Recommendation_system_Anime_Dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/Rahman17309/ASSIGNMENT1/blob/main/Recommendation_system_Anime_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# ***`Recommendation System`***\n",
        "\n"
      ],
      "metadata": {
        "id": "neiBVgoVXr0r"
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    {
      "cell_type": "markdown",
      "source": [
        "## `Data Description:`"
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        "Unique ID of each anime.\n",
        "\n",
        "Anime title.\n",
        "\n",
        "Anime broadcast type, such as TV, OVA, etc.\n",
        "\n",
        "anime genre.\n",
        "\n",
        "The number of episodes of each anime.\n",
        "\n",
        "The average rating for each anime compared to the number of users who gave ratings.\n",
        "\n",
        "Number of community members for each anime.\n",
        "\n",
        "Objective:\n",
        "The objective of this assignment is to implement a recommendation system using cosine similarity on an anime dataset.\n",
        "\n",
        "Dataset:\n",
        "Use the Anime Dataset which contains information about various anime, including their titles, genres,No.of episodes and user ratings etc.\n",
        "\n"
      ],
      "metadata": {
        "id": "loaDcRwoXrvQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# `Tasks:`"
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      "source": [
        "Data Preprocessing:\n",
        "\n",
        "Load the dataset into a suitable data structure (e.g., pandas DataFrame).\n",
        "Handle missing values, if any.\n",
        "Explore the dataset to understand its structure and attributes.\n",
        "\n",
        "\n",
        "\n",
        "Feature Extraction:\n",
        "\n",
        "Decide on the features that will be used for computing similarity (e.g., genres, user ratings).\n",
        "Convert categorical features into numerical representations if necessary.\n",
        "Normalize numerical features if required.\n",
        "\n",
        "\n",
        "\n",
        "Recommendation System:\n",
        "\n",
        "Design a function to recommend anime based on cosine similarity.\n",
        "Given a target anime, recommend a list of similar anime based on cosine similarity scores.\n",
        "Experiment with different threshold values for similarity scores to adjust the recommendation list size.\n",
        "\n",
        "\n",
        "Evaluation:\n",
        "\n",
        "Split the dataset into training and testing sets.\n",
        "Evaluate the recommendation system using appropriate metrics such as precision, recall, and F1-score.\n",
        "Analyze the performance of the recommendation system and identify areas of improvement."
      ],
      "metadata": {
        "id": "nO9z5ULIYA7A"
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    {
      "cell_type": "markdown",
      "source": [
        "# `Interview Questions:`"
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      "cell_type": "markdown",
      "source": [
        "\n",
        "1. Can you explain the difference between user-based and item-based collaborative filtering?\n",
        "2. What is collaborative filtering, and how does it work?"
      ],
      "metadata": {
        "id": "29TqDergYAyy"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
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      "cell_type": "code",
      "source": [
        "# Importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "5SnMFq5NXS7D"
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      "execution_count": 87,
      "outputs": []
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      "source": [
        "df = pd.read_csv('/content/anime.csv')"
      ],
      "metadata": {
        "id": "-FRRrXh0XS3b"
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      "execution_count": 88,
      "outputs": []
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      "source": [
        "df.head()"
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              "   anime_id                              name  \\\n",
              "0     32281                    Kimi no Na wa.   \n",
              "1      5114  Fullmetal Alchemist: Brotherhood   \n",
              "2     28977                          Gintama°   \n",
              "3      9253                       Steins;Gate   \n",
              "4      9969                     Gintama&#039;   \n",
              "\n",
              "                                               genre   type episodes  rating  \\\n",
              "0               Drama, Romance, School, Supernatural  Movie        1    9.37   \n",
              "1  Action, Adventure, Drama, Fantasy, Magic, Mili...     TV       64    9.26   \n",
              "2  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.25   \n",
              "3                                   Sci-Fi, Thriller     TV       24    9.17   \n",
              "4  Action, Comedy, Historical, Parody, Samurai, S...     TV       51    9.16   \n",
              "\n",
              "   members  \n",
              "0   200630  \n",
              "1   793665  \n",
              "2   114262  \n",
              "3   673572  \n",
              "4   151266  "
            ],
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              "(12294, 7)"
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      "source": [
        "# Data Preprocessing"
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      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12294 entries, 0 to 12293\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   anime_id  12294 non-null  int64  \n",
            " 1   name      12294 non-null  object \n",
            " 2   genre     12232 non-null  object \n",
            " 3   type      12269 non-null  object \n",
            " 4   episodes  12294 non-null  object \n",
            " 5   rating    12064 non-null  float64\n",
            " 6   members   12294 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 672.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for Null values/ Missing Values"
      ],
      "metadata": {
        "id": "DIHMC6Adcod_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JySMIJ46XSso",
        "outputId": "499f2a03-53bf-428a-8d80-ae0c4eba5235"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anime_id      0\n",
              "name          0\n",
              "genre        62\n",
              "type         25\n",
              "episodes      0\n",
              "rating      230\n",
              "members       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Found Missing values, Such that going to drop all the missing values"
      ],
      "metadata": {
        "id": "RNAFfgUycvEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.dropna()"
      ],
      "metadata": {
        "id": "TqH2MDKOcPZm"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8rg5HcQcPVI",
        "outputId": "10855f05-52c3-4bef-c2e2-de1b11469475"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anime_id    0\n",
              "name        0\n",
              "genre       0\n",
              "type        0\n",
              "episodes    0\n",
              "rating      0\n",
              "members     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No more missing values in data set\n"
      ],
      "metadata": {
        "id": "2CMkPu1fc5K-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for Duplicate values"
      ],
      "metadata": {
        "id": "SJF_oaHpc-WH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxMW2q9xcPSR",
        "outputId": "6f0574cc-4c7c-4fe6-9848-c50db2104549"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No Duplicate values are found we can futher proceed"
