant deaths</th>\n",
293 |        "      <th>Alcohol</th>\n",
294 |        "      <th>percentage expenditure</th>\n",
295 |        "      <th>Hepatitis B</th>\n",
296 |        "      <th>Measles</th>\n",
297 |        "      <th>...</th>\n",
298 |        "      <th>Polio</th>\n",
299 |        "      <th>Total expenditure</th>\n",
300 |        "      <th>Diphtheria</th>\n",
301 |        "      <th>HIV/AIDS</th>\n",
302 |        "      <th>GDP</th>\n",
303 |        "      <th>Population</th>\n",
304 |        "      <th>thinness 1-19 years</th>\n",
305 |        "      <th>thinness 5-9 years</th>\n",
306 |        "      <th>Income composition of resources</th>\n",
307 |        "      <th>Schooling</th>\n",
308 |        "    </tr>\n",
309 |        "  </thead>\n",
310 |        "  <tbody>\n",
311 |        "  </tbody>\n",
312 |        "</table>\n",
313 |        "<p>0 rows × 22 columns</p>\n",
314 |        "</div>"
315 |       ],
316 |       "text/plain": [
317 |        "Empty DataFrame\n",
318 |        "Columns: [Country, Year, Status, life-expect, Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles, BMI, under-five deaths, Polio, Total expenditure, Diphtheria, HIV/AIDS, GDP, Population, thinness 1-19 years, thinness 5-9 years, Income composition of resources, Schooling]\n",
319 |        "Index: []\n",
320 |        "\n",
321 |        "[0 rows x 22 columns]"
322 |       ]
323 |      },
324 |      "execution_count": 9,
325 |      "metadata": {},
326 |      "output_type": "execute_result"
327 |     }
328 |    ],
329 |    "source": [
330 |     "df[df.isnull().any(axis=1)]"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 11,
336 |    "metadata": {},
337 |    "outputs": [
338 |     {
339 |      "name": "stdout",
340 |      "output_type": "stream",
341 |      "text": [
342 |       "Collecting sklearn\n",
343 |       "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
344 |       "Collecting scikit-learn\n",
345 |       "  Downloading scikit_learn-0.24.0-cp39-cp39-win_amd64.whl (6.9 MB)\n",
346 |       "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\zinmy\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from scikit-learn->sklearn) (1.19.4)\n",
347 |       "Collecting joblib>=0.11\n",
348 |       "  Downloading joblib-1.0.0-py3-none-any.whl (302 kB)\n",
349 |       "Collecting scipy>=0.19.1\n",
350 |       "  Downloading scipy-1.5.4-cp39-cp39-win_amd64.whl (31.4 MB)\n",
351 |       "Collecting threadpoolctl>=2.0.0\n",
352 |       "  Downloading threadpoolctl-2.1.0-py3-none-any.whl (12 kB)\n",
353 |       "Using legacy 'setup.py install' for sklearn, since package 'wheel' is not installed.\n",
354 |       "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn, sklearn\n",
355 |       "    Running setup.py install for sklearn: started\n",
356 |       "    Running setup.py install for sklearn: finished with status 'done'\n",
357 |       "Successfully installed joblib-1.0.0 scikit-learn-0.24.0 scipy-1.5.4 sklearn-0.0 threadpoolctl-2.1.0\n"
358 |      ]
359 |     }
360 |    ],
361 |    "source": [
362 |     "!pip install sklearn"
363 |    ]
364 |   },
365 |   {
366 |    "cell_type": "code",
367 |    "execution_count": 12,
368 |    "metadata": {},
369 |    "outputs": [
370 |     {
371 |      "data": {
372 |       "text/plain": [
373 |        "LinearRegression()"
374 |       ]
375 |      },
376 |      "execution_count": 12,
377 |      "metadata": {},
378 |      "output_type": "execute_result"
379 |     }
380 |    ],
381 |    "source": [
382 |     "from sklearn.linear_model import LinearRegression\n",
383 |     "\n",
384 |     "slr = LinearRegression()\n",
