├── AB_testing.ipynb
├── README.md
└── campaigns.csv


/AB_testing.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# A/B Testing on Marketing Campaigns"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 1,
  13 |    "metadata": {},
  14 |    "outputs": [],
  15 |    "source": [
  16 |     "import pandas as pd\n",
  17 |     "import matplotlib.pyplot as plt\n",
  18 |     "import seaborn as sns\n",
  19 |     "from scipy import stats\n",
  20 |     "sns.set()"
  21 |    ]
  22 |   },
  23 |   {
  24 |    "cell_type": "code",
  25 |    "execution_count": 2,
  26 |    "metadata": {
  27 |     "colab": {
  28 |      "base_uri": "https://localhost:8080/",
  29 |      "height": 355
  30 |     },
  31 |     "colab_type": "code",
  32 |     "executionInfo": {
  33 |      "elapsed": 1618,
  34 |      "status": "ok",
  35 |      "timestamp": 1573997963947,
  36 |      "user": {
  37 |       "displayName": "Rajeev Ratan",
  38 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
  39 |       "userId": "08597265227091462140"
  40 |      },
  41 |      "user_tz": 0
  42 |     },
  43 |     "id": "JFrtn4mCiqyP",
  44 |     "outputId": "9376b70e-ac45-4c88-8ccf-47d1a91d0c4b"
  45 |    },
  46 |    "outputs": [
  47 |     {
  48 |      "data": {
  49 |       "text/html": [
  50 |        "<div>\n",
  51 |        "<style scoped>\n",
  52 |        "    .dataframe tbody tr th:only-of-type {\n",
  53 |        "        vertical-align: middle;\n",
  54 |        "    }\n",
  55 |        "\n",
  56 |        "    .dataframe tbody tr th {\n",
  57 |        "        vertical-align: top;\n",
  58 |        "    }\n",
  59 |        "\n",
  60 |        "    .dataframe thead th {\n",
  61 |        "        text-align: right;\n",
  62 |        "    }\n",
  63 |        "</style>\n",
  64 |        "<table border=\"1\" class=\"dataframe\">\n",
  65 |        "  <thead>\n",
  66 |        "    <tr style=\"text-align: right;\">\n",
  67 |        "      <th></th>\n",
  68 |        "      <th>OutletID</th>\n",
  69 |        "      <th>MarketSize</th>\n",
  70 |        "      <th>AgeofOutlets</th>\n",
  71 |        "      <th>Campaigns</th>\n",
  72 |        "      <th>Week</th>\n",
  73 |        "      <th>SalesInThousands</th>\n",
  74 |        "    </tr>\n",
  75 |        "  </thead>\n",
  76 |        "  <tbody>\n",
  77 |        "    <tr>\n",
  78 |        "      <th>0</th>\n",
  79 |        "      <td>1</td>\n",
  80 |        "      <td>Medium</td>\n",
  81 |        "      <td>4</td>\n",
  82 |        "      <td>3</td>\n",
  83 |        "      <td>1</td>\n",
  84 |        "      <td>33.73</td>\n",
  85 |        "    </tr>\n",
  86 |        "    <tr>\n",
  87 |        "      <th>1</th>\n",
  88 |        "      <td>1</td>\n",
  89 |        "      <td>Medium</td>\n",
  90 |        "      <td>4</td>\n",
  91 |        "      <td>3</td>\n",
  92 |        "      <td>2</td>\n",
  93 |        "      <td>35.67</td>\n",
  94 |        "    </tr>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>2</th>\n",
  97 |        "      <td>1</td>\n",
  98 |        "      <td>Medium</td>\n",
  99 |        "      <td>4</td>\n",
 100 |        "      <td>3</td>\n",
 101 |        "      <td>3</td>\n",
 102 |        "      <td>29.03</td>\n",
 103 |        "    </tr>\n",
 104 |        "    <tr>\n",
 105 |        "      <th>3</th>\n",
 106 |        "      <td>1</td>\n",
 107 |        "      <td>Medium</td>\n",
 108 |        "      <td>4</td>\n",
 109 |        "      <td>3</td>\n",
 110 |        "      <td>4</td>\n",
 111 |        "      <td>39.25</td>\n",
 112 |        "    </tr>\n",
 113 |        "    <tr>\n",
 114 |        "      <th>4</th>\n",
 115 |        "      <td>2</td>\n",
 116 |        "      <td>Medium</td>\n",
 117 |        "      <td>5</td>\n",
 118 |        "      <td>2</td>\n",
 119 |        "      <td>1</td>\n",
 120 |        "      <td>27.81</td>\n",
 121 |        "    </tr>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>5</th>\n",
 124 |        "      <td>2</td>\n",
 125 |        "      <td>Medium</td>\n",
 126 |        "      <td>5</td>\n",
 127 |        "      <td>2</td>\n",
 128 |        "      <td>2</td>\n",
 129 |        "      <td>34.67</td>\n",
 130 |        "    </tr>\n",
 131 |        "    <tr>\n",
 132 |        "      <th>6</th>\n",
 133 |        "      <td>2</td>\n",
 134 |        "      <td>Medium</td>\n",
 135 |        "      <td>5</td>\n",
 136 |        "      <td>2</td>\n",
 137 |        "      <td>3</td>\n",
 138 |        "      <td>27.98</td>\n",
 139 |        "    </tr>\n",
 140 |        "    <tr>\n",
 141 |        "      <th>7</th>\n",
 142 |        "      <td>2</td>\n",
 143 |        "      <td>Medium</td>\n",
 144 |        "      <td>5</td>\n",
 145 |        "      <td>2</td>\n",
 146 |        "      <td>4</td>\n",
 147 |        "      <td>27.72</td>\n",
 148 |        "    </tr>\n",
 149 |        "    <tr>\n",
 150 |        "      <th>8</th>\n",
 151 |        "      <td>3</td>\n",
 152 |        "      <td>Medium</td>\n",
 153 |        "      <td>12</td>\n",
 154 |        "      <td>1</td>\n",
 155 |        "      <td>1</td>\n",
 156 |        "      <td>44.54</td>\n",
 157 |        "    </tr>\n",
 158 |        "    <tr>\n",
 159 |        "      <th>9</th>\n",
 160 |        "      <td>3</td>\n",
 161 |        "      <td>Medium</td>\n",
 162 |        "      <td>12</td>\n",
 163 |        "      <td>1</td>\n",
 164 |        "      <td>2</td>\n",
 165 |        "      <td>37.94</td>\n",
 166 |        "    </tr>\n",
 167 |        "  </tbody>\n",
 168 |        "</table>\n",
 169 |        "</div>"
 170 |       ],
 171 |       "text/plain": [
 172 |        "   OutletID MarketSize  AgeofOutlets  Campaigns  Week  SalesInThousands\n",
 173 |        "0         1     Medium             4          3     1             33.73\n",
 174 |        "1         1     Medium             4          3     2             35.67\n",
 175 |        "2         1     Medium             4          3     3             29.03\n",
 176 |        "3         1     Medium             4          3     4             39.25\n",
 177 |        "4         2     Medium             5          2     1             27.81\n",
 178 |        "5         2     Medium             5          2     2             34.67\n",
 179 |        "6         2     Medium             5          2     3             27.98\n",
 180 |        "7         2     Medium             5          2     4             27.72\n",
 181 |        "8         3     Medium            12          1     1             44.54\n",
 182 |        "9         3     Medium            12          1     2             37.94"
 183 |       ]
 184 |      },
 185 |      "execution_count": 2,
 186 |      "metadata": {},
 187 |      "output_type": "execute_result"
 188 |     }
 189 |    ],
 190 |    "source": [
 191 |     "df = pd.read_csv('campaigns.csv')\n",
 192 |     "df.head(10)"
 193 |    ]
 194 |   },
 195 |   {
 196 |    "cell_type": "code",
 197 |    "execution_count": 3,
 198 |    "metadata": {},
 199 |    "outputs": [
 200 |     {
 201 |      "name": "stdout",
 202 |      "output_type": "stream",
 203 |      "text": [
 204 |       "Rows            : 548\n",
 205 |       "Columns         : 6\n",
 206 |       "Features        : ['OutletID', 'MarketSize', 'AgeofOutlets', 'Campaigns', 'Week', 'SalesInThousands']\n",
 207 |       "Missing Values  : 0\n",
 208 |       "Unique Values   : \n",
 209 |       "\n",
 210 |       "OutletID            137\n",
 211 |       "MarketSize            3\n",
 212 |       "AgeofOutlets         25\n",
 213 |       "Campaigns             3\n",
 214 |       "Week                  4\n",
 215 |       "SalesInThousands    517\n",
 216 |       "dtype: int64\n"
 217 |      ]
 218 |     }
 219 |    ],
 220 |    "source": [
 221 |     "print(f'Rows            : {df.shape[0]}')\n",
 222 |     "print(f'Columns         : {df.shape[1]}')\n",
 223 |     "print(f'Features        : {df.columns.tolist()}')\n",
 224 |     "print(f'Missing Values  : {df.isnull().values.sum()}')\n",
 225 |     "print(f'Unique Values   : \\n\\n{df.nunique()}')"
 226 |    ]
 227 |   },
 228 |   {
 229 |    "cell_type": "code",
 230 |    "execution_count": 4,
 231 |    "metadata": {
 232 |     "colab": {
 233 |      "base_uri": "https://localhost:8080/",
 234 |      "height": 294
 235 |     },
 236 |     "colab_type": "code",
 237 |     "executionInfo": {
 238 |      "elapsed": 991,
 239 |      "status": "ok",
 240 |      "timestamp": 1573997963951,
 241 |      "user": {
 242 |       "displayName": "Rajeev Ratan",
 243 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 244 |       "userId": "08597265227091462140"
 245 |      },
