Recommendation_Systems.ipynb

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              "   userId             movie  rating\n",
              "0       3  Toy Story (1995)     4.0\n",
              "1       6  Toy Story (1995)     5.0\n",
              "2       8  Toy Story (1995)     4.0\n",
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      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv('/content/Movie.csv')\n",
        "df.head()"
      ]
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        "df.tail()"
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              "      userId             movie  rating\n",
              "8987    7087  GoldenEye (1995)     3.0\n",
              "8988    7088  GoldenEye (1995)     1.0\n",
              "8989    7105  GoldenEye (1995)     2.0\n",
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      "source": [
        "df.shape"
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      "execution_count": 3,
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            "text/plain": [
              "(8992, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of unique users in dataset\n",
        "len(df.userId.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmZVc4jJlhDE",
        "outputId": "68d9aa18-4990-44f8-971b-dec4d263d20d"
      },
      "execution_count": 2,
      "outputs": [
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            "text/plain": [
              "4081"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of unique moives in dataset\n",
        "len(df.movie.unique())"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aQ0gsFQltWk",
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      },
      "execution_count": 5,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert the dataset rows to coulms and vice-versa / convert long format data to wide format.\n",
        "user_df = df.pivot(index = 'userId', columns = 'movie', values = 'rating').reset_index(drop=True)"
      ],
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              "movie  Father of the Bride Part II (1995)  GoldenEye (1995)  \\\n",
              "0                                     NaN               NaN   \n",
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              "4076                                  4.0               NaN   \n",
              "4077                                  3.5               NaN   \n",
              "4078                                  NaN               3.0   \n",
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              "movie  Grumpier Old Men (1995)  Heat (1995)  Jumanji (1995)  Sabrina (1995)  \\\n",
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              "movie  Sudden Death (1995)  Tom and Huck (1995)  Toy Story (1995)  \\\n",
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              "4076                   NaN                  NaN               NaN   \n",
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              "4078                   1.0                  NaN               4.0   \n",
              "4079                   NaN                  NaN               5.0   \n",
              "4080                   NaN                  NaN               4.5   \n",
              "\n",
              "movie  Waiting to Exhale (1995)  \n",
              "0                           NaN  \n",
              "1                           NaN  \n",
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              "...                         ...  \n",
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              "4080                        NaN  \n",
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              "[4081 rows x 10 columns]"
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9393b9fb-afc1-4111-adfa-b4c84c91788c');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_df.index = df.userId.unique()"
      ],
      "metadata": {
        "id": "zK6_eZyLnTMb"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_df"
      ],
      "metadata": {
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          "height": 520
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        "id": "T8-45WzZnTI7",
        "outputId": "c51454bc-d2e1-4f1f-9c41-15fc846bfc0c"
      },
      "execution_count": 10,
      "outputs": [
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              "    [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-96abeeb9-cdac-4afd-827d-17c64d0789cf 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-96abeeb9-cdac-4afd-827d-17c64d0789cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#impute na values with 0, as in the cx has not watched the movie\n",
        "user_df.fillna(0, inplace = True)"
      ],
      "metadata": {
        "id": "gKhz8s9ymuav"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "37SxxQNdnwYS",
        "outputId": "c431400c-f5f0-4d14-c89e-282ce2e254c5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie  Father of the Bride Part II (1995)  GoldenEye (1995)  \\\n",
              "3                                     0.0               0.0   \n",
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              "\n",
