CollabMRS.ipynb

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      "source": [
        "**Import Dependencies**"
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        "id": "-qvK1yx9oZYb"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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      "cell_type": "markdown",
      "source": [
        "**Load Data**"
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      "cell_type": "code",
      "source": [
        "movies = pd.read_csv('movies.csv')\n",
        "ratings = pd.read_csv('ratings.csv')"
      ],
      "metadata": {
        "id": "Mn075wu1oyru"
      },
      "execution_count": 66,
      "outputs": []
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      "source": [
        "movies.head(5)"
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            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movies",
              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 9742,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52160,\n        \"min\": 1,\n        \"max\": 193609,\n        \"num_unique_values\": 9742,\n        \"samples\": [\n          45635,\n          1373,\n          7325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9737,\n        \"samples\": [\n          \"Teenage Mutant Ninja Turtles (2014)\",\n          \"America's Sweethearts (2001)\",\n          \"Cast Away (2000)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 951,\n        \"samples\": [\n          \"Crime|Mystery|Romance|Thriller\",\n          \"Action|Adventure|Comedy|Western\",\n          \"Crime|Drama|Musical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "ratings.head(5)"
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            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
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              "\n",
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              "<div id=\"df-ece3b046-95f4-4b44-98b6-16ef039e07ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ece3b046-95f4-4b44-98b6-16ef039e07ef')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ece3b046-95f4-4b44-98b6-16ef039e07ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ratings"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "XKByiYuqrY4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_ratings = len(ratings)\n",
        "n_movies = ratings['movieId'].nunique()\n",
        "n_users = ratings['userId'].nunique()\n",
        "\n",
        "print(f\"Number of ratings: {n_ratings}\")\n",
        "print(f\"Number of unique movieId's: {n_movies}\")\n",
        "print(f\"Number of unique users: {n_users}\")\n",
        "print(f\"Average number of ratings per user: {round(n_ratings/n_users, 2)}\")\n",
        "print(f\"Average number of ratings per movie: {round(n_ratings/n_movies, 2)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnBymBkvpBHC",
        "outputId": "108f75e0-391e-4e0c-c3db-1b48843fe11b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of ratings: 100836\n",
            "Number of unique movieId's: 9724\n",
            "Number of unique users: 610\n",
            "Average number of ratings per user: 165.3\n",
            "Average number of ratings per movie: 10.37\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Distribution of movie ratings"
      ],
      "metadata": {
        "id": "-JDexeuNsLBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='rating',data = ratings, palette=\"pastel\")\n",
        "plt.title(\"Distribution of movie ratings\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "kJTM2_VysKHc",
        "outputId": "67537db3-9ed4-4a99-ea7f-f1202d1125d0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-fd10740cabcd>:1: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='rating',data = ratings, palette=\"pastel\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Mean global rating: {round(ratings['rating'].mean(),2)}.\")\n",
        "\n",
        "mean_ratings = ratings.groupby('userId')['rating'].mean()\n",
        "print(f\"Mean rating per user: {round(mean_ratings.mean(),2)}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AAwnZxjFrB6d",
        "outputId": "5ffb2eeb-f79a-4733-fcad-caf4ca3c3c26"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean global rating: 3.5.\n",
            "Mean rating per user: 3.66.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Which movies are most frequently played?"
      ],
      "metadata": {
        "id": "_hbcp4G6tUoW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_ratings = ratings.merge(movies, on='movieId')\n",
        "movie_ratings['title'].value_counts()[0:10]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "CLUFwK_StTQr",
        "outputId": "58c7bc68-6657-432d-8d8a-ee098b23100f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title\n",
              "Forrest Gump (1994)                          329\n",
              "Shawshank Redemption, The (1994)             317\n",
              "Pulp Fiction (1994)                          307\n",
              "Silence of the Lambs, The (1991)             279\n",
              "Matrix, The (1999)                           278\n",
              "Star Wars: Episode IV - A New Hope (1977)    251\n",
              "Jurassic Park (1993)                         238\n",
              "Braveheart (1995)                            237\n",
              "Terminator 2: Judgment Day (1991)            224\n",
              "Schindler's List (1993)                      220\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Forrest Gump (1994)</th>\n",
              "      <td>329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shawshank Redemption, The (1994)</th>\n",
              "      <td>317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pulp Fiction (1994)</th>\n",
              "      <td>307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Silence of the Lambs, The (1991)</th>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matrix, The (1999)</th>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n",
              "      <td>251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jurassic Park (1993)</th>\n",
              "      <td>238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Braveheart (1995)</th>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Terminator 2: Judgment Day (1991)</th>\n",
              "      <td>224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Schindler's List (1993)</th>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie_ratings.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5MjSEqyvuRNI",
        "outputId": "e9c4a7e6-825e-423e-e015-54f711db453b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp                        title  \\\n",
              "0       1        1     4.0  964982703             Toy Story (1995)   \n",
              "1       1        3     4.0  964981247      Grumpier Old Men (1995)   \n",
              "2       1        6     4.0  964982224                  Heat (1995)   \n",
              "3       1       47     5.0  964983815  Seven (a.k.a. Se7en) (1995)   \n",
              "4       1       50     5.0  964982931   Usual Suspects, The (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                               Comedy|Romance  \n",
              "2                        Action|Crime|Thriller  \n",
              "3                             Mystery|Thriller  \n",
              "4                       Crime|Mystery|Thriller  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-947f97c4-70d7-4321-a698-0c92de5c5347\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982703</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964981247</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>964982224</td>\n",
              "      <td>Heat (1995)</td>\n",
              "      <td>Action|Crime|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964983815</td>\n",
              "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
              "      <td>Mystery|Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>964982931</td>\n",
              "      <td>Usual Suspects, The (1995)</td>\n",
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              "                                                    [key], {});\n",
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              "\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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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "    width: 32px;\n",
              "  }\n",
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              "      spin 1s steps(1) infinite;\n",
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              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movie_ratings"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Least Rated and Most Rated movies."