 246 |      "user_tz": 0
 247 |     },
 248 |     "id": "nXf1B035hQg7",
 249 |     "outputId": "ee48ccdb-5b41-4193-e21c-9eef41646494"
 250 |    },
 251 |    "outputs": [
 252 |     {
 253 |      "data": {
 254 |       "text/html": [
 255 |        "<div>\n",
 256 |        "<style scoped>\n",
 257 |        "    .dataframe tbody tr th:only-of-type {\n",
 258 |        "        vertical-align: middle;\n",
 259 |        "    }\n",
 260 |        "\n",
 261 |        "    .dataframe tbody tr th {\n",
 262 |        "        vertical-align: top;\n",
 263 |        "    }\n",
 264 |        "\n",
 265 |        "    .dataframe thead th {\n",
 266 |        "        text-align: right;\n",
 267 |        "    }\n",
 268 |        "</style>\n",
 269 |        "<table border=\"1\" class=\"dataframe\">\n",
 270 |        "  <thead>\n",
 271 |        "    <tr style=\"text-align: right;\">\n",
 272 |        "      <th></th>\n",
 273 |        "      <th>OutletID</th>\n",
 274 |        "      <th>AgeofOutlets</th>\n",
 275 |        "      <th>Campaigns</th>\n",
 276 |        "      <th>Week</th>\n",
 277 |        "      <th>SalesInThousands</th>\n",
 278 |        "    </tr>\n",
 279 |        "  </thead>\n",
 280 |        "  <tbody>\n",
 281 |        "    <tr>\n",
 282 |        "      <th>count</th>\n",
 283 |        "      <td>548.000000</td>\n",
 284 |        "      <td>548.000000</td>\n",
 285 |        "      <td>548.000000</td>\n",
 286 |        "      <td>548.000000</td>\n",
 287 |        "      <td>548.000000</td>\n",
 288 |        "    </tr>\n",
 289 |        "    <tr>\n",
 290 |        "      <th>mean</th>\n",
 291 |        "      <td>479.656934</td>\n",
 292 |        "      <td>8.503650</td>\n",
 293 |        "      <td>2.029197</td>\n",
 294 |        "      <td>2.500000</td>\n",
 295 |        "      <td>53.466204</td>\n",
 296 |        "    </tr>\n",
 297 |        "    <tr>\n",
 298 |        "      <th>std</th>\n",
 299 |        "      <td>287.973679</td>\n",
 300 |        "      <td>6.638345</td>\n",
 301 |        "      <td>0.810729</td>\n",
 302 |        "      <td>1.119055</td>\n",
 303 |        "      <td>16.755216</td>\n",
 304 |        "    </tr>\n",
 305 |        "    <tr>\n",
 306 |        "      <th>min</th>\n",
 307 |        "      <td>1.000000</td>\n",
 308 |        "      <td>1.000000</td>\n",
 309 |        "      <td>1.000000</td>\n",
 310 |        "      <td>1.000000</td>\n",
 311 |        "      <td>17.340000</td>\n",
 312 |        "    </tr>\n",
 313 |        "    <tr>\n",
 314 |        "      <th>25%</th>\n",
 315 |        "      <td>216.000000</td>\n",
 316 |        "      <td>4.000000</td>\n",
 317 |        "      <td>1.000000</td>\n",
 318 |        "      <td>1.750000</td>\n",
 319 |        "      <td>42.545000</td>\n",
 320 |        "    </tr>\n",
 321 |        "    <tr>\n",
 322 |        "      <th>50%</th>\n",
 323 |        "      <td>504.000000</td>\n",
 324 |        "      <td>7.000000</td>\n",
 325 |        "      <td>2.000000</td>\n",
 326 |        "      <td>2.500000</td>\n",
 327 |        "      <td>50.200000</td>\n",
 328 |        "    </tr>\n",
 329 |        "    <tr>\n",
 330 |        "      <th>75%</th>\n",
 331 |        "      <td>708.000000</td>\n",
 332 |        "      <td>12.000000</td>\n",
 333 |        "      <td>3.000000</td>\n",
 334 |        "      <td>3.250000</td>\n",
 335 |        "      <td>60.477500</td>\n",
 336 |        "    </tr>\n",
 337 |        "    <tr>\n",
 338 |        "      <th>max</th>\n",
 339 |        "      <td>920.000000</td>\n",
 340 |        "      <td>28.000000</td>\n",
 341 |        "      <td>3.000000</td>\n",
 342 |        "      <td>4.000000</td>\n",
 343 |        "      <td>99.650000</td>\n",
 344 |        "    </tr>\n",
 345 |        "  </tbody>\n",
 346 |        "</table>\n",
 347 |        "</div>"
 348 |       ],
 349 |       "text/plain": [
 350 |        "         OutletID  AgeofOutlets   Campaigns        Week  SalesInThousands\n",
 351 |        "count  548.000000    548.000000  548.000000  548.000000        548.000000\n",
 352 |        "mean   479.656934      8.503650    2.029197    2.500000         53.466204\n",
 353 |        "std    287.973679      6.638345    0.810729    1.119055         16.755216\n",
 354 |        "min      1.000000      1.000000    1.000000    1.000000         17.340000\n",
 355 |        "25%    216.000000      4.000000    1.000000    1.750000         42.545000\n",
 356 |        "50%    504.000000      7.000000    2.000000    2.500000         50.200000\n",
 357 |        "75%    708.000000     12.000000    3.000000    3.250000         60.477500\n",
 358 |        "max    920.000000     28.000000    3.000000    4.000000         99.650000"
 359 |       ]
 360 |      },
 361 |      "execution_count": 4,
 362 |      "metadata": {},
 363 |      "output_type": "execute_result"
 364 |     }
 365 |    ],
 366 |    "source": [
 367 |     "df.describe()"
 368 |    ]
 369 |   },
 370 |   {
 371 |    "cell_type": "markdown",
 372 |    "metadata": {
 373 |     "colab_type": "text",
 374 |     "id": "lc6EtBPmT2jt"
 375 |    },
 376 |    "source": [
 377 |     "# EDA and Visualizations"
 378 |    ]
 379 |   },
 380 |   {
 381 |    "cell_type": "code",
 382 |    "execution_count": 5,
 383 |    "metadata": {
 384 |     "colab": {
 385 |      "base_uri": "https://localhost:8080/",
 386 |      "height": 483
 387 |     },
 388 |     "colab_type": "code",
 389 |     "executionInfo": {
 390 |      "elapsed": 1296,
 391 |      "status": "ok",
 392 |      "timestamp": 1573997964880,
 393 |      "user": {
 394 |       "displayName": "Rajeev Ratan",
 395 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 396 |       "userId": "08597265227091462140"
 397 |      },
 398 |      "user_tz": 0
 399 |     },
 400 |     "id": "doSO_gqViqyZ",
 401 |     "outputId": "a84827fc-fe67-41ea-db14-e1fced3cc5e7"
 402 |    },
 403 |    "outputs": [
 404 |     {
 405 |      "data": {
 406 |       "image/png": 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\n",
 407 |       "text/plain": [
 408 |        "<Figure size 576x576 with 1 Axes>"
 409 |       ]
 410 |      },
 411 |      "metadata": {},
 412 |      "output_type": "display_data"
 413 |     }
 414 |    ],
 415 |    "source": [
 416 |     "# Create some plots to visual our sales and marketing data\n",
 417 |     "# Using fivethirtyeight style on matplotlib\n",
 418 |     "\n",
 419 |     "plt.style.use('fivethirtyeight')\n",
 420 |     "ax = df.groupby('Campaigns').sum()['SalesInThousands'].plot.pie(figsize=(8,8),\n",
 421 |     "                                                                autopct='%1.0f%%',\n",
 422 |     "                                                                explode = (0, 0.1, 0),\n",
 423 |     "                                                                textprops = {'fontsize' : 15})\n",
 424 |     "ax.set_ylabel('')\n",
 425 |     "ax.set_title('Sales Distribution Across the 3 Different Promotions', fontsize = 20)\n",
 426 |     "\n",
 427 |     "plt.show()"
 428 |    ]
 429 |   },
 430 |   {
 431 |    "cell_type": "code",
 432 |    "execution_count": 6,
 433 |    "metadata": {
 434 |     "colab": {
 435 |      "base_uri": "https://localhost:8080/",
 436 |      "height": 208
 437 |     },
 438 |     "colab_type": "code",
 439 |     "executionInfo": {
 440 |      "elapsed": 1746,
 441 |      "status": "ok",
 442 |      "timestamp": 1573997965632,
 443 |      "user": {
 444 |       "displayName": "Rajeev Ratan",
 445 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 446 |       "userId": "08597265227091462140"
 447 |      },
 448 |      "user_tz": 0
 449 |     },
 450 |     "id": "KQ_wDqmWSwLW",
 451 |     "outputId": "34725d40-119a-44fe-d560-9036535e9741"
 452 |    },
 453 |    "outputs": [
 454 |     {
 455 |      "data": {
 456 |       "text/plain": [
 457 |        "Campaigns  MarketSize\n",
 458 |        "1          Large          56\n",
 459 |        "           Medium         96\n",
 460 |        "           Small          20\n",
 461 |        "2          Large          64\n",
 462 |        "           Medium        108\n",
 463 |        "           Small          16\n",
 464 |        "3          Large          48\n",
 465 |        "           Medium        116\n",
 466 |        "           Small          24\n",
 467 |        "Name: OutletID, dtype: int64"
 468 |       ]
 469 |      },
 470 |      "execution_count": 6,
 471 |      "metadata": {},
 472 |      "output_type": "execute_result"
 473 |     }
 474 |    ],
 475 |    "source": [
 476 |     "# Now let's view the promotions for each market size\n",
 477 |     "df.groupby(['Campaigns', 'MarketSize']).count()['OutletID']"
