├── Amazon_Co-purchased Product Analysis.ipynb
├── Amazon_clean_meta.csv
├── Extraction.py
├── README.md
├── SNA_COMPLETE_PPT.pdf
└── clean_amazon.csv


/Amazon_Co-purchased Product Analysis.ipynb:
--------------------------------------------------------------------------------
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   5 |     "colab": {
   6 |       "provenance": []
   7 |     },
   8 |     "kernelspec": {
   9 |       "name": "python3",
  10 |       "display_name": "Python 3"
  11 |     },
  12 |     "language_info": {
  13 |       "name": "python"
  14 |     }
  15 |   },
  16 |   "cells": [
  17 |     {
  18 |       "cell_type": "markdown",
  19 |       "source": [
  20 |         "# Welcome To My Analysis File of Amazon Co-purchasing Network"
  21 |       ],
  22 |       "metadata": {
  23 |         "id": "FVj3f9DYQBXV"
  24 |       }
  25 |     },
  26 |     {
  27 |       "cell_type": "markdown",
  28 |       "source": [
  29 |         "Note That This Is Only A Sub-part Of Whole Dataset"
  30 |       ],
  31 |       "metadata": {
  32 |         "id": "WcK0gDAuQUs8"
  33 |       }
  34 |     },
  35 |     {
  36 |       "cell_type": "code",
  37 |       "execution_count": 1,
  38 |       "metadata": {
  39 |         "id": "YemvM6338rWw"
  40 |       },
  41 |       "outputs": [],
  42 |       "source": [
  43 |         "import networkx as nx\n",
  44 |         "import pandas as pd\n",
  45 |         "import numpy as np\n",
  46 |         "import matplotlib.pyplot as plt\n",
  47 |         "from itertools import count\n"
  48 |       ]
  49 |     },
  50 |     {
  51 |       "cell_type": "markdown",
  52 |       "source": [
  53 |         "\n",
  54 |         "# The extraction and preparation step of the nodes' CSV file has been done earlier, and its code is in the separate file.\n"
  55 |       ],
  56 |       "metadata": {
  57 |         "id": "hGwQ3v2tY-f6"
  58 |       }
  59 |     },
  60 |     {
  61 |       "cell_type": "code",
  62 |       "source": [
  63 |         "df=pd.read_csv('/content/clean_amazon.csv')\n",
  64 |         "df.head()"
  65 |       ],
  66 |       "metadata": {
  67 |         "colab": {
  68 |           "base_uri": "https://localhost:8080/",
  69 |           "height": 206
  70 |         },
  71 |         "id": "tN1zsDZKA-sq",
  72 |         "outputId": "bd71dd4d-9de5-467e-9966-20f58e21915d"
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  75 |       "outputs": [
  76 |         {
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  78 |           "data": {
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  80 |               "   FromNodeId  ToNodeId\n",
  81 |               "0           0         1\n",
  82 |               "1           0         2\n",
  83 |               "2           0         3\n",
  84 |               "3           0         4\n",
  85 |               "4           0         5"
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 137 |               "      <td>5</td>\n",
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 190 |               "\n",
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 194 |               "        buttonEl.style.display =\n",
 195 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 196 |               "\n",
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 198 |               "          const element = document.querySelector('#df-594d9fab-2f74-46f4-9a9d-99d5aaf06733');\n",
 199 |               "          const dataTable =\n",
 200 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 201 |               "                                                     [key], {});\n",
 202 |               "          if (!dataTable) return;\n",
 203 |               "\n",
 204 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 205 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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 207 |               "          element.innerHTML = '';\n",
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 221 |           "execution_count": 2
 222 |         }
 223 |       ]
 224 |     },
 225 |     {
 226 |       "cell_type": "markdown",
 227 |       "source": [
 228 |         "# PreparingTthe Product Meta Data CSV"
 229 |       ],
 230 |       "metadata": {
 231 |         "id": "pFjjbuHgYTS1"
 232 |       }
 233 |     },
 234 |     {
 235 |       "cell_type": "code",
 236 |       "source": [
 237 |         "import csv\n",
 238 |         " \n",
 239 |         "input_file = 'amazon-meta.txt'\n",
 240 |         "output_file = 'amazon-meta.csv'\n",
 241 |         "\n",
 242 |         "with open(input_file, 'r', encoding='utf-8') as F:\n",
 243 |         "    content=F.readlines()\n",
 244 |         "content=[x.strip()for x in content]\n",
 245 |         "\n",
 246 |         "file= open('Amazon_clean_meta.txt','w',encoding='utf-8')\n",
