Item_Item_Memory_Based.ipynb

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      "cell_type": "markdown",
      "source": [
        "#Mounting Drive"
      ],
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        "id": "E9SapW60cR9T"
      }
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
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        "id": "w1KO8PWAccUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Libraries...\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "MY9Hg88XUACK"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Data"
      ],
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        "id": "lRYMCJ_Cc2UP"
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      "cell_type": "code",
      "source": [
        "# Loading Data...\n",
        "ratings = pd.read_csv('/content/drive/MyDrive/Semester-2/Recommendation System/Assignment_2/RAW_interactions.csv')\n",
        "recipes = pd.read_csv('/content/drive/MyDrive/Semester-2/Recommendation System/Assignment_2/RAW_recipes.csv')"
      ],
      "metadata": {
        "id": "vg8ZDclxUMLR"
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      "execution_count": 61,
      "outputs": []
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        "ratings.head()"
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              "   user_id  recipe_id        date  rating  \\\n",
              "0    38094      40893  2003-02-17       4   \n",
              "1  1293707      40893  2011-12-21       5   \n",
              "2     8937      44394  2002-12-01       4   \n",
              "3   126440      85009  2010-02-27       5   \n",
              "4    57222      85009  2011-10-01       5   \n",
              "\n",
              "                                              review  \n",
              "0  Great with a salad. Cooked on top of stove for...  \n",
              "1  So simple, so delicious! Great for chilly fall...  \n",
              "2  This worked very well and is EASY.  I used not...  \n",
              "3  I made the Mexican topping and took it to bunk...  \n",
              "4  Made the cheddar bacon topping, adding a sprin...  "
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              "                                         name      id  minutes  \\\n",
              "0  arriba   baked winter squash mexican style  137739       55   \n",
              "1            a bit different  breakfast pizza   31490       30   \n",
              "2                   all in the kitchen  chili  112140      130   \n",
              "3                          alouette  potatoes   59389       45   \n",
              "4          amish  tomato ketchup  for canning   44061      190   \n",
              "\n",
              "   contributor_id   submitted  \\\n",
              "0           47892  2005-09-16   \n",
              "1           26278  2002-06-17   \n",
              "2          196586  2005-02-25   \n",
              "3           68585  2003-04-14   \n",
              "4           41706  2002-10-25   \n",
              "\n",
              "                                                tags  \\\n",
              "0  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "1  ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "2  ['time-to-make', 'course', 'preparation', 'mai...   \n",
              "3  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4  ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "\n",
              "                                    nutrition  n_steps  \\\n",
              "0       [51.5, 0.0, 13.0, 0.0, 2.0, 0.0, 4.0]       11   \n",
              "1   [173.4, 18.0, 0.0, 17.0, 22.0, 35.0, 1.0]        9   \n",
              "2  [269.8, 22.0, 32.0, 48.0, 39.0, 27.0, 5.0]        6   \n",
              "3   [368.1, 17.0, 10.0, 2.0, 14.0, 8.0, 20.0]       11   \n",
              "4   [352.9, 1.0, 337.0, 23.0, 3.0, 0.0, 28.0]        5   \n",
              "\n",
              "                                               steps  \\\n",
              "0  ['make a choice and proceed with recipe', 'dep...   \n",
              "1  ['preheat oven to 425 degrees f', 'press dough...   \n",
              "2  ['brown ground beef in large pot', 'add choppe...   \n",
              "3  ['place potatoes in a large pot of lightly sal...   \n",
              "4  ['mix all ingredients& boil for 2 1 / 2 hours ...   \n",
              "\n",
              "                                         description  \\\n",
              "0  autumn is my favorite time of year to cook! th...   \n",
              "1  this recipe calls for the crust to be prebaked...   \n",
              "2  this modified version of 'mom's' chili was a h...   \n",
              "3  this is a super easy, great tasting, make ahea...   \n",
              "4  my dh's amish mother raised him on this recipe...   \n",
              "\n",
              "                                         ingredients  n_ingredients  \n",
              "0  ['winter squash', 'mexican seasoning', 'mixed ...              7  \n",
              "1  ['prepared pizza crust', 'sausage patty', 'egg...              6  \n",
              "2  ['ground beef', 'yellow onions', 'diced tomato...             13  \n",
              "3  ['spreadable cheese with garlic and herbs', 'n...             11  \n",
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              "      <td>arriba   baked winter squash mexican style</td>\n",
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              "      <td>55</td>\n",
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              "      <td>autumn is my favorite time of year to cook! th...</td>\n",
              "      <td>['winter squash', 'mexican seasoning', 'mixed ...</td>\n",
              "      <td>7</td>\n",
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              "      <td>9</td>\n",
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              "      <td>this recipe calls for the crust to be prebaked...</td>\n",
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              "      <td>6</td>\n",
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              "      <th>2</th>\n",
              "      <td>all in the kitchen  chili</td>\n",
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              "      <td>this modified version of 'mom's' chili was a h...</td>\n",
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              "      <td>2003-04-14</td>\n",
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              "      <td>11</td>\n",
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              "      <th>4</th>\n",
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              "      <td>my dh's amish mother raised him on this recipe...</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d9523e68-8463-443a-a91b-027176f8c7fa');\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",
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              "          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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for duplicates\n",
        "print(ratings.duplicated().sum())\n",
        "print(recipes.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUTHkyNNU1Fu",
        "outputId": "6e0b14dc-c79e-46bb-cb6e-4633b80aa8d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ratings.shape)\n",
        "print(recipes.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12vYNyCXVLiQ",
        "outputId": "95a56541-0f19-4355-eb5f-57bac3224b86"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1132367, 5)\n",
            "(231637, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "ejXNIb1uc9tB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replacing column name of recipes dataframe...\n",
        "recipes.rename(columns={'id':'recipe_id'},inplace=True)\n",
        "recipes.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "m9Ma2lB6Veua",
        "outputId": "8c62d214-fb55-473d-e4cd-78211bb7e4f9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         name  recipe_id  minutes  \\\n",
              "0  arriba   baked winter squash mexican style     137739       55   \n",
              "1            a bit different  breakfast pizza      31490       30   \n",
              "2                   all in the kitchen  chili     112140      130   \n",
              "3                          alouette  potatoes      59389       45   \n",
              "4          amish  tomato ketchup  for canning      44061      190   \n",
              "\n",
              "   contributor_id   submitted  \\\n",
              "0           47892  2005-09-16   \n",
              "1           26278  2002-06-17   \n",
              "2          196586  2005-02-25   \n",
              "3           68585  2003-04-14   \n",
