gym_exs_recommendation_kmodes.ipynb

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      "source": [
        "## Gym Dataset implementing into the Recommendation system using K means Clustering"
      ],
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        "id": "WST6pcMs1z4P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part-I [Loading the Dataset]"
      ],
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        "id": "Vh-DC5EmPF0b"
      }
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      "source": [
        "# Importing all the libraries required to work with the dataset\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing drive from google.colab to mount and select the dataset uploded in the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
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        "colab": {
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        },
        "id": "ffjpx3f549O0",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the dataset as variable named df\n",
        "df = pd.read_csv('/content/drive/MyDrive/folder_data_test/prediction_model_datasets/megaGymDataset.csv')"
      ],
      "metadata": {
        "id": "op3IpQx45CtY"
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      "outputs": []
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        "df"
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              "      Unnamed: 0                                           Title  \\\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2918,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 842,\n        \"min\": 0,\n        \"max\": 2917,\n        \"num_unique_values\": 2918,\n        \"samples\": [\n          2008,\n          1041,\n          2358\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1050,\n        \"samples\": [\n          \"The wall calf stretch is a stretch targeting the calf muscles. This is a simple way to stretch these often-tight muscles without any equipment needed.\",\n          \"The barbell forward lunge is a popular lower-body exercise targeting the quads, glutes, and hamstrings. Using a barbell allows you to overload the exercise beyond body weight and perform the movement in strength or muscle-focused rep ranges. The forward lunge can be performed as part of a barbell complex, in a circuit, or on its own in the lower-body portion of any workout.\",\n          \"The single-arm triceps kick-back is a popular movement to increase size and strength of the triceps. It is usually performed for moderate to high reps, at least 8-12 reps, as part of an upper-body or arm-focused workout.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Abdominals\",\n          \"Adductors\",\n          \"Chest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Foam Roll\",\n          \"Exercise Ball\",\n          \"Bands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5846070996570325,\n        \"min\": 0.0,\n        \"max\": 9.6,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          7.0,\n          3.1,\n          8.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part-II [EDA]"
      ],
      "metadata": {
        "id": "gmipsBad5bLd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### General Info of the dataset"
      ],
      "metadata": {
        "id": "lfWo4qFLPP-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGEhBNPl7IqQ",
        "outputId": "8fc79879-190b-40d3-8630-b3d4f240e0e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2918, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Grasping the insights about missing data\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4v2HeKk5YsR",
        "outputId": "6b0bfac3-e648-49e1-8170-5d0173164a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0       0\n",
              "Title            0\n",
              "Desc          1550\n",
              "Type             0\n",
              "BodyPart         0\n",
              "Equipment       32\n",
              "Level            0\n",
              "Rating        1887\n",
              "RatingDesc    2056\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attributes data types in dataset\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIkQTrCh5peu",
        "outputId": "905ab546-9d68-4460-ab74-80d04d76266b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0      int64\n",
              "Title          object\n",
              "Desc           object\n",
              "Type           object\n",
              "BodyPart       object\n",
              "Equipment      object\n",
              "Level          object\n",
              "Rating        float64\n",
              "RatingDesc     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unique vales in columns\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEfPmSaF5vnM",
        "outputId": "d7d3e606-e0a0-4f76-ad64-efa17e8f0a3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    2918\n",
              "Title         2909\n",
              "Desc          1050\n",
              "Type             7\n",
              "BodyPart        17\n",
              "Equipment       12\n",
              "Level            3\n",
              "Rating          72\n",
              "RatingDesc       1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFpgfcYq7b3H",
        "outputId": "d2b76392-4b63-4d9c-b085-c35e2702ced2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2918 entries, 0 to 2917\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  2918 non-null   int64  \n",
            " 1   Title       2918 non-null   object \n",
            " 2   Desc        1368 non-null   object \n",
            " 3   Type        2918 non-null   object \n",
            " 4   BodyPart    2918 non-null   object \n",
            " 5   Equipment   2886 non-null   object \n",
            " 6   Level       2918 non-null   object \n",
            " 7   Rating      1031 non-null   float64\n",
            " 8   RatingDesc  862 non-null    object \n",
            "dtypes: float64(1), int64(1), object(7)\n",
            "memory usage: 205.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Central tendency and stastistical insights of overall dataset\n",
        "df.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "it3p_Ph47sgJ",
        "outputId": "9fc93756-9e75-4e60-d2ef-235fc3ffbe10"
      },
      "execution_count": null,
      "outputs": [
        {
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              "         Unnamed: 0              Title  \\\n",
              "count   2918.000000               2918   \n",
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              "                                                     Desc      Type  \\\n",
              "count                                                1368      2918   \n",
              "unique                                               1050         7   \n",
              "top     The barbell back squat is a popular compound m...  Strength   \n",
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              "mean                                                  NaN       NaN   \n",
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              "          BodyPart  Equipment         Level       Rating RatingDesc  \n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7fb971c0-f2ee-479c-a6d8-46cf7646c921')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7fb971c0-f2ee-479c-a6d8-46cf7646c921 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1051.442390147602,\n        \"min\": 0.0,\n        \"max\": 2918.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2918.0,\n          1458.5,\n          2187.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2909,\n          \"3\",\n          \"2918\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1050,\n          \"10\",\n          \"1368\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"2545\",\n          \"2918\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          17,\n          \"662\",\n          \"2918\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          12,\n          \"1078\",\n          \"2886\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"2446\",\n          \"2918\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.5730944690922,\n        \"min\": 0.0,\n        \"max\": 1031.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.919689621726479,\n          7.9,\n          1031.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"862\",\n          1,\n          \"Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking duplicates\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HE13Qq5N_nK-",
        "outputId": "cd4c6a37-9a10-4452-84e5-bd4b2c63f3a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handeling the missing values in dataset"
      ],
      "metadata": {
        "id": "jPppVvxDPXFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handeling the missing values\n",
        "df['Desc'].fillna('Description Unavailable', inplace=True)\n",
        "df['Equipment'].fillna('Data Unavailable', inplace=True)\n",
        "df['Rating'].fillna('Unavailable', inplace=True)"
      ],
      "metadata": {
