Diet_recommendation.ipynb

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        "<a href=\"https://colab.research.google.com/github/akash-r34/CalCheck/blob/main/Diet_recommendation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Step 1: Import User macros dataset and diet plan dataset"
      ],
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        "id": "yBkcOD6HXwLo"
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      "source": [
        "user_macros_df = pd.read_excel(\"/content/Min_macros_for_height_and_weight.xlsx\")\n",
        "diet_id_df = pd.read_excel(\"/content/Food_data_generated_with_dietIDs.xlsx\", index_col=0)"
      ],
      "metadata": {
        "id": "FLryGo9iiBQG"
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      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "diet_id_df"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dietID                                 Food name  Protein(g)  \\\n",
              "0      1005                                      Idli        6.36   \n",
              "1      1003                               Dosa, plain        5.70   \n",
              "2      1005                        Dosa, with filling        5.46   \n",
              "3      1005                               Bread, puri        6.84   \n",
              "4      1001                                      Vada       12.80   \n",
              "..      ...                                       ...         ...   \n",
              "140    1004                              Lentil curry        3.67   \n",
              "141    1005                    Ghee, clarified butter        0.28   \n",
              "142    1002                                      Date        2.45   \n",
              "143    1006  Mayonnaise, reduced fat,  with olive oil        0.37   \n",
              "144    1006                      Romaine lettuce, raw        1.36   \n",
              "\n",
              "     Total lipid (fat)(g)  Carbohydrate, by difference(g)  Energy(kcal)  \\\n",
              "0                    0.35                           25.00           128   \n",
              "1                    4.05                           37.00           210   \n",
              "2                    4.27                           30.80           184   \n",
              "3                   24.90                           39.20           409   \n",
              "4                    9.73                           33.20           266   \n",
              "..                    ...                             ...           ...   \n",
              "140                  5.61                           12.30           110   \n",
              "141                 99.50                            0.00           876   \n",
              "142                  0.39                           75.00           282   \n",
              "143                 40.00                            0.00           361   \n",
              "144                  0.15                            2.87            15   \n",
              "\n",
              "     Alcohol, ethyl(g)  Water(g)  Caffeine(mg)  Theobromine(mg)  ...  \\\n",
              "0                    0     67.20             0                0  ...   \n",
              "1                    0     51.80             0                0  ...   \n",
              "2                    0     57.90             0                0  ...   \n",
              "3                    0     28.00             0                0  ...   \n",
              "4                    0     42.00             0                0  ...   \n",
              "..                 ...       ...           ...              ...  ...   \n",
              "140                  0     76.70             0                0  ...   \n",
              "141                  0      0.24             0                0  ...   \n",
              "142                  0     20.50             0                0  ...   \n",
              "143                  0     57.60             0                0  ...   \n",
              "144                  0     95.00             0                0  ...   \n",
              "\n",
              "     PUFA 20:4(g)  PUFA 22:6 n-3 (DHA)(g)  MUFA 16:1(g)  PUFA 18:4(g)  \\\n",
              "0           0.000                     0.0         0.001           0.0   \n",
              "1           0.000                     0.0         0.014           0.0   \n",
              "2           0.000                     0.0         0.014           0.0   \n",
              "3           0.001                     0.0         0.083           0.0   \n",
              "4           0.000                     0.0         0.034           0.0   \n",
              "..            ...                     ...           ...           ...   \n",
              "140         0.000                     0.0         0.018           0.0   \n",
              "141         0.000                     0.0         2.230           0.0   \n",
              "142         0.000                     0.0         0.001           0.0   \n",
              "143         0.000                     0.0         0.464           0.0   \n",
              "144         0.000                     0.0         0.002           0.0   \n",
              "\n",
              "     MUFA 20:1(g)  PUFA 20:5 n-3 (EPA)(g)  MUFA 22:1(g)  \\\n",
              "0           0.002                     0.0         0.000   \n",
              "1           0.027                     0.0         0.003   \n",
              "2           0.026                     0.0         0.002   \n",
              "3           0.167                     0.0         0.000   \n",
              "4           0.066                     0.0         0.000   \n",
              "..            ...                     ...           ...   \n",
