Corruption Technique.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96cTKwhkFHne"
      },
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      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as exp\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZTdu_qyaFPiD"
      },
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      "source": [
        "df = pd.read_csv('/content/Life Expectancy Data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LGw_JUCewqvc"
      },
      "outputs": [],
      "source": [
        "df = df.drop(range(20,17000))"
      ]
    },
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            "text/plain": [
              "          Country  Year      Status  Unnamed: 3  Adult Mortality  \\\n",
              "0     Afghanistan  2015  Developing         NaN            263.0   \n",
              "1     Afghanistan  2014  Developing         NaN            271.0   \n",
              "2     Afghanistan  2013  Developing         NaN            268.0   \n",
              "3     Afghanistan  2012  Developing         NaN            272.0   \n",
              "4     Afghanistan  2011  Developing         NaN            275.0   \n",
              "...           ...   ...         ...         ...              ...   \n",
              "2933     Zimbabwe  2004  Developing         NaN            723.0   \n",
              "2934     Zimbabwe  2003  Developing         NaN            715.0   \n",
              "2935     Zimbabwe  2002  Developing         NaN             73.0   \n",
              "2936     Zimbabwe  2001  Developing         NaN            686.0   \n",
              "2937     Zimbabwe  2000  Developing         NaN            665.0   \n",
              "\n",
              "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
              "0                62     0.01               71.279624         65.0      1154   \n",
              "1                64     0.01               73.523582         62.0       492   \n",
              "2                66     0.01               73.219243         64.0       430   \n",
              "3                69     0.01               78.184215         67.0      2787   \n",
              "4                71     0.01                7.097109         68.0      3013   \n",
              "...             ...      ...                     ...          ...       ...   \n",
              "2933             27     4.36                0.000000         68.0        31   \n",
              "2934             26     4.06                0.000000          7.0       998   \n",
              "2935             25     4.43                0.000000         73.0       304   \n",
              "2936             25     1.72                0.000000         76.0       529   \n",
              "2937             24     1.68                0.000000         79.0      1483   \n",
              "\n",
              "      ...  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "0     ...               8.16         65.0        0.1  584.259210  33736494.0   \n",
              "1     ...               8.18         62.0        0.1  612.696514    327582.0   \n",
              "2     ...               8.13         64.0        0.1  631.744976  31731688.0   \n",
              "3     ...               8.52         67.0        0.1  669.959000   3696958.0   \n",
              "4     ...               7.87         68.0        0.1   63.537231   2978599.0   \n",
              "...   ...                ...          ...        ...         ...         ...   \n",
              "2933  ...               7.13         65.0       33.6  454.366654  12777511.0   \n",
              "2934  ...               6.52         68.0       36.7  453.351155  12633897.0   \n",
              "2935  ...               6.53         71.0       39.8   57.348340    125525.0   \n",
              "2936  ...               6.16         75.0       42.1  548.587312  12366165.0   \n",
              "2937  ...               7.10         78.0       43.5  547.358878  12222251.0   \n",
              "\n",
              "       thinness  1-19 years   thinness 5-9 years  \\\n",
              "0                      17.2                 17.3   \n",
              "1                      17.5                 17.5   \n",
              "2                      17.7                 17.7   \n",
              "3                      17.9                 18.0   \n",
              "4                      18.2                 18.2   \n",
              "...                     ...                  ...   \n",
              "2933                    9.4                  9.4   \n",
              "2934                    9.8                  9.9   \n",
              "2935                    1.2                  1.3   \n",
              "2936                    1.6                  1.7   \n",
              "2937                   11.0                 11.2   \n",
              "\n",
              "      Income composition of resources  Schooling  Life expectancy   \n",
              "0                               0.479       10.1              65.0  \n",
              "1                               0.476       10.0              59.9  \n",
              "2                               0.470        9.9              59.9  \n",
              "3                               0.463        9.8              59.5  \n",
              "4                               0.454        9.5              59.2  \n",
              "...                               ...        ...               ...  \n",
              "2933                            0.407        9.2              44.3  \n",
              "2934                            0.418        9.5              44.5  \n",
              "2935                            0.427       10.0              44.8  \n",
              "2936                            0.427        9.8              45.3  \n",
              "2937                            0.434        9.8              46.0  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>Country</th>\n",
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              "      <th>Unnamed: 3</th>\n",
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              "      <th>thinness  1-19 years</th>\n",
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              "      <th>Income composition of resources</th>\n",
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              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2015</td>\n",
              "      <td>Developing</td>\n",
              "      <td>NaN</td>\n",
              "      <td>263.0</td>\n",
              "      <td>62</td>\n",
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              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2014</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>271.0</td>\n",
              "      <td>64</td>\n",
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              "      <td>62.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2013</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>268.0</td>\n",
              "      <td>66</td>\n",
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              "      <td>64.0</td>\n",
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              "      <td>8.13</td>\n",
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              "      <td>0.1</td>\n",
              "      <td>631.744976</td>\n",
              "      <td>31731688.0</td>\n",
              "      <td>17.7</td>\n",
              "      <td>17.7</td>\n",
              "      <td>0.470</td>\n",
              "      <td>9.9</td>\n",
              "      <td>59.9</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2012</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>272.0</td>\n",
              "      <td>69</td>\n",
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              "      <td>17.9</td>\n",