        "id": "UzXUqZNh-Ggz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Droping some unrequired columns\n",
        "df.drop(columns=['RatingDesc', 'Unnamed: 0'], inplace=True)"
      ],
      "metadata": {
        "id": "zVYeLMww_Njq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# imputing the Rating Descripting based on the rating values obtained accordingly\n",
        "def impute_ratingDesc(row):\n",
        "  if row['Rating'] == 'Unavailable':\n",
        "    return 'Unavailable'\n",
        "  elif row['Rating'] == 0.0:\n",
        "    return 'No Rating'\n",
        "  elif row['Rating'] <= 4.0:\n",
        "    return 'Below Average'\n",
        "  elif row['Rating'] <= 7.0:\n",
        "    return 'Average'\n",
        "  else:\n",
        "    return 'Above Average'"
      ],
      "metadata": {
        "id": "TKwzTM_RDibX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calling the function to impute Rating Description\n",
        "df['RatingDesc'] = df.apply(lambda row: impute_ratingDesc(row), axis=1)"
      ],
      "metadata": {
        "id": "D1AnT6IkEBdZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s5hVpEDg_b1T",
        "outputId": "3dd48c2b-551f-43ed-b4db-01d0ea8d844d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                             Partner plank band row   \n",
              "1                       Banded crunch isometric hold   \n",
              "2                              FYR Banded Plank Jack   \n",
              "3                                      Banded crunch   \n",
              "4                                             Crunch   \n",
              "...                                              ...   \n",
              "2913                            EZ-bar skullcrusher-   \n",
              "2914  Lying Close-Grip Barbell Triceps Press To Chin   \n",
              "2915          EZ-Bar Skullcrusher - Gethin Variation   \n",
              "2916                                TBS Skullcrusher   \n",
              "2917                     30 Arms EZ-Bar Skullcrusher   \n",
              "\n",
              "                                                   Desc      Type    BodyPart  \\\n",
              "0     The partner plank band row is an abdominal exe...  Strength  Abdominals   \n",
              "1     The banded crunch isometric hold is an exercis...  Strength  Abdominals   \n",
              "2     The banded plank jack is a variation on the pl...  Strength  Abdominals   \n",
              "3     The banded crunch is an exercise targeting the...  Strength  Abdominals   \n",
              "4     The crunch is a popular core exercise targetin...  Strength  Abdominals   \n",
              "...                                                 ...       ...         ...   \n",
              "2913  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2914                            Description Unavailable  Strength     Triceps   \n",
              "2915  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2916  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2917                            Description Unavailable  Strength     Triceps   \n",
              "\n",
              "         Equipment         Level       Rating     RatingDesc  \n",
              "0            Bands  Intermediate          0.0      No Rating  \n",
              "1            Bands  Intermediate  Unavailable    Unavailable  \n",
              "2            Bands  Intermediate  Unavailable    Unavailable  \n",
              "3            Bands  Intermediate  Unavailable    Unavailable  \n",
              "4            Bands  Intermediate  Unavailable    Unavailable  \n",
              "...            ...           ...          ...            ...  \n",
              "2913  E-Z Curl Bar  Intermediate          8.1  Above Average  \n",
              "2914  E-Z Curl Bar      Beginner          8.1  Above Average  \n",
              "2915  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "2916  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "2917  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "\n",
              "[2918 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Desc</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Partner plank band row</td>\n",
              "      <td>The partner plank band row is an abdominal exe...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No Rating</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Banded crunch isometric hold</td>\n",
              "      <td>The banded crunch isometric hold is an exercis...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYR Banded Plank Jack</td>\n",
              "      <td>The banded plank jack is a variation on the pl...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Banded crunch</td>\n",
              "      <td>The banded crunch is an exercise targeting the...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crunch</td>\n",
              "      <td>The crunch is a popular core exercise targetin...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>EZ-bar skullcrusher-</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Lying Close-Grip Barbell Triceps Press To Chin</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>EZ-Bar Skullcrusher - Gethin Variation</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>TBS Skullcrusher</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>30 Arms EZ-Bar Skullcrusher</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2918 rows × 8 columns</p>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-52a5f9c0-eb58-4bfe-a17f-7fa47728ccb8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-52a5f9c0-eb58-4bfe-a17f-7fa47728ccb8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\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",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3af4497-8dc4-4060-9cc7-e71a9870c106 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0b598e58-92e7-403c-b668-324ba58b3753\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0b598e58-92e7-403c-b668-324ba58b3753 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
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              "  </div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2918,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1051,\n        \"samples\": [\n          \"The leg press calf raise is a variation of the standing calf raise performed in a leg press machine. This allows for heavy loading, but also shoulder comfort from the pads of the leg press. Calf raise variations are often performed for relatively high reps, such as 10-20 reps per set or more.\",\n          \"The barbell front squat is a compound movement that emphasizes building the lower-body muscle groups. With the barbell racked in front of the body on the anterior delts, the front squat emphasizes the quads and glutes, but also upper back and core strength to remain upright. Front squats can be trained as an alternative to back squats, as an accessory movement for either squats or deadlifts, or for strength and muscle on their own. Many lifters and athletes prefer them to back squats, although they can be uncomfortable and difficult to learn at first.\",\n          \"The kettlebell thruster is a popular kettlebell movement that combines a kettlebell front squat with a kettlebell press, while holding the bell in a goblet or bottoms-up grip. It is preceded by a clean at the start of each set, and you can perform a clean between each rep if you choose. It can be trained in traditional strength or muscle-building rep ranges, in circuit or fat-loss training, or as part of a larger kettlebell combination or complex.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Abdominals\",\n          \"Adductors\",\n          \"Chest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Foam Roll\",\n          \"Data Unavailable\",\n          \"Bands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8.3,\n          3.1,\n          8.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Unavailable\",\n          \"Below Average\",\n          \"Above Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUDxHWUw_exL",
        "outputId": "143ed13a-431d-48d5-e9f2-a92ad2b76e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2918 entries, 0 to 2917\n",
            "Data columns (total 8 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Title       2918 non-null   object\n",
            " 1   Desc        2918 non-null   object\n",
            " 2   Type        2918 non-null   object\n",
            " 3   BodyPart    2918 non-null   object\n",
            " 4   Equipment   2918 non-null   object\n",
            " 5   Level       2918 non-null   object\n",
            " 6   Rating      2918 non-null   object\n",
            " 7   RatingDesc  2918 non-null   object\n",
            "dtypes: object(8)\n",
            "memory usage: 182.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the null counts in the dataset\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFIbUhhV_hRU",
        "outputId": "6c3c3b87-57dc-48d1-ceb0-0c40082ac804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Title         0\n",
