KNNfrequency_vectorizer.ipynb

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        "MUCHINA FADZAI ETHEL(M.TECH AIML)\n",
        "AP22122040013"
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        "The K-Nearest Neighbors (KNN) classifier is a simple yet effective algorithm for classification tasks. In the context of natural language processing (NLP), KNN can be used in combination with a frequency vectorizer to classify text documents.\n",
        "The frequency vectorizer works by creating a \"bag of words\" representation of the text data, where each word in the text is treated as a separate feature. The frequency vectorizer then counts the frequency of each word in the text and creates a vector of numbers that represent the frequency of each word in the text.\n",
        "\n",
        "KNN using frequency vectorizer\n",
        "steps\n",
        "\n",
        "1.importing the necessary packages\n",
        "2. load datasets\n",
        "3.Concatenate the datasets for better visualization and easy computation(check for missing values and replace with mean ,mode , median)\n",
        "4.Split the data into training and testing sets\n",
        "5.Convert the text data into frequency vectors\n",
        "6.Train the KNN classifier\n",
        "7.Make predictions on the test data\n",
        "8.Evaluate the accuracy of the model\n",
        "9.To plot the results, use a confusion matrix to visualize the performance of the KNN classifier on the test data.\n"
      ],
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      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "khfTM0pQLsva"
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      "execution_count": 1,
      "outputs": []
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      "source": [
        "nisha_df = pd.read_csv('/content/nisha_preprocessing.csv')\n",
        "kabita_df = pd.read_csv('/content/kabitakitchen.csv', encoding = 'latin1')\n",
        "kabita_pre_df = pd.read_csv('/content/kabita_preprocessing.csv')\n",
        "nishafin_df = pd.read_csv('/content/Nishafin.csv')"
      ],
      "metadata": {
        "id": "pIXChnBZIGb_"
      },
      "execution_count": 2,
      "outputs": []
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              "      <td>...</td>\n",
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              "      <th>4895</th>\n",
              "      <td>UgyGSYkiLHY3dWl4AZt4AaABAg</td>\n",
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              "      <td>3.666667</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4896</th>\n",
              "      <td>Ugzx3OSiIndEsUN54IB4AaABAg</td>\n",
              "      <td>ky hum condensed milk k jagha par amul powder ...</td>\n",
              "      <td>7</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3.750000</td>\n",
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              "      <th>4897</th>\n",
              "      <td>UgxRwq2MsR9csHLc7MJ4AaABAg</td>\n",
              "      <td>very yummy cake mamcondensed milk k jagah aur ...</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>79</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4898</th>\n",
              "      <td>UgxxO1vtL-g7PPRVXBp4AaABAg</td>\n",
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              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>56</td>\n",
              "      <td>4.600000</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4899</th>\n",
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              "      <td>3.928571</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>4900 rows × 10 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([nisha_df, kabita_df, kabita_pre_df, nishafin_df], axis=0).reset_index(drop=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "_R7KV9WOIx8W",
        "outputId": "b8df0f2a-5b9b-4fc3-e548-a312a7fe6747"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      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-fe2df3cd-a642-46dd-bc90-439e7ec86932 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-fe2df3cd-a642-46dd-bc90-439e7ec86932');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-VoBapWJF4p",
        "outputId": "fdecb97c-fe84-4b4a-829a-922a5e664b48"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                0\n",
              "commentText       2\n",
              "Labels            0\n",
              "stopwords      9800\n",
              "upper          9800\n",
              "hastags        9800\n",
              "word_count     9800\n",
              "char_count     9800\n",
              "avg_word       9802\n",
              "numerics       9800\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#\n",
        "# Box plot\n",
        "#\n",
        "sns.boxplot(df.stopwords)\n",
        "\n",
        "#\n",
        "# Distribution plot\n",
        "#\n",
        "sns.displot(df.stopwords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        },
        "id": "9AS_Iy5LJUxo",
        "outputId": "ecf4f31e-3de4-485f-903f-9dc733a7b933"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe9d5bc18e0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df.upper)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "chMXqKf4Eet7",
        "outputId": "0f28ef62-3788-46d1-8336-ce0ef71204f8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df['stopwords'] = df['stopwords'].fillna(df['stopwords'].median())\n",
        "\n",
        "#To insert the mean value of each column into its missing rows:\n",
        "\n",
        "df.fillna(df.median(numeric_only=True).round(1), inplace=True)\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "3QGVUEFNKesE",
        "outputId": "07ab5481-0c61-40ed-ba44-88b0ff68d808"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               id  \\\n",
              "0      Ugz7LQV0ZWpuNnjBaxJ4AaABAg   \n",
              "1      UgxvWJ_kJL2nePTkg2N4AaABAg   \n",
              "2      UgxxeUmiH9f8XOZZWr14AaABAg   \n",
              "3      Ugz-UPvwHMx4nSKVoCB4AaABAg   \n",
              "4      UgzfXhbesBABQzi4-m94AaABAg   \n",
              "...                           ...   \n",
              "19595  UgyGSYkiLHY3dWl4AZt4AaABAg   \n",
              "19596  Ugzx3OSiIndEsUN54IB4AaABAg   \n",
              "19597  UgxRwq2MsR9csHLc7MJ4AaABAg   \n",
              "19598  UgxxO1vtL-g7PPRVXBp4AaABAg   \n",
              "19599  UgyNWcXDvbZF1I7S8rh4AaABAg   \n",
              "\n",
              "                                             commentText  Labels  stopwords  \\\n",
              "0                                  love potato ke prathe       2        1.0   \n",
              "1      wwaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaww       3        0.0   \n",
              "2                                                   nice       3        0.0   \n",
              "3                                            wow amazing       3        0.0   \n",
              "4                                            thank madam       1        1.0   \n",
              "...                                                  ...     ...        ...   \n",
              "19595  Mam kya hum butter Ki bjaye desi ghee bhi daal...       7        1.0   \n",