 247 |         "all_row=['Id','title', 'group', 'categories', 'totalreviews','avgrating'] # Column names\n",
 248 |         "\n",
 249 |         "for line in content:\n",
 250 |         "    lines = line.split(':')\n",
 251 |         "\n",
 252 |         "    if lines[0]=='Id':\n",
 253 |         "        if (len(all_row)==6):\n",
 254 |         "          for comp in all_row[0:5]:\n",
 255 |         "            file.write(comp)\n",
 256 |         "            file.write(',')\n",
 257 |         "\n",
 258 |         "          file.write(all_row[5])\n",
 259 |         "          file.write('\\n')\n",
 260 |         "          all_row=[]\n",
 261 |         "          all_row.append(lines[1].strip())\n",
 262 |         "    if lines[0]=='title':\n",
 263 |         "      title=':'.join(lines[1:]).strip().replace(',',' ').replace('\\n',' ').strip()\n",
 264 |         "      all_row.append(title)\n",
 265 |         "    if lines[0]=='group':\n",
 266 |         "      all_row.append(lines[1].strip())\n",
 267 |         "    if lines[0]=='categories':\n",
 268 |         "      all_row.append(lines[1].strip())\n",
 269 |         "    if lines[0]=='reviews' and lines[1].strip()=='total':\n",
 270 |         "      all_row.append(lines[2].split(' ')[1])\n",
 271 |         "      all_row.append(lines[4].strip())\n",
 272 |         "file.close()\n",
 273 |         "print(1)\n",
 274 |         "\n"
 275 |       ],
 276 |       "metadata": {
 277 |         "colab": {
 278 |           "base_uri": "https://localhost:8080/"
 279 |         },
 280 |         "id": "Q4E_eBJa1Q9M",
 281 |         "outputId": "d00a70a5-16a4-4aec-c09c-960e5b12bfb7"
 282 |       },
 283 |       "execution_count": 3,
 284 |       "outputs": [
 285 |         {
 286 |           "output_type": "stream",
 287 |           "name": "stdout",
 288 |           "text": [
 289 |             "1\n"
 290 |           ]
 291 |         }
 292 |       ]
 293 |     },
 294 |     {
 295 |       "cell_type": "code",
 296 |       "source": [
 297 |         "new_df=pd.read_csv('Amazon_clean_meta.txt',sep=',') # Reading the cleaned file\n",
 298 |         "new_df.head()"
 299 |       ],
 300 |       "metadata": {
 301 |         "colab": {
 302 |           "base_uri": "https://localhost:8080/",
 303 |           "height": 206
 304 |         },
 305 |         "id": "w-VgVG1K1uL_",
 306 |         "outputId": "78321053-5da5-4e10-810d-af70a6011917"
 307 |       },
 308 |       "execution_count": 4,
 309 |       "outputs": [
 310 |         {
 311 |           "output_type": "execute_result",
 312 |           "data": {
 313 |             "text/plain": [
 314 |               "   Id                                              title group  categories  \\\n",
 315 |               "0   0            Patterns of Preaching: A Sermon Sampler  Book           2   \n",
 316 |               "1   2                         Candlemas: Feast of Flames  Book           2   \n",
 317 |               "2   3   World War II Allied Fighter Planes Trading Cards  Book           1   \n",
 318 |               "3   4  Life Application Bible Commentary: 1 and 2 Tim...  Book           5   \n",
 319 |               "4   5    Prayers That Avail Much for Business: Executive  Book           2   \n",
 320 |               "\n",
 321 |               "   totalreviews  avgrating  \n",
 322 |               "0             2        5.0  \n",
 323 |               "1            12        4.5  \n",
 324 |               "2             1        5.0  \n",
 325 |               "3             1        4.0  \n",
 326 |               "4             0        0.0  "
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 455 |               "\n",
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 458 |               "          document.querySelector('#df-644ba583-a789-43df-ae44-64ed50110205 button.colab-df-convert');\n",
 459 |               "        buttonEl.style.display =\n",
 460 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 461 |               "\n",
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 463 |               "          const element = document.querySelector('#df-644ba583-a789-43df-ae44-64ed50110205');\n",
 464 |               "          const dataTable =\n",
 465 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 466 |               "                                                     [key], {});\n",
 467 |               "          if (!dataTable) return;\n",
 468 |               "\n",
 469 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 470 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 471 |               "            + ' to learn more about interactive tables.';\n",
 472 |               "          element.innerHTML = '';\n",
 473 |               "          dataTable['output_type'] = 'display_data';\n",