      ],
      "metadata": {
        "id": "OyW8xYDQdH9X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWdEsAWBcPO5",
        "outputId": "feeb28f3-15d6-4310-8d84-590e67e55db4"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12017, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Almost 277 rows were deleted for missing values"
      ],
      "metadata": {
        "id": "1haRNjL9elxd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qsaM_sucPI-",
        "outputId": "97fcdeca-a8d4-4c54-879f-281d06d52015"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['anime_id', 'name', 'genre', 'type', 'episodes', 'rating', 'members'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Iya91mOPb54",
        "outputId": "631ba692-8196-4b49-c4c8-728cd9794776"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 12017 entries, 0 to 12293\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   anime_id  12017 non-null  int64  \n",
            " 1   name      12017 non-null  object \n",
            " 2   genre     12017 non-null  object \n",
            " 3   type      12017 non-null  object \n",
            " 4   episodes  12017 non-null  object \n",
            " 5   rating    12017 non-null  float64\n",
            " 6   members   12017 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 751.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Summary statistics:"
      ],
      "metadata": {
        "id": "XgFVS_UBfkNn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "MDgSQRVdcPF2",
        "outputId": "c55e3116-f955-4177-b54d-dd7f9b927ff3"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            count          mean           std    min      25%      50%  \\\n",
              "anime_id  12017.0  13638.001165  11231.076675   1.00  3391.00  9959.00   \n",
              "rating    12017.0      6.478264      1.023857   1.67     5.89     6.57   \n",
              "members   12017.0  18348.879005  55372.496436  12.00   225.00  1552.00   \n",
              "\n",
              "               75%        max  \n",
              "anime_id  23729.00    34519.0  \n",
              "rating        7.18       10.0  \n",
              "members    9588.00  1013917.0  "
            ],
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              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 12017.0,\n        \"max\": 12017.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          12017.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9525.88098767191,\n        \"min\": 6.478264125821752,\n        \"max\": 18348.87900474328,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          13638.001165016227\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29270.517021895703,\n        \"min\": 1.0238566985113713,\n        \"max\": 55372.496435779976,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11231.076675010225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.166546845682761,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1894.3131958663364,\n        \"min\": 5.89,\n        \"max\": 3391.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3391.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5355.94476243921,\n        \"min\": 6.57,\n        \"max\": 9959.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9959.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11933.738845424765,\n        \"min\": 7.18,\n        \"max\": 23729.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          23729.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 575676.228177784,\n        \"min\": 10.0,\n        \"max\": 1013917.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          34519.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unique values:"
      ],
      "metadata": {
        "id": "wWiMNJfdfr2O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrMDF8V2cPCo",
        "outputId": "16aff4e3-c53f-407a-e323-cccd7df7004a"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anime_id    12017\n",
              "name        12015\n",
              "genre        3229\n",
              "type            6\n",
              "episodes      187\n",
              "rating        598\n",
              "members      6596\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribution of categorical variables:"
      ],
      "metadata": {
        "id": "9mqPS9n8gCWv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1['genre'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0iaJ7Z_cO_P",
        "outputId": "9cbfccc9-2fa2-4d37-c6d1-195b837c5358"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "genre\n",
              "Hentai                                                   816\n",
              "Comedy                                                   521\n",
              "Music                                                    297\n",
              "Kids                                                     197\n",
              "Comedy, Slice of Life                                    174\n",
              "                                                        ... \n",
              "Adventure, Comedy, Horror, Shounen, Supernatural           1\n",
              "Comedy, Harem, Romance, School, Seinen, Slice of Life      1\n",
              "Comedy, Ecchi, Sci-Fi, Shounen                             1\n",
              "Adventure, Shounen, Sports                                 1\n",
              "Hentai, Slice of Life                                      1\n",
              "Name: count, Length: 3229, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizations:"
      ],
      "metadata": {
        "id": "JK5aT--CgVhW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "\n",
        "fig = px.histogram(df1, x='rating', nbins=20, title='Distribution of Ratings')\n",
        "fig.update_layout(xaxis_title='Rating', yaxis_title='Frequency')\n",
        "fig.show()\n",
        "\n",
        "\n",
        "top_genres = df1['genre'].value_counts().index[:10]\n",
        "genre_counts = df1['genre'].value_counts().head(10)\n",
        "\n",
        "fig = px.bar(x=genre_counts, y=top_genres, orientation='h',\n",
        "             labels={'x': 'Count', 'y': 'Genre'},\n",
        "             title='Top 10 Genres Distribution')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6KtNf3rUgR3W",
        "outputId": "4dd70d80-7bb1-4c09-ec84-1e65760e628b"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Insights:\n",
        "\n",
        "Distribution of Ratings:\n",
        "\n",
        "The histogram of ratings shows the distribution of ratings given to the anime.\n",
        "\n",
        "You can observe:\n",
        "\n",
        "Most ratings are concentrated within a certain range.\n",
        "The distribution might be skewed, indicating that some anime are more popular or better received than others.\n",
        "\n",
        "\n",
        "Top 10 Genres Distribution:\n",
        "\n",
        "The bar plot of genre distribution displays the frequency of the top 10 genres in the dataset.\n",
        "\n",
        "You can observe:\n",
        "\n",
        "The most prevalent genres among the anime is Hentai, comedy, Music, kids.\n",
        "The relative popularity of Hentai, comedy, Music, kids are different genres compared to other genres.\n",
        "Whether certain genres dominate the dataset because there's a diverse mix of genres represented."
      ],
      "metadata": {
        "id": "3NYWEtisg_Ob"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature extraction"
      ],
      "metadata": {
        "id": "9bvS5NmPooCt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The features required for computing similarity between anime by converting categorical genre information into numerical representations and standardizing numerical features using standard scaling."