      ],
      "metadata": {
        "id": "SO-B5fRntldO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# least rated\n",
        "print(\"Least Rated Movie:\")\n",
        "mean_ratings = ratings.groupby('movieId')[['rating']].mean()\n",
        "lowest_rated = mean_ratings['rating'].idxmin()\n",
        "movies[movies['movieId'] == lowest_rated]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "gZxPmVFettv6",
        "outputId": "ec95f404-a035-4cc7-d501-d53d31bdeed0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Least Rated Movie:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movieId         title   genres\n",
              "2689     3604  Gypsy (1962)  Musical"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7f626a7-7a10-4b41-8236-5ea066473516\" class=\"colab-df-container\">\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2689</th>\n",
              "      <td>3604</td>\n",
              "      <td>Gypsy (1962)</td>\n",
              "      <td>Musical</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d7f626a7-7a10-4b41-8236-5ea066473516 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-d7f626a7-7a10-4b41-8236-5ea066473516');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movies[movies['movieId'] == lowest_rated]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3604,\n        \"max\": 3604,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Gypsy (1962)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Musical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Highest rated Movie: \")\n",
        "highest_rated = mean_ratings['rating'].idxmax()\n",
        "movies[movies['movieId'] == highest_rated]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "wEzSdDsowXmd",
        "outputId": "b5076715-87e9-4def-892a-3a932b05fe1b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highest rated Movie: \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    movieId            title           genres\n",
              "48       53  Lamerica (1994)  Adventure|Drama"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bba9c65a-9eab-4f98-9d90-89c59f7986a3\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "                                                    [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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movies[movies['movieId'] == highest_rated]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 53,\n        \"max\": 53,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Lamerica (1994)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Adventure|Drama\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_of_ratings_for_highest_rated = ratings[ratings['movieId']==highest_rated]\n",
        "print(\"Number of ratings for highest rated movie: \")\n",
        "print(no_of_ratings_for_highest_rated['rating'])\n",
        "no_of_ratings_for_highest_rated['rating'].count()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNwvr_IwwgB-",
        "outputId": "cbb77f47-4ee3-4809-e196-8d4ecf6c5660"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of ratings for highest rated movie: \n",
            "13368    5.0\n",
            "96115    5.0\n",
            "Name: rating, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
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    {
      "cell_type": "markdown",
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        "- here if we observe it averaging the ratings irrespective of total no of ratings. hence we need to do bayesian average :\n",
        "![bayesian_average.svg](data:image/svg+xml;base64,<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="19.782ex" height="6.176ex" style="vertical-align: -2.171ex;" viewBox="0 -1724.2 8517.2 2659.1" role="img" focusable="false" xmlns="http://www.w3.org/2000/svg" aria-labelledby="MathJax-SVG-1-Title">
<title id="MathJax-SVG-1-Title">{\displaystyle {\bar {x}}={Cm+\sum _{i=1}^{n}x_{i} \over C+n}}</title>
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</svg>)\n",
        "\n",
        "\n",
        "where C represents our confidence, m represents our prior, and n is the total number of reviews for movie i. In this case, our prior m will be the average mean rating of average ratings across all movies. By defintion, C represents \"the typical data set size\". Let's make C be the average number of ratings for a given movie."
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        "id": "5ZZvRs32w633"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_stats = ratings.groupby('movieId')['rating'].agg(['count', 'mean'])\n",
        "movie_stats.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "kjgCBKscw4R9",
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      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         count      mean\n",
              "movieId                 \n",
              "1          215  3.920930\n",
              "2          110  3.431818\n",
              "3           52  3.259615\n",
              "4            7  2.357143\n",
              "5           49  3.071429"
            ],
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movie_stats",
              "summary": "{\n  \"name\": \"movie_stats\",\n  \"rows\": 9724,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52191,\n        \"min\": 1,\n        \"max\": 193609,\n        \"num_unique_values\": 9724,\n        \"samples\": [\n          1500,\n          114662,\n          5490\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 1,\n        \"max\": 329,\n        \"num_unique_values\": 177,\n        \"samples\": [\n          89,\n          17,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8698736344877788,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 1286,\n        \"samples\": [\n          3.9934210526315788,\n          3.657142857142857,\n          3.4423076923076925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "C = movie_stats['count'].mean()\n",
        "m = movie_stats['mean'].mean()\n",
        "print(f\"Average number of ratings for a given movie: {C:.2f}\")\n",
        "print(f\"Average rating for a given movie: {m:.2f}\")\n",
        "def bayesian_avg(ratings):\n",
        "    bayesian_avg = (C*m + ratings.sum())/(C + ratings.count())\n",
        "    return bayesian_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43fwVKzRzV2k",
        "outputId": "6f818fc7-8fe6-481c-f753-1425d73eefdf"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average number of ratings for a given movie: 10.37\n",
            "Average rating for a given movie: 3.26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Weighted Average of lamerica: {bayesian_avg(pd.Series([5,5]))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ux8MuBWq05GZ",
        "outputId": "a7fe78d2-35c9-4d06-948a-44150a3e0a46"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weighted Average of lamerica: 3.5433826131392228\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bayesian_avg_ratings = ratings.groupby('movieId')['rating'].agg(bayesian_avg).reset_index()\n",
        "bayesian_avg_ratings.columns = ['movieId','bayesian_avg']\n",
        "movie_stats = movie_stats.merge(bayesian_avg_ratings, on='movieId')\n",
        "movie_stats.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "l5GVbHRV1pe1",
        "outputId": "46c3edbc-47b5-4e88-f3d4-57bdad8b6d8e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId  count      mean  bayesian_avg\n",
              "0        1    215  3.920930      3.890632\n",
              "1        2    110  3.431818      3.417227\n",
