kNN_Code.ipynb

{
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      "execution_count": 1,
      "metadata": {
        "id": "gqpwLCCoNv2V"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "beeEHxcRTz39"
      },
      "outputs": [],
      "source": [
        "#Reading Dataset\n",
        "df = pd.read_csv('/content/adult.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education.num</th>\n",
              "      <th>marital.status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital.gain</th>\n",
              "      <th>capital.loss</th>\n",
              "      <th>hours.per.week</th>\n",
              "      <th>native.country</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>90</td>\n",
              "      <td>?</td>\n",
              "      <td>77053</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>?</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>82</td>\n",
              "      <td>Private</td>\n",
              "      <td>132870</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
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              "      <td>4356</td>\n",
              "      <td>18</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66</td>\n",
              "      <td>?</td>\n",
              "      <td>186061</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>?</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>4356</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>Private</td>\n",
              "      <td>140359</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>Private</td>\n",
              "      <td>264663</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>3900</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
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            "text/plain": [
              "   age workclass  fnlwgt  ... hours.per.week  native.country income\n",
              "0   90         ?   77053  ...             40   United-States  <=50K\n",
              "1   82   Private  132870  ...             18   United-States  <=50K\n",
              "2   66         ?  186061  ...             40   United-States  <=50K\n",
              "3   54   Private  140359  ...             40   United-States  <=50K\n",
              "4   41   Private  264663  ...             40   United-States  <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Showing the tope 5 obersrvation of the dataset\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eB8JkuuScvA",
        "outputId": "8f7a6188-0263-4bfe-8a0e-171bfa8afc8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education.num   32561 non-null  int64 \n",
            " 5   marital.status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   sex             32561 non-null  object\n",
            " 10  capital.gain    32561 non-null  int64 \n",
            " 11  capital.loss    32561 non-null  int64 \n",
            " 12  hours.per.week  32561 non-null  int64 \n",
            " 13  native.country  32561 non-null  object\n",
            " 14  income          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ]
        }
      ],
      "source": [
        "# show data information\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CW1MmHUSgZ5",
        "outputId": "797d26b4-4eb0-4993-80f0-fc1204d4541c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                int64\n",
              "workclass         object\n",
              "fnlwgt             int64\n",
              "education         object\n",
              "education.num      int64\n",
              "marital.status    object\n",
              "occupation        object\n",
              "relationship      object\n",
              "race              object\n",
              "sex               object\n",
              "capital.gain       int64\n",
              "capital.loss       int64\n",
              "hours.per.week     int64\n",
              "native.country    object\n",
              "income            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# show data type of all the attributes\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7H6vk89SiOe",
        "outputId": "1448e0a4-6fa0-4684-eec2-0f80a4319499"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#show the number of attributes and observations\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBZG4GTUSkO6",
        "outputId": "85ad87ca-e9b0-40da-e08b-ced1e214addd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Following columns contains int data values\n",
            "\n",
            "\n",
            "Attribute: age\n",
            "-------------------\n",
            "Attribute: fnlwgt\n",
            "-------------------\n",
            "Attribute: education.num\n",
            "-------------------\n",
            "Attribute: capital.gain\n",
            "-------------------\n",
            "Attribute: capital.loss\n",
            "-------------------\n",
            "Attribute: hours.per.week\n",
            "-------------------\n"
          ]
        }
      ],
      "source": [
        "print(\"Following columns contains int data values\\n\\n\")\n",
        "for col in df.columns:\n",
        "  if df[col].dtype == \"int64\":\n",
        "    print('Attribute:',col)\n",
        "    print('-------------------')\n",
        "    #print(df[col].value_counts(),\"\\n\\n\")\n",
        "    #print('-------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nadMTpZ4SsTp"
      },
      "outputs": [],
      "source": [
        "# convert \"?\" values to NaN for further processing\n",
        "for col in df.columns:\n",
        "  df[[col]] = df[[col]].replace('?',np.NaN)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBof9hE-Svti",
        "outputId": "6ee06fa2-332e-439f-fc00-453af283e443"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                  0\n",
              "workclass         1836\n",
              "fnlwgt               0\n",
              "education            0\n",
              "education.num        0\n",
              "marital.status       0\n",
              "occupation        1843\n",
              "relationship         0\n",
              "race                 0\n",
              "sex                  0\n",
              "capital.gain         0\n",
              "capital.loss         0\n",
              "hours.per.week       0\n",
              "native.country     583\n",
              "income               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#find null value for all attributes and sum total number\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "l0KYkA37S3WD"
      },
      "outputs": [],
      "source": [
        "#drop all rows that has null values\n",
        "df = df.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we can Apply Forward Fill mothod also to Replace 'NaN'\n",
        "df.fillna(method = 'ffill', inplace = True)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "ZOv9xyMN7nDv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Checking again\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ziTaBV8Z_SAD",
        "outputId": "8fae864c-7cad-4930-82b8-fc3d4e55d7cb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "workclass         0\n",
              "fnlwgt            0\n",
              "education         0\n",
              "education.num     0\n",
              "marital.status    0\n",
              "occupation        0\n",
              "relationship      0\n",
              "race              0\n",
              "sex               0\n",
              "capital.gain      0\n",
              "capital.loss      0\n",
              "hours.per.week    0\n",
              "native.country    0\n",
              "income            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#assing income column to y as target value\n",
        "x = df.drop(columns = 'income')\n",
        "y = df['income']\n",
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQKLjkI9rRXt",
        "outputId": "6c927030-474b-4a45-82cf-afefce2b62a5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<=50K    22654\n",
              ">50K      7508\n",
              "Name: income, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Performing Undersampling For balancing Data\n",
        "Undersampling can be defined as removing some observations of the majority class. This is done until the majority and minority class is balanced out."