 478 |    ]
 479 |   },
 480 |   {
 481 |    "cell_type": "code",
 482 |    "execution_count": 7,
 483 |    "metadata": {
 484 |     "colab": {
 485 |      "base_uri": "https://localhost:8080/",
 486 |      "height": 171
 487 |     },
 488 |     "colab_type": "code",
 489 |     "executionInfo": {
 490 |      "elapsed": 1454,
 491 |      "status": "ok",
 492 |      "timestamp": 1573997965633,
 493 |      "user": {
 494 |       "displayName": "Rajeev Ratan",
 495 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 496 |       "userId": "08597265227091462140"
 497 |      },
 498 |      "user_tz": 0
 499 |     },
 500 |     "id": "7XsltrtKiOf7",
 501 |     "outputId": "94f3880a-9301-46fd-9fca-cfeba26253b8"
 502 |    },
 503 |    "outputs": [
 504 |     {
 505 |      "data": {
 506 |       "text/html": [
 507 |        "<div>\n",
 508 |        "<style scoped>\n",
 509 |        "    .dataframe tbody tr th:only-of-type {\n",
 510 |        "        vertical-align: middle;\n",
 511 |        "    }\n",
 512 |        "\n",
 513 |        "    .dataframe tbody tr th {\n",
 514 |        "        vertical-align: top;\n",
 515 |        "    }\n",
 516 |        "\n",
 517 |        "    .dataframe thead th {\n",
 518 |        "        text-align: right;\n",
 519 |        "    }\n",
 520 |        "</style>\n",
 521 |        "<table border=\"1\" class=\"dataframe\">\n",
 522 |        "  <thead>\n",
 523 |        "    <tr style=\"text-align: right;\">\n",
 524 |        "      <th>MarketSize</th>\n",
 525 |        "      <th>Large</th>\n",
 526 |        "      <th>Medium</th>\n",
 527 |        "      <th>Small</th>\n",
 528 |        "    </tr>\n",
 529 |        "    <tr>\n",
 530 |        "      <th>Campaigns</th>\n",
 531 |        "      <th></th>\n",
 532 |        "      <th></th>\n",
 533 |        "      <th></th>\n",
 534 |        "    </tr>\n",
 535 |        "  </thead>\n",
 536 |        "  <tbody>\n",
 537 |        "    <tr>\n",
 538 |        "      <th>1</th>\n",
 539 |        "      <td>56</td>\n",
 540 |        "      <td>96</td>\n",
 541 |        "      <td>20</td>\n",
 542 |        "    </tr>\n",
 543 |        "    <tr>\n",
 544 |        "      <th>2</th>\n",
 545 |        "      <td>64</td>\n",
 546 |        "      <td>108</td>\n",
 547 |        "      <td>16</td>\n",
 548 |        "    </tr>\n",
 549 |        "    <tr>\n",
 550 |        "      <th>3</th>\n",
 551 |        "      <td>48</td>\n",
 552 |        "      <td>116</td>\n",
 553 |        "      <td>24</td>\n",
 554 |        "    </tr>\n",
 555 |        "  </tbody>\n",
 556 |        "</table>\n",
 557 |        "</div>"
 558 |       ],
 559 |       "text/plain": [
 560 |        "MarketSize  Large  Medium  Small\n",
 561 |        "Campaigns                       \n",
 562 |        "1              56      96     20\n",
 563 |        "2              64     108     16\n",
 564 |        "3              48     116     24"
 565 |       ]
 566 |      },
 567 |      "execution_count": 7,
 568 |      "metadata": {},
 569 |      "output_type": "execute_result"
 570 |     }
 571 |    ],
 572 |    "source": [
 573 |     "# Using unstack\n",
 574 |     "df.groupby(['Campaigns', 'MarketSize']).count()['OutletID'].unstack('MarketSize')"
 575 |    ]
 576 |   },
 577 |   {
 578 |    "cell_type": "code",
 579 |    "execution_count": 8,
 580 |    "metadata": {
 581 |     "colab": {
 582 |      "base_uri": "https://localhost:8080/",
 583 |      "height": 622
 584 |     },
 585 |     "colab_type": "code",
 586 |     "executionInfo": {
 587 |      "elapsed": 1861,
 588 |      "status": "ok",
 589 |      "timestamp": 1573997966382,
 590 |      "user": {
 591 |       "displayName": "Rajeev Ratan",
 592 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 593 |       "userId": "08597265227091462140"
 594 |      },
 595 |      "user_tz": 0
 596 |     },
 597 |     "id": "SoJNWkPAiqyc",
 598 |     "outputId": "3467fbfc-2ecd-4ceb-ca20-9577fbcf58f3"
 599 |    },
 600 |    "outputs": [
 601 |     {
 602 |      "data": {
 603 |       "image/png": 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\n",
 604 |       "text/plain": [
 605 |        "<Figure size 720x576 with 1 Axes>"
 606 |       ]
 607 |      },
 608 |      "metadata": {},
 609 |      "output_type": "display_data"
 610 |     }
 611 |    ],
 612 |    "source": [
 613 |     "# Put this into a plot\n",
 614 |     "ax = df.groupby(['Campaigns', 'MarketSize']).count()['OutletID'].unstack('MarketSize').plot(\n",
 615 |     "    kind='bar',\n",
 616 |     "    figsize=(10,8),\n",
 617 |     "    grid=True)\n",
 618 |     "\n",
 619 |     "ax.tick_params(axis = 'x', labelsize= 15)\n",
 620 |     "plt.xticks(rotation = 'horizontal')\n",
 621 |     "ax.tick_params(axis = 'y', labelsize = 15)\n",
 622 |     "ax.set_ylabel('Count')\n",
 623 |     "ax.set_title('Breakdowns of Market Sizes across Different Campaigns', fontsize = 20)\n",
 624 |     "\n",
 625 |     "plt.show()"
 626 |    ]
 627 |   },
 628 |   {
 629 |    "cell_type": "code",
 630 |    "execution_count": 9,
 631 |    "metadata": {
 632 |     "colab": {
 633 |      "base_uri": "https://localhost:8080/",
 634 |      "height": 465
 635 |     },
 636 |     "colab_type": "code",
 637 |     "executionInfo": {
 638 |      "elapsed": 2437,
 639 |      "status": "ok",
 640 |      "timestamp": 1573997967593,
 641 |      "user": {
 642 |       "displayName": "Rajeev Ratan",
 643 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 644 |       "userId": "08597265227091462140"
 645 |      },
 646 |      "user_tz": 0
 647 |     },
 648 |     "id": "ALayjDr3iqyh",
 649 |     "outputId": "b488bea3-6c8e-4891-b83e-7e77ec898653"
 650 |    },
 651 |    "outputs": [
 652 |     {
 653 |      "data": {
 654 |       "image/png": 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\n",
 655 |       "text/plain": [
 656 |        "<Figure size 864x504 with 1 Axes>"
 657 |       ]
 658 |      },
 659 |      "metadata": {},
 660 |      "output_type": "display_data"
 661 |     }
 662 |    ],
 663 |    "source": [
 664 |     "ax = df.groupby('AgeofOutlets').count()['OutletID'].plot(\n",
 665 |     "    kind='bar', \n",
 666 |     "    figsize=(12,7),\n",
 667 |     "    grid=True)\n",
 668 |     "\n",
 669 |     "ax.set_xlabel('Age')\n",
 670 |     "ax.set_ylabel('Count')\n",
 671 |     "ax.set_title('Overall Distributions Outlet Ages')\n",
 672 |     "\n",
 673 |     "plt.show()"
 674 |    ]
 675 |   },
 676 |   {
 677 |    "cell_type": "code",
 678 |    "execution_count": 10,
 679 |    "metadata": {
 680 |     "colab": {
 681 |      "base_uri": "https://localhost:8080/",
 682 |      "height": 1000
 683 |     },
 684 |     "colab_type": "code",
 685 |     "executionInfo": {
 686 |      "elapsed": 2224,
 687 |      "status": "ok",
 688 |      "timestamp": 1573997968059,
 689 |      "user": {
 690 |       "displayName": "Rajeev Ratan",
 691 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 692 |       "userId": "08597265227091462140"
 693 |      },
 694 |      "user_tz": 0
 695 |     },
 696 |     "id": "yHZPohk6iqyk",
 697 |     "outputId": "8e257555-930a-4f8c-aed9-e18c5c4fb8b4"
 698 |    },
 699 |    "outputs": [
 700 |     {
 701 |      "data": {
 702 |       "image/png": 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\n",
 703 |       "text/plain": [
 704 |        "<Figure size 1008x1296 with 1 Axes>"
 705 |       ]
 706 |      },
 707 |      "metadata": {},
 708 |      "output_type": "display_data"
 709 |     }
 710 |    ],
 711 |    "source": [
 712 |     "# Visualize this summary\n",
 713 |     "ax = df.groupby(['AgeofOutlets', 'Campaigns']).count()['OutletID'].unstack('Campaigns').iloc[::-1].plot(\n",
 714 |     "    kind='barh', \n",
 715 |     "    figsize=(14,18),\n",
 716 |     "    grid=True)\n",
 717 |     "                                                                                                               \n",
 718 |     "ax.set_ylabel('Age')\n",
 719 |     "ax.set_xlabel('Count')\n",
 720 |     "ax.set_title('Overall Distributions of Age of Outlets')\n",
 721 |     "\n",
 722 |     "plt.show()"
 723 |    ]
 724 |   },
 725 |   {
 726 |    "cell_type": "code",
 727 |    "execution_count": 11,
 728 |    "metadata": {
 729 |     "colab": {
 730 |      "base_uri": "https://localhost:8080/",
 731 |      "height": 171
 732 |     },
 733 |     "colab_type": "code",
 734 |     "executionInfo": {
 735 |      "elapsed": 1916,
 736 |      "status": "ok",
 737 |      "timestamp": 1573997968061,
 738 |      "user": {
 739 |       "displayName": "Rajeev Ratan",
 740 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 741 |       "userId": "08597265227091462140"