 474 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 475 |               "          const docLink = document.createElement('div');\n",
 476 |               "          docLink.innerHTML = docLinkHtml;\n",
 477 |               "          element.appendChild(docLink);\n",
 478 |               "        }\n",
 479 |               "      </script>\n",
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 481 |               "  </div>\n",
 482 |               "  "
 483 |             ]
 484 |           },
 485 |           "metadata": {},
 486 |           "execution_count": 4
 487 |         }
 488 |       ]
 489 |     },
 490 |     {
 491 |       "cell_type": "code",
 492 |       "source": [
 493 |         "new_df['Id'].iloc[0]=1 # Setting the initial id to 1 , earlier it was 0"
 494 |       ],
 495 |       "metadata": {
 496 |         "colab": {
 497 |           "base_uri": "https://localhost:8080/"
 498 |         },
 499 |         "id": "J5EoiMOGVtvw",
 500 |         "outputId": "b518f27c-450b-4e92-c065-0ec9891d4cd2"
 501 |       },
 502 |       "execution_count": 5,
 503 |       "outputs": [
 504 |         {
 505 |           "output_type": "stream",
 506 |           "name": "stderr",
 507 |           "text": [
 508 |             "<ipython-input-5-7362422a8e52>:1: SettingWithCopyWarning: \n",
 509 |             "A value is trying to be set on a copy of a slice from a DataFrame\n",
 510 |             "\n",
 511 |             "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
 512 |             "  new_df['Id'].iloc[0]=1 # Setting the initial id to 1 , earlier it was 0\n"
 513 |           ]
 514 |         }
 515 |       ]
 516 |     },
 517 |     {
 518 |       "cell_type": "code",
 519 |       "source": [
 520 |         "new_df.to_csv('Amazon_clean_meta.csv') # Saving for future use"
 521 |       ],
 522 |       "metadata": {
 523 |         "id": "GbSVEsOOJGsA"
 524 |       },
 525 |       "execution_count": 6,
 526 |       "outputs": []
 527 |     },
 528 |     {
 529 |       "cell_type": "code",
 530 |       "source": [
 531 |         "new_df.head() # Printing 1st 5 columns"
 532 |       ],
 533 |       "metadata": {
 534 |         "colab": {
 535 |           "base_uri": "https://localhost:8080/",
 536 |           "height": 206
 537 |         },
 538 |         "id": "Wkj-lRONWgzV",
 539 |         "outputId": "41e138be-7067-427c-f3e3-e80ad17c189f"
 540 |       },
 541 |       "execution_count": 7,
 542 |       "outputs": [
 543 |         {
 544 |           "output_type": "execute_result",
 545 |           "data": {
 546 |             "text/plain": [
 547 |               "   Id                                              title group  categories  \\\n",
 548 |               "0   1            Patterns of Preaching: A Sermon Sampler  Book           2   \n",
 549 |               "1   2                         Candlemas: Feast of Flames  Book           2   \n",
 550 |               "2   3   World War II Allied Fighter Planes Trading Cards  Book           1   \n",
 551 |               "3   4  Life Application Bible Commentary: 1 and 2 Tim...  Book           5   \n",
 552 |               "4   5    Prayers That Avail Much for Business: Executive  Book           2   \n",
 553 |               "\n",
 554 |               "   totalreviews  avgrating  \n",
 555 |               "0             2        5.0  \n",
 556 |               "1            12        4.5  \n",
 557 |               "2             1        5.0  \n",
 558 |               "3             1        4.0  \n",
 559 |               "4             0        0.0  "
 560 |             ],
 561 |             "text/html": [
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 565 |               "      <div>\n",
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 567 |               "    .dataframe tbody tr th:only-of-type {\n",
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 585 |               "      <th>group</th>\n",
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 588 |               "      <th>avgrating</th>\n",
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 594 |               "      <td>1</td>\n",
 595 |               "      <td>Patterns of Preaching: A Sermon Sampler</td>\n",
 596 |               "      <td>Book</td>\n",
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 598 |               "      <td>2</td>\n",
 599 |               "      <td>5.0</td>\n",
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 602 |               "      <th>1</th>\n",
 603 |               "      <td>2</td>\n",
 604 |               "      <td>Candlemas: Feast of Flames</td>\n",
 605 |               "      <td>Book</td>\n",
 606 |               "      <td>2</td>\n",
 607 |               "      <td>12</td>\n",
 608 |               "      <td>4.5</td>\n",
 609 |               "    </tr>\n",
 610 |               "    <tr>\n",