              "4           41706  2002-10-25   \n",
              "\n",
              "                                                tags  \\\n",
              "0  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "1  ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "2  ['time-to-make', 'course', 'preparation', 'mai...   \n",
              "3  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4  ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "\n",
              "                                    nutrition  n_steps  \\\n",
              "0       [51.5, 0.0, 13.0, 0.0, 2.0, 0.0, 4.0]       11   \n",
              "1   [173.4, 18.0, 0.0, 17.0, 22.0, 35.0, 1.0]        9   \n",
              "2  [269.8, 22.0, 32.0, 48.0, 39.0, 27.0, 5.0]        6   \n",
              "3   [368.1, 17.0, 10.0, 2.0, 14.0, 8.0, 20.0]       11   \n",
              "4   [352.9, 1.0, 337.0, 23.0, 3.0, 0.0, 28.0]        5   \n",
              "\n",
              "                                               steps  \\\n",
              "0  ['make a choice and proceed with recipe', 'dep...   \n",
              "1  ['preheat oven to 425 degrees f', 'press dough...   \n",
              "2  ['brown ground beef in large pot', 'add choppe...   \n",
              "3  ['place potatoes in a large pot of lightly sal...   \n",
              "4  ['mix all ingredients& boil for 2 1 / 2 hours ...   \n",
              "\n",
              "                                         description  \\\n",
              "0  autumn is my favorite time of year to cook! th...   \n",
              "1  this recipe calls for the crust to be prebaked...   \n",
              "2  this modified version of 'mom's' chili was a h...   \n",
              "3  this is a super easy, great tasting, make ahea...   \n",
              "4  my dh's amish mother raised him on this recipe...   \n",
              "\n",
              "                                         ingredients  n_ingredients  \n",
              "0  ['winter squash', 'mexican seasoning', 'mixed ...              7  \n",
              "1  ['prepared pizza crust', 'sausage patty', 'egg...              6  \n",
              "2  ['ground beef', 'yellow onions', 'diced tomato...             13  \n",
              "3  ['spreadable cheese with garlic and herbs', 'n...             11  \n",
              "4  ['tomato juice', 'apple cider vinegar', 'sugar...              8  "
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              "      <td>this modified version of 'mom's' chili was a h...</td>\n",
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              "      <td>[368.1, 17.0, 10.0, 2.0, 14.0, 8.0, 20.0]</td>\n",
              "      <td>11</td>\n",
              "      <td>['place potatoes in a large pot of lightly sal...</td>\n",
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              "      <td>41706</td>\n",
              "      <td>2002-10-25</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merging Data"
      ],
      "metadata": {
        "id": "uFRrwDemdBxj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merging the ratings and recipes dataframe on recipe_id\n",
        "ratings_with_name = ratings.merge(recipes, on='recipe_id')\n",
        "ratings_with_name.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "MpXYxgzKWDvg",
        "outputId": "b3a7b78a-7055-445c-9c40-85e144eaa08d"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user_id  recipe_id        date  rating  \\\n",
              "0    38094      40893  2003-02-17       4   \n",
              "1  1293707      40893  2011-12-21       5   \n",
              "2     8937      44394  2002-12-01       4   \n",
              "3   126440      85009  2010-02-27       5   \n",
              "4    57222      85009  2011-10-01       5   \n",
              "\n",
              "                                              review  \\\n",
              "0  Great with a salad. Cooked on top of stove for...   \n",
              "1  So simple, so delicious! Great for chilly fall...   \n",
              "2  This worked very well and is EASY.  I used not...   \n",
              "3  I made the Mexican topping and took it to bunk...   \n",
              "4  Made the cheddar bacon topping, adding a sprin...   \n",
              "\n",
              "                                   name  minutes  contributor_id   submitted  \\\n",
              "0  white bean   green chile pepper soup      495            1533  2002-09-21   \n",
              "1  white bean   green chile pepper soup      495            1533  2002-09-21   \n",
              "2      devilicious cookie cake delights       20           56824  2002-10-27   \n",
              "3                 baked potato toppings       10           64342  2004-02-25   \n",
              "4                 baked potato toppings       10           64342  2004-02-25   \n",
              "\n",
              "                                                tags  \\\n",
              "0  ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "1  ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "2  ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "3  ['15-minutes-or-less', 'time-to-make', 'course...   \n",
              "4  ['15-minutes-or-less', 'time-to-make', 'course...   \n",
              "\n",
              "                                           nutrition  n_steps  \\\n",
              "0           [204.8, 5.0, 9.0, 26.0, 24.0, 2.0, 10.0]        4   \n",
              "1           [204.8, 5.0, 9.0, 26.0, 24.0, 2.0, 10.0]        4   \n",
              "2           [132.3, 11.0, 39.0, 5.0, 4.0, 11.0, 5.0]        5   \n",
              "3  [2786.2, 342.0, 134.0, 290.0, 161.0, 301.0, 42.0]        3   \n",
              "4  [2786.2, 342.0, 134.0, 290.0, 161.0, 301.0, 42.0]        3   \n",
              "\n",
              "                                               steps  \\\n",
              "0  ['combine beans , onion , chilies , 1 / 2 teas...   \n",
              "1  ['combine beans , onion , chilies , 1 / 2 teas...   \n",
              "2  ['blend together cake mix , oil and eggs', 'ad...   \n",
              "3  ['pick whichever topping you want to use and c...   \n",
              "4  ['pick whichever topping you want to use and c...   \n",
              "\n",
              "                                         description  \\\n",
              "0                        easy soup for the crockpot.   \n",
              "1                        easy soup for the crockpot.   \n",
              "2                                                NaN   \n",
              "3  these toppings sure makes a nice change from p...   \n",
              "4  these toppings sure makes a nice change from p...   \n",
              "\n",
              "                                         ingredients  n_ingredients  \n",
              "0  ['great northern beans', 'yellow onion', 'dice...              9  \n",
              "1  ['great northern beans', 'yellow onion', 'dice...              9  \n",
              "2  [\"devil's food cake mix\", 'vegetable oil', 'eg...              4  \n",
              "3  ['mayonnaise', 'salsa', 'cheddar cheese', 'ref...             13  \n",
              "4  ['mayonnaise', 'salsa', 'cheddar cheese', 'ref...             13  "
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              "      <td>38094</td>\n",
              "      <td>40893</td>\n",
              "      <td>2003-02-17</td>\n",
              "      <td>4</td>\n",
              "      <td>Great with a salad. Cooked on top of stove for...</td>\n",
              "      <td>white bean   green chile pepper soup</td>\n",
              "      <td>495</td>\n",
              "      <td>1533</td>\n",
              "      <td>2002-09-21</td>\n",
              "      <td>['weeknight', 'time-to-make', 'course', 'main-...</td>\n",
              "      <td>[204.8, 5.0, 9.0, 26.0, 24.0, 2.0, 10.0]</td>\n",
              "      <td>4</td>\n",
              "      <td>['combine beans , onion , chilies , 1 / 2 teas...</td>\n",
              "      <td>easy soup for the crockpot.</td>\n",
              "      <td>['great northern beans', 'yellow onion', 'dice...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1293707</td>\n",
              "      <td>40893</td>\n",
              "      <td>2011-12-21</td>\n",
              "      <td>5</td>\n",
              "      <td>So simple, so delicious! Great for chilly fall...</td>\n",
              "      <td>white bean   green chile pepper soup</td>\n",
              "      <td>495</td>\n",
              "      <td>1533</td>\n",
              "      <td>2002-09-21</td>\n",
              "      <td>['weeknight', 'time-to-make', 'course', 'main-...</td>\n",
              "      <td>[204.8, 5.0, 9.0, 26.0, 24.0, 2.0, 10.0]</td>\n",
              "      <td>4</td>\n",