385 |     "SLRX = df[['Schooling']]\n",
386 |     "Y = df['life-expect']\n",
387 |     "slr.fit(SLRX, Y)"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 13,
393 |    "metadata": {},
394 |    "outputs": [],
395 |    "source": [
396 |     "SLRYhat = slr.predict(SLRX)"
397 |    ]
398 |   },
399 |   {
400 |    "cell_type": "code",
401 |    "execution_count": 14,
402 |    "metadata": {},
403 |    "outputs": [
404 |     {
405 |      "name": "stdout",
406 |      "output_type": "stream",
407 |      "text": [
408 |       "[64.6771856  64.44820695 64.2192283  63.99024965]\n"
409 |      ]
410 |     }
411 |    ],
412 |    "source": [
413 |     "print(SLRYhat[0:4])"
414 |    ]
415 |   },
416 |   {
417 |    "cell_type": "code",
418 |    "execution_count": 16,
419 |    "metadata": {},
420 |    "outputs": [
421 |     {
422 |      "name": "stdout",
423 |      "output_type": "stream",
424 |      "text": [
425 |       "MSE for Simple Linear Regression :  36.39144686879598\n"
426 |      ]
427 |     }
428 |    ],
429 |    "source": [
430 |     "from sklearn.metrics import mean_squared_error\n",
431 |     "\n",
432 |     "SLR_MSE = mean_squared_error(df['life-expect'], SLRYhat)\n",
433 |     "print('MSE for Simple Linear Regression : ', SLR_MSE)"
434 |    ]
435 |   },
436 |   {
437 |    "cell_type": "code",
438 |    "execution_count": 17,
439 |    "metadata": {},
440 |    "outputs": [
441 |     {
442 |      "name": "stdout",
443 |      "output_type": "stream",
444 |      "text": [
445 |       "R2 for Simple Linear Regression :  0.5294454639356108\n"
446 |      ]
447 |     }
448 |    ],
449 |    "source": [
450 |     "SLR_R2 = slr.score(SLRX, Y)\n",
451 |     "print('R2 for Simple Linear Regression : ', SLR_R2)"
452 |    ]
453 |   },
454 |   {
455 |    "cell_type": "code",
456 |    "execution_count": 18,
457 |    "metadata": {},
458 |    "outputs": [
459 |     {
460 |      "data": {
461 |       "text/plain": [
462 |        "LinearRegression()"
463 |       ]
464 |      },
465 |      "execution_count": 18,
466 |      "metadata": {},
467 |      "output_type": "execute_result"
468 |     }
469 |    ],
470 |    "source": [
471 |     "MLRX = df[['GDP', 'Alcohol', 'BMI', 'Schooling']]\n",
472 |     "mlr = LinearRegression()\n",
473 |     "mlr.fit(MLRX, Y)"
474 |    ]
475 |   },
476 |   {
477 |    "cell_type": "code",
478 |    "execution_count": 19,
479 |    "metadata": {},
480 |    "outputs": [
481 |     {
482 |      "name": "stdout",
483 |      "output_type": "stream",
484 |      "text": [
485 |       "[64.24861418 64.01295717 63.77621795 63.54168785]\n"
486 |      ]
487 |     }
488 |    ],
489 |    "source": [
490 |     "MLRYhat = mlr.predict(MLRX)\n",
491 |     "print(MLRYhat[0:4])"
492 |    ]
493 |   },
494 |   {
495 |    "cell_type": "code",
496 |    "execution_count": 20,
497 |    "metadata": {},
498 |    "outputs": [
499 |     {
500 |      "name": "stdout",
501 |      "output_type": "stream",
502 |      "text": [
503 |       "MSE for Multiple Linear Regression :  32.66263978796142\n"
504 |      ]
505 |     }
506 |    ],
507 |    "source": [
508 |     "MLR_MSE = mean_squared_error(df['life-expect'], MLRYhat)\n",
509 |     "print('MSE for Multiple Linear Regression : ', MLR_MSE)"
510 |    ]
511 |   },
512 |   {
513 |    "cell_type": "code",
514 |    "execution_count": 22,
515 |    "metadata": {},
516 |    "outputs": [
517 |     {
518 |      "name": "stdout",
519 |      "output_type": "stream",
520 |      "text": [
521 |       "R2 for Multiple Linear Regression :  0.5776602846411929\n"
522 |      ]
523 |     }
524 |    ],
525 |    "source": [
