ML-Notebooks/Affairs.ipynb

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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ],
      "metadata": {
        "id": "wFF3usp4KaCG"
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We will be predicting, the number of affairs a person might have, interesting right ;)\n",
        "- Note that the affairs column is a float type and not an int type. So we are to assume the round off value."
      ],
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        "id": "3a0N9SNxK9PW"
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      "source": [
        "affairs = pd.read_csv(\"/content/drive/MyDrive/Dataset/affairs.csv\")\n",
        "affairs.head()"
      ],
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              "   Unnamed: 0  rate_marriage   age  yrs_married  children  religious  educ  \\\n",
              "0           0            3.0  32.0          9.0       3.0        3.0  17.0   \n",
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              "\n",
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        "affairs.shape"
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              "(6366, 10)"
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    {
      "cell_type": "code",
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        "affairs.isnull().sum()"
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            "text/plain": [
              "Unnamed: 0         0\n",
              "rate_marriage      0\n",
              "age                0\n",
              "yrs_married        0\n",
              "children           0\n",
              "religious          0\n",
              "educ               0\n",
              "occupation         0\n",
              "occupation_husb    0\n",
              "affairs            0\n",
              "dtype: int64"
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        "affairs = affairs.drop(['Unnamed: 0'], axis = 1)"
      ],
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      "execution_count": 6,
      "outputs": []
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        "def wholeNumber(x):\n",
        "  return int(x + 0.5)\n",
        "\n",
        "affairs['affairs'] = affairs['affairs'].map(wholeNumber)"
      ],
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      "execution_count": 7,
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              "   rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
              "0            3.0  32.0          9.0       3.0        3.0  17.0         2.0   \n",
              "1            3.0  27.0         13.0       3.0        1.0  14.0         3.0   \n",
              "\n",
              "   occupation_husb  affairs  \n",
              "0              5.0        0  \n",
              "1              4.0        3  "
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        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(affairs.corr(),annot=True,cmap='viridis',linewidths=.5)"
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              "<Figure size 576x576 with 2 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- From the above graph, we can see that there is a high correlation between columns like age and years_married or years_married and children\n",
        "- the person is a parent of.\n",
        "- Now if we have to remove the dimensions of the data, to make it more compact without losing on much information we can remove\n",
        "- years_married column."
      ],
      "metadata": {
        "id": "yHzxXwm1LVDb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gaining more insight on the data:\n",
        "\n",
        "affairs.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "e36nFb6sKagY",
        "outputId": "79098d1a-a474-418e-9e28-ea1a8ca359a4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       rate_marriage          age  yrs_married     children    religious  \\\n",
              "count    6366.000000  6366.000000  6366.000000  6366.000000  6366.000000   \n",
              "mean        4.109645    29.082862     9.009425     1.396874     2.426170   \n",
              "std         0.961430     6.847882     7.280120     1.433471     0.878369   \n",
              "min         1.000000    17.500000     0.500000     0.000000     1.000000   \n",
              "25%         4.000000    22.000000     2.500000     0.000000     2.000000   \n",
              "50%         4.000000    27.000000     6.000000     1.000000     2.000000   \n",
              "75%         5.000000    32.000000    16.500000     2.000000     3.000000   \n",
              "max         5.000000    42.000000    23.000000     5.500000     4.000000   \n",
              "\n",
              "              educ   occupation  occupation_husb      affairs  \n",
              "count  6366.000000  6366.000000      6366.000000  6366.000000  \n",
              "mean     14.209865     3.424128         3.850141     0.697769  \n",
              "std       2.178003     0.942399         1.346435     2.218132  \n",
              "min       9.000000     1.000000         1.000000     0.000000  \n",
              "25%      12.000000     3.000000         3.000000     0.000000  \n",
              "50%      14.000000     3.000000         4.000000     0.000000  \n",
              "75%      16.000000     4.000000         5.000000     0.000000  \n",
              "max      20.000000     6.000000         6.000000    58.000000  "
            ],
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              "      <th></th>\n",
              "      <th>rate_marriage</th>\n",
              "      <th>age</th>\n",
              "      <th>yrs_married</th>\n",
              "      <th>children</th>\n",
              "      <th>religious</th>\n",
              "      <th>educ</th>\n",
              "      <th>occupation</th>\n",
              "      <th>occupation_husb</th>\n",
              "      <th>affairs</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6366.000000</td>\n",
              "      <td>6366.000000</td>\n",
              "      <td>6366.000000</td>\n",
              "      <td>6366.000000</td>\n",
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              "      <td>6366.000000</td>\n",
              "      <td>6366.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.109645</td>\n",
              "      <td>29.082862</td>\n",
              "      <td>9.009425</td>\n",
              "      <td>1.396874</td>\n",
              "      <td>2.426170</td>\n",
              "      <td>14.209865</td>\n",
              "      <td>3.424128</td>\n",
              "      <td>3.850141</td>\n",
              "      <td>0.697769</td>\n",
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              "      <th>std</th>\n",
              "      <td>0.961430</td>\n",
              "      <td>6.847882</td>\n",
              "      <td>7.280120</td>\n",
              "      <td>1.433471</td>\n",
              "      <td>0.878369</td>\n",
              "      <td>2.178003</td>\n",
              "      <td>0.942399</td>\n",
              "      <td>1.346435</td>\n",
              "      <td>2.218132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>58.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can see that ranges for different columns that are related to different aspects of a people and his/her qualifications.\n",
        "- We can even see that the mean is 0.69 for affairs, while more than 50 percentile have not committed Adultery."