              "      <td>['combine beans , onion , chilies , 1 / 2 teas...</td>\n",
              "      <td>easy soup for the crockpot.</td>\n",
              "      <td>['great northern beans', 'yellow onion', 'dice...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8937</td>\n",
              "      <td>44394</td>\n",
              "      <td>2002-12-01</td>\n",
              "      <td>4</td>\n",
              "      <td>This worked very well and is EASY.  I used not...</td>\n",
              "      <td>devilicious cookie cake delights</td>\n",
              "      <td>20</td>\n",
              "      <td>56824</td>\n",
              "      <td>2002-10-27</td>\n",
              "      <td>['30-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[132.3, 11.0, 39.0, 5.0, 4.0, 11.0, 5.0]</td>\n",
              "      <td>5</td>\n",
              "      <td>['blend together cake mix , oil and eggs', 'ad...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[\"devil's food cake mix\", 'vegetable oil', 'eg...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>126440</td>\n",
              "      <td>85009</td>\n",
              "      <td>2010-02-27</td>\n",
              "      <td>5</td>\n",
              "      <td>I made the Mexican topping and took it to bunk...</td>\n",
              "      <td>baked potato toppings</td>\n",
              "      <td>10</td>\n",
              "      <td>64342</td>\n",
              "      <td>2004-02-25</td>\n",
              "      <td>['15-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[2786.2, 342.0, 134.0, 290.0, 161.0, 301.0, 42.0]</td>\n",
              "      <td>3</td>\n",
              "      <td>['pick whichever topping you want to use and c...</td>\n",
              "      <td>these toppings sure makes a nice change from p...</td>\n",
              "      <td>['mayonnaise', 'salsa', 'cheddar cheese', 'ref...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57222</td>\n",
              "      <td>85009</td>\n",
              "      <td>2011-10-01</td>\n",
              "      <td>5</td>\n",
              "      <td>Made the cheddar bacon topping, adding a sprin...</td>\n",
              "      <td>baked potato toppings</td>\n",
              "      <td>10</td>\n",
              "      <td>64342</td>\n",
              "      <td>2004-02-25</td>\n",
              "      <td>['15-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[2786.2, 342.0, 134.0, 290.0, 161.0, 301.0, 42.0]</td>\n",
              "      <td>3</td>\n",
              "      <td>['pick whichever topping you want to use and c...</td>\n",
              "      <td>these toppings sure makes a nice change from p...</td>\n",
              "      <td>['mayonnaise', 'salsa', 'cheddar cheese', 'ref...</td>\n",
              "      <td>13</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filtering based on users"
      ],
      "metadata": {
        "id": "zr3mgt5fdGEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtering merged dataframe based on the users...\n",
        "X = ratings_with_name.groupby('user_id').count()['rating'] > 100\n",
        "genuine_users = X[X].index\n",
        "genuine_users"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FiKrFKf9WPt_",
        "outputId": "b954d93c-6fdc-4e5e-fc57-d877e08967db"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([      1533,       1535,       2310,       2312,       3288,\n",
              "                  4291,       4439,       4470,       4740,       5060,\n",
              "            ...\n",
              "               2206637,    2324285,    2399085,    2549237,    2597942,\n",
              "            1800054678, 1802849661, 2000431901, 2000498330, 2001102678],\n",
              "           dtype='int64', name='user_id', length=1368)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtered ratings...\n",
        "filtered_ratings = ratings_with_name[ratings_with_name['user_id'].isin(genuine_users)]"
      ],
      "metadata": {
        "id": "cQZF0mXsW9r9"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filtering Based on Recipes"
      ],
      "metadata": {
        "id": "g1jXpmekdLtn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtering Based on recipes...\n",
        "y = filtered_ratings.groupby('name').count()['rating']>=50\n",
        "famous_recipes = y[y].index"
      ],
      "metadata": {
        "id": "t3TvwH6oX65f"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Final ratings dataframe...\n",
        "final_ratings = filtered_ratings[filtered_ratings['name'].isin(famous_recipes)]"
      ],
      "metadata": {
        "id": "p4FXi06vY1WL"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping Duplicates if any...\n",
        "final_ratings.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ey3OoxigZimB",
        "outputId": "259b341c-56a5-4d2a-ac09-01ce1d45c74f"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         user_id  recipe_id        date  rating  \\\n",
              "4212      149363      96886  2005-05-11       5   \n",
              "4214      209441      96886  2005-06-06       5   \n",
              "4216       73836      96886  2005-09-07       5   \n",
              "4217      160974      96886  2005-09-11       5   \n",
              "4218      195175      96886  2005-09-15       5   \n",
              "...          ...        ...         ...     ...   \n",
              "1123827   130531     131018  2014-12-14       5   \n",
              "1123829   121690     131018  2015-01-08       5   \n",
              "1123855    51931     131018  2015-11-29       5   \n",
              "1123860    59686     131018  2016-03-18       5   \n",
              "1123868   481092     131018  2017-01-13       5   \n",
              "\n",
              "                                                    review  \\\n",
              "4212     This was awesome! Our whole family loved this ...   \n",
              "4214     Awesome IS the word for this, I made this for ...   \n",
              "4216     This was scrumptious.  I halved the recipe suc...   \n",
              "4217     I'm a spinach lover but the rest of the family...   \n",
              "4218     This makes a great side dish for company. It's...   \n",
              "...                                                    ...   \n",
              "1123827  I have been making this recipe for a couple of...   \n",
              "1123829  I&#039;ve made this twice without substitution...   \n",
              "1123855  I am reviewing the recipe two ways.  One as wr...   \n",
              "1123860  Since we butcher our own meat, I used a 4-1/2 ...   \n",
              "1123868                       Easy but terrific. Huge hit.   \n",
              "\n",
              "                                           name  minutes  contributor_id  \\\n",
              "4212     kittencal s spinach parmesan rice bake       40           89831   \n",
              "4214     kittencal s spinach parmesan rice bake       40           89831   \n",
              "4216     kittencal s spinach parmesan rice bake       40           89831   \n",
              "4217     kittencal s spinach parmesan rice bake       40           89831   \n",
              "4218     kittencal s spinach parmesan rice bake       40           89831   \n",
              "...                                         ...      ...             ...   \n",
              "1123827                  pulled pork  crock pot     1110           50509   \n",
              "1123829                  pulled pork  crock pot     1110           50509   \n",
              "1123855                  pulled pork  crock pot     1110           50509   \n",
              "1123860                  pulled pork  crock pot     1110           50509   \n",
              "1123868                  pulled pork  crock pot     1110           50509   \n",
              "\n",
              "          submitted                                               tags  \\\n",
              "4212     2004-08-04  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4214     2004-08-04  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4216     2004-08-04  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4217     2004-08-04  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "4218     2004-08-04  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "...             ...                                                ...   \n",
              "1123827  2005-07-25  ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "1123829  2005-07-25  ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "1123855  2005-07-25  ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "1123860  2005-07-25  ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "1123868  2005-07-25  ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "\n",