526 |     "MLR_R2 = mlr.score(MLRX, Y)\n",
527 |     "print('R2 for Multiple Linear Regression : ', MLR_R2)"
528 |    ]
529 |   },
530 |   {
531 |    "cell_type": "code",
532 |    "execution_count": 23,
533 |    "metadata": {},
534 |    "outputs": [],
535 |    "source": [
536 |     "PRX = df['Schooling']\n",
537 |     "f = np.polyfit(PRX, Y, 3)\n",
538 |     "PR = np.poly1d(f)"
539 |    ]
540 |   },
541 |   {
542 |    "cell_type": "code",
543 |    "execution_count": 24,
544 |    "metadata": {},
545 |    "outputs": [
546 |     {
547 |      "name": "stdout",
548 |      "output_type": "stream",
549 |      "text": [
550 |       "[63.54170723 63.28970051 63.04117145 62.79627248]\n"
551 |      ]
552 |     }
553 |    ],
554 |    "source": [
555 |     "PRYhat = PR(PRX)\n",
556 |     "print(PRYhat[0:4])"
557 |    ]
558 |   },
559 |   {
560 |    "cell_type": "code",
561 |    "execution_count": 25,
562 |    "metadata": {},
563 |    "outputs": [
564 |     {
565 |      "name": "stdout",
566 |      "output_type": "stream",
567 |      "text": [
568 |       "MSE for Polynomial Regression :  34.73390295312004\n"
569 |      ]
570 |     }
571 |    ],
572 |    "source": [
573 |     "PR_MSE = mean_squared_error(df['life-expect'], PRYhat)\n",
574 |     "print('MSE for Polynomial Regression : ', PR_MSE)"
575 |    ]
576 |   },
577 |   {
578 |    "cell_type": "code",
579 |    "execution_count": 26,
580 |    "metadata": {},
581 |    "outputs": [
582 |     {
583 |      "name": "stdout",
584 |      "output_type": "stream",
585 |      "text": [
586 |       "R2 for Polynomial Regression :  0.5508781047168166\n"
587 |      ]
588 |     }
589 |    ],
590 |    "source": [
591 |     "from sklearn.metrics import r2_score\n",
592 |     "PR_R2 = r2_score(df['life-expect'], PRYhat)\n",
593 |     "print('R2 for Polynomial Regression : ', PR_R2)"
594 |    ]
595 |   },
596 |   {
597 |    "cell_type": "code",
598 |    "execution_count": 27,
599 |    "metadata": {},
600 |    "outputs": [
601 |     {
602 |      "name": "stdout",
603 |      "output_type": "stream",
604 |      "text": [
605 |       "Simple Linear Regression\n",
606 |       "MSE for Simple Linear Regression :  36.39144686879598\n",
607 |       "R2 for Simple Linear Regression :  0.5294454639356108\n"
608 |      ]
609 |     }
610 |    ],
611 |    "source": [
612 |     "print('Simple Linear Regression')\n",
613 |     "print('MSE for Simple Linear Regression : ', SLR_MSE)\n",
614 |     "print('R2 for Simple Linear Regression : ', SLR_R2)"
615 |    ]
616 |   },
617 |   {
618 |    "cell_type": "code",
619 |    "execution_count": 28,
620 |    "metadata": {},
621 |    "outputs": [
622 |     {
623 |      "name": "stdout",
624 |      "output_type": "stream",
625 |      "text": [
626 |       "Multiple Linear Regression\n",
627 |       "MSE for Multiple Linear Regression :  32.66263978796142\n",
628 |       "R2 for Multiple Linear Regression :  0.5776602846411929\n"
629 |      ]
630 |     }
631 |    ],
632 |    "source": [
633 |     "print('Multiple Linear Regression')\n",
634 |     "print('MSE for Multiple Linear Regression : ', MLR_MSE)\n",
635 |     "print('R2 for Multiple Linear Regression : ', MLR_R2)"
636 |    ]
637 |   },
638 |   {
639 |    "cell_type": "code",
640 |    "execution_count": 29,
641 |    "metadata": {},
642 |    "outputs": [
643 |     {
644 |      "name": "stdout",
645 |      "output_type": "stream",
646 |      "text": [
647 |       "Polynomial Regression\n",
648 |       "MSE for Polynomial Regression :  34.73390295312004\n",
649 |       "R2 for Polynomial Regression :  0.5508781047168166\n"
650 |      ]
651 |     }