              "19596  Ky hum condensed milk k jagha par amul powder ...       7        1.0   \n",
              "19597  Very yummy cake mam...condensed milk k jagah a...       5        1.0   \n",
              "19598  mad aapne powder sugar or condensed milk dono ...       7        1.0   \n",
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      "execution_count": 12,
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              "commentText    2\n",
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          "execution_count": 12
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        "\n",
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              "             Labels     stopwords         upper       hastags    word_count  \\\n",
              "count  19600.000000  19600.000000  19600.000000  19600.000000  19600.000000   \n",
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              "\n",
              "         char_count      avg_word  numerics  \n",
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              "25%       25.000000      4.400000       0.0  \n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a3a538-00b9-49b4-988a-a7e8f61154ee')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84a3a538-00b9-49b4-988a-a7e8f61154ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PmOik5rIFV3q",
        "outputId": "bdf3ebc4-bd62-435f-8587-87c2879c75d1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Labels  stopwords     upper   hastags  word_count  char_count  \\\n",
              "Labels      1.000000   0.085706  0.024036  0.006236    0.210392    0.200276   \n",
              "stopwords   0.085706   1.000000  0.327144  0.033824    0.480476    0.539262   \n",
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              "char_count  0.200276   0.539262  0.255595  0.040033    0.983369    1.000000   \n",
              "avg_word   -0.068400   0.097716 -0.045037  0.026645   -0.083463    0.019804   \n",
              "numerics         NaN        NaN       NaN       NaN         NaN         NaN   \n",
              "\n",
              "            avg_word  numerics  \n",
              "Labels     -0.068400       NaN  \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0deec22e-3362-40ea-be3a-662c2662669f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0deec22e-3362-40ea-be3a-662c2662669f 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-0deec22e-3362-40ea-be3a-662c2662669f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df['commentText'], df['Labels'], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "4j2wroPzQWFg"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfHz3uHxQmhO",
        "outputId": "9ebac7cb-30d5-4df5-cc0b-143b86e78318"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19600, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "vectorizer = CountVectorizer()\n",
        "X_train_vec = vectorizer.fit_transform(X_train)# fits the CountVectorizer to the training data X_train and transforms it into a matrix of token counts. The resulting matrix is assigned to the variable X_train_vec.\n",
        "\n",
        "X_test_vec = vectorizer.transform(X_test)##fits the vectorizer to the training data and returns a transformed matrix,\n",
        " # while the transform() method applies the vectorizer to new data using the same vocabulary that was learned from the training data."
      ],
      "metadata": {
        "id": "8jFPgly1QunS"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k = 1\n",
        "knn = KNeighborsClassifier(n_neighbors=k)\n",
        "knn.fit(X_train_vec, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "yqYDCddNRAK2",
        "outputId": "11a3df5e-e5af-4c07-f2e2-70194ddf67a5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ],
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test_vec)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTK6REM0RIkU",
        "outputId": "f6085918-84d1-46ac-a59f-69fa3b4e5de2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 7 2 ... 6 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-Gx9WSYRPsR",
        "outputId": "920179ce-4364-486c-937c-0ca633688f95"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8017857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_pred,y_test))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47ZsVTpaeC_f",
        "outputId": "ea300d0c-2732-4a05-e44c-c3e56fb0656a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.93      0.83      0.88       635\n",
            "           2       0.90      0.60      0.72       805\n",
            "           3       0.66      0.75      0.70       487\n",
            "           4       0.78      0.89      0.83       511\n",
            "           5       0.64      0.94      0.76       394\n",
            "           6       0.87      0.78      0.82       607\n",
            "           7       0.84      0.98      0.91       481\n",
            "\n",
            "    accuracy                           0.80      3920\n",
            "   macro avg       0.80      0.82      0.80      3920\n",
            "weighted avg       0.82      0.80      0.80      3920\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The precision of the model is highest for class 1 (93%) and lowest for class 5 (64%). This means that the model is best at correctly identifying class 1 and worst at correctly identifying class 5.\n",
        "The recall of the model is highest for class 7 (98%) and lowest for class 2 (60%). This means that the model is best at correctly identifying class 7 and worst at correctly identifying class 2.\n",
        "The F1 score of the model is highest for class 7 (91%) and lowest for class 2 (72%). This metric takes into account both precision and recall, so it provides a more balanced view of the model's performance.\n",
        "The accuracy of the model is 80%, which means that it correctly predicts the class label for 81% of the samples in the test set.\n"
      ],
      "metadata": {
        "id": "fulBJGTelLkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create confusion matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "print(conf_mat)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "sns.heatmap(conf_mat, annot=True, cmap=\"Blues\", fmt='g',\n",
        "            xticklabels=['Gratitude', 'About the recipe', 'About the video', 'Praising', 'Hybrid', 'Undefined', 'Suggestions and queries'],\n",
        "            yticklabels=['Gratitude', 'About the recipe', 'About the video', 'Praising', 'Hybrid', 'Undefined', 'Suggestions and queries'])\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "id": "-YXOFiTtRe6x",
        "outputId": "35eef8b9-e08e-441f-f1c0-611eda6eb261"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[528   7   3   3  11  14   0]\n",
            " [  3 481  25  10   5  10   0]\n",
            " [  1 174 363   8   0   4   0]\n",
            " [  7  37  47 454   6  32   1]\n",
            " [ 83  70  20  23 371  13   0]\n",
            " [  6  27  23   8   1 474   8]\n",
            " [  7   9   6   5   0  60 472]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "N3nQBoKn9wtO"
      },
      "execution_count": 24,
      "outputs": []
    }
  ]
}