      ],
      "metadata": {
        "id": "ipkWpRUnE48G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# class count\n",
        "income_count_0, income_count_1 = df['income'].value_counts()\n",
        "\n",
        "# Separate class\n",
        "income_0 = df[df['income'] == 0]\n",
        "income_1 = df[df['income'] == 1]\n",
        "# print the shape of the class\n",
        "print('income 0:', income_0.shape)\n",
        "print('income 1:', income_1.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oS9SEJowDv-4",
        "outputId": "c0205f03-c59f-4989-f77f-66f5f353d2b8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "income 0: (0, 15)\n",
            "income 1: (0, 15)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting Categorical Values In Numerical  \n",
        "y = y.replace({'<=50K': 0, '>50K': 1}) #1 Represents >50K & 0 represents <=50K\n",
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tM8yEP46rgM0",
        "outputId": "1e20a872-b925-4503-de24-7f40aad51a18"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    22654\n",
              "1     7508\n",
              "Name: income, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding features"
      ],
      "metadata": {
        "id": "Bej3qTz0Bum4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "JGdiXmAIsrx_"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.apply(le.fit_transform)\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 270
        },
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      "execution_count": 24,
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          document.querySelector('#df-beed9d8f-c047-4770-9df3-2c62d9f4a12b button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-beed9d8f-c047-4770-9df3-2c62d9f4a12b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   age  workclass  fnlwgt  ...  hours.per.week  native.country  income\n",
              "1   65          2    6096  ...              17              38       0\n",
              "3   37          2    6564  ...              39              38       0\n",
              "4   24          2   15770  ...              39              38       0\n",
              "5   17          2   13278  ...              44              38       0\n",
              "6   21          2    7296  ...              39              38       0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Observing Random 4 rows from dataset\n",
        "df.sample(5, random_state=999)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "mbtEN2cSsjMw",
        "outputId": "e9e2ad6b-80da-4e57-8de7-27c133ea4c10"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afd47646-a110-4332-bf38-839102a08ac1')\"\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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              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "          document.querySelector('#df-afd47646-a110-4332-bf38-839102a08ac1 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-afd47646-a110-4332-bf38-839102a08ac1');\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",
              "  "
            ],
            "text/plain": [
              "       age  workclass  fnlwgt  ...  hours.per.week  native.country  income\n",
              "16112   20          2   18761  ...              68              29       0\n",
              "8998    12          2   20166  ...              39              38       0\n",
              "29837   18          2   13015  ...              37              22       0\n",
              "27802    8          2   10045  ...              54              38       0\n",
              "21104   21          2   12636  ...              39              38       1\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "tpScxwo0yfgI",
        "outputId": "61a866e0-f62c-4620-8d67-a71a67d15810"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th>hours.per.week</th>\n",
              "      <th>native.country</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "      <td>30162.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>21.435482</td>\n",
              "      <td>2.199324</td>\n",
              "      <td>9825.221504</td>\n",
              "      <td>10.333764</td>\n",
              "      <td>9.121312</td>\n",
              "      <td>2.580134</td>\n",
              "      <td>5.959850</td>\n",
              "      <td>1.418341</td>\n",
              "      <td>3.678602</td>\n",
              "      <td>0.675685</td>\n",
              "      <td>6.552450</td>\n",
              "      <td>2.072641</td>\n",
              "      <td>39.871527</td>\n",
              "      <td>36.382567</td>\n",
              "      <td>0.248922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.125355</td>\n",
              "      <td>0.953925</td>\n",
              "      <td>5671.017927</td>\n",
              "      <td>3.812292</td>\n",
              "      <td>2.549995</td>\n",
              "      <td>1.498016</td>\n",
              "      <td>4.029566</td>\n",
              "      <td>1.601338</td>\n",
              "      <td>0.834709</td>\n",
              "      <td>0.468126</td>\n",
              "      <td>23.284819</td>\n",
              "      <td>10.028470</td>\n",
              "      <td>11.771826</td>\n",
              "      <td>6.105372</td>\n",
              "      <td>0.432396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5025.250000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9689.500000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14520.750000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>71.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>20262.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3863ccc-9462-4336-8941-fd4b31aab34e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-c3863ccc-9462-4336-8941-fd4b31aab34e 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-c3863ccc-9462-4336-8941-fd4b31aab34e');\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",