 611 |               "      <th>2</th>\n",
 612 |               "      <td>3</td>\n",
 613 |               "      <td>World War II Allied Fighter Planes Trading Cards</td>\n",
 614 |               "      <td>Book</td>\n",
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 616 |               "      <td>1</td>\n",
 617 |               "      <td>5.0</td>\n",
 618 |               "    </tr>\n",
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 620 |               "      <th>3</th>\n",
 621 |               "      <td>4</td>\n",
 622 |               "      <td>Life Application Bible Commentary: 1 and 2 Tim...</td>\n",
 623 |               "      <td>Book</td>\n",
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 625 |               "      <td>1</td>\n",
 626 |               "      <td>4.0</td>\n",
 627 |               "    </tr>\n",
 628 |               "    <tr>\n",
 629 |               "      <th>4</th>\n",
 630 |               "      <td>5</td>\n",
 631 |               "      <td>Prayers That Avail Much for Business: Executive</td>\n",
 632 |               "      <td>Book</td>\n",
 633 |               "      <td>2</td>\n",
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 648 |               "  </svg>\n",
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 650 |               "      \n",
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 655 |               "      gap: 12px;\n",
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 657 |               "\n",
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 659 |               "      background-color: #E8F0FE;\n",
 660 |               "      border: none;\n",
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 664 |               "      fill: #1967D2;\n",
 665 |               "      height: 32px;\n",
 666 |               "      padding: 0 0 0 0;\n",
 667 |               "      width: 32px;\n",
 668 |               "    }\n",
 669 |               "\n",
 670 |               "    .colab-df-convert:hover {\n",
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 672 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 673 |               "      fill: #174EA6;\n",
 674 |               "    }\n",
 675 |               "\n",
 676 |               "    [theme=dark] .colab-df-convert {\n",
 677 |               "      background-color: #3B4455;\n",
 678 |               "      fill: #D2E3FC;\n",
 679 |               "    }\n",
 680 |               "\n",
 681 |               "    [theme=dark] .colab-df-convert:hover {\n",
 682 |               "      background-color: #434B5C;\n",
 683 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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 685 |               "      fill: #FFFFFF;\n",
 686 |               "    }\n",
 687 |               "  </style>\n",
 688 |               "\n",
 689 |               "      <script>\n",
 690 |               "        const buttonEl =\n",
 691 |               "          document.querySelector('#df-baebf810-1e3f-4a5a-997d-3c3fac154da4 button.colab-df-convert');\n",
 692 |               "        buttonEl.style.display =\n",
 693 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 694 |               "\n",
 695 |               "        async function convertToInteractive(key) {\n",
 696 |               "          const element = document.querySelector('#df-baebf810-1e3f-4a5a-997d-3c3fac154da4');\n",
 697 |               "          const dataTable =\n",
 698 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 699 |               "                                                     [key], {});\n",
 700 |               "          if (!dataTable) return;\n",
 701 |               "\n",
 702 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 703 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 704 |               "            + ' to learn more about interactive tables.';\n",
 705 |               "          element.innerHTML = '';\n",
 706 |               "          dataTable['output_type'] = 'display_data';\n",
 707 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 708 |               "          const docLink = document.createElement('div');\n",
 709 |               "          docLink.innerHTML = docLinkHtml;\n",
 710 |               "          element.appendChild(docLink);\n",
 711 |               "        }\n",
 712 |               "      </script>\n",
 713 |               "    </div>\n",
 714 |               "  </div>\n",
 715 |               "  "
 716 |             ]
 717 |           },
 718 |           "metadata": {},
 719 |           "execution_count": 7
 720 |         }
 721 |       ]
 722 |     },
 723 |     {
 724 |       "cell_type": "code",
 725 |       "source": [
 726 |         "set(new_df['group']) # Checking all the different products available, Note that 3 Products have been deleted from the original dataset"
 727 |       ],