              "140         0.030                     0.0         0.000   \n",
              "141         0.000                     0.0         0.000   \n",
              "142         0.000                     0.0         0.000   \n",
              "143         0.125                     0.0         0.000   \n",
              "144         0.000                     0.0         0.000   \n",
              "\n",
              "     PUFA 22:5 n-3 (DPA)(g)  Fatty acids, total monounsaturated(g)  \\\n",
              "0                       0.0                                  0.090   \n",
              "1                       0.0                                  1.590   \n",
              "2                       0.0                                  1.610   \n",
              "3                       0.0                                  9.710   \n",
              "4                       0.0                                  3.810   \n",
              "..                      ...                                    ...   \n",
              "140                     0.0                                  1.890   \n",
              "141                     0.0                                 28.700   \n",
              "142                     0.0                                  0.036   \n",
              "143                     0.0                                 29.500   \n",
              "144                     0.0                                  0.006   \n",
              "\n",
              "     Fatty acids, total polyunsaturated(g)  \n",
              "0                                    0.142  \n",
              "1                                    1.610  \n",
              "2                                    1.720  \n",
              "3                                   10.000  \n",
              "4                                    3.950  \n",
              "..                                     ...  \n",
              "140                                  1.990  \n",
              "141                                  3.690  \n",
              "142                                  0.019  \n",
              "143                                  4.010  \n",
              "144                                  0.082  \n",
              "\n",
              "[145 rows x 67 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dietID</th>\n",
              "      <th>Food name</th>\n",
              "      <th>Protein(g)</th>\n",
              "      <th>Total lipid (fat)(g)</th>\n",
              "      <th>Carbohydrate, by difference(g)</th>\n",
              "      <th>Energy(kcal)</th>\n",
              "      <th>Alcohol, ethyl(g)</th>\n",
              "      <th>Water(g)</th>\n",
              "      <th>Caffeine(mg)</th>\n",
              "      <th>Theobromine(mg)</th>\n",
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              "      <th>PUFA 20:4(g)</th>\n",
              "      <th>PUFA 22:6 n-3 (DHA)(g)</th>\n",
              "      <th>MUFA 16:1(g)</th>\n",
              "      <th>PUFA 18:4(g)</th>\n",
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              "      <th>PUFA 20:5 n-3 (EPA)(g)</th>\n",
              "      <th>MUFA 22:1(g)</th>\n",
              "      <th>PUFA 22:5 n-3 (DPA)(g)</th>\n",
              "      <th>Fatty acids, total monounsaturated(g)</th>\n",
              "      <th>Fatty acids, total polyunsaturated(g)</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1005</td>\n",
              "      <td>Idli</td>\n",
              "      <td>6.36</td>\n",
              "      <td>0.35</td>\n",
              "      <td>25.00</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>67.20</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>0.090</td>\n",
              "      <td>0.142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1003</td>\n",
              "      <td>Dosa, plain</td>\n",
              "      <td>5.70</td>\n",
              "      <td>4.05</td>\n",
              "      <td>37.00</td>\n",
              "      <td>210</td>\n",
              "      <td>0</td>\n",
              "      <td>51.80</td>\n",
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              "      <td>0.027</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.590</td>\n",
              "      <td>1.610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1005</td>\n",
              "      <td>Dosa, with filling</td>\n",
              "      <td>5.46</td>\n",
              "      <td>4.27</td>\n",
              "      <td>30.80</td>\n",
              "      <td>184</td>\n",
              "      <td>0</td>\n",
              "      <td>57.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.610</td>\n",
              "      <td>1.720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1005</td>\n",
              "      <td>Bread, puri</td>\n",
              "      <td>6.84</td>\n",
              "      <td>24.90</td>\n",
              "      <td>39.20</td>\n",
              "      <td>409</td>\n",
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              "      <td>28.00</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.083</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.710</td>\n",
              "      <td>10.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1001</td>\n",
              "      <td>Vada</td>\n",
              "      <td>12.80</td>\n",
              "      <td>9.73</td>\n",
              "      <td>33.20</td>\n",
              "      <td>266</td>\n",
              "      <td>0</td>\n",
              "      <td>42.00</td>\n",
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              "      <th>140</th>\n",
              "      <td>1004</td>\n",
              "      <td>Lentil curry</td>\n",
              "      <td>3.67</td>\n",
              "      <td>5.61</td>\n",