              "2        3     52  3.259615      3.260086\n",
              "3        4      7  2.357143      2.897612\n",
              "4        5     49  3.071429      3.104793"
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movie_stats",
              "summary": "{\n  \"name\": \"movie_stats\",\n  \"rows\": 9724,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52191,\n        \"min\": 1,\n        \"max\": 193609,\n        \"num_unique_values\": 9724,\n        \"samples\": [\n          1500,\n          114662,\n          5490\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 1,\n        \"max\": 329,\n        \"num_unique_values\": 177,\n        \"samples\": [\n          89,\n          17,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8698736344877788,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 1286,\n        \"samples\": [\n          3.9934210526315788,\n          3.657142857142857,\n          3.4423076923076925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bayesian_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.22759764606539928,\n        \"min\": 2.190377301320899,\n        \"max\": 4.392069545182311,\n        \"num_unique_values\": 1889,\n        \"samples\": [\n          3.5123734592885425,\n          3.2437576146727314,\n          3.4126431044273455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie_stats = movie_stats.merge(movies[['movieId', 'title']])\n",
        "movie_stats.sort_values('bayesian_avg', ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FmmrDPSj2nY_",
        "outputId": "e217f56d-fb23-49df-ceca-fbe65f4c6afe"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movieId  count      mean  bayesian_avg  \\\n",
              "277       318    317  4.429022      4.392070   \n",
              "659       858    192  4.289062      4.236457   \n",
              "2224     2959    218  4.272936      4.227052   \n",
              "224       260    251  4.231076      4.192646   \n",
              "46         50    204  4.237745      4.190567   \n",
              "\n",
              "                                          title  \n",
              "277            Shawshank Redemption, The (1994)  \n",
              "659                       Godfather, The (1972)  \n",
              "2224                          Fight Club (1999)  \n",
              "224   Star Wars: Episode IV - A New Hope (1977)  \n",
              "46                   Usual Suspects, The (1995)  "
            ],
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              "      <td>4.236457</td>\n",
              "      <td>Godfather, The (1972)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2224</th>\n",
              "      <td>2959</td>\n",
              "      <td>218</td>\n",
              "      <td>4.272936</td>\n",
              "      <td>4.227052</td>\n",
              "      <td>Fight Club (1999)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>260</td>\n",
              "      <td>251</td>\n",
              "      <td>4.231076</td>\n",
              "      <td>4.192646</td>\n",
              "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>50</td>\n",
              "      <td>204</td>\n",
              "      <td>4.237745</td>\n",
              "      <td>4.190567</td>\n",
              "      <td>Usual Suspects, The (1995)</td>\n",
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              "        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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              "    }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movie_stats\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1194,\n        \"min\": 50,\n        \"max\": 2959,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          858,\n          50,\n          2959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": 192,\n        \"max\": 317,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          192,\n          204,\n          218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08031575867960633,\n        \"min\": 4.231075697211155,\n        \"max\": 4.429022082018927,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.2890625,\n          4.237745098039215,\n          4.272935779816514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bayesian_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08320235439968333,\n        \"min\": 4.190566628019545,\n        \"max\": 4.392069545182311,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.236456870686314,\n          4.190566628019545,\n          4.2270516053002245\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Godfather, The (1972)\",\n          \"Usual Suspects, The (1995)\",\n          \"Fight Club (1999)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the Bayesian average, we see that Shawshank Redemption, The Godfather, and The Usual Suspects are the most highly rated movies. This result makes much more sense since these movies are critically acclaimed films.\n"
      ],
      "metadata": {
        "id": "C34ydAVI3mUa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_stats.sort_values('bayesian_avg', ascending=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DfsX5CVB3di2",
        "outputId": "d5053150-05fb-4110-d470-67a76305dd56"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movieId  count      mean  bayesian_avg  \\\n",
              "1172     1556     19  1.605263      2.190377   \n",
              "2679     3593     19  1.657895      2.224426   \n",
              "1372     1882     33  1.954545      2.267268   \n",
              "1144     1499     27  1.925926      2.296800   \n",
              "1988     2643     16  1.687500      2.306841   \n",
              "\n",
              "                                        title  \n",
              "1172           Speed 2: Cruise Control (1997)  \n",
              "2679                 Battlefield Earth (2000)  \n",
              "1372                          Godzilla (1998)  \n",
              "1144                          Anaconda (1997)  \n",
              "1988  Superman IV: The Quest for Peace (1987)  "
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movie_stats\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 885,\n        \"min\": 1499,\n        \"max\": 3593,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3593,\n          2643,\n          1882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 16,\n        \"max\": 33,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          33,\n          16,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16187245402857117,\n        \"min\": 1.605263157894737,\n        \"max\": 1.9545454545454546,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.6578947368421053,\n          1.6875,\n          1.9545454545454546\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bayesian_avg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0491760630689789,\n        \"min\": 2.190377301320899,\n        \"max\": 2.306841253563834,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.2244258741100533,\n          2.306841253563834,\n          2.267267609072289\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Battlefield Earth (2000)\",\n          \"Superman IV: The Quest for Peace (1987)\",\n          \"Godzilla (1998)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With Bayesian averaging, it looks like Speed 2: Cruise Control, Battlefield Earth, and Godzilla are the worst rated movies. Gypsy isn't so bad after all."