              "Desc          0\n",
              "Type          0\n",
              "BodyPart      0\n",
              "Equipment     0\n",
              "Level         0\n",
              "Rating        0\n",
              "RatingDesc    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-checking the duplicates in the modified dataset\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YidPTEJW_wcO",
        "outputId": "5098dd4c-dc0f-433f-d7ed-1f04daffacdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the duplicates in modified dataset\n",
        "df = df.drop_duplicates()\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6veuz0R_zSt",
        "outputId": "e9cd14f1-de12-4d6b-9875-90873f26c6b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyzing the dataset with Visualization"
      ],
      "metadata": {
        "id": "pssKmuzzPclc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the dataset with various plots on different columns\n",
        "sns.set(style='whitegrid')\n",
        "# Visualization of the dataset for Exercise Types\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(data=df, x='Type', order=df['Type'].value_counts().index)\n",
        "plt.title('Distribution of Exercise Types')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "rNkYsauy_9c3",
        "outputId": "bf27069d-c942-47c3-f989-0a18f17acb68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of the dataset for Body Part column\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(data=df, x='BodyPart', order=df['BodyPart'].value_counts().index)\n",
        "plt.title('Distribution of Body Parts Targeted')\n",
        "plt.xlabel('BodyPart')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "dhdpTXmE7u0o",
        "outputId": "40471523-403e-4d61-e13a-e2fcce50c2e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of the dataset for Equipments column\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(data=df, x='Equipment', order=df['Equipment'].value_counts().index)\n",
        "plt.title('Distribution of Equipment Used')\n",
        "plt.xlabel('Equipment')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "Rc9aJ8cO8Rip",
        "outputId": "a18c1010-6a88-46d6-b22e-6cefcbcdedf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of the dataset based on Exercise Level column\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(data=df, x='Level', order=df['Level'].value_counts().index)\n",
        "plt.title('Distribution of Difficulty Levels')\n",
        "plt.xlabel('Level')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "qgQ4UErUANm2",
        "outputId": "9d951f74-40cf-443e-87c0-0f67cbff268a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of the dataset based on Rating Description column\n",
        "\n",
        "plt.figure(figsize=(20,12))\n",
        "sns.countplot(data=df, x='RatingDesc', order=df['RatingDesc'].value_counts().index)\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "e2W0QeXmAa16",
        "outputId": "7bb068d1-2ff0-4ffe-c0d8-3402485c732d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AjWCEWRJfnrY",
        "outputId": "c8e53758-ddb4-4056-9731-8af03a6c251a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                             Partner plank band row   \n",
              "1                       Banded crunch isometric hold   \n",
              "2                              FYR Banded Plank Jack   \n",
              "3                                      Banded crunch   \n",
              "4                                             Crunch   \n",
              "...                                              ...   \n",
              "2913                            EZ-bar skullcrusher-   \n",
              "2914  Lying Close-Grip Barbell Triceps Press To Chin   \n",
              "2915          EZ-Bar Skullcrusher - Gethin Variation   \n",
              "2916                                TBS Skullcrusher   \n",
              "2917                     30 Arms EZ-Bar Skullcrusher   \n",
              "\n",
              "                                                   Desc      Type    BodyPart  \\\n",
              "0     The partner plank band row is an abdominal exe...  Strength  Abdominals   \n",
              "1     The banded crunch isometric hold is an exercis...  Strength  Abdominals   \n",
              "2     The banded plank jack is a variation on the pl...  Strength  Abdominals   \n",
              "3     The banded crunch is an exercise targeting the...  Strength  Abdominals   \n",
              "4     The crunch is a popular core exercise targetin...  Strength  Abdominals   \n",
              "...                                                 ...       ...         ...   \n",
              "2913  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2914                            Description Unavailable  Strength     Triceps   \n",
              "2915  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2916  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2917                            Description Unavailable  Strength     Triceps   \n",
              "\n",
              "         Equipment         Level       Rating     RatingDesc  \n",
              "0            Bands  Intermediate          0.0      No Rating  \n",
              "1            Bands  Intermediate  Unavailable    Unavailable  \n",
              "2            Bands  Intermediate  Unavailable    Unavailable  \n",
              "3            Bands  Intermediate  Unavailable    Unavailable  \n",
              "4            Bands  Intermediate  Unavailable    Unavailable  \n",
              "...            ...           ...          ...            ...  \n",
              "2913  E-Z Curl Bar  Intermediate          8.1  Above Average  \n",
              "2914  E-Z Curl Bar      Beginner          8.1  Above Average  \n",
              "2915  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "2916  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "2917  E-Z Curl Bar  Intermediate  Unavailable    Unavailable  \n",
              "\n",
              "[2909 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-045e8572-8f20-45ef-9a53-1d86188deb2d\" 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>Title</th>\n",
              "      <th>Desc</th>\n",
              "      <th>Type</th>\n",
              "      <th>BodyPart</th>\n",
              "      <th>Equipment</th>\n",
              "      <th>Level</th>\n",
              "      <th>Rating</th>\n",
              "      <th>RatingDesc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Partner plank band row</td>\n",
              "      <td>The partner plank band row is an abdominal exe...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No Rating</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Banded crunch isometric hold</td>\n",
              "      <td>The banded crunch isometric hold is an exercis...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYR Banded Plank Jack</td>\n",
              "      <td>The banded plank jack is a variation on the pl...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Banded crunch</td>\n",
              "      <td>The banded crunch is an exercise targeting the...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crunch</td>\n",
              "      <td>The crunch is a popular core exercise targetin...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>EZ-bar skullcrusher-</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Lying Close-Grip Barbell Triceps Press To Chin</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>EZ-Bar Skullcrusher - Gethin Variation</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>TBS Skullcrusher</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>30 Arms EZ-Bar Skullcrusher</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2909 rows × 8 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2909,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1051,\n        \"samples\": [\n          \"The leg press calf raise is a variation of the standing calf raise performed in a leg press machine. This allows for heavy loading, but also shoulder comfort from the pads of the leg press. Calf raise variations are often performed for relatively high reps, such as 10-20 reps per set or more.\",\n          \"The barbell front squat is a compound movement that emphasizes building the lower-body muscle groups. With the barbell racked in front of the body on the anterior delts, the front squat emphasizes the quads and glutes, but also upper back and core strength to remain upright. Front squats can be trained as an alternative to back squats, as an accessory movement for either squats or deadlifts, or for strength and muscle on their own. Many lifters and athletes prefer them to back squats, although they can be uncomfortable and difficult to learn at first.\",\n          \"The kettlebell thruster is a popular kettlebell movement that combines a kettlebell front squat with a kettlebell press, while holding the bell in a goblet or bottoms-up grip. It is preceded by a clean at the start of each set, and you can perform a clean between each rep if you choose. It can be trained in traditional strength or muscle-building rep ranges, in circuit or fat-loss training, or as part of a larger kettlebell combination or complex.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Abdominals\",\n          \"Adductors\",\n          \"Chest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Foam Roll\",\n          \"Data Unavailable\",\n          \"Bands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8.3,\n          3.1,\n          8.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Unavailable\",\n          \"Below Average\",\n          \"Above Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finalizing Cleaned Dataset"