              "      <td>12.30</td>\n",
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              "      <td>0.018</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030</td>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.890</td>\n",
              "      <td>1.990</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1005</td>\n",
              "      <td>Ghee, clarified butter</td>\n",
              "      <td>0.28</td>\n",
              "      <td>99.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>876</td>\n",
              "      <td>0</td>\n",
              "      <td>0.24</td>\n",
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              "      <th>142</th>\n",
              "      <td>1002</td>\n",
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              "      <td>2.45</td>\n",
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              "      <td>0.036</td>\n",
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              "      <th>143</th>\n",
              "      <td>1006</td>\n",
              "      <td>Mayonnaise, reduced fat,  with olive oil</td>\n",
              "      <td>0.37</td>\n",
              "      <td>40.00</td>\n",
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              "      <td>361</td>\n",
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              "      <td>57.60</td>\n",
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              "      <td>1006</td>\n",
              "      <td>Romaine lettuce, raw</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.15</td>\n",
              "      <td>2.87</td>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7953585-2e50-4f6c-b250-214348cfaf65');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Converting the string string values to integer values"
      ],
      "metadata": {
        "id": "blUiBL8HYAip"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_macros_df = user_macros_df.dropna()\n",
        "user_macros_df[\"Food Energy (Calories/day)\"] = user_macros_df[\"Food Energy (Calories/day)\"].str.replace(',','')\n",
        "user_macros_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "JsB1RHiDiram",
        "outputId": "d701bc70-ce50-4549-aa71-e2d02068ab16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-26743a48dbcc>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  user_macros_df[\"Food Energy (Calories/day)\"] = user_macros_df[\"Food Energy (Calories/day)\"].str.replace(',','')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0  Height  Weight    bmi    bmi_range  Protein (grams/day)  \\\n",
              "0            0  177.74   74.74  23.66       Normal                146.0   \n",
              "1            1  149.72   38.52  17.18  Underweight                101.0   \n",
              "2            2  142.78   45.87  22.50       Normal                103.0   \n",
              "3            3  160.85   61.73  23.86       Normal                126.0   \n",
              "4            4  182.25  105.28  31.70      Obesity                174.0   \n",
              "..         ...     ...     ...    ...          ...                  ...   \n",
              "95          95  147.82   45.29  20.73       Normal                105.0   \n",
              "96          96  180.27  103.48  31.84      Obesity                171.0   \n",
              "97          97  183.51  107.12  31.81      Obesity                176.0   \n",
              "98          98  164.39   85.51  31.64      Obesity                148.0   \n",
              "99          99  179.51   95.86  29.75   Overweight                164.0   \n",
              "\n",
              "    Carbs (grams/day)  Fat (grams/day)  Sugar (grams/day)  \\\n",
              "0               319.0             68.0               64.0   \n",
              "1               220.0             47.0               44.0   \n",
              "2               226.0             48.0               45.0   \n",
              "3               275.0             59.0               55.0   \n",
              "4               380.0             81.0               76.0   \n",
              "..                ...              ...                ...   \n",
              "95              230.0             49.0               46.0   \n",
              "96              374.0             80.0               75.0   \n",
              "97              385.0             82.0               77.0   \n",
              "98              323.0             69.0               65.0   \n",
              "99              359.0             77.0               72.0   \n",
              "\n",
              "    Saturated Fat (grams/day) Food Energy (Calories/day)  \n",
              "0                        27.0                       2390  \n",
              "1                        19.0                       1651  \n",
              "2                        19.0                       1693  \n",
              "3                        23.0                       2066  \n",
              "4                        32.0                       2849  \n",
              "..                        ...                        ...  \n",
              "95                       20.0                       1728  \n",
              "96                       32.0                       2807  \n",
              "97                       33.0                       2885  \n",
              "98                       28.0                       2423  \n",
              "99                       31.0                       2696  \n",
              "\n",
              "[99 rows x 11 columns]"
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
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              "      <th>bmi</th>\n",
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              "      <th>0</th>\n",