      ],
      "metadata": {
        "id": "KRQr9VBj3uTR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lets Look into genre"
      ],
      "metadata": {
        "id": "0J3jcww4340B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies['genres'] = movies['genres'].apply(lambda x: x.split(\"|\"))\n",
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UTbD-A1n31Y2",
        "outputId": "8dcd4c48-40ff-486d-b463-e0c1885ac0ae"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                              genres  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
              "1                     [Adventure, Children, Fantasy]  \n",
              "2                                  [Comedy, Romance]  \n",
              "3                           [Comedy, Drama, Romance]  \n",
              "4                                           [Comedy]  "
            ],
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              "                                                    [key], {});\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movies",
              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 9742,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52160,\n        \"min\": 1,\n        \"max\": 193609,\n        \"num_unique_values\": 9742,\n        \"samples\": [\n          45635,\n          1373,\n          7325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9737,\n        \"samples\": [\n          \"Teenage Mutant Ninja Turtles (2014)\",\n          \"America's Sweethearts (2001)\",\n          \"Cast Away (2000)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "  - genres is expressed as a string with a pipe | separating each genre. We will manipulate this string into a list, which will make it much easier to analyze.\n",
        "  - title currently has (year) appended at the end. We will extract year from each title string and create a new column for it.\n"
      ],
      "metadata": {
        "id": "0bNFMZXK4bgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "genre_frequency = Counter(g for genres in movies['genres'] for g in genres)\n",
        "print(f\"There are {len(genre_frequency)} genres.\")\n",
        "\n",
        "genre_frequency"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzPpxWfh4hWT",
        "outputId": "f0194a3a-eef3-4a35-d361-e0d229eb1584"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 20 genres.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({'Adventure': 1263,\n",
              "         'Animation': 611,\n",
              "         'Children': 664,\n",
              "         'Comedy': 3756,\n",
              "         'Fantasy': 779,\n",
              "         'Romance': 1596,\n",
              "         'Drama': 4361,\n",
              "         'Action': 1828,\n",
              "         'Crime': 1199,\n",
              "         'Thriller': 1894,\n",
              "         'Horror': 978,\n",
              "         'Mystery': 573,\n",
              "         'Sci-Fi': 980,\n",
              "         'War': 382,\n",
              "         'Musical': 334,\n",
              "         'Documentary': 440,\n",
              "         'IMAX': 158,\n",
              "         'Western': 167,\n",
              "         'Film-Noir': 87,\n",
              "         '(no genres listed)': 34})"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The 5 most common genres: \\n\", genre_frequency.most_common(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZbb_Ztv5ABM",
        "outputId": "ba356672-6674-469a-8f15-0181c69fdedc"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The 5 most common genres: \n",
            " [('Drama', 4361), ('Comedy', 3756), ('Thriller', 1894), ('Action', 1828), ('Romance', 1596)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genre_frequency_df = pd.DataFrame([genre_frequency]).T.reset_index()\n",
        "genre_frequency_df.columns = ['genre', 'count']"
      ],
      "metadata": {
        "id": "nn10YuFL5DIS"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='genre', y='count', data=genre_frequency_df.sort_values(by='count', ascending=False))\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "vtQby4n-5lhY",
        "outputId": "f234ed58-cfbb-4706-de25-51c3924e1532"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Data PreProcessing\n",
        "To generate recommendations, we will use a technique called collaborative filtering. This method is based on the principle that users with similar preferences are likely to enjoy similar items.\n",
        "\n",
        "The initial step involves transforming our data into a user-item matrix, also referred to as a \"utility matrix.\" In this matrix:\n",
        "\n",
        "    Rows represent users.\n",
        "    Columns represent movies.\n",
        "\n",
        "One of the key advantages of collaborative filtering is that it relies solely on user-item interactions, making it independent of additional information about the users or the movies."
      ],
      "metadata": {
        "id": "p-b1jZH-6ZCj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The create_utility() function outputs a sparse matrix\n",
        "\n",
        "with four mapper dictionaries:\n",
        "\n",
        "    user_mapper: maps user id to user index\n",
        "    movie_mapper: maps movie id to movie index\n",
        "    user_inv_mapper: maps user index to user id\n",
        "    movie_inv_mapper: maps movie index to movie id\n",
        "\n",
        "We need these dictionaries because they map which row/column of the utility matrix corresponds to which user/movie id."
      ],
      "metadata": {
        "id": "-WDZ9yYl6wfP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "def create_utility(df):\n",
        "  M = df['userId'].nunique()\n",
        "  N = df['movieId'].nunique()\n",
        "\n",
        "  user_mapper = dict(zip(np.unique(df[\"userId\"]), list(range(M))))\n",
        "  movie_mapper = dict(zip(np.unique(df[\"movieId\"]), list(range(N))))\n",
        "  user_inv_mapper = dict(zip(list(range(M)), np.unique(df[\"userId\"])))\n",
        "  movie_inv_mapper = dict(zip(list(range(N)), np.unique(df[\"movieId\"])))\n",
        "  user_index = [user_mapper[i] for i in df['userId']]\n",
        "  movie_index = [movie_mapper[i] for i in df['movieId']]\n",
        "  X = csr_matrix((df[\"rating\"], (user_index, movie_index)), shape=(M, N))\n",
        "  return X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper\n",
        "\n",
        "X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_utility(ratings)\n"
      ],
      "metadata": {
        "id": "mXouwcz85oW8"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Mekr-p58fOt",
        "outputId": "bc85b1f1-ca78-4597-adb6-63d4ece71490"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(610, 9724)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluating sparsity"
      ],
      "metadata": {
        "id": "SxevVkym9SP5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_total = X.shape[0]*X.shape[1]\n",
        "n_ratings = X.nnz\n",
        "sparsity = n_ratings/n_total\n",
        "print(f\"Matrix sparsity: {round(sparsity*100,2)}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPan6IoY9Rtu",
        "outputId": "2a61ac89-7a9b-41df-e38a-ff72cb833bb5"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matrix sparsity: 1.7%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The cold start problem is when there are new users and movies in our matrix that do not have any ratings. In our Movielens dataset, all users and movies have at least one rating but in general, it's useful to check which users and movies have few interactions."