      ],
      "metadata": {
        "id": "JKeflGr4PgYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Droping the rating dataset since it Rating Description column can help further\n",
        "df_2 = df\n",
        "df_2.drop(columns = ['Rating'], inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXsfwlGaHNgl",
        "outputId": "65f690f4-ef88-4e3c-ea98-d9a524ed7eca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-76ab70a83ffd>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_2.drop(columns = ['Rating'], inplace = True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting the dataset after cleaning and before working with model\n",
        "# The dataset is non-encoded\n",
        "df.to_csv('cleaned_data.csv', index=False)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "I8zN7Manpzbs",
        "outputId": "2077aba5-a84b-4ec2-eded-ec8d47d2b6d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                             Partner plank band row   \n",
              "1                       Banded crunch isometric hold   \n",
              "2                              FYR Banded Plank Jack   \n",
              "3                                      Banded crunch   \n",
              "4                                             Crunch   \n",
              "...                                              ...   \n",
              "2913                            EZ-bar skullcrusher-   \n",
              "2914  Lying Close-Grip Barbell Triceps Press To Chin   \n",
              "2915          EZ-Bar Skullcrusher - Gethin Variation   \n",
              "2916                                TBS Skullcrusher   \n",
              "2917                     30 Arms EZ-Bar Skullcrusher   \n",
              "\n",
              "                                                   Desc      Type    BodyPart  \\\n",
              "0     The partner plank band row is an abdominal exe...  Strength  Abdominals   \n",
              "1     The banded crunch isometric hold is an exercis...  Strength  Abdominals   \n",
              "2     The banded plank jack is a variation on the pl...  Strength  Abdominals   \n",
              "3     The banded crunch is an exercise targeting the...  Strength  Abdominals   \n",
              "4     The crunch is a popular core exercise targetin...  Strength  Abdominals   \n",
              "...                                                 ...       ...         ...   \n",
              "2913  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2914                            Description Unavailable  Strength     Triceps   \n",
              "2915  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2916  The EZ-bar skullcrusher is a popular exercise ...  Strength     Triceps   \n",
              "2917                            Description Unavailable  Strength     Triceps   \n",
              "\n",
              "         Equipment         Level     RatingDesc  \n",
              "0            Bands  Intermediate      No Rating  \n",
              "1            Bands  Intermediate    Unavailable  \n",
              "2            Bands  Intermediate    Unavailable  \n",
              "3            Bands  Intermediate    Unavailable  \n",
              "4            Bands  Intermediate    Unavailable  \n",
              "...            ...           ...            ...  \n",
              "2913  E-Z Curl Bar  Intermediate  Above Average  \n",
              "2914  E-Z Curl Bar      Beginner  Above Average  \n",
              "2915  E-Z Curl Bar  Intermediate    Unavailable  \n",
              "2916  E-Z Curl Bar  Intermediate    Unavailable  \n",
              "2917  E-Z Curl Bar  Intermediate    Unavailable  \n",
              "\n",
              "[2909 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Desc</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Partner plank band row</td>\n",
              "      <td>The partner plank band row is an abdominal exe...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>No Rating</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Banded crunch isometric hold</td>\n",
              "      <td>The banded crunch isometric hold is an exercis...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYR Banded Plank Jack</td>\n",
              "      <td>The banded plank jack is a variation on the pl...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Banded crunch</td>\n",
              "      <td>The banded crunch is an exercise targeting the...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crunch</td>\n",
              "      <td>The crunch is a popular core exercise targetin...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>EZ-bar skullcrusher-</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Lying Close-Grip Barbell Triceps Press To Chin</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>EZ-Bar Skullcrusher - Gethin Variation</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>TBS Skullcrusher</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>30 Arms EZ-Bar Skullcrusher</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2909 rows × 7 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2909,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1051,\n        \"samples\": [\n          \"The leg press calf raise is a variation of the standing calf raise performed in a leg press machine. This allows for heavy loading, but also shoulder comfort from the pads of the leg press. Calf raise variations are often performed for relatively high reps, such as 10-20 reps per set or more.\",\n          \"The barbell front squat is a compound movement that emphasizes building the lower-body muscle groups. With the barbell racked in front of the body on the anterior delts, the front squat emphasizes the quads and glutes, but also upper back and core strength to remain upright. Front squats can be trained as an alternative to back squats, as an accessory movement for either squats or deadlifts, or for strength and muscle on their own. Many lifters and athletes prefer them to back squats, although they can be uncomfortable and difficult to learn at first.\",\n          \"The kettlebell thruster is a popular kettlebell movement that combines a kettlebell front squat with a kettlebell press, while holding the bell in a goblet or bottoms-up grip. It is preceded by a clean at the start of each set, and you can perform a clean between each rep if you choose. It can be trained in traditional strength or muscle-building rep ranges, in circuit or fat-loss training, or as part of a larger kettlebell combination or complex.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Abdominals\",\n          \"Adductors\",\n          \"Chest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Foam Roll\",\n          \"Data Unavailable\",\n          \"Bands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Unavailable\",\n          \"Below Average\",\n          \"Above Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoding the final dataset"
      ],
      "metadata": {
        "id": "M7sqm3knPnOW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding the dataset for further works\n",
        "from sklearn.preprocessing import LabelEncoder # Importing the label encoder\n",
        "label_encoder = {}\n",
        "\n",
        "columns = df_2[['BodyPart', 'Equipment']]\n",
        "\n",
        "# Encoding the dataset since all columns are object types\n",
        "for col in columns:\n",
        "  le = LabelEncoder()\n",
        "  df_2[col] = le.fit_transform(columns[col])\n",
        "  label_encoder[col] = le\n",
        "\n",
        "columns # Checking the encoded dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "0dut1OnXFrZT",
        "outputId": "5ca87909-4984-4302-e20d-5fc7d7d60f17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-72652c857439>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_2[col] = le.fit_transform(columns[col])\n",
            "<ipython-input-31-72652c857439>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_2[col] = le.fit_transform(columns[col])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        BodyPart     Equipment\n",
              "0     Abdominals         Bands\n",
              "1     Abdominals         Bands\n",
              "2     Abdominals         Bands\n",
              "3     Abdominals         Bands\n",
              "4     Abdominals         Bands\n",
              "...          ...           ...\n",
              "2913     Triceps  E-Z Curl Bar\n",