652 |    ],
653 |    "source": [
654 |     "print('Polynomial Regression')\n",
655 |     "print('MSE for Polynomial Regression : ', PR_MSE)\n",
656 |     "print('R2 for Polynomial Regression : ', PR_R2)"
657 |    ]
658 |   },
659 |   {
660 |    "cell_type": "code",
661 |    "execution_count": null,
662 |    "metadata": {},
663 |    "outputs": [],
664 |    "source": []
665 |   }
666 |  ],
667 |  "metadata": {
668 |   "kernelspec": {
669 |    "display_name": "Python 3",
670 |    "language": "python",
671 |    "name": "python3"
672 |   },
673 |   "language_info": {
674 |    "codemirror_mode": {
675 |     "name": "ipython",
676 |     "version": 3
677 |    },
678 |    "file_extension": ".py",
679 |    "mimetype": "text/x-python",
680 |    "name": "python",
681 |    "nbconvert_exporter": "python",
682 |    "pygments_lexer": "ipython3",
683 |    "version": "3.9.1"
684 |   }
685 |  },
686 |  "nbformat": 4,
687 |  "nbformat_minor": 4
688 | }
689 | 


--------------------------------------------------------------------------------
/P09-LogisticRegression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Practical - Logistic Regression\n",
  8 |     "\n",
  9 |     "Please refer to two parts of lecture before proceeding to this lab session.\n",
 10 |     "Part 1 - https://www.youtube.com/watch?v=jLe1nILDRbU\n",
 11 |     "Part 2 - https://www.youtube.com/watch?v=wD5wJvwVohU\n",
 12 |     "For those who would like to refresh Python skill, we would like to recommend our \"Programming for Data Science Series\" where we covered almost all aspects of Python programming in data science domain. Refer below URL for full playlist of almost 10 hours video lesson in Burmese Language. URL : https://www.youtube.com/watch?v=jOZNjVVZIVs&list=PLD_eiqVVLZDi9GZZJDC8Zx4-3Np8LHs52"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 1,
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "text/html": [
 23 |        "<div>\n",
 24 |        "<style scoped>\n",
 25 |        "    .dataframe tbody tr th:only-of-type {\n",
 26 |        "        vertical-align: middle;\n",
 27 |        "    }\n",
 28 |        "\n",
 29 |        "    .dataframe tbody tr th {\n",
 30 |        "        vertical-align: top;\n",
 31 |        "    }\n",
 32 |        "\n",
 33 |        "    .dataframe thead th {\n",
 34 |        "        text-align: right;\n",
 35 |        "    }\n",
 36 |        "</style>\n",
 37 |        "<table border=\"1\" class=\"dataframe\">\n",
 38 |        "  <thead>\n",
 39 |        "    <tr style=\"text-align: right;\">\n",
 40 |        "      <th></th>\n",
 41 |        "      <th>age</th>\n",
 42 |        "      <th>education</th>\n",
 43 |        "      <th>experience</th>\n",
 44 |        "      <th>selected</th>\n",
 45 |        "    </tr>\n",
 46 |        "  </thead>\n",
 47 |        "  <tbody>\n",
 48 |        "    <tr>\n",
 49 |        "      <th>0</th>\n",
 50 |        "      <td>24</td>\n",
 51 |        "      <td>1.0</td>\n",
 52 |        "      <td>2</td>\n",
 53 |        "      <td>0</td>\n",
 54 |        "    </tr>\n",
 55 |        "    <tr>\n",
 56 |        "      <th>1</th>\n",
 57 |        "      <td>35</td>\n",
 58 |        "      <td>3.5</td>\n",
 59 |        "      <td>8</td>\n",
 60 |        "      <td>1</td>\n",
 61 |        "    </tr>\n",
 62 |        "    <tr>\n",
 63 |        "      <th>2</th>\n",
 64 |        "      <td>23</td>\n",
 65 |        "      <td>1.0</td>\n",
 66 |        "      <td>1</td>\n",
 67 |        "      <td>0</td>\n",
 68 |        "    </tr>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>3</th>\n",