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              "      <td>177.74</td>\n",
              "      <td>74.74</td>\n",
              "      <td>23.66</td>\n",
              "      <td>Normal</td>\n",
              "      <td>146.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>27.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>38.52</td>\n",
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              "      <td>101.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1651</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>142.78</td>\n",
              "      <td>45.87</td>\n",
              "      <td>22.50</td>\n",
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              "      <td>103.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1693</td>\n",
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              "      <th>3</th>\n",
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              "      <td>160.85</td>\n",
              "      <td>61.73</td>\n",
              "      <td>23.86</td>\n",
              "      <td>Normal</td>\n",
              "      <td>126.0</td>\n",
              "      <td>275.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2066</td>\n",
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              "      <th>4</th>\n",
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              "      <td>182.25</td>\n",
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              "      <td>31.70</td>\n",
              "      <td>Obesity</td>\n",
              "      <td>174.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2849</td>\n",
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              "      <td>147.82</td>\n",
              "      <td>45.29</td>\n",
              "      <td>20.73</td>\n",
              "      <td>Normal</td>\n",
              "      <td>105.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <th>96</th>\n",
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              "      <td>103.48</td>\n",
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              "      <td>374.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>75.0</td>\n",
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              "      <td>2807</td>\n",
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              "      <th>97</th>\n",
              "      <td>97</td>\n",
              "      <td>183.51</td>\n",
              "      <td>107.12</td>\n",
              "      <td>31.81</td>\n",
              "      <td>Obesity</td>\n",
              "      <td>176.0</td>\n",
              "      <td>385.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2885</td>\n",
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              "      <td>98</td>\n",
              "      <td>164.39</td>\n",
              "      <td>85.51</td>\n",
              "      <td>31.64</td>\n",
              "      <td>Obesity</td>\n",
              "      <td>148.0</td>\n",
              "      <td>323.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>65.0</td>\n",
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              "      <td>31.0</td>\n",
              "      <td>2696</td>\n",
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              "<p>99 rows × 11 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Step 2: Aggregate each default diet plans by their macro levels"
      ],
      "metadata": {
        "id": "TdliFmJIYSHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_diet_id = diet_id_df.groupby(by=\"dietID\")\n",
        "macro_names = [\"Protein(g)\", \"Carbohydrate, by difference(g)\", \"Total lipid (fat)(g)\", \"Sugars, total including NLEA(g)\", \"Energy(kcal)\"]\n",
        "grouped_macros = grouped_diet_id[macro_names].sum()\n",
        "grouped_macros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "cRBM6ycbjZZ9",
        "outputId": "69e2bf87-c0ca-40ff-b8f2-1aee3140c5c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Protein(g)  Carbohydrate, by difference(g)  Total lipid (fat)(g)  \\\n",
              "dietID                                                                     \n",
              "1001        121.94                          462.87                137.07   \n",
              "1002        141.36                          464.99                282.09   \n",
              "1003        194.94                          638.27                219.39   \n",
              "1004        174.13                          399.36                158.96   \n",
              "1005        233.33                          603.83                390.02   \n",
              "1006        168.29                          748.45                209.06   \n",
              "\n",
              "        Sugars, total including NLEA(g)  Energy(kcal)  \n",
              "dietID                                                 \n",
              "1001                             107.18          3565  \n",
              "1002                             210.02          4898  \n",
              "1003                             149.12          5271  \n",
              "1004                             134.79          3714  \n",
              "1005                             121.57          6812  \n",
              "1006                             185.22          5520  "
            ],
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              "      <th>1001</th>\n",
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              "      <td>603.83</td>\n",
              "      <td>390.02</td>\n",
              "      <td>121.57</td>\n",
              "      <td>6812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1006</th>\n",
              "      <td>168.29</td>\n",
              "      <td>748.45</td>\n",