 742 |      },
 743 |      "user_tz": 0
 744 |     },
 745 |     "id": "O7nhk6hqiqyp",
 746 |     "outputId": "e7a10493-976b-468c-f088-4d318288f0af"
 747 |    },
 748 |    "outputs": [
 749 |     {
 750 |      "data": {
 751 |       "text/html": [
 752 |        "<div>\n",
 753 |        "<style scoped>\n",
 754 |        "    .dataframe tbody tr th:only-of-type {\n",
 755 |        "        vertical-align: middle;\n",
 756 |        "    }\n",
 757 |        "\n",
 758 |        "    .dataframe tbody tr th {\n",
 759 |        "        vertical-align: top;\n",
 760 |        "    }\n",
 761 |        "\n",
 762 |        "    .dataframe thead th {\n",
 763 |        "        text-align: right;\n",
 764 |        "    }\n",
 765 |        "</style>\n",
 766 |        "<table border=\"1\" class=\"dataframe\">\n",
 767 |        "  <thead>\n",
 768 |        "    <tr style=\"text-align: right;\">\n",
 769 |        "      <th></th>\n",
 770 |        "      <th>count</th>\n",
 771 |        "      <th>mean</th>\n",
 772 |        "      <th>std</th>\n",
 773 |        "      <th>min</th>\n",
 774 |        "      <th>25%</th>\n",
 775 |        "      <th>50%</th>\n",
 776 |        "      <th>75%</th>\n",
 777 |        "      <th>max</th>\n",
 778 |        "    </tr>\n",
 779 |        "    <tr>\n",
 780 |        "      <th>Campaigns</th>\n",
 781 |        "      <th></th>\n",
 782 |        "      <th></th>\n",
 783 |        "      <th></th>\n",
 784 |        "      <th></th>\n",
 785 |        "      <th></th>\n",
 786 |        "      <th></th>\n",
 787 |        "      <th></th>\n",
 788 |        "      <th></th>\n",
 789 |        "    </tr>\n",
 790 |        "  </thead>\n",
 791 |        "  <tbody>\n",
 792 |        "    <tr>\n",
 793 |        "      <th>1</th>\n",
 794 |        "      <td>172.0</td>\n",
 795 |        "      <td>8.279070</td>\n",
 796 |        "      <td>6.636160</td>\n",
 797 |        "      <td>1.0</td>\n",
 798 |        "      <td>3.0</td>\n",
 799 |        "      <td>6.0</td>\n",
 800 |        "      <td>12.0</td>\n",
 801 |        "      <td>27.0</td>\n",
 802 |        "    </tr>\n",
 803 |        "    <tr>\n",
 804 |        "      <th>2</th>\n",
 805 |        "      <td>188.0</td>\n",
 806 |        "      <td>7.978723</td>\n",
 807 |        "      <td>6.597648</td>\n",
 808 |        "      <td>1.0</td>\n",
 809 |        "      <td>3.0</td>\n",
 810 |        "      <td>7.0</td>\n",
 811 |        "      <td>10.0</td>\n",
 812 |        "      <td>28.0</td>\n",
 813 |        "    </tr>\n",
 814 |        "    <tr>\n",
 815 |        "      <th>3</th>\n",
 816 |        "      <td>188.0</td>\n",
 817 |        "      <td>9.234043</td>\n",
 818 |        "      <td>6.651646</td>\n",
 819 |        "      <td>1.0</td>\n",
 820 |        "      <td>5.0</td>\n",
 821 |        "      <td>8.0</td>\n",
 822 |        "      <td>12.0</td>\n",
 823 |        "      <td>24.0</td>\n",
 824 |        "    </tr>\n",
 825 |        "  </tbody>\n",
 826 |        "</table>\n",
 827 |        "</div>"
 828 |       ],
 829 |       "text/plain": [
 830 |        "           count      mean       std  min  25%  50%   75%   max\n",
 831 |        "Campaigns                                                      \n",
 832 |        "1          172.0  8.279070  6.636160  1.0  3.0  6.0  12.0  27.0\n",
 833 |        "2          188.0  7.978723  6.597648  1.0  3.0  7.0  10.0  28.0\n",
 834 |        "3          188.0  9.234043  6.651646  1.0  5.0  8.0  12.0  24.0"
 835 |       ]
 836 |      },
 837 |      "execution_count": 11,
 838 |      "metadata": {},
 839 |      "output_type": "execute_result"
 840 |     }
 841 |    ],
 842 |    "source": [
 843 |     "# Every campaigns has similar age profiles and the average outlet ages is around 8 ~ 9 years old\n",
 844 |     "# Majority of the outlet are around 10 ~ 12 years old or younger\n",
 845 |     "\n",
 846 |     "df.groupby('Campaigns').describe()['AgeofOutlets']"
 847 |    ]
 848 |   },
 849 |   {
 850 |    "cell_type": "markdown",
 851 |    "metadata": {
 852 |     "colab_type": "text",
 853 |     "id": "APnBnPBKUz2y"
 854 |    },
 855 |    "source": [
 856 |     "# Performing A/B Testing on Three Marketing Campaigns"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "code",
 861 |    "execution_count": 12,
 862 |    "metadata": {
 863 |     "colab": {
 864 |      "base_uri": "https://localhost:8080/",
 865 |      "height": 277
 866 |     },
 867 |     "colab_type": "code",
 868 |     "executionInfo": {
 869 |      "elapsed": 580,
 870 |      "status": "ok",
 871 |      "timestamp": 1573998191625,
 872 |      "user": {
 873 |       "displayName": "Rajeev Ratan",
 874 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 875 |       "userId": "08597265227091462140"
 876 |      },
 877 |      "user_tz": 0
 878 |     },
 879 |     "id": "5TBx6yEXiqys",
 880 |     "outputId": "21492f62-5a1e-4d47-e271-b5179d2337d4"
 881 |    },
 882 |    "outputs": [
 883 |     {
 884 |      "name": "stdout",
 885 |      "output_type": "stream",
 886 |      "text": [
 887 |       "===== MEAN ====== \n",
 888 |       "Campaigns\n",
 889 |       "1    58.099012\n",
 890 |       "2    47.329415\n",
 891 |       "3    55.364468\n",
 892 |       "Name: SalesInThousands, dtype: float64\n",
 893 |       "\n",
 894 |       "===== STANDARD DEVIATION ====== \n",
 895 |       "Campaigns\n",
 896 |       "1    16.553782\n",
 897 |       "2    15.108955\n",
 898 |       "3    16.766231\n",
 899 |       "Name: SalesInThousands, dtype: float64\n",
 900 |       "\n",
 901 |       "===== OBSERVATIONS ====== \n",
 902 |       "Campaigns\n",
 903 |       "1    172\n",
 904 |       "2    188\n",
 905 |       "3    188\n",
 906 |       "Name: SalesInThousands, dtype: int64\n",
 907 |       "\n"
 908 |      ]
 909 |     }
 910 |    ],
 911 |    "source": [
 912 |     "means = df.groupby('Campaigns').mean()['SalesInThousands']\n",
 913 |     "stdev = df.groupby('Campaigns').std()['SalesInThousands']\n",
 914 |     "obs = df.groupby('Campaigns').count()['SalesInThousands']\n",
 915 |     "print(f'===== MEAN ====== \\n{means}\\n')\n",
 916 |     "print(f'===== STANDARD DEVIATION ====== \\n{stdev}\\n')\n",
 917 |     "print(f'===== OBSERVATIONS ====== \\n{obs}\\n')"
 918 |    ]
 919 |   },
 920 |   {
 921 |    "cell_type": "markdown",
 922 |    "metadata": {
 923 |     "colab_type": "text",
 924 |     "id": "BgAgZHlAVRsS"
 925 |    },
 926 |    "source": [
 927 |     "# A/B Testing: 1st Campaigns vs 2nd Campaigns"
 928 |    ]
 929 |   },
 930 |   {
 931 |    "cell_type": "code",
 932 |    "execution_count": 13,
 933 |    "metadata": {
 934 |     "colab": {
 935 |      "base_uri": "https://localhost:8080/",
 936 |      "height": 52
 937 |     },
 938 |     "colab_type": "code",
 939 |     "executionInfo": {
 940 |      "elapsed": 593,
 941 |      "status": "ok",
 942 |      "timestamp": 1573998248738,
 943 |      "user": {
 944 |       "displayName": "Rajeev Ratan",
 945 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
 946 |       "userId": "08597265227091462140"
 947 |      },
 948 |      "user_tz": 0
 949 |     },
 950 |     "id": "9viIzw-Tiqyz",
 951 |     "outputId": "007fc8b4-0fc0-4a1f-c352-65b7565a9899"
 952 |    },
 953 |    "outputs": [
 954 |     {
 955 |      "name": "stdout",
 956 |      "output_type": "stream",
 957 |      "text": [
 958 |       "t-value = 6.42752867090748\n",
 959 |       "p-value = 4.2903687179871785e-10\n"
 960 |      ]
 961 |     }
 962 |    ],
 963 |    "source": [
 964 |     "# Computing the t and p values using scipy \n",
 965 |     "\n",
 966 |     "t, p = stats.ttest_ind(df.loc[df['Campaigns'] == 1, 'SalesInThousands'].values,\n",
 967 |     "                       df.loc[df['Campaigns'] == 2, 'SalesInThousands'].values, \n",
 968 |     "                       equal_var=False)\n",
 969 |     "print(f't-value = {str(t)}')\n",
 970 |     "print(f'p-value = {str(p)}')"
 971 |    ]
 972 |   },
 973 |   {
 974 |    "cell_type": "markdown",
 975 |    "metadata": {
 976 |     "colab_type": "text",
 977 |     "id": "unxLmuGYGSZ2"
 978 |    },
 979 |    "source": [
 980 |     "# Analysis of P and t-values (1 vs 2)\n",
 981 |     "\n",
 982 |     "Our p-Value is close to 0 which means that there is good evidence to **reject the null hypothesis**. Meaning the there is a statistical difference between the two groups. Our threshold rejectings the Null is usually less than 0.05.\n",
 983 |     "\n",
 984 |     "Furthermore, the t-test shows that the marketing performances for these two groups are significantly different and that first marketing campaigns outperforms second marketing campaigns."