 71 |        "      <td>35</td>\n",
 72 |        "      <td>3.0</td>\n",
 73 |        "      <td>7</td>\n",
 74 |        "      <td>1</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>4</th>\n",
 78 |        "      <td>45</td>\n",
 79 |        "      <td>1.0</td>\n",
 80 |        "      <td>10</td>\n",
 81 |        "      <td>0</td>\n",
 82 |        "    </tr>\n",
 83 |        "  </tbody>\n",
 84 |        "</table>\n",
 85 |        "</div>"
 86 |       ],
 87 |       "text/plain": [
 88 |        "   age  education  experience  selected\n",
 89 |        "0   24        1.0           2         0\n",
 90 |        "1   35        3.5           8         1\n",
 91 |        "2   23        1.0           1         0\n",
 92 |        "3   35        3.0           7         1\n",
 93 |        "4   45        1.0          10         0"
 94 |       ]
 95 |      },
 96 |      "execution_count": 1,
 97 |      "metadata": {},
 98 |      "output_type": "execute_result"
 99 |     }
100 |    ],
101 |    "source": [
102 |     "import pandas as pd\n",
103 |     "from sklearn.model_selection import train_test_split\n",
104 |     "from sklearn.linear_model import LogisticRegression\n",
105 |     "from sklearn import metrics\n",
106 |     "import seaborn as sn\n",
107 |     "\n",
108 |     "df = pd.read_csv('https://raw.githubusercontent.com/myanmards/resource_files/master/job-fit.csv')\n",
109 |     "df.head()"
110 |    ]
111 |   },
112 |   {
113 |    "cell_type": "code",
114 |    "execution_count": 2,
115 |    "metadata": {},
116 |    "outputs": [],
117 |    "source": [
118 |     "X = df[['age', 'education', 'experience']]\n",
119 |     "y = df['selected']"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": 3,
125 |    "metadata": {},
126 |    "outputs": [],
127 |    "source": [
128 |     "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 4,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/plain": [
139 |        "LogisticRegression()"
140 |       ]
141 |      },
142 |      "execution_count": 4,
143 |      "metadata": {},
144 |      "output_type": "execute_result"
145 |     }
146 |    ],
147 |    "source": [
148 |     "LR = LogisticRegression()\n",
149 |     "LR.fit(X_train, y_train)"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 5,
155 |    "metadata": {},
156 |    "outputs": [
157 |     {
158 |      "data": {
159 |       "text/plain": [
160 |        "array([1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1], dtype=int64)"
161 |       ]
162 |      },
163 |      "execution_count": 5,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "y_pred = LR.predict(X_test)\n",
170 |     "y_pred"
171 |    ]
172 |   },
173 |   {
174 |    "cell_type": "code",
175 |    "execution_count": 6,
176 |    "metadata": {},
177 |    "outputs": [
178 |     {
179 |      "data": {
180 |       "text/plain": [
181 |        "<AxesSubplot:xlabel='Predicted', ylabel='Actual'>"
182 |       ]
183 |      },
184 |      "execution_count": 6,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     },
188 |     {
189 |      "data": {
190 |       "image/png": 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\n",
191 |       "text/plain": [
192 |        "<Figure size 432x288 with 2 Axes>"
193 |       ]
194 |      },
195 |      "metadata": {
196 |       "needs_background": "light"
197 |      },
198 |      "output_type": "display_data"
199 |     }
200 |    ],
201 |    "source": [
202 |     "confusion_matrix = pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
203 |     "sn.heatmap(confusion_matrix, annot=True)"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 7,
209 |    "metadata": {},