              "                                          nutrition  n_steps  \\\n",
              "4212      [351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]        7   \n",
              "4214      [351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]        7   \n",
              "4216      [351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]        7   \n",
              "4217      [351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]        7   \n",
              "4218      [351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]        7   \n",
              "...                                             ...      ...   \n",
              "1123827  [335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]       26   \n",
              "1123829  [335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]       26   \n",
              "1123855  [335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]       26   \n",
              "1123860  [335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]       26   \n",
              "1123868  [335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]       26   \n",
              "\n",
              "                                                     steps  \\\n",
              "4212     ['set oven to 350f', 'generously grease a 11 x...   \n",
              "4214     ['set oven to 350f', 'generously grease a 11 x...   \n",
              "4216     ['set oven to 350f', 'generously grease a 11 x...   \n",
              "4217     ['set oven to 350f', 'generously grease a 11 x...   \n",
              "4218     ['set oven to 350f', 'generously grease a 11 x...   \n",
              "...                                                    ...   \n",
              "1123827  ['slice one onion and place in crock pot', 'pu...   \n",
              "1123829  ['slice one onion and place in crock pot', 'pu...   \n",
              "1123855  ['slice one onion and place in crock pot', 'pu...   \n",
              "1123860  ['slice one onion and place in crock pot', 'pu...   \n",
              "1123868  ['slice one onion and place in crock pot', 'pu...   \n",
              "\n",
              "                                               description  \\\n",
              "4212     this is a wonderful easy side dish to chicken ...   \n",
              "4214     this is a wonderful easy side dish to chicken ...   \n",
              "4216     this is a wonderful easy side dish to chicken ...   \n",
              "4217     this is a wonderful easy side dish to chicken ...   \n",
              "4218     this is a wonderful easy side dish to chicken ...   \n",
              "...                                                    ...   \n",
              "1123827  i found this pulled pork recipe years ago, and...   \n",
              "1123829  i found this pulled pork recipe years ago, and...   \n",
              "1123855  i found this pulled pork recipe years ago, and...   \n",
              "1123860  i found this pulled pork recipe years ago, and...   \n",
              "1123868  i found this pulled pork recipe years ago, and...   \n",
              "\n",
              "                                               ingredients  n_ingredients  \n",
              "4212     ['frozen chopped spinach', 'cooked white rice'...             13  \n",
              "4214     ['frozen chopped spinach', 'cooked white rice'...             13  \n",
              "4216     ['frozen chopped spinach', 'cooked white rice'...             13  \n",
              "4217     ['frozen chopped spinach', 'cooked white rice'...             13  \n",
              "4218     ['frozen chopped spinach', 'cooked white rice'...             13  \n",
              "...                                                    ...            ...  \n",
              "1123827  ['pork roast', 'onions', 'ginger ale', 'barbec...              4  \n",
              "1123829  ['pork roast', 'onions', 'ginger ale', 'barbec...              4  \n",
              "1123855  ['pork roast', 'onions', 'ginger ale', 'barbec...              4  \n",
              "1123860  ['pork roast', 'onions', 'ginger ale', 'barbec...              4  \n",
              "1123868  ['pork roast', 'onions', 'ginger ale', 'barbec...              4  \n",
              "\n",
              "[24339 rows x 16 columns]"
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              "      <td>2005-05-11</td>\n",
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              "      <td>5</td>\n",
              "      <td>Awesome IS the word for this, I made this for ...</td>\n",
              "      <td>kittencal s spinach parmesan rice bake</td>\n",
              "      <td>40</td>\n",
              "      <td>89831</td>\n",
              "      <td>2004-08-04</td>\n",
              "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]</td>\n",
              "      <td>7</td>\n",
              "      <td>['set oven to 350f', 'generously grease a 11 x...</td>\n",
              "      <td>this is a wonderful easy side dish to chicken ...</td>\n",
              "      <td>['frozen chopped spinach', 'cooked white rice'...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4216</th>\n",
              "      <td>73836</td>\n",
              "      <td>96886</td>\n",
              "      <td>2005-09-07</td>\n",
              "      <td>5</td>\n",
              "      <td>This was scrumptious.  I halved the recipe suc...</td>\n",
              "      <td>kittencal s spinach parmesan rice bake</td>\n",
              "      <td>40</td>\n",
              "      <td>89831</td>\n",
              "      <td>2004-08-04</td>\n",
              "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]</td>\n",
              "      <td>7</td>\n",
              "      <td>['set oven to 350f', 'generously grease a 11 x...</td>\n",
              "      <td>this is a wonderful easy side dish to chicken ...</td>\n",
              "      <td>['frozen chopped spinach', 'cooked white rice'...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4217</th>\n",
              "      <td>160974</td>\n",
              "      <td>96886</td>\n",
              "      <td>2005-09-11</td>\n",
              "      <td>5</td>\n",
              "      <td>I'm a spinach lover but the rest of the family...</td>\n",
              "      <td>kittencal s spinach parmesan rice bake</td>\n",
              "      <td>40</td>\n",
              "      <td>89831</td>\n",
              "      <td>2004-08-04</td>\n",
              "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]</td>\n",
              "      <td>7</td>\n",
              "      <td>['set oven to 350f', 'generously grease a 11 x...</td>\n",
              "      <td>this is a wonderful easy side dish to chicken ...</td>\n",
              "      <td>['frozen chopped spinach', 'cooked white rice'...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4218</th>\n",
              "      <td>195175</td>\n",
              "      <td>96886</td>\n",
              "      <td>2005-09-15</td>\n",
              "      <td>5</td>\n",
              "      <td>This makes a great side dish for company. It's...</td>\n",
              "      <td>kittencal s spinach parmesan rice bake</td>\n",
              "      <td>40</td>\n",
              "      <td>89831</td>\n",
              "      <td>2004-08-04</td>\n",
              "      <td>['60-minutes-or-less', 'time-to-make', 'course...</td>\n",
              "      <td>[351.2, 36.0, 3.0, 20.0, 35.0, 70.0, 5.0]</td>\n",
              "      <td>7</td>\n",
              "      <td>['set oven to 350f', 'generously grease a 11 x...</td>\n",
              "      <td>this is a wonderful easy side dish to chicken ...</td>\n",
              "      <td>['frozen chopped spinach', 'cooked white rice'...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123827</th>\n",
              "      <td>130531</td>\n",
              "      <td>131018</td>\n",
              "      <td>2014-12-14</td>\n",
              "      <td>5</td>\n",
              "      <td>I have been making this recipe for a couple of...</td>\n",
              "      <td>pulled pork  crock pot</td>\n",
              "      <td>1110</td>\n",
              "      <td>50509</td>\n",
              "      <td>2005-07-25</td>\n",
              "      <td>['time-to-make', 'course', 'main-ingredient', ...</td>\n",
              "      <td>[335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]</td>\n",
              "      <td>26</td>\n",
              "      <td>['slice one onion and place in crock pot', 'pu...</td>\n",
              "      <td>i found this pulled pork recipe years ago, and...</td>\n",
              "      <td>['pork roast', 'onions', 'ginger ale', 'barbec...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123829</th>\n",