      ],
      "metadata": {
        "id": "XLh7dYmK9qdn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_ratings_per_user = X.getnnz(axis=1)\n",
        "len(n_ratings_per_user)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8RKj8_q9mOv",
        "outputId": "747f4866-dee9-408a-b81a-5c0dc298211d"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "610"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Most active user rated {n_ratings_per_user.max()} movies.\")\n",
        "print(f\"Least active user rated {n_ratings_per_user.min()} movies.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7v786XKw-OKa",
        "outputId": "a7cf3628-b890-4cf9-8567-9101d6f1ef65"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most active user rated 2698 movies.\n",
            "Least active user rated 20 movies.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_ratings_per_movie = X.getnnz(axis=0)\n",
        "len(n_ratings_per_movie)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAKXhnO7-d5z",
        "outputId": "2ad08f16-ec83-4f2d-bf51-b89ac9668c0e"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9724"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Most rated movie has {n_ratings_per_movie.max()} ratings.\")\n",
        "print(f\"Least rated movie has {n_ratings_per_movie.min()} ratings.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzzAHKOt-hb6",
        "outputId": "988ebe64-0ca9-454a-c0db-b6be16a0cd0b"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most rated movie has 329 ratings.\n",
            "Least rated movie has 1 ratings.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,4))\n",
        "plt.subplot(1,2,1)\n",
        "sns.kdeplot(n_ratings_per_user, shade=True)\n",
        "plt.xlim(0)\n",
        "plt.title(\"Number of Ratings Per User\", fontsize=14)\n",
        "plt.xlabel(\"number of ratings per user\")\n",
        "plt.ylabel(\"density\")\n",
        "plt.subplot(1,2,2)\n",
        "sns.kdeplot(n_ratings_per_movie, shade=True)\n",
        "plt.xlim(0)\n",
        "plt.title(\"Number of Ratings Per Movie\", fontsize=14)\n",
        "plt.xlabel(\"number of ratings per movie\")\n",
        "plt.ylabel(\"density\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "kI0FaTOG-SRu",
        "outputId": "1e47cc25-6c3a-469d-cbf7-cd342b3a8818"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-103-44089890dcd7>:3: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(n_ratings_per_user, shade=True)\n",
            "<ipython-input-103-44089890dcd7>:9: FutureWarning: \n",
            "\n",
            "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
            "This will become an error in seaborn v0.14.0; please update your code.\n",
            "\n",
            "  sns.kdeplot(n_ratings_per_movie, shade=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Item-Item Recommendation with k-Nearest Neighbors\n",
        "We are going to find the k movies that have the most similar user engagement vectors for movie i."
      ],
      "metadata": {
        "id": "E0qK6O2g-Q8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "def find_similar_movies(movie_id, X, movie_mapper,movie_inv_mapper,k, metric='cosine'):\n",
        "  X = X.T\n",
        "  neighbour_ids = []\n",
        "  movie_ind = movie_mapper[movie_id]\n",
        "  movie_vec = X[movie_ind]\n",
        "  if isinstance(movie_vec, (np.ndarray)):\n",
        "    movie_vec = movie_vec.reshape(1,-1)\n",
        "  print(movie_vec.shape)\n",
        "  knn = NearestNeighbors(n_neighbors=k+1, algorithm=\"brute\", metric=metric)\n",
        "  knn.fit(X)\n",
        "  neighbour = knn.kneighbors(movie_vec,return_distance = False)\n",
        "  for i in range(0,k):\n",
        "    n = neighbour.item(i)\n",
        "    neighbour_ids.append(movie_inv_mapper[n])\n",
        "  neighbour_ids.pop(0)\n",
        "  return neighbour_ids"
      ],
      "metadata": {
        "id": "FcpgqsQI-wVm"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "find_similar_movies() takes in a movieId and X matrix, and outputs a list of\n",
        "\n",
        "movies that are similar to the movieId of interest.\n",
        "\n",
        "Let's see how it works in action. We will first create another mapper that maps movieId to title so that our results are interpretable."
      ],
      "metadata": {
        "id": "-Q1uyJz-_k_3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "similar_movies = find_similar_movies(1, X, movie_mapper, movie_inv_mapper, k=10)\n",
        "similar_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvVywql3_i_Y",
        "outputId": "e40e0ed0-3862-45fc-aecb-0e1574d0e140"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 610)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3114, 480, 780, 260, 356, 364, 1210, 648, 1265]"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "find_similar_movies() returns a list of movieId's that are most similar to your movie of interest. Let's convert these id's to titles so that we can interpret our results. To make things easier, we will create a dictionary that maps movieId to title."