              "      <td>209.06</td>\n",
              "      <td>185.22</td>\n",
              "      <td>5520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb8afca0-a81c-4830-a2ec-9de1c344ab07')\"\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-fb8afca0-a81c-4830-a2ec-9de1c344ab07 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-fb8afca0-a81c-4830-a2ec-9de1c344ab07');\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": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create a heatmap for the aggregated data\n",
        "Just for better interpretation"
      ],
      "metadata": {
        "id": "SURW6PgDYeE4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(grouped_macros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "NUtNZR7NnTHt",
        "outputId": "0dc19977-edd3-4804-f6c8-300b5bfded26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='dietID'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convert the aggregated data into pandas dataframe"
      ],
      "metadata": {
        "id": "8hnLW7cjYngC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "macros_dietID_aggr = pd.DataFrame(grouped_macros)\n",
        "macros_dietID_aggr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "rbbA0g-Dn2q-",
        "outputId": "f71e6cc5-d86a-4793-c37d-990300678a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Protein(g)  Carbohydrate, by difference(g)  Total lipid (fat)(g)  \\\n",
              "dietID                                                                     \n",
              "1001        121.94                          462.87                137.07   \n",
              "1002        141.36                          464.99                282.09   \n",
              "1003        194.94                          638.27                219.39   \n",
              "1004        174.13                          399.36                158.96   \n",
              "1005        233.33                          603.83                390.02   \n",
              "1006        168.29                          748.45                209.06   \n",
              "\n",
              "        Sugars, total including NLEA(g)  Energy(kcal)  \n",
              "dietID                                                 \n",
              "1001                             107.18          3565  \n",
              "1002                             210.02          4898  \n",
              "1003                             149.12          5271  \n",
              "1004                             134.79          3714  \n",
              "1005                             121.57          6812  \n",
              "1006                             185.22          5520  "
            ],
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Step 3: Assign recommended dietIDs to all the users\n",
        "If the minimum macro levels intake of the user is less than or equal to a diet plan's macro level, then recommend that plan to the user."
      ],
      "metadata": {
        "id": "ceY3L0bqYvnZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_diet_recom=[]\n",
        "user_diet = dict()\n",
        "for uid, uprotein, ucarbs, ufat, usugar, uenergy in zip(user_macros_df[\"Unnamed: 0\"], user_macros_df[\"Protein (grams/day)\"], user_macros_df[\"Carbs (grams/day)\"], user_macros_df[\"Fat (grams/day)\"], user_macros_df[\"Sugar (grams/day)\"], user_macros_df[\"Food Energy (Calories/day)\"]):\n",
        "  for id, protein, carbs, fat, sugar, energy in zip(macros_dietID_aggr.index, macros_dietID_aggr[\"Protein(g)\"], macros_dietID_aggr[\"Carbohydrate, by difference(g)\"], macros_dietID_aggr[\"Total lipid (fat)(g)\"], macros_dietID_aggr[\"Sugars, total including NLEA(g)\"], macros_dietID_aggr[\"Energy(kcal)\"]):\n",
        "    if(float(uprotein)<=float(protein) and float(ucarbs)<=float(carbs) and float(ufat)<=float(fat) and float(usugar)<=float(sugar) and float(uenergy)<=float(energy)):\n",
        "      user_diet [\"UserID\"] = \"User \" + str(uid)\n",
        "      user_diet [\"Protein (grams/day)\"] = float(uprotein) \n",
        "      user_diet [\"Carbs (grams/day)\"] = float(ucarbs)\n",
        "      user_diet [\"Fat (grams/day)\"] = float(ufat)\n",
        "      user_diet [\"Sugar (grams/day)\"] = float(usugar)\n",
        "      user_diet [\"Food Energy (Calories/day)\"] = float(uenergy)\n",
        "      user_diet[\"Diet Protein(g)\"] = float(protein) \n",
        "      user_diet[\"Diet Carbohydrate, by difference(g)\"] = float(carbs)\n",
        "      user_diet[\"Diet Total lipid (fat)(g)\"] = float(fat)\n",
        "      user_diet[\"Diet Sugars, total including NLEA(g)\"] = float(sugar)\n",
        "      user_diet[\"Diet Energy(cal)\"] = float(energy)\n",
        "      user_diet [\"DietID\"] = id\n",
        "      user_diet_recom.append(user_diet)\n",
        "      user_diet = dict()\n",
        "\n",
        "user_diet_recom_df = pd.DataFrame(user_diet_recom)\n",
        "user_diet_recom_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "pjPCfyinoM_9",
        "outputId": "13d34b23-727f-4f9e-ebea-d1a5dd9e01aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      UserID  Protein (grams/day)  Carbs (grams/day)  Fat (grams/day)  \\\n",
              "0     User 0                146.0              319.0             68.0   \n",
              "1     User 0                146.0              319.0             68.0   \n",
              "2     User 0                146.0              319.0             68.0   \n",
              "3     User 0                146.0              319.0             68.0   \n",
              "4     User 1                101.0              220.0             47.0   \n",
              "..       ...                  ...                ...              ...   \n",
              "428  User 98                148.0              323.0             69.0   \n",