      ],
      "metadata": {
        "id": "XYq4c2efOd9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(affairs['educ'], affairs['affairs'], cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "1nSbOTXmObav",
        "outputId": "0eb84e3a-c397-4a88-e464-248403d520a1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff785df6580>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- This shows that with education of level 14 on a scale of (9(lowest) to 20(highest)) most people cheat.\n",
        "- What is even more suprising is that people on level 20 in education too cheat but the intensity is moderate,\n",
        "- while on level 10 and 18 of education nobody cheated (loyal people are at level 10 and 18 guys and gals)."
      ],
      "metadata": {
        "id": "S_Rtwl_2OlMJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograms\n",
        "\n",
        "plt.bar(affairs['children'],  affairs['affairs'], color='#0504aa')\n",
        "plt.grid(axis='y', alpha=0.7)\n",
        "plt.xlabel('Number of children')\n",
        "plt.ylabel('Frequency of Affairs')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "hklp6D29Oios",
        "outputId": "32889811-46e3-42e3-f458-5d28250cda4b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Again quite a suprise that  a person with a child had the highest affair count."
      ],
      "metadata": {
        "id": "JEbu7WA7Ospi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "sns.boxplot(affairs['affairs'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "aTOwmh0DOqG0",
        "outputId": "7efa95f8-e2dd-4089-dcc4-51834cc791f1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff785dbae80>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This shows that the data is filled with zeros and will cause error later on as the data is imbalanced.\n",
        "# To fix this we will remove most of all the zero values in affairs column.\n",
        "\n",
        "affairs_zero = affairs[affairs['affairs'] == 0]\n",
        "affairs = affairs[affairs['affairs'] > 0]"
      ],
      "metadata": {
        "id": "YUVmUTN5OwQE"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "affairs_zero.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHQb4VWnOyYV",
        "outputId": "1254e49b-3d11-4399-bffc-74566406fb4e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4788, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs = affairs.append(affairs_zero.iloc[:500])\n",
        "\n",
        "affairs.reset_index(inplace = True, drop = True)\n",
        "affairs.tail(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "gW2FtRmtO0R1",
        "outputId": "bfada9ed-482c-407d-f726-608e2cdb4a8d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      rate_marriage   age  yrs_married  children  religious  educ  occupation  \\\n",
              "2075            3.0  27.0          9.0       0.0        2.0  16.0         5.0   \n",
              "2076            4.0  27.0          2.5       1.0        3.0  14.0         3.0   \n",
              "2077            5.0  22.0          2.5       0.0        1.0  12.0         3.0   \n",
              "\n",
              "      occupation_husb  affairs  \n",
              "2075              2.0        0  \n",
              "2076              5.0        0  \n",
              "2077              4.0        0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f024b881-71e7-4656-a3c0-e4a038dc0414\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>rate_marriage</th>\n",
              "      <th>age</th>\n",
              "      <th>yrs_married</th>\n",
              "      <th>children</th>\n",
              "      <th>religious</th>\n",
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              "      <th>occupation</th>\n",
              "      <th>occupation_husb</th>\n",
              "      <th>affairs</th>\n",
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              "      <th>2075</th>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2076</th>\n",
              "      <td>4.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2077</th>\n",
              "      <td>5.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f024b881-71e7-4656-a3c0-e4a038dc0414')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f024b881-71e7-4656-a3c0-e4a038dc0414 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-f024b881-71e7-4656-a3c0-e4a038dc0414');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwz6YJTeO1xs",
        "outputId": "5a75a5c4-ca5c-485d-9738-1525f614842f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2078, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs['children'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0PWO3GoO3fb",
        "outputId": "503eb31e-c003-4b07-8cd7-6674b87413d1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    612\n",
              "0.0    515\n",
              "1.0    417\n",
              "3.0    321\n",
              "4.0    133\n",
              "5.5     80\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs['educ'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GjmhcpBO5eM",
        "outputId": "1997dbb7-57c9-4fa4-8ccb-bf380f45ed37"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.0    818\n",
              "12.0    728\n",
              "16.0    279\n",
              "17.0    143\n",
              "20.0     89\n",
              "9.0      21\n",
              "Name: educ, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Moving on we look for categorical data types\n",
        "\n",
        "affairs['rate_marriage'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQlWiE0gO854",
        "outputId": "50110a54-4f18-4bb8-c55d-8285309901da"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    733\n",
              "3.0    550\n",
              "5.0    500\n",
              "2.0    221\n",
              "1.0     74\n",
              "Name: rate_marriage, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs['religious'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EA1N2WxYPAGo",
        "outputId": "f5536a4c-9b49-439e-cb05-11529202f42c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    830\n",
              "3.0    716\n",
              "1.0    412\n",