 728 |       "metadata": {
 729 |         "colab": {
 730 |           "base_uri": "https://localhost:8080/"
 731 |         },
 732 |         "id": "N6K-9zXLW3e9",
 733 |         "outputId": "c8a8eaea-d0bf-4272-8391-6e0d904c56c3"
 734 |       },
 735 |       "execution_count": 8,
 736 |       "outputs": [
 737 |         {
 738 |           "output_type": "execute_result",
 739 |           "data": {
 740 |             "text/plain": [
 741 |               "{'Book', 'DVD', 'Music', 'Toy', 'Video'}"
 742 |             ]
 743 |           },
 744 |           "metadata": {},
 745 |           "execution_count": 8
 746 |         }
 747 |       ]
 748 |     },
 749 |     {
 750 |       "cell_type": "markdown",
 751 |       "source": [
 752 |         "# Now Let's Attach The Group Attribute From Meta-Data To Nodes Id "
 753 |       ],
 754 |       "metadata": {
 755 |         "id": "tb8G6CqLZbvP"
 756 |       }
 757 |     },
 758 |     {
 759 |       "cell_type": "code",
 760 |       "source": [
 761 |         "G=nx.from_pandas_edgelist(df,'FromNodeId','ToNodeId')  # Creating A Networkx Graph of node csv file"
 762 |       ],
 763 |       "metadata": {
 764 |         "id": "ENNA_Zw1DCc1"
 765 |       },
 766 |       "execution_count": 9,
 767 |       "outputs": []
 768 |     },
 769 |     {
 770 |       "cell_type": "code",
 771 |       "source": [
 772 |         "combined_values=dict(zip(new_df.Id,new_df.group)) # combining the id and group to 1 file\n"
 773 |       ],
 774 |       "metadata": {
 775 |         "id": "pPIiE_IXaDG3"
 776 |       },
 777 |       "execution_count": 10,
 778 |       "outputs": []
 779 |     },
 780 |     {
 781 |       "cell_type": "code",
 782 |       "source": [
 783 |         "nx.set_node_attributes(G, combined_values, 'group') # Setting the attribute of the nodes "
 784 |       ],
 785 |       "metadata": {
 786 |         "id": "MJxXElpwauny"
 787 |       },
 788 |       "execution_count": 11,
 789 |       "outputs": []
 790 |     },
 791 |     {
 792 |       "cell_type": "code",
 793 |       "source": [
 794 |         "G.number_of_nodes(), G.number_of_edges() # Checking the number of nodes and number of edges in the graph"
 795 |       ],
 796 |       "metadata": {
 797 |         "colab": {
 798 |           "base_uri": "https://localhost:8080/"
 799 |         },
 800 |         "id": "1yPgoSW4a_UT",
 801 |         "outputId": "3b1c131b-476f-45ba-8f9c-868d2dcf582a"
 802 |       },
 803 |       "execution_count": 12,
 804 |       "outputs": [
 805 |         {
 806 |           "output_type": "execute_result",
 807 |           "data": {
 808 |             "text/plain": [
 809 |               "(137260, 394188)"
 810 |             ]
 811 |           },
 812 |           "metadata": {},
 813 |           "execution_count": 12
 814 |         }
 815 |       ]
 816 |     },
 817 |     {
 818 |       "cell_type": "markdown",
 819 |       "source": [
 820 |         "# Now That We Have Created The Graph, Lets Start The Visualising "
 821 |       ],
 822 |       "metadata": {
 823 |         "id": "mK_vBFIqcna6"
 824 |       }
 825 |     },
 826 |     {
 827 |       "cell_type": "code",
 828 |       "source": [
 829 |         "import matplotlib.pyplot as plt"
 830 |       ],
 831 |       "metadata": {
 832 |         "id": "QInLNPA7oE4x"
 833 |       },
 834 |       "execution_count": 13,
 835 |       "outputs": []
 836 |     },
 837 |     {
 838 |       "cell_type": "code",
 839 |       "source": [
 840 |         "Nodes_Group=list(nx.get_node_attributes(G,'group').values()) # Checking the individual count of each product category\n",
 841 |         "valuecount=pd.value_counts(Nodes_Group)\n",
 842 |         "valuecount\n",
 843 |         "\n",
 844 |         "\n"
 845 |       ],
 846 |       "metadata": {
 847 |         "colab": {
 848 |           "base_uri": "https://localhost:8080/"
 849 |         },
 850 |         "id": "xOQDW-1FbHQs",
 851 |         "outputId": "fc52b3d1-c73c-4f88-e12b-c8421e17918c"
 852 |       },
 853 |       "execution_count": 14,
 854 |       "outputs": [
 855 |         {
 856 |           "output_type": "execute_result",
 857 |           "data": {
 858 |             "text/plain": [
 859 |               "Book     8588\n",
 860 |               "Music    2479\n",
 861 |               "Video     581\n",
 862 |               "DVD       461\n",
 863 |               "Toy         2\n",
 864 |               "dtype: int64"
 865 |             ]
 866 |           },
 867 |           "metadata": {},
 868 |           "execution_count": 14
 869 |         }
 870 |       ]
 871 |     },
 872 |     {
 873 |       "cell_type": "code",
 874 |       "source": [
 875 |         "colors = [\"red\", \"orange\", \"green\", \"yellow\",'purple','blue']  \n",
 876 |         "X= valuecount.plot.bar(color=colors) # Bar Graph \n"
 877 |       ],
 878 |       "metadata": {