 985 |    ]
 986 |   },
 987 |   {
 988 |    "cell_type": "markdown",
 989 |    "metadata": {},
 990 |    "source": [
 991 |     "***"
 992 |    ]
 993 |   },
 994 |   {
 995 |    "cell_type": "markdown",
 996 |    "metadata": {
 997 |     "colab_type": "text",
 998 |     "id": "c5Li9WaMVfZF"
 999 |    },
1000 |    "source": [
1001 |     "# A/B Testing: 1st Campaigns vs 3rd Campaigns\n",
1002 |     "\n",
1003 |     "However, if we run a t-test between the promotion group 1 and promotion group 3, we see different results:"
1004 |    ]
1005 |   },
1006 |   {
1007 |    "cell_type": "code",
1008 |    "execution_count": 14,
1009 |    "metadata": {
1010 |     "colab": {
1011 |      "base_uri": "https://localhost:8080/",
1012 |      "height": 52
1013 |     },
1014 |     "colab_type": "code",
1015 |     "executionInfo": {
1016 |      "elapsed": 640,
1017 |      "status": "ok",
1018 |      "timestamp": 1573998527053,
1019 |      "user": {
1020 |       "displayName": "Rajeev Ratan",
1021 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
1022 |       "userId": "08597265227091462140"
1023 |      },
1024 |      "user_tz": 0
1025 |     },
1026 |     "id": "SLLtch3xiqy4",
1027 |     "outputId": "62ed7463-5720-4450-9b89-4b077af12649"
1028 |    },
1029 |    "outputs": [
1030 |     {
1031 |      "name": "stdout",
1032 |      "output_type": "stream",
1033 |      "text": [
1034 |       "t-value = 1.5560224307758634\n",
1035 |       "p-value = 0.12059147742229478\n"
1036 |      ]
1037 |     }
1038 |    ],
1039 |    "source": [
1040 |     "t, p = stats.ttest_ind(\n",
1041 |     "    df.loc[df['Campaigns'] == 1, 'SalesInThousands'].values, \n",
1042 |     "    df.loc[df['Campaigns'] == 3, 'SalesInThousands'].values, \n",
1043 |     "    equal_var = False)\n",
1044 |     "\n",
1045 |     "print(f't-value = {str(t)}')\n",
1046 |     "print(f'p-value = {str(p)}')"
1047 |    ]
1048 |   },
1049 |   {
1050 |    "cell_type": "markdown",
1051 |    "metadata": {
1052 |     "colab_type": "text",
1053 |     "id": "QURnWYZmKqDE"
1054 |    },
1055 |    "source": [
1056 |     "# Analysis of P and t-values (1 vs 3)\n",
1057 |     "\n",
1058 |     "We note that the average sales from first marketing campaigns (58.1) is higher than those from third marketing campaigns (55.36). \n",
1059 |     "But, running a t-test between these two groups, gives us a t-value of 1.556 and a p-value of 0.121. \n",
1060 |     "The computed **p-value is a lot higher than 0.05**, past the threshold for statistical significance."
1061 |    ]
1062 |   },
1063 |   {
1064 |    "cell_type": "markdown",
1065 |    "metadata": {},
1066 |    "source": [
1067 |     "***"
1068 |    ]
1069 |   },
1070 |   {
1071 |    "cell_type": "markdown",
1072 |    "metadata": {
1073 |     "colab_type": "text",
1074 |     "id": "c5Li9WaMVfZF"
1075 |    },
1076 |    "source": [
1077 |     "# A/B Testing: 2nd Campaigns vs 3rd Campaigns"
1078 |    ]
1079 |   },
1080 |   {
1081 |    "cell_type": "code",
1082 |    "execution_count": 15,
1083 |    "metadata": {
1084 |     "colab": {
1085 |      "base_uri": "https://localhost:8080/",
1086 |      "height": 52
1087 |     },
1088 |     "colab_type": "code",
1089 |     "executionInfo": {
1090 |      "elapsed": 640,
1091 |      "status": "ok",
1092 |      "timestamp": 1573998527053,
1093 |      "user": {
1094 |       "displayName": "Rajeev Ratan",
1095 |       "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mCGo6aIm0tOcd5EhqWlYb0rime9sBvHS9YMpx0D2w=s64",
1096 |       "userId": "08597265227091462140"
1097 |      },
1098 |      "user_tz": 0
1099 |     },
1100 |     "id": "SLLtch3xiqy4",
1101 |     "outputId": "62ed7463-5720-4450-9b89-4b077af12649"
1102 |    },
1103 |    "outputs": [
1104 |     {
1105 |      "name": "stdout",
1106 |      "output_type": "stream",
1107 |      "text": [
1108 |       "t-value = -4.88139271089348\n",
1109 |       "p-value = 1.5692733176039892e-06\n"
1110 |      ]
1111 |     }
1112 |    ],
1113 |    "source": [
1114 |     "t, p = stats.ttest_ind(\n",
1115 |     "    df.loc[df['Campaigns'] == 2, 'SalesInThousands'].values, \n",
1116 |     "    df.loc[df['Campaigns'] == 3, 'SalesInThousands'].values, \n",
1117 |     "    equal_var = False)\n",
1118 |     "\n",
1119 |     "print(f't-value = {str(t)}')\n",
1120 |     "print(f'p-value = {str(p)}')"
1121 |    ]
1122 |   },
1123 |   {
1124 |    "cell_type": "markdown",
1125 |    "metadata": {
1126 |     "colab_type": "text",
1127 |     "id": "QURnWYZmKqDE"
1128 |    },
1129 |    "source": [
1130 |     "# Analysis of P and t-values (2 vs 3)\n",
1131 |     "\n",
1132 |     "Based on the average sales, second marketing campaigns (47.33) is higher than those from third marketing campaigns (55.36).\n",
1133 |     "If we run the t-test and between these two campaigns, results shows that the p-value (0.000001569) is lower than 0.05 threshold, which concludes that it **rejects null hypotheses**. In other words, there's statistical difference between second marketing campaigns and third marketing campaigns."
1134 |    ]
1135 |   }
1136 |  ],
1137 |  "metadata": {
1138 |   "colab": {
1139 |    "collapsed_sections": [],
1140 |    "name": "18.0 Case Study 9 - AB Testing.ipynb",
1141 |    "provenance": [
1142 |     {
1143 |      "file_id": "1vFy9KQ5ILPKPp-1wNjNtZprg_vr9Goqd",
1144 |      "timestamp": 1573158327351
1145 |     }
1146 |    ]
1147 |   },
1148 |   "kernelspec": {
1149 |    "display_name": "Python 3",
1150 |    "language": "python",
1151 |    "name": "python3"
1152 |   },
1153 |   "language_info": {
1154 |    "codemirror_mode": {
1155 |     "name": "ipython",
1156 |     "version": 3
1157 |    },
1158 |    "file_extension": ".py",
1159 |    "mimetype": "text/x-python",
1160 |    "name": "python",
1161 |    "nbconvert_exporter": "python",
1162 |    "pygments_lexer": "ipython3",
1163 |    "version": "3.8.5"
1164 |   }
1165 |  },
1166 |  "nbformat": 4,
1167 |  "nbformat_minor": 1
1168 | }
1169 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Marketing Campaigns A/B Testing
 2 | A/B Testing using Python to determine the most effective marketing campaigns.