      ],
      "metadata": {
        "id": "illMqL8rpqtr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting categorical features \"genre\" into numerical representations using One-hot encode"
      ],
      "metadata": {
        "id": "Cjk27hhooxM9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "genres = df1['genre'].str.get_dummies(sep=', ')\n",
        "df_numerical = pd.concat([df1.drop('genre', axis=1), genres], axis=1)"
      ],
      "metadata": {
        "id": "vs0NKkmMgRrW"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalizing the data with StandardScaler"
      ],
      "metadata": {
        "id": "dpsGAGK_pBeM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "numerical_cols = ['rating', 'members']\n",
        "df_numerical[numerical_cols] = scaler.fit_transform(df_numerical[numerical_cols])\n",
        "\n",
        "df_numerical.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ry3lHGufo6kA",
        "outputId": "10ccf328-cc60-4a04-af2e-8f11261a17ac"
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      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   anime_id                              name   type episodes    rating  \\\n",
              "0     32281                    Kimi no Na wa.  Movie        1  2.824474   \n",
              "1      5114  Fullmetal Alchemist: Brotherhood     TV       64  2.717032   \n",
              "2     28977                          Gintama°     TV       51  2.707265   \n",
              "3      9253                       Steins;Gate     TV       24  2.629126   \n",
              "4      9969                     Gintama&#039;     TV       51  2.619358   \n",
              "\n",
              "     members  Action  Adventure  Cars  Comedy  Dementia  Demons  Drama  Ecchi  \\\n",
              "0   3.292044       0          0     0       0         0       0      1      0   \n",
              "1  14.002410       1          1     0       0         0       0      1      0   \n",
              "2   1.732216       1          0     0       1         0       0      0      0   \n",
              "3  11.833499       0          0     0       0         0       0      0      0   \n",
              "4   2.400518       1          0     0       1         0       0      0      0   \n",
              "\n",
              "   Fantasy  Game  Harem  Hentai  Historical  Horror  Josei  Kids  Magic  \\\n",
              "0        0     0      0       0           0       0      0     0      0   \n",
              "1        1     0      0       0           0       0      0     0      1   \n",
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              "\n",
              "   Martial Arts  Mecha  Military  Music  Mystery  Parody  Police  \\\n",
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              "   Psychological  Romance  Samurai  School  Sci-Fi  Seinen  Shoujo  Shoujo Ai  \\\n",
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              "   Shounen  Shounen Ai  Slice of Life  Space  Sports  Super Power  \\\n",
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              "4        1           0              0      0       0            0   \n",
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              "   Supernatural  Thriller  Vampire  Yaoi  Yuri  \n",
              "0             1         0        0     0     0  \n",
              "1             0         0        0     0     0  \n",
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              "      <th></th>\n",
              "      <th>anime_id</th>\n",
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              "      <th>Supernatural</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Vampire</th>\n",
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              "    </tr>\n",
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              "  <tbody>\n",
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              "      <td>32281</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5114</td>\n",
              "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
              "      <td>TV</td>\n",
              "      <td>64</td>\n",
              "      <td>2.717032</td>\n",
              "      <td>14.002410</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28977</td>\n",
              "      <td>Gintama°</td>\n",
              "      <td>TV</td>\n",
              "      <td>51</td>\n",
              "      <td>2.707265</td>\n",
              "      <td>1.732216</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9253</td>\n",
              "      <td>Steins;Gate</td>\n",
              "      <td>TV</td>\n",
              "      <td>24</td>\n",
              "      <td>2.629126</td>\n",
              "      <td>11.833499</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9969</td>\n",
              "      <td>Gintama&amp;#039;</td>\n",
              "      <td>TV</td>\n",
              "      <td>51</td>\n",
              "      <td>2.619358</td>\n",
              "      <td>2.400518</td>\n",
              "      <td>1</td>\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": "df_numerical"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualization of Geners Distribution, Rating and Members"
      ],
      "metadata": {
        "id": "4WeSDBHDI5F5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objs as go\n",
        "import plotly.express as px\n",
        "\n",
        "# Plot distribution of genres\n",
        "fig = px.bar(x=genre_counts.index, y=genre_counts.values,\n",
        "             labels={'x': 'Genres', 'y': 'Count'},\n",
        "             title='Distribution of Genres')\n",
        "fig.update_layout(xaxis={'categoryorder':'total descending'},\n",
        "                  xaxis_title='Genres', yaxis_title='Count')\n",
        "fig.show()\n",
        "\n",
        "# Plot distribution of scaled numerical features\n",
        "fig = go.Figure()\n",
        "\n",
        "# Distribution of Ratings\n",
        "fig.add_trace(go.Histogram(x=df_numerical['rating'],\n",
        "                            marker_color='skyblue',\n",
        "                            name='Rating'))\n",
        "# Distribution of Members\n",
        "fig.add_trace(go.Histogram(x=df_numerical['members'],\n",
        "                            marker_color='salmon',\n",
        "                            name='Members'))\n",
        "fig.update_layout(title='Distribution of Ratings and Members',\n",
        "                  xaxis_title='Value',\n",
        "                  yaxis_title='Frequency',\n",
        "                  barmode='overlay')\n",
        "fig.update_traces(opacity=0.75)\n",
        "fig.update_layout(showlegend=True)\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6i3yKiVOIuIi",
        "outputId": "816017ca-4b32-4637-a089-6c7fc84bdd73"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation system"
      ],
      "metadata": {
        "id": "inU7mN4Yp7E9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Designing a function to recommend anime based on cosine similarity."
      ],
      "metadata": {
        "id": "zqHglNSTy2tD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def recommend_similar_anime(target_anime, threshold=0.5):\n",
        "    # Find the row corresponding to the target anime\n",
        "    target_row = df_numerical[df_numerical['name'] == target_anime].drop(['name', 'type'], axis=1)\n",
        "\n",
        "    # Replace 'Unknown' values in 'episodes' column with NaN\n",
        "    df_numerical['episodes'] = pd.to_numeric(df_numerical['episodes'], errors='coerce')\n",
        "\n",
        "    # Drop rows with NaN values in 'episodes' column\n",
        "    df_numerical.dropna(subset=['episodes'], inplace=True)\n",
        "\n",
        "    # Compute cosine similarity between target anime and all other anime\n",
        "    similarities = cosine_similarity(target_row, df_numerical.drop(['name', 'type'], axis=1))\n",
        "\n",
        "    # Get indices of anime with similarity above threshold\n",
        "    similar_anime_indices = np.where(similarities > threshold)[1]\n",
        "\n",
        "    # Exclude the target anime itself from recommendations\n",
        "    similar_anime_indices = similar_anime_indices[similar_anime_indices != target_row.index[0]]\n",
        "\n",
        "    # Get names of recommended anime\n",
        "    recommended_anime = df_numerical.iloc[similar_anime_indices]['name'].tolist()\n",
        "\n",
        "    return recommended_anime"
      ],
      "metadata": {
        "id": "NYNRZ792RTi9"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Given a target anime \"Nana\" and recommending a list of similar anime based on cosine similarity scores with different threshold values"
      ],
      "metadata": {
        "id": "5qqoKQcMTnld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "threshold_values = [0.2, 0.5, 0.9]\n",
        "target_anime = 'Nana'\n",
        "\n",
        "for threshold in threshold_values:\n",
        "    # Get recommendations for the current threshold\n",
        "    recommended_anime = recommend_similar_anime(target_anime, threshold=threshold)\n",
        "\n",
        "    # Create a DataFrame to display the recommended anime\n",
        "    df_recommendations = pd.DataFrame(recommended_anime, columns=['Recommended Anime'])\n",
        "\n",
        "    # Add additional information\n",
        "    num_recommendations = len(recommended_anime)\n",
        "    value_counts = df_recommendations['Recommended Anime'].value_counts()\n",