 879 |         "colab": {
 880 |           "base_uri": "https://localhost:8080/",
 881 |           "height": 455
 882 |         },
 883 |         "id": "SqK70KUWpqJN",
 884 |         "outputId": "3d2c4e88-05f9-4685-ce2e-36c46a9188f6"
 885 |       },
 886 |       "execution_count": 15,
 887 |       "outputs": [
 888 |         {
 889 |           "output_type": "display_data",
 890 |           "data": {
 891 |             "text/plain": [
 892 |               "<Figure size 640x480 with 1 Axes>"
 893 |             ],
 894 |             "image/png": 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\n"
 895 |           },
 896 |           "metadata": {}
 897 |         }
 898 |       ]
 899 |     },
 900 |     {
 901 |       "cell_type": "markdown",
 902 |       "source": [
 903 |         "# Now lets connect 2 products see how they relate to each other while purchasing"
 904 |       ],
 905 |       "metadata": {
 906 |         "id": "fABrlSj4qon6"
 907 |       }
 908 |     },
 909 |     {
 910 |       "cell_type": "markdown",
 911 |       "source": [
 912 |         "Since the book has maximum count so we will take book as 1 product \n",
 913 |         "and DVD( People might buy DVD based on the books or vice-versa) as another"
 914 |       ],
 915 |       "metadata": {
 916 |         "id": "RLAKxXuoMoH0"
 917 |       }
 918 |     },
 919 |     {
 920 |       "cell_type": "code",
 921 |       "source": [
 922 |         "dvd_nodes=set(new_df[new_df['group']=='DVD'].Id) # I have extracted the id of those nodes whose group is DVD\n"
 923 |       ],
 924 |       "metadata": {
 925 |         "id": "uXvhBUP-oYxe"
 926 |       },
 927 |       "execution_count": 19,
 928 |       "outputs": []
 929 |     },
 930 |     {
 931 |       "cell_type": "code",
 932 |       "source": [
 933 |         "dvd_df=df[df.FromNodeId.isin(dvd_nodes) |df.ToNodeId.isin(dvd_nodes) ] # Making the dataframe of the DVD nodes"
 934 |       ],
 935 |       "metadata": {
 936 |         "id": "WZJrERdeNmRO"
 937 |       },
 938 |       "execution_count": 20,
 939 |       "outputs": []
 940 |     },
 941 |     {
 942 |       "cell_type": "markdown",
 943 |       "source": [
 944 |         "# Lets make new network for DVD dataframe"
 945 |       ],
 946 |       "metadata": {
 947 |         "id": "7QFSE4XbPH7l"
 948 |       }
 949 |     },
 950 |     {
 951 |       "cell_type": "code",
 952 |       "source": [
 953 |         "DVD_G=nx.from_pandas_edgelist(dvd_df,'FromNodeId','ToNodeId') # Making the network "
 954 |       ],
 955 |       "metadata": {
 956 |         "id": "YPVQMHF6PEXE"
 957 |       },
 958 |       "execution_count": 21,
 959 |       "outputs": []
 960 |     },
 961 |     {
 962 |       "cell_type": "code",
 963 |       "source": [
 964 |         "nx.set_node_attributes(DVD_G, combined_values, 'group') # Setting the attribute of the nodes "
 965 |       ],
 966 |       "metadata": {
 967 |         "id": "RYF7Tp-RPEar"
 968 |       },
 969 |       "execution_count": 22,
 970 |       "outputs": []
 971 |     },
 972 |     {
 973 |       "cell_type": "code",
 974 |       "source": [
 975 |         "DVD_G.number_of_edges(), DVD_G.number_of_nodes() # Checking the nodes and edges"
 976 |       ],
 977 |       "metadata": {
 978 |         "colab": {
 979 |           "base_uri": "https://localhost:8080/"
 980 |         },
 981 |         "id": "kgej-XkVPEdu",
 982 |         "outputId": "543b49fe-4ce3-4080-88ce-7d66c5d37cf8"
 983 |       },
 984 |       "execution_count": 30,
 985 |       "outputs": [
 986 |         {
 987 |           "output_type": "execute_result",
 988 |           "data": {
 989 |             "text/plain": [
 990 |               "(4375, 4225)"
 991 |             ]
 992 |           },
 993 |           "metadata": {},
 994 |           "execution_count": 30
 995 |         }
 996 |       ]
 997 |     },
 998 |     {
 999 |       "cell_type": "code",
1000 |       "source": [
1001 |         "\n",
1002 |         "connected_components = list(nx.connected_components(DVD_G))\n",
1003 |         "connected_component_subgraphs = [DVD_G.subgraph(component) for component in connected_components]\n",
1004 |         "connected_component_lengths = [len(subgraph) for subgraph in connected_component_subgraphs]\n",
1005 |         "\n",
1006 |         "plt.hist(connected_component_lengths)\n",
1007 |         "\n",
1008 |         "\n"
1009 |       ],
1010 |       "metadata": {
1011 |         "colab": {
1012 |           "base_uri": "https://localhost:8080/",
1013 |           "height": 499
1014 |         },
1015 |         "id": "fhqY43D7PEg_",
1016 |         "outputId": "4a774cc5-1091-451a-eaf5-0e73cfd62a08"
1017 |       },
1018 |       "execution_count": 48,
1019 |       "outputs": [
1020 |         {
1021 |           "output_type": "execute_result",
1022 |           "data": {
1023 |             "text/plain": [