 3 | 
 4 | ## Case Study
 5 | A fashion retail company is preparing to launch new product to their apparel catalog. Nevertheless, the management hasn't decided which one of three marketing campaigns in order to promote their new product. Therefore, they decide to disperse and introduce its new product in random selected outlets to examine which marketing campaigns has the substantial impact on product sales. So the company uses different approach of marketing campaigns for each outlets and new product is evaluated by assessing the weekly sales on this month (4 effective weeks).
 6 | 
 7 | ## Dataset Description
 8 | 
 9 | Our data set consists of 548 observations which includes:
10 | - OutletID: Unique identifier for store location. Each location is identified by a number. The total number of stores is 137.
11 | - AgeofOutlets: Age of store in years (1–28). The mean age of a store is 8.5 years.
12 | - Campaigns: One of three promotions that were tested (1, 2, 3).
13 | - Sales in Thousands: Sales amount for a specific LocationID, Promotion and week. The mean sales are 53.5 thousand dollars.
14 | - Market size: there are three types of market size: small, medium and large.
15 | - Week: One of four weeks when the promotions were run (1–4).
16 | 
17 | ## Libraries
18 | 1. Pandas
19 | 2. Matplotlib
20 | 3. Seaborn
21 | 4. Scipy
22 | 


--------------------------------------------------------------------------------
/campaigns.csv:
--------------------------------------------------------------------------------
  1 | OutletID,MarketSize,AgeofOutlets,Campaigns,Week,SalesInThousands
  2 | 1,Medium,4,3,1,33.73
  3 | 1,Medium,4,3,2,35.67
  4 | 1,Medium,4,3,3,29.03
  5 | 1,Medium,4,3,4,39.25
  6 | 2,Medium,5,2,1,27.81
  7 | 2,Medium,5,2,2,34.67
  8 | 2,Medium,5,2,3,27.98
  9 | 2,Medium,5,2,4,27.72
 10 | 3,Medium,12,1,1,44.54
 11 | 3,Medium,12,1,2,37.94
 12 | 3,Medium,12,1,3,45.49
 13 | 3,Medium,12,1,4,34.75
 14 | 4,Medium,1,2,1,39.28
 15 | 4,Medium,1,2,2,39.8
 16 | 4,Medium,1,2,3,24.77
 17 | 4,Medium,1,2,4,30.98
 18 | 5,Medium,10,2,1,30.37
 19 | 5,Medium,10,2,2,24.82
 20 | 5,Medium,10,2,3,37.47
 21 | 5,Medium,10,2,4,23.35
 22 | 6,Medium,10,3,1,32.9
 23 | 6,Medium,10,3,2,22.18
 24 | 6,Medium,10,3,3,42.98
 25 | 6,Medium,10,3,4,26.68
 26 | 7,Medium,15,1,1,42.92
 27 | 7,Medium,15,1,2,42.16
 28 | 7,Medium,15,1,3,51.72
 29 | 7,Medium,15,1,4,36.17
 30 | 8,Medium,10,2,1,30.08
 31 | 8,Medium,10,2,2,28.62
 32 | 8,Medium,10,2,3,25.4
 33 | 8,Medium,10,2,4,27.26
 34 | 9,Medium,6,1,1,37.41
 35 | 9,Medium,6,1,2,38.64
 36 | 9,Medium,6,1,3,40.9
 37 | 9,Medium,6,1,4,41.11
 38 | 10,Medium,5,2,1,34.27
 39 | 10,Medium,5,2,2,29.3
 40 | 10,Medium,5,2,3,23.93
 41 | 10,Medium,5,2,4,35.16
 42 | 11,Medium,5,3,1,33.42
 43 | 11,Medium,5,3,2,37.93
 44 | 11,Medium,5,3,3,33.85
 45 | 11,Medium,5,3,4,40.25
 46 | 12,Medium,12,1,1,35.85
 47 | 12,Medium,12,1,2,36.24
 48 | 12,Medium,12,1,3,41.73
 49 | 12,Medium,12,1,4,37.32
 50 | 13,Medium,12,1,1,50.48
 51 | 13,Medium,12,1,2,36.88
 52 | 13,Medium,12,1,3,35.68
 53 | 13,Medium,12,1,4,46.45
 54 | 101,Small,22,1,1,67.48
 55 | 101,Small,22,1,2,65.57
 56 | 101,Small,22,1,3,68.42
 57 | 101,Small,22,1,4,60.93
 58 | 102,Small,8,3,1,61.59
 59 | 102,Small,8,3,2,63.64
 60 | 102,Small,8,3,3,54.68
 61 | 102,Small,8,3,4,61.24
 62 | 103,Small,22,3,1,62.93
 63 | 103,Small,22,3,2,58.77
 64 | 103,Small,22,3,3,70.6
 65 | 103,Small,22,3,4,65.06
 66 | 104,Small,19,3,1,59.76
 67 | 104,Small,19,3,2,66.11
 68 | 104,Small,19,3,3,62.16
 69 | 104,Small,19,3,4,64.04
 70 | 105,Small,8,3,1,59.65
 71 | 105,Small,8,3,2,57.04
 72 | 105,Small,8,3,3,59.73
 73 | 105,Small,8,3,4,56.72
 74 | 106,Small,5,3,1,58.55
 75 | 106,Small,5,3,2,62.19
 76 | 106,Small,5,3,3,53.14
 77 | 106,Small,5,3,4,62.27
 78 | 201,Large,4,3,1,86.14
 79 | 201,Large,4,3,2,82.56
 80 | 201,Large,4,3,3,79.36
 81 | 201,Large,4,3,4,77.17
 82 | 202,Large,8,1,1,88.73
 83 | 202,Large,8,1,2,85.21
 84 | 202,Large,8,1,3,81.55
 85 | 202,Large,8,1,4,94.17
 86 | 203,Large,12,3,1,89.7
 87 | 203,Large,12,3,2,78.43
 88 | 203,Large,12,3,3,84.05
 89 | 203,Large,12,3,4,81.18
 90 | 204,Large,1,2,1,87.43
 91 | 204,Large,1,2,2,81.79
 92 | 204,Large,1,2,3,88.12
 93 | 204,Large,1,2,4,75.29
 94 | 205,Large,19,3,1,83.02
 95 | 205,Large,19,3,2,90.3
 96 | 205,Large,19,3,3,85.18
 97 | 205,Large,19,3,4,89.77
 98 | 206,Large,11,3,1,82.64
 99 | 206,Large,11,3,2,82.89
100 | 206,Large,11,3,3,82.13
101 | 206,Large,11,3,4,88.91
102 | 207,Large,1,3,1,87.7
103 | 207,Large,1,3,2,81.16
104 | 207,Large,1,3,3,81.58
105 | 207,Large,1,3,4,94.21
106 | 208,Large,1,3,1,96.48
107 | 208,Large,1,3,2,84.13
108 | 208,Large,1,3,3,91.98
109 | 208,Large,1,3,4,81.72
110 | 209,Large,1,1,1,93.71
111 | 209,Large,1,1,2,96.01
112 | 209,Large,1,1,3,93.03
113 | 209,Large,1,1,4,97.61
114 | 210,Large,19,1,1,85.11
115 | 210,Large,19,1,2,88.07
116 | 210,Large,19,1,3,94.43
117 | 210,Large,19,1,4,89.44
118 | 211,Large,13,2,1,88.64
119 | 211,Large,13,2,2,81.37
120 | 211,Large,13,2,3,82.14
121 | 211,Large,13,2,4,79.64
122 | 212,Large,3,3,1,84.34
123 | 212,Large,3,3,2,87.9
124 | 212,Large,3,3,3,76.12
125 | 212,Large,3,3,4,82.72
126 | 213,Large,4,2,1,73.22
127 | 213,Large,4,2,2,75.88
128 | 213,Large,4,2,3,78.01
129 | 213,Large,4,2,4,80.17
130 | 214,Large,5,1,1,91.6
131 | 214,Large,5,1,2,80.61
132 | 214,Large,5,1,3,93.86
133 | 214,Large,5,1,4,83.43
134 | 215,Large,7,2,1,82.65
135 | 215,Large,7,2,2,77.39
136 | 215,Large,7,2,3,80.83
137 | 215,Large,7,2,4,80.75
138 | 216,Large,4,3,1,94.89
139 | 216,Large,4,3,2,74.75
140 | 216,Large,4,3,3,93.63
141 | 216,Large,4,3,4,80.82
142 | 217,Large,5,3,1,91.61
143 | 217,Large,5,3,2,79.02
144 | 217,Large,5,3,3,86.11
145 | 217,Large,5,3,4,82.88
146 | 218,Large,2,1,1,99.65
147 | 218,Large,2,1,2,88.64
148 | 218,Large,2,1,3,86.96
149 | 218,Large,2,1,4,89.25
150 | 219,Large,7,2,1,66.22