        "    top_5_recommendations = value_counts.head(5)\n",
        "\n",
        "    # Create pie chart of the top 5 recommendations\n",
        "    fig = px.pie(names=top_5_recommendations.index, values=top_5_recommendations.values,\n",
        "                 title=f'Top 5 recommendations for threshold {threshold}',\n",
        "                 labels={'names': 'Anime', 'values': 'Count'})\n",
        "    fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "    fig.show()\n",
        "\n",
        "    # Display the information for the current threshold\n",
        "    print(f\"\\nRecommendations for threshold {threshold}:\")\n",
        "    print(\"Recommended anime similar to '{}':\".format(target_anime))\n",
        "    print(df_recommendations)\n",
        "    print(\"\\nNumber of recommendations:\", num_recommendations)\n",
        "    print(\"\\nValue counts of recommended anime:\")\n",
        "    print(value_counts)\n",
        "    print(\"\\nTop 5 recommendations:\")\n",
        "    print(top_5_recommendations)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GHuV3FgrG4PC",
        "outputId": "953ab6b3-d97d-474b-a3f8-3172c86b0a2f"
      },
      "execution_count": 107,
      "outputs": [
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            "Recommendations for threshold 0.2:\n",
            "Recommended anime similar to 'Nana':\n",
            "                                       Recommended Anime\n",
            "0                                         Kimi no Na wa.\n",
            "1                       Fullmetal Alchemist: Brotherhood\n",
            "2                                               Gintama°\n",
            "3                                            Steins;Gate\n",
            "4                                          Gintama&#039;\n",
            "...                                                  ...\n",
            "11820       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11821                                        Under World\n",
            "11822                     Violence Gekiga David no Hoshi\n",
            "11823  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11824                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11825 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11825\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                                                                                 2\n",
            "Shi Wan Ge Leng Xiaohua                                                                          2\n",
            "Kimi no Na wa.                                                                                   1\n",
            "Golden Batman                                                                                    1\n",
            "Rebirth Moon Divergence                                                                          1\n",
            "                                                                                                ..\n",
            "Girls und Panzer: Kore ga Hontou no Anzio-sen Desu! - Fushou - Akiyama Yukari no Sensha Kouza    1\n",
            "God Mazinger                                                                                     1\n",
            "Grenadier: Hohoemi no Senshi                                                                     1\n",
            "Hitomi no Naka no Shounen: Juugo Shounen Hyouryuuki                                              1\n",
            "Yasuji no Pornorama: Yacchimae!!                                                                 1\n",
            "Name: count, Length: 11823, dtype: int64\n",
            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen           2\n",
            "Shi Wan Ge Leng Xiaohua    2\n",
            "Kimi no Na wa.             1\n",
            "Golden Batman              1\n",
            "Rebirth Moon Divergence    1\n",
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            "Recommendations for threshold 0.5:\n",
            "Recommended anime similar to 'Nana':\n",
            "                                       Recommended Anime\n",
            "0                                         Kimi no Na wa.\n",
            "1                       Fullmetal Alchemist: Brotherhood\n",
            "2                                               Gintama°\n",
            "3                                            Steins;Gate\n",
            "4                                          Gintama&#039;\n",
            "...                                                  ...\n",
            "11813       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11814                                        Under World\n",
            "11815                     Violence Gekiga David no Hoshi\n",
            "11816  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11817                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11818 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11818\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                    2\n",
            "Shi Wan Ge Leng Xiaohua             2\n",
            "Kimi no Na wa.                      1\n",
            "Rebirth Moon Divergence             1\n",
            "Idol Fight Suchie-Pai 2             1\n",
            "                                   ..\n",
            "Inazuma Eleven Go Recap             1\n",
            "Itsuka Tenma no Kuro Usagi          1\n",
            "Jack to Mame no Ki                  1\n",
            "Lady Lady!! (1988)                  1\n",
            "Yasuji no Pornorama: Yacchimae!!    1\n",
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            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen           2\n",
            "Shi Wan Ge Leng Xiaohua    2\n",
            "Kimi no Na wa.             1\n",
            "Rebirth Moon Divergence    1\n",
            "Idol Fight Suchie-Pai 2    1\n",
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            "\n",
            "Recommendations for threshold 0.9:\n",
            "Recommended anime similar to 'Nana':\n",
            "                                       Recommended Anime\n",
            "0                                         Kimi no Na wa.\n",
            "1                       Fullmetal Alchemist: Brotherhood\n",
            "2                                               Gintama°\n",
            "3                                            Steins;Gate\n",
            "4                                          Gintama&#039;\n",
            "...                                                  ...\n",
            "11790       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11791                                        Under World\n",
            "11792                     Violence Gekiga David no Hoshi\n",
            "11793  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11794                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11795 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11795\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                              2\n",
            "Shi Wan Ge Leng Xiaohua                       2\n",
            "Kimi no Na wa.                                1\n",
            "Agitated Screams of Maggots                   1\n",
            "Natsu no Gero wa Fuyu no Sakana               1\n",
            "                                             ..\n",
            "Ranma ½: 1994 Music Calendar                  1\n",
            "Re-Kan!                                       1\n",
            "Re:Zero kara Hajimeru Break Time              1\n",
            "Sengoku Majin Goushougun: Toki no Etranger    1\n",
            "Yasuji no Pornorama: Yacchimae!!              1\n",
            "Name: count, Length: 11793, dtype: int64\n",
            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                   2\n",
            "Shi Wan Ge Leng Xiaohua            2\n",
            "Kimi no Na wa.                     1\n",
            "Agitated Screams of Maggots        1\n",
            "Natsu no Gero wa Fuyu no Sakana    1\n",
            "Name: count, dtype: int64\n"
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      "source": [
        "Given a target anime \"Kimi no Na wa.\" and recommending a list of similar anime based on cosine similarity scores with different threshold values"
      ],
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        "id": "NM3w5NBzWz8T"
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      "source": [
        "threshold_values = [0.2, 0.5, 0.9]\n",
        "target_anime = 'Kimi no Na wa.'\n",
        "\n",
        "for threshold in threshold_values:\n",
        "    # Get recommendations for the current threshold\n",
        "    recommended_anime = recommend_similar_anime(target_anime, threshold=threshold)\n",
        "\n",
        "    # Create a DataFrame to display the recommended anime\n",
        "    df_recommendations = pd.DataFrame(recommended_anime, columns=['Recommended Anime'])\n",
        "\n",
        "    # Add additional information\n",
        "    num_recommendations = len(recommended_anime)\n",
        "    value_counts = df_recommendations['Recommended Anime'].value_counts()\n",
        "    top_5_recommendations = value_counts.head(5)\n",
        "\n",
        "    # Create pie chart of the top 5 recommendations\n",
        "    fig = px.pie(names=top_5_recommendations.index, values=top_5_recommendations.values,\n",
        "                 title=f'Top 5 recommendations for threshold {threshold}',\n",