              "      <td>121690</td>\n",
              "      <td>131018</td>\n",
              "      <td>2015-01-08</td>\n",
              "      <td>5</td>\n",
              "      <td>I&amp;#039;ve made this twice without substitution...</td>\n",
              "      <td>pulled pork  crock pot</td>\n",
              "      <td>1110</td>\n",
              "      <td>50509</td>\n",
              "      <td>2005-07-25</td>\n",
              "      <td>['time-to-make', 'course', 'main-ingredient', ...</td>\n",
              "      <td>[335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]</td>\n",
              "      <td>26</td>\n",
              "      <td>['slice one onion and place in crock pot', 'pu...</td>\n",
              "      <td>i found this pulled pork recipe years ago, and...</td>\n",
              "      <td>['pork roast', 'onions', 'ginger ale', 'barbec...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123855</th>\n",
              "      <td>51931</td>\n",
              "      <td>131018</td>\n",
              "      <td>2015-11-29</td>\n",
              "      <td>5</td>\n",
              "      <td>I am reviewing the recipe two ways.  One as wr...</td>\n",
              "      <td>pulled pork  crock pot</td>\n",
              "      <td>1110</td>\n",
              "      <td>50509</td>\n",
              "      <td>2005-07-25</td>\n",
              "      <td>['time-to-make', 'course', 'main-ingredient', ...</td>\n",
              "      <td>[335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]</td>\n",
              "      <td>26</td>\n",
              "      <td>['slice one onion and place in crock pot', 'pu...</td>\n",
              "      <td>i found this pulled pork recipe years ago, and...</td>\n",
              "      <td>['pork roast', 'onions', 'ginger ale', 'barbec...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123860</th>\n",
              "      <td>59686</td>\n",
              "      <td>131018</td>\n",
              "      <td>2016-03-18</td>\n",
              "      <td>5</td>\n",
              "      <td>Since we butcher our own meat, I used a 4-1/2 ...</td>\n",
              "      <td>pulled pork  crock pot</td>\n",
              "      <td>1110</td>\n",
              "      <td>50509</td>\n",
              "      <td>2005-07-25</td>\n",
              "      <td>['time-to-make', 'course', 'main-ingredient', ...</td>\n",
              "      <td>[335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]</td>\n",
              "      <td>26</td>\n",
              "      <td>['slice one onion and place in crock pot', 'pu...</td>\n",
              "      <td>i found this pulled pork recipe years ago, and...</td>\n",
              "      <td>['pork roast', 'onions', 'ginger ale', 'barbec...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123868</th>\n",
              "      <td>481092</td>\n",
              "      <td>131018</td>\n",
              "      <td>2017-01-13</td>\n",
              "      <td>5</td>\n",
              "      <td>Easy but terrific. Huge hit.</td>\n",
              "      <td>pulled pork  crock pot</td>\n",
              "      <td>1110</td>\n",
              "      <td>50509</td>\n",
              "      <td>2005-07-25</td>\n",
              "      <td>['time-to-make', 'course', 'main-ingredient', ...</td>\n",
              "      <td>[335.1, 10.0, 72.0, 24.0, 80.0, 10.0, 8.0]</td>\n",
              "      <td>26</td>\n",
              "      <td>['slice one onion and place in crock pot', 'pu...</td>\n",
              "      <td>i found this pulled pork recipe years ago, and...</td>\n",
              "      <td>['pork roast', 'onions', 'ginger ale', 'barbec...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24339 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b13fa6b5-4567-49d6-b742-6a2b2dd86c2c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train-Test Split"
      ],
      "metadata": {
        "id": "5vbhQnM1dTGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-Test Split...\n",
        "train_data, test_data = train_test_split(final_ratings, test_size=0.2)"
      ],
      "metadata": {
        "id": "EW2kEdaH2USQ"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dWnlJTk5flhA",
        "outputId": "98bd7e0a-f9ea-4651-b53d-5ceb939841c4"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         user_id  recipe_id        date  rating  \\\n",
              "642812    195175      16371  2007-01-02       5   \n",
              "18441      85873     150384  2006-01-18       5   \n",
              "982442     64203      63786  2007-03-04       5   \n",
              "495298    542159     117194  2009-12-24       5   \n",
              "436810     64203      95086  2007-01-29       5   \n",
              "...          ...        ...         ...     ...   \n",
              "9902      122877      26110  2009-10-26       5   \n",
              "1049339   132383     135350  2010-01-13       5   \n",
              "634543    105601      87925  2008-08-03       5   \n",
              "936565    207176      82102  2008-03-30       5   \n",
              "740891     56003      31717  2003-11-05       5   \n",
              "\n",
              "                                                    review  \\\n",
              "642812   These were great and easy to make.I followed t...   \n",
              "18441    A restaurant quality recipe worth more then 5*...   \n",
              "982442   These are better than any restaurant fajitas I...   \n",
              "495298   The perfect quick, healthy snack. So easy and ...   \n",
              "436810   Andypandy - \\nThank you so much for sharing th...   \n",
              "...                                                    ...   \n",
              "9902     Rushed making them, used 1/2 whole wheat 1/2 w...   \n",
              "1049339  The best mac and cheese we have had yet.  My h...   \n",
              "634543   This was so, so fabulous!!  I love frosted bro...   \n",
              "936565   This was a winner with the family and it was s...   \n",
              "740891   This was great!  Easy, too!  I did forget to s...   \n",
              "\n",
              "                                                      name  minutes  \\\n",
              "642812                            streamlined chimichangas       20   \n",
              "18441                          panera broccoli cheese soup       70   \n",
              "982442                          steak  or chicken  fajitas       20   \n",
              "495298                                         apple bites        5   \n",
              "436810                            rustic italian bread abm      215   \n",
              "...                                                    ...      ...   \n",
              "9902                          southern buttermilk biscuits       22   \n",
              "1049339    fannie farmer s classic baked macaroni   cheese       40   \n",
              "634543              kittencal s extreme chocolate brownies       50   \n",
              "936565   kittencal s moist cheddar garlic oven fried ch...       60   \n",
              "740891   olive garden pasta e fagioli soup in a crock p...      435   \n",
              "\n",
              "         contributor_id   submitted  \\\n",
              "642812            22015  2001-12-29   \n",
              "18441            192957  2006-01-04   \n",
              "982442            82367  2003-06-05   \n",
              "495298           193516  2005-04-14   \n",
              "436810           106624  2004-07-05   \n",
              "...                 ...         ...   \n",
              "9902              38532  2002-04-22   \n",
              "1049339          148316  2005-08-29   \n",
              "634543            89831  2004-03-31   \n",
              "936565            89831  2004-01-26   \n",
              "740891            45338  2002-06-20   \n",
              "\n",
              "                                                      tags  \\\n",
              "642812   ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "18441    ['time-to-make', 'course', 'main-ingredient', ...   \n",
              "982442   ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "495298   ['15-minutes-or-less', 'time-to-make', 'course...   \n",
              "436810   ['time-to-make', 'course', 'cuisine', 'prepara...   \n",
              "...                                                    ...   \n",
              "9902     ['30-minutes-or-less', 'time-to-make', 'course...   \n",
              "1049339  ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "634543   ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "936565   ['60-minutes-or-less', 'time-to-make', 'course...   \n",
              "740891   ['weeknight', 'time-to-make', 'course', 'main-...   \n",