151 | 219,Large,7,2,2,82.86
152 | 219,Large,7,2,3,83.4
153 | 219,Large,7,2,4,75.61
154 | 220,Large,3,1,1,87.08
155 | 220,Large,3,1,2,89.32
156 | 220,Large,3,1,3,99.12
157 | 220,Large,3,1,4,85.85
158 | 221,Large,23,2,1,79.53
159 | 221,Large,23,2,2,74.03
160 | 221,Large,23,2,3,78.53
161 | 221,Large,23,2,4,76.71
162 | 222,Large,6,1,1,93.32
163 | 222,Large,6,1,2,85.71
164 | 222,Large,6,1,3,77.36
165 | 222,Large,6,1,4,91.29
166 | 301,Small,1,2,1,49.61
167 | 301,Small,1,2,2,43.69
168 | 301,Small,1,2,3,54.49
169 | 301,Small,1,2,4,61.25
170 | 302,Small,7,3,1,51.47
171 | 302,Small,7,3,2,53.47
172 | 302,Small,7,3,3,46.83
173 | 302,Small,7,3,4,56.7
174 | 303,Small,9,1,1,55.94
175 | 303,Small,9,1,2,61.36
176 | 303,Small,9,1,3,56.19
177 | 303,Small,9,1,4,62.06
178 | 304,Small,3,1,1,54.01
179 | 304,Small,3,1,2,63.48
180 | 304,Small,3,1,3,61.96
181 | 304,Small,3,1,4,55.3
182 | 305,Small,2,2,1,47.89
183 | 305,Small,2,2,2,52.37
184 | 305,Small,2,2,3,49.11
185 | 305,Small,2,2,4,52.88
186 | 306,Small,13,1,1,53.79
187 | 306,Small,13,1,2,66.96
188 | 306,Small,13,1,3,57.1
189 | 306,Small,13,1,4,65.12
190 | 307,Small,7,2,1,46.98
191 | 307,Small,7,2,2,53.41
192 | 307,Small,7,2,3,36.17
193 | 307,Small,7,2,4,55.98
194 | 308,Small,28,2,1,51.87
195 | 308,Small,28,2,2,58.01
196 | 308,Small,28,2,3,47.93
197 | 308,Small,28,2,4,51.33
198 | 309,Small,8,1,1,62.16
199 | 309,Small,8,1,2,54.06
200 | 309,Small,8,1,3,51.72
201 | 309,Small,8,1,4,59.64
202 | 401,Medium,5,3,1,52.23
203 | 401,Medium,5,3,2,51.68
204 | 401,Medium,5,3,3,46.22
205 | 401,Medium,5,3,4,46.03
206 | 402,Medium,25,2,1,40.4
207 | 402,Medium,25,2,2,50.28
208 | 402,Medium,25,2,3,52.72
209 | 402,Medium,25,2,4,38.3
210 | 403,Medium,19,2,1,51.79
211 | 403,Medium,19,2,2,42.56
212 | 403,Medium,19,2,3,44.64
213 | 403,Medium,19,2,4,45.11
214 | 404,Medium,8,2,1,42.15
215 | 404,Medium,8,2,2,42.59
216 | 404,Medium,8,2,3,49.56
217 | 404,Medium,8,2,4,39.25
218 | 405,Medium,4,2,1,48.18
219 | 405,Medium,4,2,2,48.25
220 | 405,Medium,4,2,3,46.42
221 | 405,Medium,4,2,4,43.77
222 | 406,Medium,3,2,1,45.3
223 | 406,Medium,3,2,2,48.84
224 | 406,Medium,3,2,3,45.08
225 | 406,Medium,3,2,4,44.67
226 | 407,Medium,6,3,1,45.56
227 | 407,Medium,6,3,2,55.19
228 | 407,Medium,6,3,3,37.84
229 | 407,Medium,6,3,4,50.07
230 | 408,Medium,9,2,1,47.33
231 | 408,Medium,9,2,2,45.42
232 | 408,Medium,9,2,3,44.31
233 | 408,Medium,9,2,4,40.26
234 | 409,Medium,14,2,1,43.27
235 | 409,Medium,14,2,2,48.33
236 | 409,Medium,14,2,3,44.14
237 | 409,Medium,14,2,4,51.5
238 | 410,Medium,11,1,1,49.95
239 | 410,Medium,11,1,2,55.11
240 | 410,Medium,11,1,3,55.28
241 | 410,Medium,11,1,4,54.7
242 | 411,Medium,23,3,1,50.11
243 | 411,Medium,23,3,2,55.59
244 | 411,Medium,23,3,3,54.37
245 | 411,Medium,23,3,4,51.91
246 | 412,Medium,6,1,1,59.34
247 | 412,Medium,6,1,2,62.63
248 | 412,Medium,6,1,3,49.08
249 | 412,Medium,6,1,4,58.04
250 | 413,Medium,1,2,1,46.26
251 | 413,Medium,1,2,2,47.71
252 | 413,Medium,1,2,3,54.09
253 | 413,Medium,1,2,4,46.89
254 | 414,Medium,1,3,1,57.37
255 | 414,Medium,1,3,2,55.53
256 | 414,Medium,1,3,3,51.16
257 | 414,Medium,1,3,4,57.06
258 | 415,Medium,12,3,1,50.59
259 | 415,Medium,12,3,2,48.64
260 | 415,Medium,12,3,3,50.55
261 | 415,Medium,12,3,4,49.08
262 | 501,Medium,6,3,1,24.75
263 | 501,Medium,6,3,2,41.47
264 | 501,Medium,6,3,3,35.1
265 | 501,Medium,6,3,4,34.33
266 | 502,Medium,19,2,1,31.94
267 | 502,Medium,19,2,2,29.64
268 | 502,Medium,19,2,3,33.64
269 | 502,Medium,19,2,4,36.05
270 | 503,Medium,2,2,1,36.7
271 | 503,Medium,2,2,2,41.53
272 | 503,Medium,2,2,3,27.71
273 | 503,Medium,2,2,4,37.2
274 | 504,Medium,4,1,1,42.16
275 | 504,Medium,4,1,2,36.8
276 | 504,Medium,4,1,3,46.98
277 | 504,Medium,4,1,4,47.35
278 | 505,Medium,1,2,1,32.21
279 | 505,Medium,1,2,2,38.41
280 | 505,Medium,1,2,3,37.9
281 | 505,Medium,1,2,4,44.84
282 | 506,Medium,12,2,1,37.45
283 | 506,Medium,12,2,2,35.46
284 | 506,Medium,12,2,3,31.85
285 | 506,Medium,12,2,4,19.26
286 | 507,Medium,5,2,1,38.65
287 | 507,Medium,5,2,2,17.34
288 | 507,Medium,5,2,3,33.14
289 | 507,Medium,5,2,4,23.44
290 | 508,Medium,5,3,1,37.17
291 | 508,Medium,5,3,2,30.52
292 | 508,Medium,5,3,3,32.51
293 | 508,Medium,5,3,4,29.12
294 | 509,Medium,8,1,1,40.46
295 | 509,Medium,8,1,2,41.25
296 | 509,Medium,8,1,3,32.05
297 | 509,Medium,8,1,4,47.5
298 | 510,Medium,7,2,1,32.77
299 | 510,Medium,7,2,2,30.26
300 | 510,Medium,7,2,3,27.55
301 | 510,Medium,7,2,4,27.37
302 | 511,Medium,24,3,1,47.48
303 | 511,Medium,24,3,2,39.41
304 | 511,Medium,24,3,3,43.29
305 | 511,Medium,24,3,4,42.59
306 | 512,Medium,4,1,1,35.3
307 | 512,Medium,4,1,2,35.86
308 | 512,Medium,4,1,3,49.3
309 | 512,Medium,4,1,4,30.81
310 | 513,Medium,7,1,1,44.98
311 | 513,Medium,7,1,2,41.71
312 | 513,Medium,7,1,3,41.1
313 | 513,Medium,7,1,4,40.97
314 | 514,Medium,6,1,1,41.1
315 | 514,Medium,6,1,2,41.56
316 | 514,Medium,6,1,3,36.39
317 | 514,Medium,6,1,4,39.67
318 | 515,Medium,8,3,1,34.46
319 | 515,Medium,8,3,2,40.13
320 | 515,Medium,8,3,3,32.18
321 | 515,Medium,8,3,4,43.73
322 | 601,Medium,7,3,1,49.01
323 | 601,Medium,7,3,2,46.29
324 | 601,Medium,7,3,3,37.14
325 | 601,Medium,7,3,4,42.82
326 | 602,Medium,4,3,1,47.22
327 | 602,Medium,4,3,2,35.24
328 | 602,Medium,4,3,3,47.22
329 | 602,Medium,4,3,4,43.15
330 | 603,Medium,4,2,1,40.71
331 | 603,Medium,4,2,2,38.26
332 | 603,Medium,4,2,3,41.44
333 | 603,Medium,4,2,4,25.7
334 | 604,Medium,11,3,1,40.29
335 | 604,Medium,11,3,2,48.35
336 | 604,Medium,11,3,3,43.26
337 | 604,Medium,11,3,4,44.2
338 | 605,Medium,8,3,1,46.14
339 | 605,Medium,8,3,2,41.12
340 | 605,Medium,8,3,3,45.03
341 | 605,Medium,8,3,4,38.85
342 | 606,Medium,8,3,1,41.96
343 | 606,Medium,8,3,2,42.27
344 | 606,Medium,8,3,3,38.51
345 | 606,Medium,8,3,4,43.51
346 | 607,Medium,3,2,1,49.39
347 | 607,Medium,3,2,2,40.25
348 | 607,Medium,3,2,3,42.46
349 | 607,Medium,3,2,4,31.62
350 | 608,Medium,18,1,1,53.38