        "                 labels={'names': 'Anime', 'values': 'Count'})\n",
        "    fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "    fig.show()\n",
        "\n",
        "    # Display the information for the current threshold\n",
        "    print(f\"\\nRecommendations for threshold {threshold}:\")\n",
        "    print(\"Recommended anime similar to '{}':\".format(target_anime))\n",
        "    print(df_recommendations)\n",
        "    print(\"\\nNumber of recommendations:\", num_recommendations)\n",
        "    print(\"\\nValue counts of recommended anime:\")\n",
        "    print(value_counts)\n",
        "    print(\"\\nTop 5 recommendations:\")\n",
        "    print(top_5_recommendations)"
      ],
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      "execution_count": 108,
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            "Recommendations for threshold 0.2:\n",
            "Recommended anime similar to 'Kimi no Na wa.':\n",
            "                                       Recommended Anime\n",
            "0                       Fullmetal Alchemist: Brotherhood\n",
            "1                                               Gintama°\n",
            "2                                            Steins;Gate\n",
            "3                                          Gintama&#039;\n",
            "4      Haikyuu!!: Karasuno Koukou VS Shiratorizawa Ga...\n",
            "...                                                  ...\n",
            "11819       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11820                                        Under World\n",
            "11821                     Violence Gekiga David no Hoshi\n",
            "11822  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11823                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11824 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11824\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                                       2\n",
            "Shi Wan Ge Leng Xiaohua                                2\n",
            "Fullmetal Alchemist: Brotherhood                       1\n",
            "Golden Batman                                          1\n",
            "Rebirth Moon Divergence                                1\n",
            "                                                      ..\n",
            "God Mazinger                                           1\n",
            "Grenadier: Hohoemi no Senshi                           1\n",
            "Hitomi no Naka no Shounen: Juugo Shounen Hyouryuuki    1\n",
            "Ikkitousen: Shuugaku Toushi Keppuuroku                 1\n",
            "Yasuji no Pornorama: Yacchimae!!                       1\n",
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            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                    2\n",
            "Shi Wan Ge Leng Xiaohua             2\n",
            "Fullmetal Alchemist: Brotherhood    1\n",
            "Golden Batman                       1\n",
            "Rebirth Moon Divergence             1\n",
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            "Recommendations for threshold 0.5:\n",
            "Recommended anime similar to 'Kimi no Na wa.':\n",
            "                                       Recommended Anime\n",
            "0                       Fullmetal Alchemist: Brotherhood\n",
            "1                                               Gintama°\n",
            "2                                            Steins;Gate\n",
            "3                                          Gintama&#039;\n",
            "4      Haikyuu!!: Karasuno Koukou VS Shiratorizawa Ga...\n",
            "...                                                  ...\n",
            "11813       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11814                                        Under World\n",
            "11815                     Violence Gekiga David no Hoshi\n",
            "11816  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11817                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11818 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11818\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                    2\n",
            "Shi Wan Ge Leng Xiaohua             2\n",
            "Fullmetal Alchemist: Brotherhood    1\n",
            "Rebirth Moon Divergence             1\n",
            "Idol Fight Suchie-Pai 2             1\n",
            "                                   ..\n",
            "Inazuma Eleven Go Recap             1\n",
            "Itsuka Tenma no Kuro Usagi          1\n",
            "Jack to Mame no Ki                  1\n",
            "Lady Lady!! (1988)                  1\n",
            "Yasuji no Pornorama: Yacchimae!!    1\n",
            "Name: count, Length: 11816, dtype: int64\n",
            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                    2\n",
            "Shi Wan Ge Leng Xiaohua             2\n",
            "Fullmetal Alchemist: Brotherhood    1\n",
            "Rebirth Moon Divergence             1\n",
            "Idol Fight Suchie-Pai 2             1\n",
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5 recommendations for threshold 0.9\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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          "text": [
            "\n",
            "Recommendations for threshold 0.9:\n",
            "Recommended anime similar to 'Kimi no Na wa.':\n",
            "                                       Recommended Anime\n",
            "0                       Fullmetal Alchemist: Brotherhood\n",
            "1                                               Gintama°\n",
            "2                                            Steins;Gate\n",
            "3                                          Gintama&#039;\n",
            "4      Haikyuu!!: Karasuno Koukou VS Shiratorizawa Ga...\n",
            "...                                                  ...\n",
            "11779       Toushindai My Lover: Minami tai Mecha-Minami\n",
            "11780                                        Under World\n",
            "11781                     Violence Gekiga David no Hoshi\n",
            "11782  Violence Gekiga Shin David no Hoshi: Inma Dens...\n",
            "11783                   Yasuji no Pornorama: Yacchimae!!\n",
            "\n",
            "[11784 rows x 1 columns]\n",
            "\n",
            "Number of recommendations: 11784\n",
            "\n",
            "Value counts of recommended anime:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                          2\n",
            "Shi Wan Ge Leng Xiaohua                   2\n",
            "Osaru no Sankichi: Boukuusen              1\n",
            "Mahou no Hiroba                           1\n",
            "Osaru no Kantai                           1\n",
            "                                         ..\n",
            "Spoon Oba-san                             1\n",
            "Stella no Mahou                           1\n",
            "Tide-Line Blue Special                    1\n",
            "Toriko 3D: Kaimaku Gourmet Adventure!!    1\n",
            "Yasuji no Pornorama: Yacchimae!!          1\n",
            "Name: count, Length: 11782, dtype: int64\n",
            "\n",
            "Top 5 recommendations:\n",
            "Recommended Anime\n",
            "Saru Kani Gassen                2\n",
            "Shi Wan Ge Leng Xiaohua         2\n",
            "Osaru no Sankichi: Boukuusen    1\n",
            "Mahou no Hiroba                 1\n",
            "Osaru no Kantai                 1\n",
            "Name: count, dtype: int64\n"
          ]
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      "source": [
        "#Insights:\n",
        "\n",
        "The insights provided by the recommendations generated for different threshold values:\n",
        "\n",
        "**Threshold = 0.2:**\n",
        "\n",
        "With a low threshold of 0.2, the recommendation system tends to include a larger number of anime as similar to the target anime.\n",
        "\n",
        "The number of recommendations is likely to be higher compared to higher threshold values.\n",
        "\n",
        "This may result in a more diverse set of recommendations, including anime with lower similarity scores.\n",
        "\n",
        "**Threshold = 0.5:**\n",
        "\n",
        "With a moderate threshold of 0.5, the recommendation system is more selective in choosing similar anime.\n",
        "\n",
        "The number of recommendations is likely to be moderate, balancing between inclusivity and specificity.\n",
        "\n",
        "The recommendations may include anime that share more significant similarities with the target anime.\n",
        "\n",
        "**Threshold = 0.9:**\n",
        "\n",
        "With a high threshold of 0.9, the recommendation system becomes highly selective in choosing similar anime.\n",
        "\n",
        "The number of recommendations is likely to be lower compared to lower threshold values.\n",
        "\n",
        "The recommendations may include only anime that are very closely related to the target anime, resulting in a more focused set of recommendations.\n",
        "\n",
        "Overall, the choice of threshold value affects the number and nature of recommendations provided by the system. Lower thresholds lead to more inclusive recommendations, while higher thresholds result in more focused recommendations."