              "\n",
              "                                            nutrition  n_steps  \\\n",
              "642812     [373.7, 34.0, 10.0, 31.0, 36.0, 50.0, 8.0]       11   \n",
              "18441    [702.6, 81.0, 46.0, 33.0, 55.0, 162.0, 10.0]       14   \n",
              "982442         [137.2, 8.0, 8.0, 14.0, 5.0, 5.0, 6.0]       12   \n",
              "495298          [97.9, 0.0, 75.0, 0.0, 1.0, 0.0, 8.0]        4   \n",
              "436810           [84.0, 2.0, 1.0, 3.0, 4.0, 1.0, 4.0]       15   \n",
              "...                                               ...      ...   \n",
              "9902         [162.6, 11.0, 5.0, 16.0, 6.0, 22.0, 6.0]       21   \n",
              "1049339  [836.2, 80.0, 11.0, 37.0, 55.0, 162.0, 21.0]       17   \n",
              "634543       [179.6, 14.0, 74.0, 3.0, 4.0, 28.0, 8.0]       13   \n",
              "936565      [452.1, 40.0, 6.0, 35.0, 69.0, 73.0, 6.0]        8   \n",
              "740891    [377.3, 20.0, 33.0, 23.0, 49.0, 24.0, 13.0]        4   \n",
              "\n",
              "                                                     steps  \\\n",
              "642812   ['preheat oven to 475 degrees', 'brown meat wi...   \n",
              "18441    ['saut onion in butter', 'set aside', 'cook me...   \n",
              "982442   ['slice steak into thin strips', 'in bowl , mi...   \n",
              "495298   ['cut up apple into bite sized chunks', 'put t...   \n",
              "436810   ['add flour , oil , salt , sugar , yeast and w...   \n",
              "...                                                    ...   \n",
              "9902     ['preheat your oven to 450f', 'combine the dry...   \n",
              "1049339  ['preheat oven to 400f', 'cook and drain macar...   \n",
              "634543   ['set oven to 325 degrees f', 'grease a 13 x 9...   \n",
              "936565   ['preheat oven to 350f', 'butter an 11 x 7-inc...   \n",
              "740891   ['brown beef in a skillet', 'drain fat from be...   \n",
              "\n",
              "                                               description  \\\n",
              "642812   this is a great tasting recipe and so easy tha...   \n",
              "18441    a very good broccoli cheese soup. served with ...   \n",
              "982442   these are so quick and tasty. you can use stri...   \n",
              "495298   a delicious, incredibly easy way to dress up f...   \n",
              "436810   this is the one i make all the time.  i use th...   \n",
              "...                                                    ...   \n",
              "9902     these buttermilk biscuits are authentic. this ...   \n",
              "1049339  to me fannie farmer's recipe is the only \"real...   \n",
              "634543   if your looking for an everyday run-of-the-mil...   \n",
              "936565   this is for serious garlic lovers. the chicken...   \n",
              "740891   this tastes exactly like the pasta e fagioli s...   \n",
              "\n",
              "                                               ingredients  n_ingredients  \n",
              "642812   ['ground beef', 'onion', 'garlic clove', 'pace...              9  \n",
              "18441    ['butter', 'onion', 'flour', 'half-and-half cr...             12  \n",
              "982442   ['top sirloin steak', 'olive oil', 'lime juice...             16  \n",
              "495298                               ['apple', 'cinnamon']              2  \n",
              "436810   ['warm water', 'olive oil', 'bread flour', 'gr...              8  \n",
              "...                                                    ...            ...  \n",
              "9902     ['unbleached all-purpose flour', 'baking soda'...              6  \n",
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              "      <td>These were great and easy to make.I followed t...</td>\n",
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              "      <td>['preheat oven to 350f', 'butter an 11 x 7-inc...</td>\n",
              "      <td>this is for serious garlic lovers. the chicken...</td>\n",
              "      <td>['butter', 'garlic', 'garlic powder', 'seasoni...</td>\n",
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              "      <td>14</td>\n",
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          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating Pivot Table"
      ],
      "metadata": {
        "id": "Pu1RtfK5dhME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create pivot table for train data\n",
        "train_pt = train_data.pivot_table(index='name', columns='user_id', values='rating')\n",
        "# calculate similarity scores\n",
        "similarity_scores = cosine_similarity(train_pt.fillna(0))\n",
        "# create pivot table for test data\n",
        "test_pt = test_data.pivot_table(index='name', columns='user_id', values='rating')\n",
        "# test_pt.fillna(0,inplace=True)\n",
        "# train_pt.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "5vgq9dug2ow3"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SqePDNWAj2JL",
        "outputId": "c1f62f9f-5e7c-425a-fa88-f6b5c3230db1"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "user_id                                         1533        1535        \\\n",
              "name                                                                     \n",
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              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
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              "\n",
              "user_id                                         2312        3288        \\\n",
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              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
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              "user_id                                         4291        4439        \\\n",
              "name                                                                     \n",
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              "4 minute spicy garlic shrimp                           NaN         NaN   \n",
              "40 minute hamburger buns                               NaN         NaN   \n",
              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
              "yes  virginia there is a great meatloaf                NaN         NaN   \n",
              "yummy crispy baked fish                                NaN         NaN   \n",
              "yummy crunchy apple pie                                NaN         NaN   \n",
              "zesty oven baked fries                                 NaN         NaN   \n",
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              "40 minute hamburger buns                               NaN         NaN   \n",
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              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
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              "user_id                                         5060        6258        ...  \\\n",
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              "ww 0 point weight watchers cabbage soup                NaN         NaN  ...   \n",
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              "\n",
              "user_id                                         1706426     1900301     \\\n",
              "name                                                                     \n",
              "24k carrots                                            NaN         NaN   \n",
              "4 minute spicy garlic shrimp                           NaN         NaN   \n",
              "40 minute hamburger buns                               NaN         NaN   \n",
              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
              "yes  virginia there is a great meatloaf                NaN         NaN   \n",
              "yummy crispy baked fish                                NaN         NaN   \n",
              "yummy crunchy apple pie                                NaN         NaN   \n",
              "zesty oven baked fries                                 NaN         NaN   \n",
              "\n",
              "user_id                                         2123645     2206637     \\\n",
              "name                                                                     \n",
              "24k carrots                                            NaN         NaN   \n",
              "4 minute spicy garlic shrimp                           NaN         NaN   \n",
              "40 minute hamburger buns                               NaN         NaN   \n",
              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