      ],
      "metadata": {
        "id": "_LhSYaNg_ugh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_titles = dict(zip(movies['movieId'],movies['title']))\n",
        "\n",
        "movie_id = 1\n",
        "\n",
        "similar_movies = find_similar_movies(movie_id, X, movie_mapper, movie_inv_mapper, k=10)\n",
        "movie_title = movie_titles[movie_id]\n",
        "\n",
        "print(f\"Since you watched {movie_title}\")\n",
        "for i in similar_movies:\n",
        "  print(movie_titles[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jN4D_cTE_r62",
        "outputId": "93cb71a6-43de-4afa-fb0b-e165edd2bbc5"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 610)\n",
            "Since you watched Toy Story (1995)\n",
            "Toy Story 2 (1999)\n",
            "Jurassic Park (1993)\n",
            "Independence Day (a.k.a. ID4) (1996)\n",
            "Star Wars: Episode IV - A New Hope (1977)\n",
            "Forrest Gump (1994)\n",
            "Lion King, The (1994)\n",
            "Star Wars: Episode VI - Return of the Jedi (1983)\n",
            "Mission: Impossible (1996)\n",
            "Groundhog Day (1993)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The results above show the 10 movies that are most similar to Toy Story. Most movies in this list are family movies from the 1990s, which seems pretty reasonable. Note that these recommendations are based solely on user-item ratings. Movie features such as genres are not used in this approach.\n",
        "\n",
        "You can also play around with the kNN distance metric and see what results you would get if you use \"manhattan\" or \"euclidean\" instead of \"cosine\".\n"
      ],
      "metadata": {
        "id": "vNUOykumASZq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_id = 1\n",
        "\n",
        "similar_movies = find_similar_movies(movie_id, X, movie_mapper, movie_inv_mapper, metric='euclidean', k=10)\n",
        "movie_title = movie_titles[movie_id]\n",
        "\n",
        "print(f\"Because you watched {movie_title}:\")\n",
        "for i in similar_movies:\n",
        "    print(movie_titles[i])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMUYUbyzAUai",
        "outputId": "dd881a08-0ac0-4ba6-b7b2-9e39f4a93382"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 610)\n",
            "Because you watched Toy Story (1995):\n",
            "Toy Story 2 (1999)\n",
            "Mission: Impossible (1996)\n",
            "Independence Day (a.k.a. ID4) (1996)\n",
            "Bug's Life, A (1998)\n",
            "Nutty Professor, The (1996)\n",
            "Willy Wonka & the Chocolate Factory (1971)\n",
            "Babe (1995)\n",
            "Groundhog Day (1993)\n",
            "Mask, The (1994)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling the cold-start problem\n",
        "\n",
        "Collaborative filtering depends entirely on user-item interactions within the utility matrix. However, this approach faces a challenge known as the cold start problem, where new users or items with no prior interactions cannot be included in the recommendation system. To address this, we can use content-based filtering, which generates recommendations based on the features of users and items.\n",
        "\n",
        "As a first step, we need to transform the genres column into binary features. Each genre will be represented as a separate column in the dataframe, with values of 0 or 1 indicating the absence or presence of that genre, respectively."
      ],
      "metadata": {
        "id": "NgcQvsCrAYRy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_movies = movies['movieId'].nunique()\n",
        "print(f\"There are {n_movies} unique movies in our movies dataset.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oadX6hiwAcZf",
        "outputId": "c1c88be6-c360-4ecb-8136-9c5c8710ebad"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 9742 unique movies in our movies dataset.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genres = set(g for G in movies['genres'] for g in G)\n",
        "\n",
        "for g in genres:\n",
        "    movies[g] = movies.genres.transform(lambda x: int(g in x))\n",
        "\n",
        "movie_genres = movies.drop(columns=['movieId', 'title','genres'])"
      ],
      "metadata": {
        "id": "pSXkH59AAdgv"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movie_genres.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "bRpVBPihAkvO",
        "outputId": "f456f27c-81c9-49ee-995e-4ae1284d8ef2"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Children  Mystery  Animation  Sci-Fi  Comedy  War  Romance  Action  \\\n",
              "0         1        0          1       0       1    0        0       0   \n",
              "1         1        0          0       0       0    0        0       0   \n",
              "2         0        0          0       0       1    0        1       0   \n",
              "3         0        0          0       0       1    0        1       0   \n",
              "4         0        0          0       0       1    0        0       0   \n",
              "\n",
              "   Thriller  Adventure  Drama  Film-Noir  Horror  Musical  Western  Fantasy  \\\n",
              "0         0          1      0          0       0        0        0        1   \n",
              "1         0          1      0          0       0        0        0        1   \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "movie_genres",
              "summary": "{\n  \"name\": \"movie_genres\",\n  \"rows\": 9742,\n  \"fields\": [\n    {\n      \"column\": \"Children\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mystery\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Animation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n      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\"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Romance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Action\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Thriller\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adventure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Film-Noir\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Horror\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Musical\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Western\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fantasy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Documentary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Crime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"(no genres listed)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cosine_sim = cosine_similarity(movie_genres, movie_genres)\n",
        "print(f\"Dimensions of our genres cosine similarity matrix: {cosine_sim.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMZJRcQgAutL",
        "outputId": "6eb5b8c2-5635-469e-eb9f-10e9931b4876"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of our genres cosine similarity matrix: (9742, 9742)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As expected, after passing the movie_genres dataframe into the cosine_similarity() function, we obtain a cosine similarity matrix with a shape of (n-movies, n-movies).\n",
        "\n",
        "This matrix contains values between 0 and 1, which represent the degree of similarity between movies along both the x and y axes.\n",
        "\n",
        "### Creating a movie finder function\n",
        "\n",
        "Let’s say we want to get movie recommendations similar to Jumanji. To retrieve results from our recommender, we need to know the exact title of a movie in our dataset.\n",
        "\n",
        "In our dataset, Jumanji is listed as 'Jumanji (1995)', so if we misspell the title or forget to include its release year, our recommender won’t be able to identify the correct movie.\n",
        "\n",
        "To improve the user experience, we can use a Python package called fuzzywuzzy, which finds the most similar title to a string you pass in. Let’s create a function called movie_finder(), which leverages fuzzywuzzy’s string matching algorithm to return the most similar title based on a user-inputted string."