              "2914     Triceps  E-Z Curl Bar\n",
              "2915     Triceps  E-Z Curl Bar\n",
              "2916     Triceps  E-Z Curl Bar\n",
              "2917     Triceps  E-Z Curl Bar\n",
              "\n",
              "[2909 rows x 2 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BodyPart</th>\n",
              "      <th>Equipment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abdominals</td>\n",
              "      <td>Bands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>Triceps</td>\n",
              "      <td>E-Z Curl Bar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2909 rows × 2 columns</p>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "columns",
              "summary": "{\n  \"name\": \"columns\",\n  \"rows\": 2909,\n  \"fields\": [\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Abdominals\",\n          \"Adductors\",\n          \"Chest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Foam Roll\",\n          \"Data Unavailable\",\n          \"Bands\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## part-III Building Models"
      ],
      "metadata": {
        "id": "MJoM5ddJJgQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kmodes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovcxNjawe70A",
        "outputId": "591f8a5e-edd5-4755-b3d8-d209350e4809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kmodes\n",
            "  Downloading kmodes-0.12.2-py2.py3-none-any.whl.metadata (8.1 kB)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from kmodes) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from kmodes) (1.3.2)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.10/dist-packages (from kmodes) (1.13.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from kmodes) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->kmodes) (3.5.0)\n",
            "Downloading kmodes-0.12.2-py2.py3-none-any.whl (20 kB)\n",
            "Installing collected packages: kmodes\n",
            "Successfully installed kmodes-0.12.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the required funtions from sklearn\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from kmodes.kmodes import KModes"
      ],
      "metadata": {
        "id": "ycMeotOeJRog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4uvO0CGyfPou",
        "outputId": "f371a85e-b7f5-4330-ea7c-c5405fea3b19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                             Partner plank band row   \n",
              "1                       Banded crunch isometric hold   \n",
              "2                              FYR Banded Plank Jack   \n",
              "3                                      Banded crunch   \n",
              "4                                             Crunch   \n",
              "...                                              ...   \n",
              "2913                            EZ-bar skullcrusher-   \n",
              "2914  Lying Close-Grip Barbell Triceps Press To Chin   \n",
              "2915          EZ-Bar Skullcrusher - Gethin Variation   \n",
              "2916                                TBS Skullcrusher   \n",
              "2917                     30 Arms EZ-Bar Skullcrusher   \n",
              "\n",
              "                                                   Desc      Type  BodyPart  \\\n",
              "0     The partner plank band row is an abdominal exe...  Strength         0   \n",
              "1     The banded crunch isometric hold is an exercis...  Strength         0   \n",
              "2     The banded plank jack is a variation on the pl...  Strength         0   \n",
              "3     The banded crunch is an exercise targeting the...  Strength         0   \n",
              "4     The crunch is a popular core exercise targetin...  Strength         0   \n",
              "...                                                 ...       ...       ...   \n",
              "2913  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2914                            Description Unavailable  Strength        16   \n",
              "2915  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2916  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2917                            Description Unavailable  Strength        16   \n",
              "\n",
              "      Equipment         Level     RatingDesc  \n",
              "0             0  Intermediate      No Rating  \n",
              "1             0  Intermediate    Unavailable  \n",
              "2             0  Intermediate    Unavailable  \n",
              "3             0  Intermediate    Unavailable  \n",
              "4             0  Intermediate    Unavailable  \n",
              "...         ...           ...            ...  \n",
              "2913          6  Intermediate  Above Average  \n",
              "2914          6      Beginner  Above Average  \n",
              "2915          6  Intermediate    Unavailable  \n",
              "2916          6  Intermediate    Unavailable  \n",
              "2917          6  Intermediate    Unavailable  \n",
              "\n",
              "[2909 rows x 7 columns]"
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              "      <th>0</th>\n",
              "      <td>Partner plank band row</td>\n",
              "      <td>The partner plank band row is an abdominal exe...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
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              "      <td>Intermediate</td>\n",
              "      <td>No Rating</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Banded crunch isometric hold</td>\n",
              "      <td>The banded crunch isometric hold is an exercis...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYR Banded Plank Jack</td>\n",
              "      <td>The banded plank jack is a variation on the pl...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Banded crunch</td>\n",
              "      <td>The banded crunch is an exercise targeting the...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crunch</td>\n",
              "      <td>The crunch is a popular core exercise targetin...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>EZ-bar skullcrusher-</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Lying Close-Grip Barbell Triceps Press To Chin</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>Above Average</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>EZ-Bar Skullcrusher - Gethin Variation</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>TBS Skullcrusher</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>30 Arms EZ-Bar Skullcrusher</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2909 rows × 7 columns</p>\n",
              "</div>\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2909,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1051,\n        \"samples\": [\n          \"The leg press calf raise is a variation of the standing calf raise performed in a leg press machine. This allows for heavy loading, but also shoulder comfort from the pads of the leg press. Calf raise variations are often performed for relatively high reps, such as 10-20 reps per set or more.\",\n          \"The barbell front squat is a compound movement that emphasizes building the lower-body muscle groups. With the barbell racked in front of the body on the anterior delts, the front squat emphasizes the quads and glutes, but also upper back and core strength to remain upright. Front squats can be trained as an alternative to back squats, as an accessory movement for either squats or deadlifts, or for strength and muscle on their own. Many lifters and athletes prefer them to back squats, although they can be uncomfortable and difficult to learn at first.\",\n          \"The kettlebell thruster is a popular kettlebell movement that combines a kettlebell front squat with a kettlebell press, while holding the bell in a goblet or bottoms-up grip. It is preceded by a clean at the start of each set, and you can perform a clean between each rep if you choose. It can be trained in traditional strength or muscle-building rep ranges, in circuit or fat-loss training, or as part of a larger kettlebell combination or complex.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 16,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 12,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          8,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Unavailable\",\n          \"Below Average\",\n          \"Above Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setring the variable for each with data values\n",
        "selected_df_main = df[['BodyPart', 'Equipment']]\n",
        "\n",
        "# Standardizing the main columns\n",
        "scaler_main = StandardScaler()\n",
        "scaled_data_main = scaler_main.fit_transform(selected_df_main)"
      ],
      "metadata": {
        "id": "OyMjn5aMolq6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data_main"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFOiaB-adsi-",
        "outputId": "cd014f1e-1f0e-4a9d-df10-6dbf729dcb85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.41176514, -1.22943483],\n",