              "movie  Grumpier Old Men (1995)  Heat (1995)  Jumanji (1995)  Sabrina (1995)  \\\n",
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              "\n",
              "movie  Sudden Death (1995)  Tom and Huck (1995)  Toy Story (1995)  \\\n",
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              "\n",
              "movie  Waiting to Exhale (1995)  \n",
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              "<p>4081 rows × 10 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77cfc0eb-6152-4152-af48-0bb091ec8a45')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-77cfc0eb-6152-4152-af48-0bb091ec8a45 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-77cfc0eb-6152-4152-af48-0bb091ec8a45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating cosine similarity between users\n",
        "from sklearn.metrics import pairwise_distances\n",
        "from scipy.spatial.distance import cosine, correlation"
      ],
      "metadata": {
        "id": "BvfrXBS5nxnn"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim = 1 - pairwise_distances(user_df.values, metric= 'cosine')"
      ],
      "metadata": {
        "id": "w4BFFJY-oJo0"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim1 = 1 - pairwise_distances(user_df.values, metric= 'correlation')"
      ],
      "metadata": {
        "id": "iSKKas-5ooXy"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "266rvHzYos-H",
        "outputId": "6a683d42-e699-4543-c7bb-12d77ee517c8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.55337157],\n",
              "       [0.        , 1.        , 0.        , ..., 0.45883147, 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 1.        , ..., 0.45883147, 1.        ,\n",
              "        0.62254302],\n",
              "       ...,\n",
              "       [0.        , 0.45883147, 0.45883147, ..., 1.        , 0.45883147,\n",
              "        0.47607054],\n",
              "       [0.        , 0.        , 1.        , ..., 0.45883147, 1.        ,\n",
              "        0.62254302],\n",
              "       [0.55337157, 0.        , 0.62254302, ..., 0.47607054, 0.62254302,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgtVWn07oz7A",
        "outputId": "e04adf0b-2dfd-497f-80d3-4ccaa73cfa57"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.        , -0.11111111, -0.11111111, ..., -0.35136418,\n",
              "        -0.11111111,  0.47898578],\n",
              "       [-0.11111111,  1.        , -0.11111111, ...,  0.35136418,\n",
              "        -0.11111111, -0.21772081],\n",
              "       [-0.11111111, -0.11111111,  1.        , ...,  0.35136418,\n",
              "         1.        ,  0.56607411],\n",
              "       ...,\n",
              "       [-0.35136418,  0.35136418,  0.35136418, ...,  1.        ,\n",
              "         0.35136418,  0.13769873],\n",
              "       [-0.11111111, -0.11111111,  1.        , ...,  0.35136418,\n",
              "         1.        ,  0.56607411],\n",
              "       [ 0.47898578, -0.21772081,  0.56607411, ...,  0.13769873,\n",
              "         0.56607411,  1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert user_sim into a dataframe\n",
        "user_sim_df = pd.DataFrame(user_sim)"
      ],
      "metadata": {
        "id": "B4eqzdzao1iY"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim_df1 = pd.DataFrame(user_sim1)"
      ],
      "metadata": {
        "id": "9F9ZfI0mpJUX"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Set index and column names to user id.\n",
        "user_sim_df.index = df.userId.unique()\n",
        "user_sim_df.columns = df.userId.unique()"
      ],
      "metadata": {
        "id": "HWsMQeBkpMoE"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_sim_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "B4JAv2HepmrX",
        "outputId": "8be1f489-f72e-4ab0-a578-5b16f30eef71"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "#Set index and column names to user id.\n",
        "user_sim_df1.index = df.userId.unique()\n",
        "user_sim_df1.columns = df.userId.unique()"
      ],
      "metadata": {
        "id": "SZLKRprnpt2D"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
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        "outputId": "2eb560c5-179c-4c58-8ce1-acaf947e5377"
      },
      "execution_count": 31,
      "outputs": [
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          },
          "metadata": {},
          "execution_count": 31
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fill diagonal values with 0\n",
        "np.fill_diagonal(user_sim, 0)\n",
        "user_sim_df.iloc[0:5, 0:5]"
      ],
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              "     3    6    8    10   11\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",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "#fill diagonal values with 0\n",
        "np.fill_diagonal(user_sim1, 0)\n",
        "user_sim_df1.iloc[0:5, 0:5]"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "5vE0rpV8qvBM",
        "outputId": "9d94896b-39a7-48d4-94fd-2fc5d7fd06c8"
      },
      "execution_count": 33,
      "outputs": [
        {