1024 |               "(array([198.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.]),\n",
1025 |               " array([   2. ,  185.9,  369.8,  553.7,  737.6,  921.5, 1105.4, 1289.3,\n",
1026 |               "        1473.2, 1657.1, 1841. ]),\n",
1027 |               " <BarContainer object of 10 artists>)"
1028 |             ]
1029 |           },
1030 |           "metadata": {},
1031 |           "execution_count": 48
1032 |         },
1033 |         {
1034 |           "output_type": "display_data",
1035 |           "data": {
1036 |             "text/plain": [
1037 |               "<Figure size 640x480 with 1 Axes>"
1038 |             ],
1039 |             "image/png": 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\n"
1040 |           },
1041 |           "metadata": {}
1042 |         }
1043 |       ]
1044 |     },
1045 |     {
1046 |       "cell_type": "markdown",
1047 |       "source": [
1048 |         "# Now Let's Visualize the subgraph"
1049 |       ],
1050 |       "metadata": {
1051 |         "id": "EkCucJHdTNyf"
1052 |       }
1053 |     },
1054 |     {
1055 |       "cell_type": "code",
1056 |       "source": [
1057 |         "\n",
1058 |         "connected_components = list(nx.connected_components(DVD_G))\n",
1059 |         "subG = [DVD_G.subgraph(component) for component in connected_components if len(component) < 300]\n",
1060 |         "\n",
1061 |         "len(subG)"
1062 |       ],
1063 |       "metadata": {
1064 |         "colab": {
1065 |           "base_uri": "https://localhost:8080/"
1066 |         },
1067 |         "id": "T8Qb9KD7RwWT",
1068 |         "outputId": "1067fbb2-127c-4d8d-eaf7-549f75792721"
1069 |       },
1070 |       "execution_count": 59,
1071 |       "outputs": [
1072 |         {
1073 |           "output_type": "execute_result",
1074 |           "data": {
1075 |             "text/plain": [
1076 |               "198"
1077 |             ]
1078 |           },
1079 |           "metadata": {},
1080 |           "execution_count": 59
1081 |         }
1082 |       ]
1083 |     },
1084 |     {
1085 |       "cell_type": "code",
1086 |       "source": [
1087 |         "L=[]                       # We have selected nodes from the subgraph and have appended them in list\n",
1088 |         "max=0\n",
1089 |         "pos=0\n",
1090 |         "for i in range(0,198):\n",
1091 |         "  N=len(subG[i])\n",
1092 |         "  L.append(N)\n",
1093 |         "  if  N>max:\n",
1094 |         "    pos=i\n",
1095 |         "    max=N\n"
1096 |       ],
1097 |       "metadata": {
1098 |         "id": "EMmCtWjcT5xe"
1099 |       },
1100 |       "execution_count": 60,
1101 |       "outputs": []
1102 |     },
1103 |     {
1104 |       "cell_type": "code",
1105 |       "source": [
1106 |         "(subG[pos].number_of_nodes()) # Checking the number of nodes"
1107 |       ],
1108 |       "metadata": {
1109 |         "colab": {
1110 |           "base_uri": "https://localhost:8080/"
1111 |         },
1112 |         "id": "4fqY-HE3V3QO",
1113 |         "outputId": "f4fc967c-2ea0-46f3-97cd-e8966abd3886"
1114 |       },
1115 |       "execution_count": 61,
1116 |       "outputs": [
1117 |         {
1118 |           "output_type": "execute_result",
1119 |           "data": {
1120 |             "text/plain": [
1121 |               "59"
1122 |             ]
1123 |           },
1124 |           "metadata": {},
1125 |           "execution_count": 61
1126 |         }
1127 |       ]
1128 |     },
1129 |     {
1130 |       "cell_type": "code",
1131 |       "source": [
1132 |         "nx.draw(subG[pos])  #Plotting the subgraph"
1133 |       ],
1134 |       "metadata": {
1135 |         "colab": {
1136 |           "base_uri": "https://localhost:8080/",
1137 |           "height": 516
1138 |         },
1139 |         "id": "-pO3RZNvWGYt",
1140 |         "outputId": "6551cff0-e802-443a-b9bd-ba86de824c75"
1141 |       },
1142 |       "execution_count": 62,
1143 |       "outputs": [
1144 |         {
1145 |           "output_type": "display_data",
1146 |           "data": {
1147 |             "text/plain": [
1148 |               "<Figure size 640x480 with 1 Axes>"
1149 |             ],
1150 |             "image/png": 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\n"
1151 |           },
1152 |           "metadata": {}
1153 |         }
1154 |       ]
1155 |     },
1156 |     {
1157 |       "cell_type": "markdown",
1158 |       "source": [
1159 |         "# Now Let's Color Code It 😀😀"
1160 |       ],
1161 |       "metadata": {
1162 |         "id": "2OsNjBSvZEid"
1163 |       }
1164 |     },
1165 |     {
1166 |       "cell_type": "markdown",
1167 |       "source": [
1168 |         "A subgraph subG[pos] with nodes colored according to their group attribute. Each group have a distinct color, and a legend was added to show the mapping between colors and groups."