      ],
      "metadata": {
        "id": "_ErXa5-pAzvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install fuzzywuzzy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJ1UEUpiBO84",
        "outputId": "afdfc5ee-26ee-4994-b3bd-b1fc333530e9"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fuzzywuzzy\n",
            "  Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl.metadata (4.9 kB)\n",
            "Downloading fuzzywuzzy-0.18.0-py2.py3-none-any.whl (18 kB)\n",
            "Installing collected packages: fuzzywuzzy\n",
            "Successfully installed fuzzywuzzy-0.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install python-Levenshtein"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdgmU_V8BX9S",
        "outputId": "b0a94dfe-6257-491a-f2bc-994812bc289d"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-Levenshtein\n",
            "  Downloading python_Levenshtein-0.26.1-py3-none-any.whl.metadata (3.7 kB)\n",
            "Collecting Levenshtein==0.26.1 (from python-Levenshtein)\n",
            "  Downloading levenshtein-0.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.2 kB)\n",
            "Collecting rapidfuzz<4.0.0,>=3.9.0 (from Levenshtein==0.26.1->python-Levenshtein)\n",
            "  Downloading rapidfuzz-3.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Downloading python_Levenshtein-0.26.1-py3-none-any.whl (9.4 kB)\n",
            "Downloading levenshtein-0.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (162 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m162.6/162.6 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading rapidfuzz-3.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein, python-Levenshtein\n",
            "Successfully installed Levenshtein-0.26.1 python-Levenshtein-0.26.1 rapidfuzz-3.11.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fuzzywuzzy import process\n",
        "\n",
        "def movie_finder(title):\n",
        "    all_titles = movies['title'].tolist()\n",
        "    closest_match = process.extractOne(title,all_titles)\n",
        "    return closest_match[0]"
      ],
      "metadata": {
        "id": "jQod44p9BK_b"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "title = movie_finder('jumaji')\n",
        "title"
      ],
      "metadata": {
        "id": "nVqgKSlWBjM2"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "To get relevant recommendations for Jumanji, we need to find its index in the cosine simialrity matrix. To identify which row we should be looking at, we can create a movie index mapper which maps a movie title to the index that it represents in our matrix.\n",
        "\n",
        "Let's create a movie index dictionary called movie_idx where the keys are movie titles and values are movie indices:\n"
      ],
      "metadata": {
        "id": "8ihqIol0Bu0S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie_idx = dict(zip(movies['title'], list(movies.index)))\n",
        "idx = movie_idx[title]\n",
        "print(f\"Movie index for Jumanji: {idx}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtPWdgDcBvcP",
        "outputId": "5f24ad0b-5472-4f9a-ad6e-bb88d3cc9f4d"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Movie index for Jumanji: 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using this handy movie_idx dictionary, we know that Jumanji is represented by index 1 in our matrix. Let's get the top 10 most similar movies to Jumanji."
      ],
      "metadata": {
        "id": "FNKl795gCRvi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_recommendations=10\n",
        "sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "sim_scores = sim_scores[1:(n_recommendations+1)]\n",
        "similar_movies = [i[0] for i in sim_scores]"
      ],
      "metadata": {
        "id": "ITzder1hCUt0"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Because you watched {title}:\")\n",
        "movies['title'].iloc[similar_movies]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "CwSjR-RvCxdQ",
        "outputId": "edd95ab5-15ec-4f38-ac3a-f7808e4a5699"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Because you watched Jumanji (1995):\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53                     Indian in the Cupboard, The (1995)\n",
              "109                     NeverEnding Story III, The (1994)\n",
              "767                       Escape to Witch Mountain (1975)\n",
              "1514            Darby O'Gill and the Little People (1959)\n",
              "1556                                  Return to Oz (1985)\n",
              "1617                        NeverEnding Story, The (1984)\n",
              "1618    NeverEnding Story II: The Next Chapter, The (1...\n",
              "1799                        Santa Claus: The Movie (1985)\n",
              "3574    Harry Potter and the Sorcerer's Stone (a.k.a. ...\n",
              "6075    Chronicles of Narnia: The Lion, the Witch and ...\n",
              "Name: title, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Indian in the Cupboard, The (1995)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>NeverEnding Story III, The (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>Escape to Witch Mountain (1975)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1514</th>\n",
              "      <td>Darby O'Gill and the Little People (1959)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1556</th>\n",
              "      <td>Return to Oz (1985)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1617</th>\n",
              "      <td>NeverEnding Story, The (1984)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1618</th>\n",
              "      <td>NeverEnding Story II: The Next Chapter, The (1...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1799</th>\n",
              "      <td>Santa Claus: The Movie (1985)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3574</th>\n",
              "      <td>Harry Potter and the Sorcerer's Stone (a.k.a. ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6075</th>\n",
              "      <td>Chronicles of Narnia: The Lion, the Witch and ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Great! Now, to make it easier for users to test the recommender with various movie titles and customize the number of recommendations, we can package everything into a single function. This function will take in the movie title of interest and the number of recommendations as input and return the relevant movie recommendations."