210 |    "outputs": [
211 |     {
212 |      "name": "stdout",
213 |      "output_type": "stream",
214 |      "text": [
215 |       "Accuracy Score :  0.8461538461538461\n"
216 |      ]
217 |     }
218 |    ],
219 |    "source": [
220 |     "print('Accuracy Score : ', metrics.accuracy_score(y_test, y_pred))"
221 |    ]
222 |   },
223 |   {
224 |    "cell_type": "code",
225 |    "execution_count": 10,
226 |    "metadata": {},
227 |    "outputs": [
228 |     {
229 |      "data": {
230 |       "text/html": [
231 |        "<div>\n",
232 |        "<style scoped>\n",
233 |        "    .dataframe tbody tr th:only-of-type {\n",
234 |        "        vertical-align: middle;\n",
235 |        "    }\n",
236 |        "\n",
237 |        "    .dataframe tbody tr th {\n",
238 |        "        vertical-align: top;\n",
239 |        "    }\n",
240 |        "\n",
241 |        "    .dataframe thead th {\n",
242 |        "        text-align: right;\n",
243 |        "    }\n",
244 |        "</style>\n",
245 |        "<table border=\"1\" class=\"dataframe\">\n",
246 |        "  <thead>\n",
247 |        "    <tr style=\"text-align: right;\">\n",
248 |        "      <th></th>\n",
249 |        "      <th>age</th>\n",
250 |        "      <th>education</th>\n",
251 |        "      <th>experience</th>\n",
252 |        "    </tr>\n",
253 |        "  </thead>\n",
254 |        "  <tbody>\n",
255 |        "    <tr>\n",
256 |        "      <th>0</th>\n",
257 |        "      <td>25</td>\n",
258 |        "      <td>3</td>\n",
259 |        "      <td>5</td>\n",
260 |        "    </tr>\n",
261 |        "  </tbody>\n",
262 |        "</table>\n",
263 |        "</div>"
264 |       ],
265 |       "text/plain": [
266 |        "   age  education  experience\n",
267 |        "0   25          3           5"
268 |       ]
269 |      },
270 |      "execution_count": 10,
271 |      "metadata": {},
272 |      "output_type": "execute_result"
273 |     }
274 |    ],
275 |    "source": [
276 |     "new_candidate = {\n",
277 |     "    'age': [25],\n",
278 |     "    'education' : [3],\n",
279 |     "    'experience' : [5]\n",
280 |     "}\n",
281 |     "\n",
282 |     "new_df = pd.DataFrame(new_candidate, columns=['age', 'education', 'experience'])\n",
283 |     "new_df"
284 |    ]
285 |   },
286 |   {
287 |    "cell_type": "code",
288 |    "execution_count": 11,
289 |    "metadata": {},
290 |    "outputs": [
291 |     {
292 |      "name": "stdout",
293 |      "output_type": "stream",
294 |      "text": [
295 |       "[1]\n"
296 |      ]
297 |     }
298 |    ],
299 |    "source": [
300 |     "y_new_pred = LR.predict(new_df)\n",
301 |     "print(y_new_pred)"
302 |    ]
303 |   },
304 |   {
305 |    "cell_type": "code",
306 |    "execution_count": null,
307 |    "metadata": {},
308 |    "outputs": [],
309 |    "source": []
310 |   }
311 |  ],
312 |  "metadata": {
313 |   "kernelspec": {
314 |    "display_name": "Python 3",
315 |    "language": "python",
316 |    "name": "python3"
317 |   },
318 |   "language_info": {
319 |    "codemirror_mode": {
320 |     "name": "ipython",
321 |     "version": 3
322 |    },
323 |    "file_extension": ".py",
324 |    "mimetype": "text/x-python",
325 |    "name": "python",
326 |    "nbconvert_exporter": "python",
327 |    "pygments_lexer": "ipython3",
328 |    "version": "3.9.1"
329 |   }
330 |  },
331 |  "nbformat": 4,
332 |  "nbformat_minor": 4
333 | }
334 | 


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/README.md:
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1 | # data-analysis


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