              "       [-1.41176514, -1.22943483],\n",
              "       [-1.41176514, -1.22943483],\n",
              "       ...,\n",
              "       [ 1.43045108,  0.4442689 ],\n",
              "       [ 1.43045108,  0.4442689 ],\n",
              "       [ 1.43045108,  0.4442689 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the function to get the wcss values to plot for elbow plot\n",
        "def elbow_method(X_scaled):\n",
        "    # Elbow method to find the optimal number of clusters\n",
        "    wcss = []\n",
        "    for i in range(1, 11):\n",
        "        kmodes = KModes(n_clusters=i, init='Cao', max_iter=300, n_init=10, random_state=42)\n",
        "        kmodes.fit(X_scaled)\n",
        "        wcss.append(kmodes.cost_)\n",
        "    return wcss"
      ],
      "metadata": {
        "id": "rubPSCooi3sw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to make the elbow plot from values obtained from wcss\n",
        "def plot_elbow(wcss,point=0):\n",
        "    # Plot the results\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(range(1, 11), wcss, marker='o', linestyle='-', color='#44AA99', markersize=8)\n",
        "    plt.title('Elbow Method for Optimal Number of Clusters', fontsize=15, fontweight='bold')\n",
        "    plt.xlabel('Number of Clusters', fontsize=12)\n",
        "    plt.ylabel('WCSS', fontsize=12)\n",
        "    plt.xticks(range(1, 11), fontsize=10)\n",
        "    plt.yticks(fontsize=10)\n",
        "    plt.grid(True, linestyle='--', linewidth=0.7)\n",
        "    if point != 0:\n",
        "        plt.axvline(x=point, color='r', linestyle='--')  # Optional: Highlights the elbow point if known\n",
        "\n",
        "    plt.tight_layout()"
      ],
      "metadata": {
        "id": "oSg05wgMioUa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making the simple recommendations from the model for records based on certain index\n",
        "def recommend_items(item_index, data, cluster_col, n_recommendations=5):\n",
        "    cluster_label = data.loc[item_index, cluster_col]\n",
        "    cluster_data = data[data[cluster_col] == cluster_label]\n",
        "    recommended_items = cluster_data.sample(n=n_recommendations).index\n",
        "    return recommended_items"
      ],
      "metadata": {
        "id": "xNrDtgGMq1xA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XCreating a function to make the scatter plot to visualize the cluster\n",
        "def scatter_clusters(y_kmodes,X_scaled,X,val):\n",
        "    # Plot the clusters\n",
        "    plt.figure(figsize=(20, 10))\n",
        "    plt.clf()\n",
        "\n",
        "    # Define colors for the clusters\n",
        "    colors = ['y', 'orange', 'b', 'c'] if val == 4 else ['y', 'orange', 'b', 'c','g']\n",
        "\n",
        "    for i in range(val):\n",
        "        plt.scatter(X_scaled[y_kmodes == i, 0], X_scaled[y_kmodes == i, 1],\n",
        "                    s=100, c=colors[i], label=f'Cluster {i+1}')\n",
        "\n",
        "    # Plotting the centroids\n",
        "    plt.scatter(kmodes_main_model.cluster_centroids_[:, 0], kmodes_main_model.cluster_centroids_[:, 1],\n",
        "                s=200, c='red', label='Centroids',marker='x')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    plt.title('Clusters of Customers', fontsize=15, fontweight='bold')\n",
        "    plt.xlabel(f'{X.columns[0]}', fontsize=12)\n",
        "    plt.ylabel(f'{X.columns[1]}', fontsize=12)\n",
        "    plt.legend()\n",
        "    plt.grid(True, linestyle='--', linewidth=0.7)\n",
        "    plt.tight_layout()\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "CC9D2THSuvs1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.spatial import Voronoi, voronoi_plot_2d # Importing voronoi functions from scipy to partationize clusters\n",
        "\n",
        "# Function to plot using voronoi\n",
        "def plot_kmeans_voronoi(scaled_data, clusters, cluster_centers, feature_names=None):\n",
        "  plt.figure(figsize=(12, 8))\n",
        "  sns.scatterplot(x=scaled_data[:, 0], y=scaled_data[:, 1], hue=clusters, palette='viridis', legend='full')\n",
        "  centers = kmodes_main_model.cluster_centroids_\n",
        "  plt.scatter(centers[:, 0], centers[:, 1], s = 300)\n",
        "  # Plot for seperating line\n",
        "  vor = Voronoi(centers)\n",
        "  voronoi_plot_2d(vor, ax = plt.gca(), show_vertices=False, line_colors = 'k', line_width=1)\n",
        "  plt.title('K-Means Clusters')\n",
        "  plt.xlabel('Feature 1')\n",
        "  plt.ylabel('Feature 2')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "-TeTGluGtUJb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the WCSS For the Elbow Method for test columns\n",
        "wcss = elbow_method(scaled_data_main)\n",
        "print(wcss)\n",
        "# Plot the Result Of the Elbow Method for test columns\n",
        "plot_elbow(wcss,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "8DMra8iPirHS",
        "outputId": "8831aad8-3335-4453-84c8-cd2e0c421ad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4080.0, 3267.0, 2971.0, 2598.0, 2413.0, 2223.0, 2152.0, 2074.0, 1986.0, 1927.0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Kmeans model for the main model\n",
        "optimal_cluster_no = 3\n",
        "kmodes_main_model = KModes(n_clusters = optimal_cluster_no, init='Cao', max_iter=300, n_init=10, random_state = 7777)\n",
        "df['Cluster_main'] = kmodes_main_model.fit_predict(scaled_data_main)\n",
        "\n",
        "print('Cluster Assignments:')\n",
        "print(df['Cluster_main'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c--v1qWimO6N",
        "outputId": "0f83d52a-a078-4060-eac2-20d71ceb0e99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster Assignments:\n",
            "Cluster_main\n",
            "0    1882\n",
            "1     731\n",
            "2     296\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-1503f03287a1>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Cluster_main'] = kmodes_main_model.fit_predict(scaled_data_main)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making the recommendation for index 2 based values\n",
        "item_index = 2\n",
        "recommendations = recommend_items(item_index, df, 'Cluster_main', n_recommendations=5)\n",
        "print(f\"Recommendations for item {item_index}: {recommendations}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJTcjS8dmky5",
        "outputId": "2d375f95-3a53-4f5c-ea4f-891c748dfd04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for item 2: Index([522, 430, 213, 1608, 573], dtype='int64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot for the main selected columns implemented clusters\n",
        "scatter_clusters(df['Cluster_main'],scaled_data_main,selected_df_main,optimal_cluster_no)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "0zr-I-Zdmy76",
        "outputId": "030ccd78-37b1-4bbc-ec77-c58fd0e28204"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Voronoi plot for the obtained model with 4 different features\n",
        "plot_kmeans_voronoi(scaled_data_main, df['Cluster_main'].values,kmodes_main_model.cluster_centroids_, ['BodyPart', 'Equipment'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "DJ6Jorl-m5a7",
        "outputId": "1c51ef12-bb34-4996-b873-010433ddfa48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting the encoded df\n",
        "df.to_csv('encoded_csv.csv', index=False)"
      ],
      "metadata": {
        "id": "-lobHzE4s-Hs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Prepare the data for KNN\n",
        "# X = df.drop(columns=['Cluster_main'])\n",
        "X = df[['BodyPart', 'Equipment']]\n",
        "y = df['Cluster_main']\n",
        "\n",
        "# Split the data 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=7777)\n",
        "\n",
        "# Fit the KNN model\n",
        "knn = KNeighborsClassifier(n_neighbors=9)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# Predict the cluster labels\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "# Compute the accuracy of the KNN model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'KNN Accuracy: {accuracy}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mD2IMnz_kfju",
        "outputId": "0a5c3171-bae3-453d-ca5c-9942e350e885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Accuracy: 0.9862542955326461\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def recommend_items(user_features, df, top_n=10):\n",
        "    # Create DataFrame for user features\n",
        "    user_df = pd.DataFrame([user_features])\n",
        "\n",
        "    # Calculate similarity between user_features and all items\n",
        "    item_features = df[['BodyPart', 'Equipment']]\n",
        "    user_feature_array = user_df[['BodyPart', 'Equipment']].values\n",
        "    item_feature_array = item_features.values\n",
        "\n",
        "    # Check if item_feature_array has elements\n",
        "    if item_feature_array.size == 0:\n",
        "        raise ValueError(\"Item feature array is empty. Check the input data.\")\n",