351 | 608,Medium,18,1,2,55.31
352 | 608,Medium,18,1,3,56.1
353 | 608,Medium,18,1,4,43.24
354 | 609,Medium,9,1,1,48.18
355 | 609,Medium,9,1,2,43.11
356 | 609,Medium,9,1,3,43.78
357 | 609,Medium,9,1,4,56.9
358 | 610,Medium,4,1,1,53.53
359 | 610,Medium,4,1,2,47.92
360 | 610,Medium,4,1,3,56.18
361 | 610,Medium,4,1,4,46.66
362 | 611,Medium,11,3,1,40.43
363 | 611,Medium,11,3,2,51.15
364 | 611,Medium,11,3,3,42.5
365 | 611,Medium,11,3,4,52.76
366 | 612,Medium,1,3,1,47.63
367 | 612,Medium,1,3,2,52.21
368 | 612,Medium,1,3,3,40.17
369 | 612,Medium,1,3,4,48.36
370 | 613,Medium,18,1,1,42.76
371 | 613,Medium,18,1,2,50.52
372 | 613,Medium,18,1,3,56.86
373 | 613,Medium,18,1,4,47.51
374 | 614,Medium,8,2,1,38.56
375 | 614,Medium,8,2,2,37.29
376 | 614,Medium,8,2,3,35.6
377 | 614,Medium,8,2,4,33.35
378 | 615,Medium,1,2,1,48.5
379 | 615,Medium,1,2,2,36.8
380 | 615,Medium,1,2,3,48.32
381 | 615,Medium,1,2,4,41.54
382 | 701,Medium,1,3,1,53.56
383 | 701,Medium,1,3,2,49.72
384 | 701,Medium,1,3,3,48.77
385 | 701,Medium,1,3,4,46.84
386 | 702,Medium,13,1,1,51.82
387 | 702,Medium,13,1,2,45.02
388 | 702,Medium,13,1,3,49.44
389 | 702,Medium,13,1,4,51.32
390 | 703,Medium,4,1,1,52.85
391 | 703,Medium,4,1,2,40.84
392 | 703,Medium,4,1,3,64.45
393 | 703,Medium,4,1,4,44.43
394 | 704,Medium,27,1,1,56.64
395 | 704,Medium,27,1,2,46.06
396 | 704,Medium,27,1,3,55.46
397 | 704,Medium,27,1,4,51.41
398 | 705,Medium,22,3,1,49.67
399 | 705,Medium,22,3,2,49.98
400 | 705,Medium,22,3,3,49.5
401 | 705,Medium,22,3,4,47.35
402 | 706,Medium,17,3,1,50.26
403 | 706,Medium,17,3,2,52.05
404 | 706,Medium,17,3,3,51.14
405 | 706,Medium,17,3,4,46.3
406 | 707,Medium,5,1,1,56.99
407 | 707,Medium,5,1,2,45.57
408 | 707,Medium,5,1,3,45.92
409 | 707,Medium,5,1,4,50.52
410 | 708,Medium,15,2,1,32.61
411 | 708,Medium,15,2,2,57.27
412 | 708,Medium,15,2,3,37.32
413 | 708,Medium,15,2,4,41.22
414 | 709,Medium,1,2,1,39.73
415 | 709,Medium,1,2,2,50.3
416 | 709,Medium,1,2,3,40.16
417 | 709,Medium,1,2,4,41.37
418 | 710,Medium,24,3,1,54.79
419 | 710,Medium,24,3,2,52.39
420 | 710,Medium,24,3,3,42.15
421 | 710,Medium,24,3,4,49.63
422 | 711,Medium,1,1,1,54.95
423 | 711,Medium,1,1,2,55.78
424 | 711,Medium,1,1,3,46.49
425 | 711,Medium,1,1,4,56.84
426 | 712,Medium,9,3,1,48.06
427 | 712,Medium,9,3,2,51.09
428 | 712,Medium,9,3,3,44.19
429 | 712,Medium,9,3,4,49.52
430 | 801,Medium,3,1,1,65.11
431 | 801,Medium,3,1,2,54.38
432 | 801,Medium,3,1,3,57.14
433 | 801,Medium,3,1,4,49.91
434 | 802,Medium,9,2,1,48.76
435 | 802,Medium,9,2,2,44.14
436 | 802,Medium,9,2,3,58.1
437 | 802,Medium,9,2,4,44.16
438 | 803,Medium,10,3,1,44.66
439 | 803,Medium,10,3,2,53.78
440 | 803,Medium,10,3,3,43.91
441 | 803,Medium,10,3,4,52.41
442 | 804,Medium,1,1,1,50.54
443 | 804,Medium,1,1,2,58.43
444 | 804,Medium,1,1,3,59.77
445 | 804,Medium,1,1,4,60.44
446 | 805,Medium,6,3,1,50.94
447 | 805,Medium,6,3,2,49.38
448 | 805,Medium,6,3,3,63.98
449 | 805,Medium,6,3,4,45.84
450 | 806,Medium,5,3,1,51.01
451 | 806,Medium,5,3,2,56.16
452 | 806,Medium,5,3,3,45.77
453 | 806,Medium,5,3,4,46.47
454 | 807,Medium,1,2,1,46.02
455 | 807,Medium,1,2,2,51.09
456 | 807,Medium,1,2,3,51.68
457 | 807,Medium,1,2,4,55.91
458 | 808,Medium,20,3,1,55.9
459 | 808,Medium,20,3,2,59.8
460 | 808,Medium,20,3,3,60.97
461 | 808,Medium,20,3,4,64.14
462 | 809,Medium,9,3,1,39.98
463 | 809,Medium,9,3,2,54.58
464 | 809,Medium,9,3,3,52.64
465 | 809,Medium,9,3,4,45.43
466 | 810,Medium,13,1,1,49.3
467 | 810,Medium,13,1,2,61.8
468 | 810,Medium,13,1,3,58.0
469 | 810,Medium,13,1,4,55.2
470 | 901,Large,7,2,1,53.76
471 | 901,Large,7,2,2,39.36
472 | 901,Large,7,2,3,47.2
473 | 901,Large,7,2,4,53.66
474 | 902,Large,1,1,1,61.53
475 | 902,Large,1,1,2,59.17
476 | 902,Large,1,1,3,63.73
477 | 902,Large,1,1,4,61.77
478 | 903,Large,7,3,1,51.89
479 | 903,Large,7,3,2,51.17
480 | 903,Large,7,3,3,54.33
481 | 903,Large,7,3,4,50.05
482 | 904,Large,10,2,1,51.09
483 | 904,Large,10,2,2,54.82
484 | 904,Large,10,2,3,44.16
485 | 904,Large,10,2,4,53.95
486 | 905,Large,2,3,1,54.34
487 | 905,Large,2,3,2,60.24
488 | 905,Large,2,3,3,56.39
489 | 905,Large,2,3,4,58.33
490 | 906,Large,13,2,1,51.26
491 | 906,Large,13,2,2,51.73
492 | 906,Large,13,2,3,51.35
493 | 906,Large,13,2,4,41.85
494 | 907,Large,10,2,1,45.21
495 | 907,Large,10,2,2,55.02
496 | 907,Large,10,2,3,45.35
497 | 907,Large,10,2,4,47.06
498 | 908,Large,3,2,1,43.59
499 | 908,Large,3,2,2,46.03
500 | 908,Large,3,2,3,47.22
501 | 908,Large,3,2,4,51.41
502 | 909,Large,1,1,1,58.19
503 | 909,Large,1,1,2,62.72
504 | 909,Large,1,1,3,66.66
505 | 909,Large,1,1,4,64.66
506 | 910,Large,1,1,1,66.34
507 | 910,Large,1,1,2,60.59
508 | 910,Large,1,1,3,61.95
509 | 910,Large,1,1,4,67.84
510 | 911,Large,1,2,1,55.39
511 | 911,Large,1,2,2,49.16
512 | 911,Large,1,2,3,58.26
513 | 911,Large,1,2,4,48.12
514 | 912,Large,6,2,1,49.98
515 | 912,Large,6,2,2,44.84
516 | 912,Large,6,2,3,55.12
517 | 912,Large,6,2,4,47.36
518 | 913,Large,24,1,1,62.33
519 | 913,Large,24,1,2,53.68
520 | 913,Large,24,1,3,62.37
521 | 913,Large,24,1,4,63.58
522 | 914,Large,9,2,1,44.7
523 | 914,Large,9,2,2,43.44
524 | 914,Large,9,2,3,52.36
525 | 914,Large,9,2,4,45.9
526 | 915,Large,3,1,1,53.51
527 | 915,Large,3,1,2,49.76
528 | 915,Large,3,1,3,68.31
529 | 915,Large,3,1,4,66.1
530 | 916,Large,7,2,1,43.61
531 | 916,Large,7,2,2,46.2
532 | 916,Large,7,2,3,51.52
533 | 916,Large,7,2,4,49.71
534 | 917,Large,14,3,1,53.5
535 | 917,Large,14,3,2,56.34
536 | 917,Large,14,3,3,51.83
537 | 917,Large,14,3,4,50.2
538 | 918,Large,6,1,1,49.62
539 | 918,Large,6,1,2,62.54
540 | 918,Large,6,1,3,50.11
541 | 918,Large,6,1,4,61.63
542 | 919,Large,2,1,1,59.87
543 | 919,Large,2,1,2,62.99
544 | 919,Large,2,1,3,57.2
545 | 919,Large,2,1,4,64.34
546 | 920,Large,14,2,1,50.2
547 | 920,Large,14,2,2,45.75
548 | 920,Large,14,2,3,44.29
549 | 920,Large,14,2,4,49.41
550 | 


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