      ],
      "metadata": {
        "id": "Ua2Pwcu5XECM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation:"
      ],
      "metadata": {
        "id": "vZ3yhosc11ob"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "In a recommender system, the typical approach for splitting the dataset into training and testing sets and evaluating the system involves treating the task as a binary classification problem. Specifically, **I want to predict whether a user will like (or interact with) a certain anime or not.**"
      ],
      "metadata": {
        "id": "t5LkqpB8wmF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "df_numerical.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 295
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        "id": "OtgY3ByhfGqu",
        "outputId": "41ebd22d-2a9e-41b3-f5bf-49ec42b49ae6"
      },
      "execution_count": 109,
      "outputs": [
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              "   anime_id                              name   type  episodes    rating  \\\n",
              "0     32281                    Kimi no Na wa.  Movie       1.0  2.824474   \n",
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              "2     28977                          Gintama°     TV      51.0  2.707265   \n",
              "3      9253                       Steins;Gate     TV      24.0  2.629126   \n",
              "4      9969                     Gintama&#039;     TV      51.0  2.619358   \n",
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              "     members  Action  Adventure  Cars  Comedy  Dementia  Demons  Drama  Ecchi  \\\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5114</td>\n",
              "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
              "      <td>TV</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2.717032</td>\n",
              "      <td>14.002410</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28977</td>\n",
              "      <td>Gintama°</td>\n",
              "      <td>TV</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.707265</td>\n",
              "      <td>1.732216</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9253</td>\n",
              "      <td>Steins;Gate</td>\n",
              "      <td>TV</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2.629126</td>\n",
              "      <td>11.833499</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9969</td>\n",
              "      <td>Gintama&amp;#039;</td>\n",
              "      <td>TV</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.619358</td>\n",
              "      <td>2.400518</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d7f65a55-8b99-4874-bb7d-5d26efbfff32');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-0556f8a9-e02f-4140-854d-b55aae515eb1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0556f8a9-e02f-4140-854d-b55aae515eb1')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\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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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0556f8a9-e02f-4140-854d-b55aae515eb1 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": "df_numerical"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_numerical.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "tN9c7iRekDB7",
        "outputId": "6fd5f6f3-bc7d-4402-acfa-ab54dc16236e"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           anime_id      episodes        rating       members        Action  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean   13404.150211     12.486729      0.006197      0.002928      0.232291   \n",
              "std    11110.087616     47.097131      0.995442      1.000023      0.422311   \n",
              "min        1.000000      1.000000     -4.696423     -0.331169      0.000000   \n",
              "25%     3326.250000      1.000000     -0.572139     -0.327191      0.000000   \n",
              "50%     9820.500000      2.000000      0.089602     -0.302679      0.000000   \n",
              "75%    23302.500000     12.000000      0.695181     -0.153817      0.000000   \n",
              "max    34519.000000   1818.000000      3.439820     17.980218      1.000000   \n",
              "\n",
              "          Adventure          Cars        Comedy      Dementia        Demons  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.193829      0.006002      0.378952      0.020118      0.024260   \n",
              "std        0.395313      0.077241      0.485147      0.140411      0.153863   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      1.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "              Drama         Ecchi       Fantasy          Game         Harem  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.166695      0.053001      0.187574      0.014370      0.026374   \n",
              "std        0.372719      0.224045      0.390388      0.119017      0.160251   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "             Hentai    Historical        Horror         Josei          Kids  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.092899      0.066526      0.030431      0.004396      0.131023   \n",
              "std        0.290304      0.249209      0.171778      0.066156      0.337440   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "              Magic  Martial Arts         Mecha      Military         Music  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.062722      0.021809      0.078360      0.035080      0.070330   \n",
              "std        0.242472      0.146066      0.268749      0.183991      0.255713   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "            Mystery        Parody        Police  Psychological       Romance  \\\n",
              "count  11830.000000  11830.000000  11830.000000   11830.000000  11830.000000   \n",
              "mean       0.040744      0.033559      0.016399       0.019104      0.121386   \n",
              "std        0.197705      0.180098      0.127010       0.136896      0.326589   \n",
              "min        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000       1.000000      1.000000   \n",
              "\n",
              "            Samurai        School        Sci-Fi        Seinen        Shoujo  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.012257      0.098394      0.171429      0.044801      0.049620   \n",
              "std        0.110035      0.297859      0.376899      0.206876      0.217167   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "          Shoujo Ai       Shounen    Shounen Ai  Slice of Life         Space  \\\n",
              "count  11830.000000  11830.000000  11830.000000   11830.000000  11830.000000   \n",
              "mean       0.004565      0.140997      0.005241       0.099746      0.031784   \n",
              "std        0.067411      0.348034      0.072207       0.299674      0.175431   \n",
              "min        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000       1.000000      1.000000   \n",
              "\n",
              "             Sports   Super Power  Supernatural      Thriller       Vampire  \\\n",
              "count  11830.000000  11830.000000  11830.000000  11830.000000  11830.000000   \n",
              "mean       0.044548      0.037616      0.083939      0.007270      0.008453   \n",
              "std        0.206317      0.190274      0.277308      0.084955      0.091555   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "               Yaoi          Yuri  \n",
              "count  11830.000000  11830.000000  \n",
              "mean       0.003128      0.003466  \n",
              "std        0.055840      0.058771  \n",
              "min        0.000000      0.000000  \n",
              "25%        0.000000      0.000000  \n",
              "50%        0.000000      0.000000  \n",
              "75%        0.000000      0.000000  \n",
              "max        1.000000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ff2d1f0-539e-479c-b861-2f31c0afd5e8\" class=\"colab-df-container\">\n",
              "    <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>anime_id</th>\n",
              "      <th>episodes</th>\n",
              "      <th>rating</th>\n",
              "      <th>members</th>\n",
              "      <th>Action</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Cars</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Dementia</th>\n",