              "yes  virginia there is a great meatloaf                5.0         NaN   \n",
              "yummy crispy baked fish                                NaN         NaN   \n",
              "yummy crunchy apple pie                                NaN         NaN   \n",
              "zesty oven baked fries                                 NaN         NaN   \n",
              "\n",
              "user_id                                         2324285     2549237     \\\n",
              "name                                                                     \n",
              "24k carrots                                            NaN         NaN   \n",
              "4 minute spicy garlic shrimp                           NaN         NaN   \n",
              "40 minute hamburger buns                               NaN         NaN   \n",
              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
              "yes  virginia there is a great meatloaf                NaN         NaN   \n",
              "yummy crispy baked fish                                NaN         NaN   \n",
              "yummy crunchy apple pie                                NaN         NaN   \n",
              "zesty oven baked fries                                 NaN         NaN   \n",
              "\n",
              "user_id                                         1800054678  2000431901  \\\n",
              "name                                                                     \n",
              "24k carrots                                            NaN         NaN   \n",
              "4 minute spicy garlic shrimp                           NaN         NaN   \n",
              "40 minute hamburger buns                               NaN         NaN   \n",
              "absolute best ever lasagna                             NaN         NaN   \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN   \n",
              "...                                                    ...         ...   \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN   \n",
              "yes  virginia there is a great meatloaf                NaN         NaN   \n",
              "yummy crispy baked fish                                NaN         NaN   \n",
              "yummy crunchy apple pie                                NaN         NaN   \n",
              "zesty oven baked fries                                 NaN         NaN   \n",
              "\n",
              "user_id                                         2000498330  2001102678  \n",
              "name                                                                    \n",
              "24k carrots                                            NaN         NaN  \n",
              "4 minute spicy garlic shrimp                           NaN         NaN  \n",
              "40 minute hamburger buns                               NaN         NaN  \n",
              "absolute best ever lasagna                             NaN         NaN  \n",
              "all purpose stir fry sauce  brown garlic sauce         NaN         NaN  \n",
              "...                                                    ...         ...  \n",
              "ww 0 point weight watchers cabbage soup                NaN         NaN  \n",
              "yes  virginia there is a great meatloaf                NaN         NaN  \n",
              "yummy crispy baked fish                                NaN         NaN  \n",
              "yummy crunchy apple pie                                NaN         NaN  \n",
              "zesty oven baked fries                                 NaN         NaN  \n",
              "\n",
              "[311 rows x 1167 columns]"
            ],
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              "\n",
              "  <div id=\"df-24ddd11c-b679-4365-aebb-cc4e6e5dcd8d\">\n",
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              "    </tr>\n",
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              "      <th>name</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>absolute best ever lasagna</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>ww 0 point weight watchers cabbage soup</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes  virginia there is a great meatloaf</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>yummy crispy baked fish</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yummy crunchy apple pie</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zesty oven baked fries</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>311 rows × 1167 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24ddd11c-b679-4365-aebb-cc4e6e5dcd8d')\"\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-24ddd11c-b679-4365-aebb-cc4e6e5dcd8d 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-24ddd11c-b679-4365-aebb-cc4e6e5dcd8d');\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": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_pt.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJlJCkEOt3uN",
        "outputId": "f3cfac5f-025b-434f-f5ad-76a69b0c362a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([      1533,       1535,       2312,       3288,       4291,\n",
              "                  4439,       4470,       4740,       5060,       6258,\n",
              "            ...\n",
              "               1706426,    1900301,    2123645,    2206637,    2324285,\n",
              "               2549237, 1800054678, 2000431901, 2000498330, 2001102678],\n",
              "           dtype='int64', name='user_id', length=1167)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation Engine"
      ],
      "metadata": {
        "id": "BzWoNqZXdpRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loop over each user in the test set\n",
        "def get_top_k_recommendations(user_id,k):\n",
        "    if user_id not in train_pt.columns:\n",
        "        return []\n",
        "    # get the recipes that the user has not rated\n",
        "    rated_recipes = train_pt[user_id]\n",
        "    # rated_recipes = rated_recipes[rated_recipes != 0]\n",
        "    rated_recipes = rated_recipes[rated_recipes.notna()]  \n",
        "    unrated_recipes = train_pt.index.difference(rated_recipes.index)\n",
        "    predicted_ratings = []\n",
        "    # loop over each unrated recipe\n",
        "    for recipe_name in unrated_recipes:\n",
        "        # calculate predicted rating using similarity scores and user ratings\n",
        "        similarity_scores_for_recipe = similarity_scores[train_pt.index.get_loc(recipe_name)]\n",
        "        user_ratings = train_pt[user_id].fillna(0)\n",
        "        numerator = (user_ratings * similarity_scores_for_recipe).sum()\n",
        "        denominator = similarity_scores_for_recipe.sum()\n",
        "        if denominator == 0:\n",
        "            predicted_rating = 0\n",
        "        else:\n",
        "            predicted_rating = numerator / denominator\n",
        "        \n",
        "        predicted_ratings.append((recipe_name, predicted_rating))\n",
        "\n",
        "    # predicted_ratings = sorted(predicted_ratings, key=lambda x: x[1], reverse=True)\n",
        "    # top_k_recommendations = [r[0] for r in predicted_ratings][:k]\n",
        "    top_recipe_names = [recipe_name for recipe_name, predicted_rating in sorted(predicted_ratings, key=lambda x: x[1], reverse=True)[:k]]\n",
        "    return top_recipe_names\n",
        "    # return top_k_recommendations"
      ],
      "metadata": {
        "id": "63FBDFEW3d8I"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_recipes = get_top_k_recommendations(47559,10)\n",
        "top_recipes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvj8TVmvftgp",
        "outputId": "5d584a93-042a-432f-ca99-7d7000d677b7"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['easy peezy pizza dough  bread machine pizza dough',\n",
              " 'chicken legs grilled',\n",
              " 'pan release  professional pan coating  better than pam spray',\n",
              " 'halloween party treat  candy corn and peanut mix',\n",
              " 'breakfast in a mug',\n",
              " 'grilled cheese  diner style',\n",
              " 'bacon wrapped jalapeno poppers',\n",
              " 'best banana bread',\n",
              " 'self rising flour',\n",
              " 'buttered egg noodles  best ever']"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating Recommendations"
      ],
      "metadata": {
        "id": "7XWYHDeW_i-4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_recommendations(user_id, k):\n",
        "    # get the actual rated recipes for the user\n",
        "    actual_rated_recipes = test_pt[user_id]\n",
        "    actual_rated_recipes = actual_rated_recipes[actual_rated_recipes.notna()]\n",