        "\n",
        "    similarities = cosine_similarity(user_feature_array, item_feature_array)\n",
        "\n",
        "    # Check if similarities result in empty array\n",
        "    if similarities.size == 0:\n",
        "        raise ValueError(\"Similarities calculation returned an empty result. Check the input data.\")\n",
        "\n",
        "    # Find the most similar item\n",
        "    closest_item_index = similarities[0].argmax()\n",
        "    closest_item = df.iloc[closest_item_index]\n",
        "\n",
        "    user_cluster = closest_item['Cluster_main']\n",
        "\n",
        "    # Filter items by cluster\n",
        "    cluster_items = df[df['Cluster_main'] == user_cluster]\n",
        "\n",
        "    if cluster_items.empty:\n",
        "        raise ValueError(f\"No items found in the cluster {user_cluster}.\")\n",
        "\n",
        "    # Compute similarity between the user features and the items in the same cluster\n",
        "    item_features = cluster_items[['BodyPart', 'Equipment']]\n",
        "    item_feature_array = item_features.values\n",
        "\n",
        "    # Check if item_feature_array has elements\n",
        "    if item_feature_array.size == 0:\n",
        "        raise ValueError(\"Cluster item feature array is empty. Check the input data.\")\n",
        "\n",
        "    similarities = cosine_similarity(user_feature_array, item_feature_array)\n",
        "\n",
        "    if similarities.size == 0:\n",
        "        raise ValueError(\"Similarities calculation for cluster items returned an empty result.\")\n",
        "\n",
        "    # Get top_n most similar items\n",
        "    similar_indices = similarities[0].argsort()[-top_n:][::-1]\n",
        "    recommended_items = cluster_items.iloc[similar_indices]\n",
        "\n",
        "    return recommended_items.index.tolist()"
      ],
      "metadata": {
        "id": "LRfEmLmXqaoN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def recommend_items_hybrid(user_features, df, top_n):\n",
        "\n",
        "    user_df = pd.DataFrame([user_features])\n",
        "\n",
        "    # One-Hot Encoding for categorical features\n",
        "    item_features = df[['BodyPart', 'Equipment']]\n",
        "    user_feature_array = user_df[['BodyPart', 'Equipment']].values\n",
        "    item_feature_array = item_features.values\n",
        "\n",
        "    # Calculate similarity between user_features and all items\n",
        "    similarities = cosine_similarity(user_feature_array, item_features)\n",
        "\n",
        "    # Check for empty similarities\n",
        "    if similarities.size == 0:\n",
        "        raise ValueError(\"Similarities calculation returned an empty result. Check the input data.\")\n",
        "\n",
        "    # Find the most similar item\n",
        "    closest_item_index = similarities[0].argmax()\n",
        "    closest_item = df.iloc[closest_item_index]\n",
        "    user_cluster = closest_item['Cluster_main']\n",
        "\n",
        "    # Filter items by cluster\n",
        "    cluster_items = df[df['Cluster_main'] == user_cluster]\n",
        "    if cluster_items.empty:\n",
        "        raise ValueError(f\"No items found in the cluster {user_cluster}.\")\n",
        "\n",
        "    # Compute similarity between the user features and the items in the same cluster\n",
        "    item_features_cluster = cluster_items[['BodyPart', 'Equipment']]\n",
        "\n",
        "    similarities = cosine_similarity(user_feature_array, item_features_cluster)\n",
        "\n",
        "    if similarities.size == 0:\n",
        "        raise ValueError(\"Similarities calculation for cluster items returned an empty result.\")\n",
        "\n",
        "    # Get top_n most similar items\n",
        "    similar_indices = similarities[0].argsort()[-top_n:][::-1]\n",
        "    recommended_items = cluster_items.iloc[similar_indices]\n",
        "\n",
        "    return recommended_items.index.tolist()"
      ],
      "metadata": {
        "id": "WUN-GyAqXHO8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_encoded = df[['BodyPart', 'Equipment', 'Cluster_main']]"
      ],
      "metadata": {
        "id": "Z4En8pfmukAy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aqkeg4C4x1nR",
        "outputId": "2885d29a-8802-445c-8d80-10315a3c01d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Title  \\\n",
              "0                             Partner plank band row   \n",
              "1                       Banded crunch isometric hold   \n",
              "2                              FYR Banded Plank Jack   \n",
              "3                                      Banded crunch   \n",
              "4                                             Crunch   \n",
              "...                                              ...   \n",
              "2913                            EZ-bar skullcrusher-   \n",
              "2914  Lying Close-Grip Barbell Triceps Press To Chin   \n",
              "2915          EZ-Bar Skullcrusher - Gethin Variation   \n",
              "2916                                TBS Skullcrusher   \n",
              "2917                     30 Arms EZ-Bar Skullcrusher   \n",
              "\n",
              "                                                   Desc      Type  BodyPart  \\\n",
              "0     The partner plank band row is an abdominal exe...  Strength         0   \n",
              "1     The banded crunch isometric hold is an exercis...  Strength         0   \n",
              "2     The banded plank jack is a variation on the pl...  Strength         0   \n",
              "3     The banded crunch is an exercise targeting the...  Strength         0   \n",
              "4     The crunch is a popular core exercise targetin...  Strength         0   \n",
              "...                                                 ...       ...       ...   \n",
              "2913  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2914                            Description Unavailable  Strength        16   \n",
              "2915  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2916  The EZ-bar skullcrusher is a popular exercise ...  Strength        16   \n",
              "2917                            Description Unavailable  Strength        16   \n",
              "\n",
              "      Equipment         Level     RatingDesc  Cluster_main  \n",
              "0             0  Intermediate      No Rating             0  \n",
              "1             0  Intermediate    Unavailable             0  \n",
              "2             0  Intermediate    Unavailable             0  \n",
              "3             0  Intermediate    Unavailable             0  \n",
              "4             0  Intermediate    Unavailable             0  \n",
              "...         ...           ...            ...           ...  \n",
              "2913          6  Intermediate  Above Average             0  \n",
              "2914          6      Beginner  Above Average             0  \n",
              "2915          6  Intermediate    Unavailable             0  \n",
              "2916          6  Intermediate    Unavailable             0  \n",
              "2917          6  Intermediate    Unavailable             0  \n",
              "\n",
              "[2909 rows x 8 columns]"
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              "  <div id=\"df-05d39ca3-8ad5-4e43-973e-3aa6e339382b\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Desc</th>\n",
              "      <th>Type</th>\n",
              "      <th>BodyPart</th>\n",
              "      <th>Equipment</th>\n",
              "      <th>Level</th>\n",
              "      <th>RatingDesc</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Partner plank band row</td>\n",
              "      <td>The partner plank band row is an abdominal exe...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>No Rating</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Banded crunch isometric hold</td>\n",
              "      <td>The banded crunch isometric hold is an exercis...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FYR Banded Plank Jack</td>\n",
              "      <td>The banded plank jack is a variation on the pl...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Banded crunch</td>\n",
              "      <td>The banded crunch is an exercise targeting the...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crunch</td>\n",
              "      <td>The crunch is a popular core exercise targetin...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2913</th>\n",
              "      <td>EZ-bar skullcrusher-</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2914</th>\n",
              "      <td>Lying Close-Grip Barbell Triceps Press To Chin</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Beginner</td>\n",
              "      <td>Above Average</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2915</th>\n",
              "      <td>EZ-Bar Skullcrusher - Gethin Variation</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2916</th>\n",