              "  "
            ],
            "text/plain": [
              "                age     workclass  ...  native.country        income\n",
              "count  30162.000000  30162.000000  ...    30162.000000  30162.000000\n",
              "mean      21.435482      2.199324  ...       36.382567      0.248922\n",
              "std       13.125355      0.953925  ...        6.105372      0.432396\n",
              "min        0.000000      0.000000  ...        0.000000      0.000000\n",
              "25%       11.000000      2.000000  ...       38.000000      0.000000\n",
              "50%       20.000000      2.000000  ...       38.000000      0.000000\n",
              "75%       30.000000      2.000000  ...       38.000000      0.000000\n",
              "max       71.000000      6.000000  ...       40.000000      1.000000\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Split for Train-Test Purpose\n"
      ],
      "metadata": {
        "id": "KINcG-InCBmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "evrokKI-v5zx"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying kNN"
      ],
      "metadata": {
        "id": "9MGSnJaSCNYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import knearest neighbors Classifier model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Create object of kNN Classifier\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "#Train the model using the training sets\n",
        "knn.fit(x_train, y_train)\n",
        "\n"
      ],
      "metadata": {
        "id": "tULi5igYwWpP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc6ba37b-843e-46dc-825e-2f0cbfcba370"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Make prediction"
      ],
      "metadata": {
        "id": "mkxi-wOvN3UH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Predict the response for test dataset\n",
        "y_pred = knn.predict(x_test)"
      ],
      "metadata": {
        "id": "xyqQn3GhN7Pa"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluating the predictions"
      ],
      "metadata": {
        "id": "xCuPOB5hOFxE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vM3kvIqlTMK_",
        "outputId": "7c30f2a4-5266-4856-8e2a-65b65dd0f3da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: \n",
            " 0.7582053265554205\n",
            "Confusion Matrix: \n",
            " [[5923  898]\n",
            " [1290  938]]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.87      0.84      6821\n",
            "           1       0.51      0.42      0.46      2228\n",
            "\n",
            "    accuracy                           0.76      9049\n",
            "   macro avg       0.67      0.64      0.65      9049\n",
            "weighted avg       0.74      0.76      0.75      9049\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(\"Accuracy: \\n\", accuracy_score(y_test, y_pred)) #Accuracy\n",
        "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, y_pred)) #Confusion Matrix\n",
        "print(\"Classification Report: \\n\", classification_report(y_test,y_pred)) "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking k values in a range of 1 to 40\n",
        "#Evaluating alternative K-values for better predictions\n",
        "error_rate = []\n",
        "\n",
        "for i in range(1,40):\n",
        "  knn = KNeighborsClassifier(n_neighbors = i)\n",
        "  knn.fit(x_train, y_train)\n",
        "  pred_i = knn.predict(x_test)\n",
        "  error_rate.append(np.mean(pred_i != y_test))\n"
      ],
      "metadata": {
        "id": "OFdH9a07ys2y"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Plotting Error rate\n"
      ],
      "metadata": {
        "id": "uL8mP9CBNWbp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure and plot error rate over k values\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.plot(range(1,40), error_rate, color='blue', linestyle='dashed', marker='o', markerfacecolor='red', markersize=10)\n",
        "plt.title('Error Rate vs. K-Values')\n",
        "plt.xlabel('K-Values')\n",
        "plt.ylabel('Error Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "wTopJw31NO1r",
        "outputId": "620489c3-7559-4479-8062-27a468867e63"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Error Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=15)\n",
        "\n",
        "\n",
        "knn.fit(x_train, y_train)\n",
        "\n",
        "y_pred = knn.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"When K Value is 15\\n\")\n",
        "print(\"Accuracy: \\n\",accuracy_score(y_test, y_pred),\"\\n\\n\")\n",
        "print(\"Confusion Matrix: \\n\", confusion_matrix(y_test, y_pred), \"\\n\\n\")\n",
        "print(\"Classification Report: \\n\", classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL1-9QLC1egM",
        "outputId": "dee8376f-9d2b-422b-b8d0-bb530eb6b002"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "When K Value is 15\n",
            "\n",
            "Accuracy: \n",
            " 0.7863852359376726 \n",
            "\n",
            "\n",
            "Confusion Matrix: \n",
            " [[6619  202]\n",
            " [1731  497]] \n",
            "\n",
            "\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.97      0.87      6821\n",
            "           1       0.71      0.22      0.34      2228\n",
            "\n",
            "    accuracy                           0.79      9049\n",
            "   macro avg       0.75      0.60      0.61      9049\n",
            "weighted avg       0.77      0.79      0.74      9049\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FXtZXt7F3xcY"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "kNN_Adult.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}