        "    actual_rated_recipes_set = set(actual_rated_recipes.index)\n",
        "    \n",
        "    # get the top-k recommended recipes for the user\n",
        "    top_k_recommendations = get_top_k_recommendations(user_id, k)\n",
        "    top_k_recommendations_set = set(top_k_recommendations)\n",
        "    \n",
        "    # calculate precision and recall\n",
        "    intersection = actual_rated_recipes_set.intersection(top_k_recommendations_set)\n",
        "    if len(top_k_recommendations_set) == 0:\n",
        "      precision = 0.0\n",
        "    else:\n",
        "      precision = len(intersection) / len(top_k_recommendations_set)\n",
        "    recall = len(intersection) / len(actual_rated_recipes_set)\n",
        "    \n",
        "    return precision, recall"
      ],
      "metadata": {
        "id": "RJWgl1bmf1GA"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "P,R = evaluate_recommendations(23333,10)\n",
        "print(\"Precision: \",P)\n",
        "print(\"Recall: \",R)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZTs3yDCg-uO",
        "outputId": "6722e85e-08d6-412c-c6b2-5028e6067729"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision:  0.0\n",
            "Recall:  0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k_values = [5, 10, 50, 60,70,80,90, 100]\n",
        "precision_scores = []\n",
        "recall_scores = []\n",
        "\n",
        "for k in k_values:\n",
        "    total_precision = 0\n",
        "    total_recall = 0\n",
        "    num_users = 0\n",
        "\n",
        "    for user_id in test_pt.columns:\n",
        "        if not test_pt[user_id].isnull().all():\n",
        "            precision, recall = evaluate_recommendations(user_id, k)\n",
        "            total_precision += precision\n",
        "            total_recall += recall\n",
        "            num_users += 1\n",
        "\n",
        "    precision_scores.append(total_precision / num_users)\n",
        "    recall_scores.append(total_recall / num_users)\n",
        "\n",
        "print(\"Precision scores:\", precision_scores)\n",
        "print(\"Recall scores:\", recall_scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Wb-IZKsij4H",
        "outputId": "8ab16d08-f5aa-4a75-f46b-1e1baaf8ab06"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision scores: [0.9917737789203084, 0.991859468723222, 0.9918423307626396, 0.9918737503570411, 0.9919329171257188, 0.9919237360754065, 0.991935637436923, 0.9919622964867184]\n",
            "Recall scores: [0.015948492526337416, 0.03189974923986558, 0.15949596412095807, 0.19140124173520176, 0.2233148119110286, 0.2552145611508899, 0.28711983876513736, 0.31901135765986255]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing Precision and Recall for Different K"
      ],
      "metadata": {
        "id": "vyHvt1Iy_oNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(k_values, precision_scores, label='Precision')\n",
        "plt.plot(k_values, recall_scores, label='Recall')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Score')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "CAO3HUAakJkp",
        "outputId": "fd59c460-753d-422d-be31-f9af41adc338"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/62poOBDGe/QJn/TPvil99M6A0ZCXF2PR0h1FFgTu3mBmtwHLCIePPurua8zsHqDC3ZcAfw/0AX5j4RPN++5+eVQ1icSmqSmcgduy6dd/FMYLe4dP+uU3ppv+wFPU9CUnIt1H4O5LgaUt7rsr4+eLo3x+kVg0NYW5djJPztr6OhzcF8YLesFJZ8KU/5hu+oPGQF5+nFVLgnWKncUiXZZ7i6ZfGZp+3YdhvKAIhkyASV/OaPqnQr7+15POQ3+NItlyh90bW3/Sr60J4/k9w9Wyzvxi+uSs0tPU9KXT01+oSFvcYc/7rS+OXrsnjOf3CBdDP+PKdNM/cRzkF8ZXs8hxUhCIuENNVeumf2BXGM8rhMHjYfzs9Bm5J46Hgh5xVi3SYRQEkizu8OGW1k1//wdhPK8gfLI/7bPppj/4dCjoGWfViVJfX09VVRW1tbVxl9IlFRUVMXz4cAoLs/92qiCQ7u3DrS2uk7sKPkqdsmL5oemfOiN9ctbg06GwV4wFS1VVFSeccAKjRo3CdKLcMXF3du7cSVVVFWVlZVn/noJAuo+921s3/X3bwpjlhR23p1ycnntn8BnQozjGgqUttbW1CoHjZGYMHDiQ6urqY/o9BYF0TfuqWzf9vVtSgxYO0Rw9Ld30h0yAHr1jK1eOjULg+B3Pe6cgkM7vo52wNaPhb6mED6tSgxbOwB31qYymfyb07BNfvSJdjIJAOpf9u9InZh1q+jXvp8cHnAwjz01v0x9yJhT1jadW6bby8/OZMGECDQ0NjBs3jsWLF1Nc3L7NiHfddRef/vSnufjitidUePjhhykuLua6665r1/McD3PvWpN5lpeXe0VFRdxlSEc4sKdF018FezKuVNq/LP0pf+jkMBdPUUk8tUrOrFu3jnHjxsVaQ58+fdi3L0wJcs0113DWWWfxjW984/B4Q0MDBQWd93N0W++hmb3q7uVtLd95X4l0L7U14SzcQ01/a2WYmuGQfp8Izf7wpGsTw8VVJNHu/t9rWLvlww59zPFD+/K9z52e9fIXXHABq1evZsWKFXz3u9+lf//+rF+/nnXr1jF//nxWrFhBXV0dt956K1/72tcAuO+++/jlL39JXl4el112Gffeey833HADs2bN4gtf+ALz589nyZIlFBQUcOmll/KjH/2I73//+/Tp04dvfetbVFZWcvPNN7N//35OPvlkHn30Ufr378+0adM455xzeO6559izZw+PPPIIF1xwQbvfEwWBdLy6va2b/s4N6fGSkTB0Iky+Nj3/TvGAmIoVObKGhgb++Mc/MmPGDABee+013njjDcrKyli0aBElJSWsXLmSuro6zj//fC699FLWr1/PH/7wB15++WWKi4vZtWtXs8fcuXMnTz/9NOvXr8fM2LNnT6vnve6663jooYe48MILueuuu7j77rv58Y9/fLimV155haVLl3L33XfzzDPPtPt1Kgikfer2hQunZE6v/MHbQGqTY9/hYdPOxDnppt97UHz1SpdyLJ/cO9KBAweYNGkSEL4RzJ07lxdeeIGpU6cePj7/T3/6E6tXr+app54CoKamhrfffptnnnmGG2+88fA+hQEDmn/IKSkpoaioiLlz5zJr1ixmzZrVbLympoY9e/Zw4YXhgo3XX389V1111eHxz3/+8wCcddZZbNy4sUNer4JAsndwf+umX/0mh5v+CUND059wVWr+nUnQ58TYyhU5Xr169aKysrLV/b17pw9Bdnceeughpk+f3myZZcuWHfWxCwoKeOWVV3j22Wd56qmn+MlPfsLy5cuzrq1nz3CWe35+Pg0NDVn/3lFr6pBHke6n/gBse6P5TJvV68GbwnifweET/ul/k276JwyJsWCR3Jo+fToLFy7kM5/5DIWFhbz11lsMGzaMSy65hHvuuYdrrrnm8KahzG8F+/btY//+/cycOZPzzz+f0aNHN3vckpIS+vfvz/PPP88FF1zA448/fvjbQVQUBAL1teFi6FteSx/Fs2MdeGMY710amv5ps9Lz7/Q9Kc6KRWJ30003sXHjRqZMmYK7U1payu9//3tmzJhBZWUl5eXl9OjRg5kzZ/LDH/7w8O/t3buX2bNnU1tbi7tz//33t3rsxYsXH95ZPHr0aH7+859H+lp0+GjSNNSlmn7GJ/0d66Ap9RWzeGB6W/7hpj9UF0eXnOkMh492dTp8VNIaDsKOtc2b/va10FQfxnv1D43+k5ekj9cvGaGmL5IwCoLuorE+fLLPnF55+xpoPBjGi0pCsz/v1nTT7/cJNX0RURB0SY0NYcdtZtPf9gY01oXxnn3DCVnn3Jxu+v3L1PRFpE0Kgs6uqTEcotmy6TccCOM9TghNf+pX09v0+5dBXl6sZYtI16Eg6EyaGsPJWJnTK29bDfX7w3hh79D0y7+Snn9nwMlq+iLSLgqCuDQ1wa53ml8ucevrUP9RGC8sDjNrTrk+3fQHngJ5+XFWLSLdkIIgF5qaYPd7rZv+wb1hvKAoNP3J16Q37ww6VU1fJCaZ01CXlZXx+OOP069fvw57/FGjRlFRUcGgQYOazXQaFwVBR3Nvu+nXpWZQzO8ZrpY18eqMpj8W8rUqRDqLzCkmrr/+ehYsWMB3vvOdeIuKkLpPe7iH+fMzL5e4tTJMuQyQ3yNcF3fCF9JNv/Q0yC+Ms2qRruOP88P8Vh1pyAS47N6sFz/vvPNYvXo1AO+88w633nor1dXVFBcX89Of/pTTTjuN7du3c/PNN/Puu2Fq9YULF/LJT36SK664gk2bNlFbW8sdd9zBvHnzOva1dBAFQbbcoWZT66Z/YHcYzyuEwaeHuXcOnZl74ngo6BFj0SLSHo2NjTz77LPMnTsXgHnz5vHwww8zZswYXn75ZW655RaWL1/O7bffzoUXXsjTTz9NY2Pj4U09jz76KAMGDODAgQOcffbZXHnllQwcODDOl9QmBUFb3OHDza2b/v6dYTyvAE4cB+M+l276g0+Hgp4xFi3SDR3DJ/eOdGga6s2bNzNu3DguueQS9u3bxwsvvNBsSui6unDuzvLly3nssceAsH+hpCRcSe/BBx/k6aefBmDTpk28/fbbCoJOyR32bm3d9D+qDuOWH5r+2MtS8+9MCU2/sCjGokUkSof2Eezfv5/p06ezYMECbrjhBvr169fm9NRtWbFiBc888wwvvvgixcXFTJs2jdra2mgLP07JC4K925pfI3drJezbHsYsL2zDH3NpetK1IWdAYa8YCxaRuBQXF/Pggw9yxRVXcMstt1BWVsZvfvMbrrrqKtyd1atXM3HiRC666CIWLlzInXfeeXjTUE1NDf3796e4uJj169fz0ksvxf1yjig5QfDqYljxP8KnfwAMSsfCyZ9p3vR79D7ao4hIwkyePJkzzzyTJ554gl/96ld8/etf5wc/+AH19fXMmTOHiRMn8sADDzBv3jweeeQR8vPzWbhwITNmzODhhx9m3LhxjB07lnPPPTful3JEkU5DbWYzgAeAfOBn7n5vi/GewGPAWcBO4Gp333i0xzzuaajfWgZ/eSp99M6QCdCzz7E/johEStNQt1+nmYbazPKBBcAlQBWw0syWuPvajMXmArvd/RQzmwPcB1wdSUGnTg//RESkmSgnqZkKbHD3d939IPAkMLvFMrOBxamfnwIuMtMUmSIiuRRlEAwDNmXcrkrd1+Yy7t4A1ACtjq0ys3lmVmFmFdXV1RGVKyKdRVe7cmJncjzvXZeYttLdF7l7ubuXl5aWxl2OiESoqKiInTt3KgyOg7uzc+dOioqO7fD2KI8a2gyMyLg9PHVfW8tUmVkBUELYaSwiCTV8+HCqqqrQt//jU1RUxPDhw4/pd6IMgpXAGDMrIzT8OcCXWyyzBLgeeBH4ArDc9TFAJNEKCwspKyuLu4xEiSwI3L3BzG4DlhEOH33U3deY2T1AhbsvAR4BHjezDcAuQliIiEgORXpCmbsvBZa2uO+ujJ9rgata/p6IiOROl9hZLCIi0Yn0zOIomFk18Ne464jJIOCDuIuIUdJfP+g90Os//tf/CXdv87DLLhcESWZmFUc6RTwJkv76Qe+BXn80r1+bhkREEk5BICKScAqCrmVR3AXELOmvH/Qe6PVHQPsIREQSTt8IREQSTkEgIpJwCoJOysxGmNlzZrbWzNaY2R2p+weY2b+b2dup//aPu9YomVm+ma0ys/+Tul1mZi+b2QYz+xcz6xF3jVExs35m9pSZrTezdWZ2XpLWv5n9p9Tf/htm9oSZFXXn9W9mj5rZDjN7I+O+Nte3BQ+m3ofVZjalPc+tIOi8GoBvuvt44FzgVjMbD8wHnnX3McCzqdvd2R3Auozb9wH/y91PAXYTrnLXXT0A/Ju7nwZMJLwPiVj/ZjYMuB0od/czCPOVHbqKYXdd/78AZrS470jr+zJgTOrfPGBhe55YQdBJuftWd38t9fNeQhMYRvOrui0GroilwBwws+HAZ4GfpW4b8BnC1eygG79+MysBPk2YmBF3P+jue0jQ+ifMhdYrNUV9MbCVbrz+3f3PhMk3Mx1pfc8GHvPgJaCfmZ10vM+tIOgCzGwUMBl4GRjs7ltTQ9uAwXHVlQM/Bv4L0JS6PRDYk7qaHbR91bvuogyoBn6e2jT2MzPrTULWv7tvBn4EvE8IgBrgVZKz/g850vrO5gqQWVMQdHJm1gf4LXCnu3+YOZa6dkO3PP7XzGYBO9z91bhriUkBMAVY6O6TgY9osRmom6///oRPvWXAUKA3rTebJEqU61tB0ImZWSEhBH7l7r9L3b390FfA1H93xFVfxM4HLjezjcCThE0CDxC+Ah+aPr2tq951F1VAlbu/nLr9FCEYkrL+Lwbec/dqd68Hfkf4m0jK+j/kSOs7mytAZk1B0Emltoc/Aqxz9/szhg5d1Y3Uf/+Q69pywd3/zt2Hu/sowk7C5e5+DfAc4Wp20L1f/zZgk5mNTd11EbCWhKx/wiahc82sOPX/wqHXn4j1n+FI63sJcF3q6KFzgZqMTUjHTGcWd1Jm9ingeeAvpLeRf5uwn+DXwEjCdNxfdPeWO5i6FTObBnzL3WeZ2WjCN4QBwCrgWnevi7G8yJjZJMKO8h7Au8CNhA9viVj/ZnY3cDXhCLpVwE2E7eDdcv2b2RPANMJU09uB7wG/p431nQrHnxA2l+0HbnT3iuN+bgWBiEiyadOQiEjCKQhERBJOQSAiknAKAhGRhFMQiIgknIJApJ3MbF/GzzPN7C0z+0ScNYkci4KPX0REsmFmFwEPAtPd/a9x1yOSLQWBSAcws08DPwVmuvs7cdcjcix0QplIO5lZPbAXmObuq+OuR+RYaR+BSPvVAy/QvS6SIgmiIBBpvybgi8BUM/t23MWIHCvtIxDpAO6+38w+CzxvZtvd/ZG4axLJloJApIOkZoWcAfzZzKrdfUncNYlkQzuLRUQSTvsIREQSTkEgIpJwCgIRkYRTEIiIJJyCQEQk4RQEIiIJpyAQEUm4/w85UvXRV1gDxgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5az_sQRIzbAy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}