              "      <td>TBS Skullcrusher</td>\n",
              "      <td>The EZ-bar skullcrusher is a popular exercise ...</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2917</th>\n",
              "      <td>30 Arms EZ-Bar Skullcrusher</td>\n",
              "      <td>Description Unavailable</td>\n",
              "      <td>Strength</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Unavailable</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2909 rows × 8 columns</p>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2909,\n  \"fields\": [\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2909,\n        \"samples\": [\n          \"Peroneals Stretch\",\n          \"FYR2 Glute Bridge Dumbbell Floor Press\",\n          \"Calf Raise On A Dumbbell\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Desc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1051,\n        \"samples\": [\n          \"The leg press calf raise is a variation of the standing calf raise performed in a leg press machine. This allows for heavy loading, but also shoulder comfort from the pads of the leg press. Calf raise variations are often performed for relatively high reps, such as 10-20 reps per set or more.\",\n          \"The barbell front squat is a compound movement that emphasizes building the lower-body muscle groups. With the barbell racked in front of the body on the anterior delts, the front squat emphasizes the quads and glutes, but also upper back and core strength to remain upright. Front squats can be trained as an alternative to back squats, as an accessory movement for either squats or deadlifts, or for strength and muscle on their own. Many lifters and athletes prefer them to back squats, although they can be uncomfortable and difficult to learn at first.\",\n          \"The kettlebell thruster is a popular kettlebell movement that combines a kettlebell front squat with a kettlebell press, while holding the bell in a goblet or bottoms-up grip. It is preceded by a clean at the start of each set, and you can perform a clean between each rep if you choose. It can be trained in traditional strength or muscle-building rep ranges, in circuit or fat-loss training, or as part of a larger kettlebell combination or complex.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Strength\",\n          \"Plyometrics\",\n          \"Strongman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BodyPart\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 16,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Equipment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 12,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          8,\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Level\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intermediate\",\n          \"Beginner\",\n          \"Expert\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatingDesc\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Unavailable\",\n          \"Below Average\",\n          \"Above Average\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster_main\",\n      \"properties\": {\n        \"dtype\": \"uint16\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attributes_values = {'BodyPart':16, 'Equipment': 6}\n",
        "\n",
        "recommended_items = recommend_items(attributes_values, df, top_n=10)\n",
        "print(\"Recommended items:\\n\", recommended_items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-B-MSKrqxh6",
        "outputId": "c4275e92-f29b-4be3-e332-67139e05d2cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommended items:\n",
            " [2917, 2912, 1346, 1347, 2911, 2916, 2913, 2914, 2915, 1018]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recommended_items = recommend_items_hybrid(attributes_values, df, top_n=12)\n",
        "print(\"Recommended items:\\n\", recommended_items)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkoMJG3eXPGr",
        "outputId": "fc2307ac-83d4-4f64-cd06-72f1798b6660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommended items:\n",
            " [2917, 2912, 1346, 1347, 2911, 2916, 2913, 2914, 2915, 1018, 1026, 1025]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the non-encoded dataset\n",
        "res_df = pd.read_csv('/content/cleaned_data.csv')"
      ],
      "metadata": {
        "id": "uBXB_0IyqRij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading the values from each index and printing the details of each recommendations made\n",
        "recommended_items_main = res_df.iloc[recommended_items].to_dict('records')\n",
        "recommended_items_main"
      ],
      "metadata": {
        "id": "5gBQz1qznr4p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5efc3d04-ba10-40b0-b8e5-5a2c25d4af17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Title': 'Seated One-Arm Dumbbell Palms-Down Wrist Curl',\n",
              "  'Desc': 'Description Unavailable',\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Above Average'},\n",
              " {'Title': 'Side plank with dumbbell external rotation',\n",
              "  'Desc': \"The side plank with dumbbell external rotation is a movement that combines an isometric side plank hold with a dumbbell rotation to target both the core and the rotator cuff muscles. You probably won't be able to use much weight without disrupting your balance, but even at light weight, this is a movement that can help address multiple muscle groups in a dynamic warm-up or as accessory work at the end of a workout.\",\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Intermediate',\n",
              "  'RatingDesc': 'Unavailable'},\n",
              " {'Title': 'Standing Olympic Plate Hand Squeeze',\n",
              "  'Desc': 'Description Unavailable',\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Other',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Above Average'},\n",
              " {'Title': \"Dumbbell farmer's walk\",\n",
              "  'Desc': \"The dumbbell farmer's walk is an exercise with roots in competitive strongman training, but which is also popular in CrossFit and functional training. It can be a challenging finisher to any workout, or work well in fat-loss circuit training or as an accessory movement to heavy lifts like the deadlift.\",\n",
              "  'Type': 'Strongman',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Other',\n",
              "  'Level': 'Intermediate',\n",
              "  'RatingDesc': 'Above Average'},\n",
              " {'Title': 'Rickshaw Carry',\n",
              "  'Desc': 'Description Unavailable',\n",
              "  'Type': 'Strongman',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Other',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Above Average'},\n",
              " {'Title': 'Wrist Roller',\n",
              "  'Desc': 'Description Unavailable',\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Other',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Above Average'},\n",
              " {'Title': 'Suitcase Dumbbell Carry',\n",
              "  'Desc': 'The dumbbell suitcase carry is similar to a dumbbell farmer’s carry, but only uses one dumbbell rather than two. This simple change provides a significant challenge to the abdominal muscles. It can be a challenging finisher to any workout, or work well in fat-loss circuit training or as an accessory movement to heavy lifts like the deadlift.',\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Intermediate',\n",
              "  'RatingDesc': 'No Rating'},\n",
              " {'Title': 'Dumbbell suitcase carry',\n",
              "  'Desc': \"The dumbbell suitcase carry is similar to a dumbbell farmer's carry, but only uses one dumbbell rather than two. This simple change provides a significant challenge to the abdominal muscles. It can be a challenging finisher to any workout, or work well in fat-loss circuit training or as an accessory movement to heavy lifts like the deadlift.\",\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Intermediate',\n",
              "  'RatingDesc': 'No Rating'},\n",
              " {'Title': \"Partner farmer's walk\",\n",
              "  'Desc': \"The partner farmer's walk is a protocol for performing farmer's carries with dumbbells or other weights. One partner walks for a predetermined distance or time, then passes the weight to the other partner who walks for the same distance or time.\",\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Average'},\n",
              " {'Title': 'Seated Dumbbell Palms-Up Wrist Curl',\n",
              "  'Desc': 'Description Unavailable',\n",
              "  'Type': 'Strength',\n",
              "  'BodyPart': 'Forearms',\n",
              "  'Equipment': 'Dumbbell',\n",
              "  'Level': 'Beginner',\n",
              "  'RatingDesc': 'Average'}]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting the model with pickle and integrating in the web app!"
      ],
      "metadata": {
        "id": "m-9xeDrqtF9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The End!**"
      ],
      "metadata": {
        "id": "F9HC-bI3NELV"
      }
    }
  ]
}