      ],
      "metadata": {
        "id": "UFG1ItQuC1kr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_content_based_recommendations(title_string, n_recommendations=10):\n",
        "    title = movie_finder(title_string)\n",
        "    idx = movie_idx[title]\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    sim_scores = sim_scores[1:(n_recommendations+1)]\n",
        "    similar_movies = [i[0] for i in sim_scores]\n",
        "    print(f\"Because you watched {title}:\")\n",
        "    print(movies['title'].iloc[similar_movies])"
      ],
      "metadata": {
        "id": "9nRU6ii7C2I0"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_content_based_recommendations('toy story', 30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsOIZtVMC3vZ",
        "outputId": "4dbf9d9e-ac22-4b32-b76f-2c9d127357fa"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Because you watched Toy Story (1995):\n",
            "1706                                          Antz (1998)\n",
            "2355                                   Toy Story 2 (1999)\n",
            "2809       Adventures of Rocky and Bullwinkle, The (2000)\n",
            "3000                     Emperor's New Groove, The (2000)\n",
            "3568                                Monsters, Inc. (2001)\n",
            "6194                                     Wild, The (2006)\n",
            "6486                               Shrek the Third (2007)\n",
            "6948                       Tale of Despereaux, The (2008)\n",
            "7760    Asterix and the Vikings (Astérix et les Viking...\n",
            "8219                                         Turbo (2013)\n",
            "8927                             The Good Dinosaur (2015)\n",
            "9430                                         Moana (2016)\n",
            "559                                      Space Jam (1996)\n",
            "3194                                         Shrek (2001)\n",
            "5490    Twelve Tasks of Asterix, The (Les douze travau...\n",
            "5977                                       Valiant (2005)\n",
            "6260                                Ant Bully, The (2006)\n",
            "6448           TMNT (Teenage Mutant Ninja Turtles) (2007)\n",
            "7355                                   Toy Story 3 (2010)\n",
            "7360    Shrek Forever After (a.k.a. Shrek: The Final C...\n",
            "7530                               Gnomeo & Juliet (2011)\n",
            "7805        Puss in Boots (Nagagutsu o haita neko) (1969)\n",
            "8357                                The Lego Movie (2014)\n",
            "8806                                          Home (2015)\n",
            "8900                                    Inside Out (2015)\n",
            "1357                                Borrowers, The (1997)\n",
            "1505                           Black Cauldron, The (1985)\n",
            "1577                        Lord of the Rings, The (1978)\n",
            "1599                             American Tail, An (1986)\n",
            "1757                                 Bug's Life, A (1998)\n",
            "Name: title, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Advanced Dimensionality Reduction with Matrix Factorization\n",
        "Matrix factorization (MF) is a powerful linear algebra technique used to uncover hidden patterns in user-item interactions, such as between users and movies. By identifying latent features, MF provides a more compact and insightful representation of user preferences and movie characteristics. This method is especially beneficial for handling sparse data and can significantly improve the quality of recommendation systems.\n",
        "\n",
        "The core idea behind matrix factorization is to decompose the original user-item interaction matrix into two smaller matrices:\n",
        "\n",
        "    User-factor matrix: of dimensions nusers×knusers​×k, where each row represents a user’s preferences across kk latent features.\n",
        "    Item-factor matrix: of dimensions k×nitemsk×nitems​, where each column represents a movie’s characteristics across the same kk latent features.\n",
        "\n",
        "Through this process, we reduce the dimensionality of the original matrix into \"taste\" dimensions. While we cannot explicitly interpret each latent feature, we can imagine that one latent factor might correspond to users who prefer romantic comedies from the 1990s, while another factor could represent movies in independent foreign languages.\n",
        "\n",
        "The matrix factorization model approximates the original user-item matrix XX as the product of the two smaller matrices PP and QQ:\n",
        "Xmn≈Pmk×QnkT=X^\n",
        "Xmn​≈Pmk​×QnkT​=X^\n",
        "\n",
        "Here:\n",
        "\n",
        "    XmnXmn​ is the original rating for user mm and movie nn.\n",
        "    X^mnX^mn​ is the predicted rating.\n",
        "    PmkPmk​ represents the user's preference for the kk-th latent feature.\n",
        "    QnkQnk​ represents the item's profile for the kk-th latent feature.\n",
        "\n",
        "This factorization allows us to make predictions for missing ratings and generate more personalized recommendations.\n",
        "\n",
        "$$ X_{mn} \\approx P_{mk} \\times Q_{nk}^T = \\hat{X} $$\n"
      ],
      "metadata": {
        "id": "D_2YgiDDC_7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import TruncatedSVD\n",
        "svd = TruncatedSVD(n_components=20,n_iter=10)\n",
        "Q = svd.fit_transform(X.T)\n",
        "Q.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvovC3ecC5Be",
        "outputId": "75067d33-ab16-4eff-819c-306ab1cd00fa"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9724, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie_id = 1\n",
        "similar_movies = find_similar_movies(movie_id, Q.T, movie_mapper, movie_inv_mapper, metric='cosine', k=10)\n",
        "movie_title = movie_titles[movie_id]\n",
        "\n",
        "print(f\"Because you watched {movie_title}:\")\n",
        "for i in similar_movies:\n",
        "    print(movie_titles[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQaOUPe1D7Yk",
        "outputId": "ae71862b-2558-47cd-bd1a-02e5f7ff0c75"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 20)\n",
            "Because you watched Toy Story (1995):\n",
            "Home Alone (1990)\n",
            "Jurassic Park (1993)\n",
            "Aladdin (1992)\n",
            "Willy Wonka & the Chocolate Factory (1971)\n",
            "Back to the Future (1985)\n",
            "Forrest Gump (1994)\n",
            "Groundhog Day (1993)\n",
            "Star Wars: Episode IV - A New Hope (1977)\n",
            "Princess Bride, The (1987)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results above are the most similar movies to Toy Story using kNN on our “compressed” movie-factor matrix. We reduced the dimensions down to n_components=20. We can think of each component representing a latent feature such as movie genre."
      ],
      "metadata": {
        "id": "KOQwqFwiECRc"
      }
    }
  ]
}