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              "      <th>4</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2011</td>\n",
              "      <td>Developing</td>\n",
              "      <td>NaN</td>\n",
              "      <td>275.0</td>\n",
              "      <td>71</td>\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XGDuQ9neTU33"
      },
      "outputs": [],
      "source": [
        "r=len(df.index)\n",
        "c=list(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vvCkymwHUbbS"
      },
      "outputs": [],
      "source": [
        "randomlist = []\n",
        "for i in range(0,1000):\n",
        "  n = random.randint(20,50)\n",
        "  randomlist.append(n)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Modified']= 'False'"
      ],
      "metadata": {
        "id": "9eSIN_n6pCWj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,1000):\n",
        "  x=random.randint(0,r-1)\n",
        "  df[c[22]][x]=df[c[22]][x]+randomlist[i]\n",
        "  df['Modified'][x] = 'True'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAXmu27MoGvq",
        "outputId": "ceb6a178-2ed9-474b-8ad0-df8fee48812c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W1qqQHVYI561"
      },
      "outputs": [],
      "source": [
        "for i in range(0,5):\n",
        "  x=random.randint(0,r-1)\n",
        "  for j in range(1,len(c)):\n",
        "    if(type(df[c[j]][x])==str):\n",
        "      break\n",
        "    else:\n",
        "      df[c[j]][x]=df[c[j]][x]+randomlist[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sgX-1FRecX6V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0146ad2-b984-4a34-d1f4-e02521e8e4a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ]
        }
      ],
      "source": [
        "for i in range(0,100):\n",
        "  z=random.randint(0,r-1)\n",
        "  y=22\n",
        "  df[c[y]][z]=np.nan\n",
        "  df['Modified'][x] = 'True'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "SWRrWlEdy2aL",
        "outputId": "7fab7ac0-01b7-4358-91fd-c534d5f3a84c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Country  Year      Status  Unnamed: 3  Adult Mortality  \\\n",
              "0     Afghanistan  2015  Developing         NaN            263.0   \n",
              "1     Afghanistan  2014  Developing         NaN            271.0   \n",
              "2     Afghanistan  2013  Developing         NaN            268.0   \n",
              "3     Afghanistan  2012  Developing         NaN            272.0   \n",
              "4     Afghanistan  2011  Developing         NaN            275.0   \n",
              "...           ...   ...         ...         ...              ...   \n",
              "2933     Zimbabwe  2004  Developing         NaN            723.0   \n",
              "2934     Zimbabwe  2003  Developing         NaN            715.0   \n",
              "2935     Zimbabwe  2002  Developing         NaN             73.0   \n",
              "2936     Zimbabwe  2001  Developing         NaN            686.0   \n",
              "2937     Zimbabwe  2000  Developing         NaN            665.0   \n",
              "\n",
              "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
              "0                62     0.01               71.279624         65.0      1154   \n",
              "1                64     0.01               73.523582         62.0       492   \n",
              "2                66     0.01               73.219243         64.0       430   \n",
              "3                69     0.01               78.184215         67.0      2787   \n",
              "4                71     0.01                7.097109         68.0      3013   \n",
              "...             ...      ...                     ...          ...       ...   \n",
              "2933             27     4.36                0.000000         68.0        31   \n",
              "2934             26     4.06                0.000000          7.0       998   \n",
              "2935             25     4.43                0.000000         73.0       304   \n",
              "2936             25     1.72                0.000000         76.0       529   \n",
              "2937             24     1.68                0.000000         79.0      1483   \n",
              "\n",
              "      ...  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
              "0     ...         65.0        0.1  584.259210  33736494.0   \n",
              "1     ...         62.0        0.1  612.696514    327582.0   \n",
              "2     ...         64.0        0.1  631.744976  31731688.0   \n",
              "3     ...         67.0        0.1  669.959000   3696958.0   \n",
              "4     ...         68.0        0.1   63.537231   2978599.0   \n",
              "...   ...          ...        ...         ...         ...   \n",
              "2933  ...         65.0       33.6  454.366654  12777511.0   \n",
              "2934  ...         68.0       36.7  453.351155  12633897.0   \n",
              "2935  ...         71.0       39.8   57.348340    125525.0   \n",
              "2936  ...         75.0       42.1  548.587312  12366165.0   \n",
              "2937  ...         78.0       43.5  547.358878  12222251.0   \n",
              "\n",
              "       thinness  1-19 years   thinness 5-9 years  \\\n",
              "0                      17.2                 17.3   \n",
              "1                      17.5                 17.5   \n",
              "2                      17.7                 17.7   \n",
              "3                      17.9                 18.0   \n",
              "4                      18.2                 18.2   \n",
              "...                     ...                  ...   \n",
              "2933                    9.4                  9.4   \n",
              "2934                    9.8                  9.9   \n",
              "2935                    1.2                  1.3   \n",
              "2936                    1.6                  1.7   \n",
              "2937                   11.0                 11.2   \n",
              "\n",
              "      Income composition of resources  Schooling  Life expectancy   Modified  \n",
              "0                               0.479       10.1             135.0      True  \n",
              "1                               0.476       10.0              59.9     False  \n",
              "2                               0.470        9.9              59.9     False  \n",
              "3                               0.463        9.8              73.5      True  \n",
              "4                               0.454        9.5              59.2     False  \n",
              "...                               ...        ...               ...       ...  \n",
              "2933                            0.407        9.2              61.3      True  \n",
              "2934                            0.418        9.5              44.5     False  \n",
              "2935                            0.427       10.0              44.8     False  \n",
              "2936                            0.427        9.8              63.3      True  \n",
              "2937                            0.434        9.8              60.0      True  \n",
              "\n",
              "[2938 rows x 24 columns]"
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5aa2e7a-e91d-4a4a-909e-cdee833df4b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a5aa2e7a-e91d-4a4a-909e-cdee833df4b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5aa2e7a-e91d-4a4a-909e-cdee833df4b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xylvwJ7ue8EJ"
      },
      "outputs": [],
      "source": [
        "df.to_csv('Corrupt1.csv')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "IEEE-1.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}