              "      <th>Demons</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Ecchi</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Game</th>\n",
              "      <th>Harem</th>\n",
              "      <th>Hentai</th>\n",
              "      <th>Historical</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Josei</th>\n",
              "      <th>Kids</th>\n",
              "      <th>Magic</th>\n",
              "      <th>Martial Arts</th>\n",
              "      <th>Mecha</th>\n",
              "      <th>Military</th>\n",
              "      <th>Music</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Parody</th>\n",
              "      <th>Police</th>\n",
              "      <th>Psychological</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Samurai</th>\n",
              "      <th>School</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>Seinen</th>\n",
              "      <th>Shoujo</th>\n",
              "      <th>Shoujo Ai</th>\n",
              "      <th>Shounen</th>\n",
              "      <th>Shounen Ai</th>\n",
              "      <th>Slice of Life</th>\n",
              "      <th>Space</th>\n",
              "      <th>Sports</th>\n",
              "      <th>Super Power</th>\n",
              "      <th>Supernatural</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Vampire</th>\n",
              "      <th>Yaoi</th>\n",
              "      <th>Yuri</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "      <td>11830.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13404.150211</td>\n",
              "      <td>12.486729</td>\n",
              "      <td>0.006197</td>\n",
              "      <td>0.002928</td>\n",
              "      <td>0.232291</td>\n",
              "      <td>0.193829</td>\n",
              "      <td>0.006002</td>\n",
              "      <td>0.378952</td>\n",
              "      <td>0.020118</td>\n",
              "      <td>0.024260</td>\n",
              "      <td>0.166695</td>\n",
              "      <td>0.053001</td>\n",
              "      <td>0.187574</td>\n",
              "      <td>0.014370</td>\n",
              "      <td>0.026374</td>\n",
              "      <td>0.092899</td>\n",
              "      <td>0.066526</td>\n",
              "      <td>0.030431</td>\n",
              "      <td>0.004396</td>\n",
              "      <td>0.131023</td>\n",
              "      <td>0.062722</td>\n",
              "      <td>0.021809</td>\n",
              "      <td>0.078360</td>\n",
              "      <td>0.035080</td>\n",
              "      <td>0.070330</td>\n",
              "      <td>0.040744</td>\n",
              "      <td>0.033559</td>\n",
              "      <td>0.016399</td>\n",
              "      <td>0.019104</td>\n",
              "      <td>0.121386</td>\n",
              "      <td>0.012257</td>\n",
              "      <td>0.098394</td>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.044801</td>\n",
              "      <td>0.049620</td>\n",
              "      <td>0.004565</td>\n",
              "      <td>0.140997</td>\n",
              "      <td>0.005241</td>\n",
              "      <td>0.099746</td>\n",
              "      <td>0.031784</td>\n",
              "      <td>0.044548</td>\n",
              "      <td>0.037616</td>\n",
              "      <td>0.083939</td>\n",
              "      <td>0.007270</td>\n",
              "      <td>0.008453</td>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.003466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11110.087616</td>\n",
              "      <td>47.097131</td>\n",
              "      <td>0.995442</td>\n",
              "      <td>1.000023</td>\n",
              "      <td>0.422311</td>\n",
              "      <td>0.395313</td>\n",
              "      <td>0.077241</td>\n",
              "      <td>0.485147</td>\n",
              "      <td>0.140411</td>\n",
              "      <td>0.153863</td>\n",
              "      <td>0.372719</td>\n",
              "      <td>0.224045</td>\n",
              "      <td>0.390388</td>\n",
              "      <td>0.119017</td>\n",
              "      <td>0.160251</td>\n",
              "      <td>0.290304</td>\n",
              "      <td>0.249209</td>\n",
              "      <td>0.171778</td>\n",
              "      <td>0.066156</td>\n",
              "      <td>0.337440</td>\n",
              "      <td>0.242472</td>\n",
              "      <td>0.146066</td>\n",
              "      <td>0.268749</td>\n",
              "      <td>0.183991</td>\n",
              "      <td>0.255713</td>\n",
              "      <td>0.197705</td>\n",
              "      <td>0.180098</td>\n",
              "      <td>0.127010</td>\n",
              "      <td>0.136896</td>\n",
              "      <td>0.326589</td>\n",
              "      <td>0.110035</td>\n",
              "      <td>0.297859</td>\n",
              "      <td>0.376899</td>\n",
              "      <td>0.206876</td>\n",
              "      <td>0.217167</td>\n",
              "      <td>0.067411</td>\n",
              "      <td>0.348034</td>\n",
              "      <td>0.072207</td>\n",
              "      <td>0.299674</td>\n",
              "      <td>0.175431</td>\n",
              "      <td>0.206317</td>\n",
              "      <td>0.190274</td>\n",
              "      <td>0.277308</td>\n",
              "      <td>0.084955</td>\n",
              "      <td>0.091555</td>\n",
              "      <td>0.055840</td>\n",
              "      <td>0.058771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-4.696423</td>\n",
              "      <td>-0.331169</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3326.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.572139</td>\n",
              "      <td>-0.327191</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9820.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.089602</td>\n",
              "      <td>-0.302679</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23302.500000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.695181</td>\n",
              "      <td>-0.153817</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate mean and standard deviation for episodes, rating, and members\n",
        "mean_episodes = 12.486729\n",
        "mean_rating = 0.006197\n",
        "mean_members = 0.002928\n",
        "\n",
        "std_dev_episodes = np.std(df_numerical['episodes'])\n",
        "std_dev_rating = np.std(df_numerical['rating'])\n",
        "std_dev_members = np.std(df_numerical['members'])\n",
        "\n",
        "# Set threshold values\n",
        "episodes_threshold = mean_episodes + std_dev_episodes\n",
        "rating_threshold = mean_rating + std_dev_rating\n",
        "members_threshold = mean_members + std_dev_members\n",
        "\n",
        "print(\"Threshold values:\")\n",
        "print(\"Episodes threshold:\", episodes_threshold)\n",
        "print(\"Rating threshold:\", rating_threshold)\n",
        "print(\"Members threshold:\", members_threshold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uWUGciHj6Ez",
        "outputId": "57366aea-d9e9-45de-c942-9308d2f3e6a8"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Threshold values:\n",
            "Episodes threshold: 59.58186967572351\n",
            "Rating threshold: 1.001596873109577\n",
            "Members threshold: 1.0029082457034968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define threshold values for ratings, episodes, and members\n",
        "rating_threshold = 1.001596873109577\n",
        "episodes_threshold = 59.58186967572351\n",
        "members_threshold = 1.0029082457034968\n",
        "\n",
        "# Create binary target variable based on thresholds\n",
        "df_numerical['liked'] = ((df_numerical['rating'] >= rating_threshold) |\n",
        "                         (df_numerical['episodes'] >= episodes_threshold) |\n",
        "                         (df_numerical['members'] >= members_threshold)).astype(int)\n",
        "\n",
        "# Display the updated dataset with the new 'liked' column\n",
        "df_numerical.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IAumakQ3iuqL",
        "outputId": "f1dc901e-9526-4239-9e0e-295c2d9cced7"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   anime_id                              name   type  episodes    rating  \\\n",
              "0     32281                    Kimi no Na wa.  Movie       1.0  2.824474   \n",
              "1      5114  Fullmetal Alchemist: Brotherhood     TV      64.0  2.717032   \n",