              "429  User 99                164.0              359.0             77.0   \n",
              "430  User 99                164.0              359.0             77.0   \n",
              "431  User 99                164.0              359.0             77.0   \n",
              "432  User 99                164.0              359.0             77.0   \n",
              "\n",
              "     Sugar (grams/day)  Food Energy (Calories/day)  Diet Protein(g)  \\\n",
              "0                 64.0                      2390.0           194.94   \n",
              "1                 64.0                      2390.0           174.13   \n",
              "2                 64.0                      2390.0           233.33   \n",
              "3                 64.0                      2390.0           168.29   \n",
              "4                 44.0                      1651.0           121.94   \n",
              "..                 ...                         ...              ...   \n",
              "428               65.0                      2423.0           168.29   \n",
              "429               72.0                      2696.0           194.94   \n",
              "430               72.0                      2696.0           174.13   \n",
              "431               72.0                      2696.0           233.33   \n",
              "432               72.0                      2696.0           168.29   \n",
              "\n",
              "     Diet Carbohydrate, by difference(g)  Diet Total lipid (fat)(g)  \\\n",
              "0                                 638.27                     219.39   \n",
              "1                                 399.36                     158.96   \n",
              "2                                 603.83                     390.02   \n",
              "3                                 748.45                     209.06   \n",
              "4                                 462.87                     137.07   \n",
              "..                                   ...                        ...   \n",
              "428                               748.45                     209.06   \n",
              "429                               638.27                     219.39   \n",
              "430                               399.36                     158.96   \n",
              "431                               603.83                     390.02   \n",
              "432                               748.45                     209.06   \n",
              "\n",
              "     Diet Sugars, total including NLEA(g)  Diet Energy(cal)  DietID  \n",
              "0                                  149.12            5271.0    1003  \n",
              "1                                  134.79            3714.0    1004  \n",
              "2                                  121.57            6812.0    1005  \n",
              "3                                  185.22            5520.0    1006  \n",
              "4                                  107.18            3565.0    1001  \n",
              "..                                    ...               ...     ...  \n",
              "428                                185.22            5520.0    1006  \n",
              "429                                149.12            5271.0    1003  \n",
              "430                                134.79            3714.0    1004  \n",
              "431                                121.57            6812.0    1005  \n",
              "432                                185.22            5520.0    1006  \n",
              "\n",
              "[433 rows x 12 columns]"
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              "      <td>68.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>User 1</td>\n",
              "      <td>101.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1651.0</td>\n",
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              "      <td>462.87</td>\n",
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              "      <td>72.0</td>\n",
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              "      <td>359.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>2696.0</td>\n",
              "      <td>233.33</td>\n",
              "      <td>603.83</td>\n",
              "      <td>390.02</td>\n",
              "      <td>121.57</td>\n",
              "      <td>6812.0</td>\n",
              "      <td>1005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>User 99</td>\n",
              "      <td>164.0</td>\n",
              "      <td>359.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>2696.0</td>\n",
              "      <td>168.29</td>\n",
              "      <td>748.45</td>\n",
              "      <td>209.06</td>\n",
              "      <td>185.22</td>\n",
              "      <td>5520.0</td>\n",
              "      <td>1006</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>433 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8208a91f-890a-4b40-9554-c34298deafc1')\"\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",
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              "  </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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8208a91f-890a-4b40-9554-c34298deafc1 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-8208a91f-890a-4b40-9554-c34298deafc1');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Step 4: Export the resulting dataset"
      ],
      "metadata": {
        "id": "t1PbB2WlZHUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_diet_recom_df.to_excel(\"Diet_recommendation_based_on_user.xlsx\")"
      ],
      "metadata": {
        "id": "l59FDhHqvnkd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}