              "4.0    120\n",
              "Name: religious, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs['occupation'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ph-xwhOiPCdn",
        "outputId": "439acdd0-8d5b-486d-df10-c21283ebb432"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0    978\n",
              "4.0    484\n",
              "5.0    313\n",
              "2.0    256\n",
              "6.0     40\n",
              "1.0      7\n",
              "Name: occupation, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "affairs['occupation_husb'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUS1XOhwPEYJ",
        "outputId": "729bcd68-ebd8-47ae-a73d-5ab9c7011252"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.0    649\n",
              "5.0    608\n",
              "2.0    435\n",
              "3.0    173\n",
              "6.0    165\n",
              "1.0     48\n",
              "Name: occupation_husb, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Frankly you can say that these are categorical data, since they are in the form on munbers, they can be easily processed\n",
        "\n",
        "plat_new = pd.get_dummies(affairs['rate_marriage'], prefix = 'rate')\n",
        "dev_new = pd.get_dummies(affairs['religious'], prefix = 'religion')\n",
        "rat_new = pd.get_dummies(affairs['occupation'], prefix = 'occ') \n",
        "rat_new = pd.get_dummies(affairs['occupation_husb'], prefix = 'husb_occ')\n",
        "y = affairs['affairs']\n",
        "\n",
        "affairs = pd.concat([affairs, plat_new, dev_new, rat_new], axis = 1)\n",
        "X = affairs.drop(['affairs', 'rate_marriage', 'religious', 'occupation_husb'], axis = 1)"
      ],
      "metadata": {
        "id": "dIHBOzPEPGv4"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "pUeL8kHCPJHI"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyyIP4wmPK_g",
        "outputId": "94c95189-8d34-4f9c-88b7-e6ae00f1da0c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2078, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "y2Da7DP-PMr_",
        "outputId": "23006a85-4eec-49b9-9b92-fa173f16db95"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  yrs_married  children  educ  occupation  rate_1.0  rate_2.0  \\\n",
              "0  27.0         13.0       3.0  14.0         3.0         0         0   \n",
              "1  22.0          2.5       0.0  16.0         3.0         0         0   \n",
              "2  37.0         16.5       4.0  16.0         5.0         0         0   \n",
              "3  27.0          9.0       1.0  14.0         3.0         0         0   \n",
              "4  27.0          9.0       0.0  14.0         3.0         0         0   \n",
              "\n",
              "   rate_3.0  rate_4.0  rate_5.0  religion_1.0  religion_2.0  religion_3.0  \\\n",
              "0         1         0         0             1             0             0   \n",
              "1         0         1         0             1             0             0   \n",
              "2         0         1         0             0             0             1   \n",
              "3         0         0         1             1             0             0   \n",
              "4         0         1         0             0             1             0   \n",
              "\n",
              "   religion_4.0  husb_occ_1.0  husb_occ_2.0  husb_occ_3.0  husb_occ_4.0  \\\n",
              "0             0             0             0             0             1   \n",
              "1             0             0             0             0             0   \n",
              "2             0             0             0             0             0   \n",
              "3             0             0             0             0             1   \n",
              "4             0             0             0             0             1   \n",
              "\n",
              "   husb_occ_5.0  husb_occ_6.0  \n",
              "0             0             0  \n",
              "1             1             0  \n",
              "2             1             0  \n",
              "3             0             0  \n",
              "4             0             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cb756b9-dd0e-4051-a661-15c3c54c4733\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <td>22.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>37.0</td>\n",
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              "      <td>4.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27.0</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components = 15)\n",
        "pca_fit = pca.fit_transform(X)"
      ],
      "metadata": {
        "id": "LgCdMBXbPOuu"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, ytrain, ytest = train_test_split(pca_fit, y, test_size=0.2, random_state = 1)"
      ],
      "metadata": {
        "id": "FWyRc6OPPQrf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_model = RandomForestRegressor(n_estimators=300, random_state = 0, n_jobs = -1)"
      ],
      "metadata": {
        "id": "NQwdcPCOPSco"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit\n",
        "random_model.fit(Xtrain, ytrain)\n",
        "\n",
        "y_pred = random_model.predict(Xtest)\n",
        "\n",
        "#Checking the accuracy\n",
        "random_model_accuracy = round(random_model.score(Xtrain, ytrain)*100,2)\n",
        "print(round(random_model_accuracy, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xse-_5htPT-6",
        "outputId": "6e89df20-4e2d-4bd0-e711-9ada5762313e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82.19 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model as a pickle string.\n",
        "import pickle \n",
        "\n",
        "saved_model = pickle.dump(random_model, open('drive/MyDrive/Dataset/Models/Affairs.pickle','wb'))"
      ],
      "metadata": {
        "id": "Od3gs4kdPYTX"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "saved_pca = pickle.dump(pca, open('drive/MyDrive/Dataset/Models/AffairsPCA.pickle','wb'))"
      ],
      "metadata": {
        "id": "2LMtK0dPPaym"
      },
      "execution_count": 34,
      "outputs": []
    }
  ]
}