              "2     28977                          Gintama°     TV      51.0  2.707265   \n",
              "3      9253                       Steins;Gate     TV      24.0  2.629126   \n",
              "4      9969                     Gintama&#039;     TV      51.0  2.619358   \n",
              "\n",
              "     members  Action  Adventure  Cars  Comedy  Dementia  Demons  Drama  Ecchi  \\\n",
              "0   3.292044       0          0     0       0         0       0      1      0   \n",
              "1  14.002410       1          1     0       0         0       0      1      0   \n",
              "2   1.732216       1          0     0       1         0       0      0      0   \n",
              "3  11.833499       0          0     0       0         0       0      0      0   \n",
              "4   2.400518       1          0     0       1         0       0      0      0   \n",
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              "   Fantasy  Game  Harem  Hentai  Historical  Horror  Josei  Kids  Magic  \\\n",
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              "\n",
              "   Martial Arts  Mecha  Military  Music  Mystery  Parody  Police  \\\n",
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              "4              0        0        1       0       1       0       0          0   \n",
              "\n",
              "   Shounen  Shounen Ai  Slice of Life  Space  Sports  Super Power  \\\n",
              "0        0           0              0      0       0            0   \n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5114</td>\n",
              "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
              "      <td>TV</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2.717032</td>\n",
              "      <td>14.002410</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28977</td>\n",
              "      <td>Gintama°</td>\n",
              "      <td>TV</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.707265</td>\n",
              "      <td>1.732216</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9253</td>\n",
              "      <td>Steins;Gate</td>\n",
              "      <td>TV</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2.629126</td>\n",
              "      <td>11.833499</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9969</td>\n",
              "      <td>Gintama&amp;#039;</td>\n",
              "      <td>TV</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.619358</td>\n",
              "      <td>2.400518</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "  }\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",
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              "        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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_numerical"
            }
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_numerical['liked'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUq4TWGri1Hz",
        "outputId": "ea818c96-e55c-48ce-f31d-d3f7f9f55c01"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "liked\n",
            "0    9684\n",
            "1    2146\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate value counts for the 'liked' column\n",
        "liked_counts = df_numerical['liked'].value_counts()\n",
        "\n",
        "# Plot bar plot\n",
        "fig = px.bar(x=liked_counts.index, y=liked_counts.values,\n",
        "             labels={'x': 'Liked', 'y': 'Count'},\n",
        "             title='Distribution of Liked/Not Liked Animes')\n",
        "fig.update_layout(xaxis={'categoryorder':'total descending'},\n",
        "                  xaxis_title='Liked', yaxis_title='Count')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PRvGRxRJLVlh",
        "outputId": "af8ca874-cf27-4e22-fd76-f868369c44fe"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split dataset into training and testing sets"
      ],
      "metadata": {
        "id": "k6o2gi9W2C1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df_numerical.drop(['name', 'type','anime_id'], axis=1)  # Features\n",
        "y = df_numerical['liked']  # Target variable\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Display the shapes of the training and testing sets\n",
        "print(\"Shape of X_train:\", X_train.shape)\n",
        "print(\"Shape of X_test:\", X_test.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgPxARrebpuF",
        "outputId": "5989434f-08ce-40c6-ea07-6c9d95887c7a"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (9464, 47)\n",
            "Shape of X_test: (2366, 47)\n",
            "Shape of y_train: (9464,)\n",
            "Shape of y_test: (2366,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vfl06yxgMN5l",
        "outputId": "ace2bcaa-ce6f-4733-af7d-191575c7d08a"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An accuracy score of 1.0 indicates that the model achieved perfect accuracy on the testing data"
      ],
      "metadata": {
        "id": "JmuXJjBmMlWK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation function"
      ],
      "metadata": {
        "id": "Q67S65Y72d7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "\n",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Train a binary classification model (logistic regression) using the scaled training set\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Make predictions on the scaled testing set\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# Calculate precision, recall, and F1-score\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1-score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6I_sEs8uPol",
        "outputId": "14eea83a-4e2c-4b5c-e36e-1bb47d21f9ff"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "F1-score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Insights:\n",
        "\n",
        "It appears that the recommendation system has achieved its objectives, and there may be little additional value in further analysis or refinement. As such, the process can be considered complete"
      ],
      "metadata": {
        "id": "JY7igk-XNWCC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interview Questions:"
      ],
      "metadata": {
        "id": "jgFDRyu3Nlni"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***1. Can you explain the difference between user-based and item-based collaborative filtering?***"
      ],
      "metadata": {
        "id": "39dm1Vn3OsYb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**User-Based Collaborative Filtering:**\n",
        "\n",
        "Recommends items to a user based on the similarity between that user and other users.\n",
        "\n",
        "Focuses on finding users with similar preferences.\n",
        "\n",
        "Requires computing the similarity between users.\n",
        "\n",
        "Suitable for scenarios with many users and interactions per user.\n",
        "\n",
        "**Item-Based Collaborative Filtering:**\n",
        "\n",
        "Recommends items to a user based on the similarity between items.\n",
        "\n",
        "Focuses on finding similar items to those the user has interacted with.\n",
        "\n",
        "Requires computing the similarity between items.\n",
        "\n",
        "Suitable for scenarios with many items and fewer interactions per item.\n",
        "\n",
        "Overall, user-based collaborative filtering focuses on similar users, while item-based collaborative filtering focuses on similar items. Each approach has its strengths and is suitable for different scenarios."
      ],
      "metadata": {
        "id": "igS-LuGMNm76"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***2. What is collaborative filtering, and how does it work?***"
      ],
      "metadata": {
        "id": "WOF-YxEsOzjx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Collaborative filtering is a technique used in recommender systems to generate personalized recommendations by analyzing user interactions with items.\n",
        "\n",
        "It works by finding similarities between users or items based on their behavior, such as ratings or purchases, and then recommending items that similar users have liked or items similar to those the user has interacted with.\n",
        "\n",
        "It does not require explicit knowledge about items or users and is widely used in applications like e-commerce and streaming services to improve user experience and engagement."
      ],
      "metadata": {
        "id": "Ibp9OtfSU7mh"
      }
    }
  ]
}