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              "          3         6         8         10        11\n",
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              "10 -0.164581 -0.164581 -0.164581  0.000000 -0.164581\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-db558439-7fad-4a56-b269-690f7998fee7 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-db558439-7fad-4a56-b269-690f7998fee7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Most similar values in top 5 rows\n",
        "user_sim_df.idxmax(axis=1)[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihj21Eo6q0sI",
        "outputId": "56015f31-8439-4a4d-e212-6deffc68b472"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3       11\n",
              "6      168\n",
              "8       16\n",
              "10    4047\n",
              "11       3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Most similar values in top 5 rows\n",
        "user_sim_df1.idxmax(axis=1)[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BRyS3G3rNhb",
        "outputId": "fa1bad2e-b704-4385-dd5f-a19181dbd6cd"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3       11\n",
              "6      667\n",
              "8       16\n",
              "10    4047\n",
              "11       3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['userId']==6) | (df['userId']==168)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "rB64y5KqrfWZ",
        "outputId": "c0f4486a-8ed7-4264-8e52-38a634b68028"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      userId                    movie  rating\n",
              "1          6         Toy Story (1995)     5.0\n",
              "60       168         Toy Story (1995)     4.5\n",
              "3725       6  Grumpier Old Men (1995)     3.0\n",
              "6464       6           Sabrina (1995)     5.0"
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-d06629b0-b364-4d12-84fa-86879e56de30 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-d06629b0-b364-4d12-84fa-86879e56de30');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['userId']==6) | (df['userId']==667)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ykjgjCDGsjoi",
        "outputId": "7869e2bc-c4d0-4a10-dc84-71439e253ab1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      userId                    movie  rating\n",
              "1          6         Toy Story (1995)     5.0\n",
              "225      667         Toy Story (1995)     4.5\n",
              "3725       6  Grumpier Old Men (1995)     3.0\n",
              "6464       6           Sabrina (1995)     5.0"
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              "    <tr>\n",
              "      <th>3725</th>\n",
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              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6464</th>\n",
              "      <td>6</td>\n",
              "      <td>Sabrina (1995)</td>\n",
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              "  </tbody>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#which movie to recommed\n",
        "user1 = df[df['userId']==6]\n",
        "user2 = df[df['userId']==11]"
      ],
      "metadata": {
        "id": "9vdf0fROsnOu"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_wrLpntGtSA9",
        "outputId": "51715e33-48ea-41fe-9163-506af5484657"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      userId                    movie  rating\n",
              "1          6         Toy Story (1995)     5.0\n",
              "3725       6  Grumpier Old Men (1995)     3.0\n",
              "6464       6           Sabrina (1995)     5.0"
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              "    <tr>\n",
              "      <th>6464</th>\n",
              "      <td>6</td>\n",
              "      <td>Sabrina (1995)</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\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",
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user2"
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          "height": 112
        },
        "id": "t7TbqELTtTRL",
        "outputId": "2f0aa5fe-4027-4aea-839b-82fdcb4c7799"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      userId             movie  rating\n",
              "4         11  Toy Story (1995)     4.5\n",
              "7446      11  GoldenEye (1995)     2.5"
            ],
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(user1, user2, on = 'movie', how = 'outer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "dsXyQd5ctU3m",
        "outputId": "c134c4e4-9c30-41a2-aff5-5ad43c12641a"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId_x                    movie  rating_x  userId_y  rating_y\n",
              "0       6.0         Toy Story (1995)       5.0      11.0       4.5\n",
              "1       6.0  Grumpier Old Men (1995)       3.0       NaN       NaN\n",
              "2       6.0           Sabrina (1995)       5.0       NaN       NaN\n",
              "3       NaN         GoldenEye (1995)       NaN      11.0       2.5"
            ],
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IR30QnzstpLq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}