1169 |       ],
1170 |       "metadata": {
1171 |         "id": "elhMVXEHd6lH"
1172 |       }
1173 |     },
1174 |     {
1175 |       "cell_type": "code",
1176 |       "source": [
1177 |         "node_groups=list(nx.get_node_attributes(G,'group').values()) # Getting  Groups\n",
1178 |         "\n",
1179 |         "groups=set(node_groups) # Making Set\n",
1180 |         "node_group_dic=nx.get_node_attributes(subG[pos],'group')\n",
1181 |         "color=[plt.cm.jet(x) for x in np.linspace(0,1,len(groups))]\n",
1182 |         "poss=nx.spring_layout(subG[pos])\n",
1183 |         "\n",
1184 |         "plt.figure(figsize=(11,11))\n",
1185 |         "for i, groups in enumerate(groups):\n",
1186 |         "  group_nodes=[node for node, value in node_group_dic.items() if value==groups]\n",
1187 |         "  nx.draw_networkx_nodes(subG[pos],poss,nodelist=group_nodes,node_color=color[i],label=groups, node_size=100,cmap=plt.cm.jet)\n",
1188 |         "nx.draw_networkx_edges(subG[pos],poss,alpha=0.2)\n",
1189 |         "plt.legend()\n",
1190 |         "plt.axis('off')\n",
1191 |         "plt.show()"
1192 |       ],
1193 |       "metadata": {
1194 |         "colab": {
1195 |           "base_uri": "https://localhost:8080/",
1196 |           "height": 973
1197 |         },
1198 |         "id": "rd_lOpmHXkn1",
1199 |         "outputId": "0697fc8b-20db-4251-c224-d8bf2e4bcf7f"
1200 |       },
1201 |       "execution_count": 63,
1202 |       "outputs": [
1203 |         {
1204 |           "output_type": "stream",
1205 |           "name": "stderr",
1206 |           "text": [
1207 |             "/usr/local/lib/python3.10/dist-packages/networkx/drawing/nx_pylab.py:433: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
1208 |             "  node_collection = ax.scatter(\n",
1209 |             "/usr/local/lib/python3.10/dist-packages/networkx/drawing/nx_pylab.py:433: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
1210 |             "  node_collection = ax.scatter(\n"
1211 |           ]
1212 |         },
1213 |         {
1214 |           "output_type": "display_data",
1215 |           "data": {
1216 |             "text/plain": [
1217 |               "<Figure size 1100x1100 with 1 Axes>"
1218 |             ],
1219 |             "image/png": 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\n"
1220 |           },
1221 |           "metadata": {}
1222 |         }
1223 |       ]
1224 |     },
1225 |     {
1226 |       "cell_type": "code",
1227 |       "source": [],
1228 |       "metadata": {
1229 |         "id": "9IGNB4Neck-s"
1230 |       },
1231 |       "execution_count": null,
1232 |       "outputs": []
1233 |     }
1234 |   ]
1235 | }


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/Extraction.py:
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 1 | import csv
 2 | import gzip
 3 | 
 4 | with gzip.open('amazon0302.txt.gz', 'rt') as f:
 5 |     reader = csv.reader(f, delimiter='\t')
 6 |     with open('amazon0302.csv', 'w') as fout:
 7 |         writer = csv.writer(fout)
 8 |         for row in reader:
 9 |             writer.writerow(row)
10 | 


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/README.md:
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 1 | # Amazon_CoPurchasing_Network
 2 | Complete Analysis of Amazon' Co-Purchasing Network
 3 | 
 4 | 
 5 | # Exploring the Amazon Product Co-Purchasing Network: A Social Network Analysis Approach
 6 | 
 7 | In this project, we dive into the Amazon product co-purchasing network to explore the connections between products and identify key patterns and communities within the network. 
 8 | By leveraging social network analysis techniques, we aim to gain a deeper understanding of the product network's structure, identify influential products, and uncover potential clusters or communities of related items.
 9 | 
10 | # Usage
11 | 1. Run "Amazon_Co-purchased Product Analysis.ipynb"
12 | * Make Sure You Have The CSV Files I have Provided
13 | 
14 | 
15 | # Dataset
16 | We utilize a large-scale dataset obtained from Amazon, which contains information about customer purchases and the co-purchasing relationships between products. 
17 | The dataset includes attributes such as product IDs, customer IDs, purchase timestamps, and additional metadata about the products.
18 | 
19 | Raw Data can be obtained from:-- [Stanford's Network Datasets](https://snap.stanford.edu/data/#amazon)
20 | 
21 | # Methodology
22 | - analysis involves several key steps:
23 | 
24 | 1. Data preprocessing: We perform data cleaning and transformation to ensure the dataset is in a suitable format for network analysis.
25 | 2. Network construction: We construct a co-purchasing network by representing products as nodes and connecting them based on their co-purchasing relationships.
26 | 3. Network analysis: We apply various social network analysis techniques to explore the structural properties of the co-purchasing network. This includes measuring      centrality, identifying communities or clusters, and examining network connectivity.
27 | 4. Visualization: We visualize the co-purchasing network and its key properties to provide a clear representation of the relationships between products and any identified patterns or communities.
28 | 


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/SNA_COMPLETE_PPT.pdf:
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https://raw.githubusercontent.com/Devank-Garg/Amazon-Network/f269a9f11151d9d9a4843b234ed64a8216aee3f0/SNA_COMPLETE_PPT.pdf


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