SalaryEstimationusingK_nearestNeighbour.ipynb

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      "source": [
        "***Importing Libraries***"
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        "import pandas as pd #useful for loading the dataset\n",
        "import numpy as np #to perform array"
      ]
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      "cell_type": "markdown",
      "source": [
        "***Choosing Dataset from Local Directory***"
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          "text": [
            "Saving sal.csv to sal.csv\n"
          ]
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    {
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      "source": [
        "***Loading Dataset***"
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    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('sal.csv')"
      ],
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        "id": "4E0qUgq2ObTI"
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      "execution_count": 6,
      "outputs": []
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      "execution_count": 7,
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              "    age  education.num  capital.gain  hours.per.week income\n",
              "0    90              8             0              40  <=50K\n",
              "1    82              5             0              18  <=50K\n",
              "2    66              6             0              40   >50K\n",
              "3    54              9             0              40  <=50K\n",
              "4    41              3             0              40  <=50K\n",
              "..  ...            ...           ...             ...    ...\n",
              "94   32              6             0              40  <=50K\n",
              "95   43              6             0              43  <=50K\n",
              "96   32             12             0              40  <=50K\n",
              "97   54              6             0              65  <=50K\n",
              "98   27             12             0              40  <=50K\n",
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              "[99 rows x 5 columns]"
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "***Mapping Salary Data to Binary Value***"
      ],
      "metadata": {
        "id": "_uLQbFsyifTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "income_set = set(dataset['income'])\n",
        "dataset['income'] = dataset['income'].map({'<=50K':0, '>50K':1})\n",
        "print(dataset.head(20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5r7R2gsil0S",
        "outputId": "db9eddd4-901d-4c91-9ed8-025a023c8213"
      },
      "execution_count": 8,
      "outputs": [
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          "name": "stdout",
          "text": [
            "    age  education.num  capital.gain  hours.per.week  income\n",
            "0    90              8             0              40       0\n",
            "1    82              5             0              18       0\n",
            "2    66              6             0              40       1\n",
            "3    54              9             0              40       0\n",
            "4    41              3             0              40       0\n",
            "5    34              2             0              45       1\n",
            "6    38              8             0              20       0\n",
            "7    74              1             0              40       0\n",
            "8    68              9             0              60       0\n",
            "9    41             12             0              40       1\n",
            "10   45             15             0              35       0\n",
            "11   38              1             0              45       0\n",
            "12   52              3             0              20       0\n",
            "13   32              5             0              40       0\n",
            "14   51              5             0              55       0\n",
            "15   46              9             0              40       0\n",
            "16   45              9             0              76       0\n",
            "17   57              2             0              40       1\n",
            "18   22              5             0              50       1\n",
            "19   23             11             0              40       1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Segregate dataset into X(input/independent variable) and Y(output/dependent variable)***"
      ],
      "metadata": {
        "id": "xvI2b7JTjHqb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.iloc[:, :-1].values\n",
        "X"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giw40DiQjXz6",
        "outputId": "ee568d03-a608-4d14-a9e4-6b99b470a6a6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[90,  8,  0, 40],\n",
              "       [82,  5,  0, 18],\n",
              "       [66,  6,  0, 40],\n",
              "       [54,  9,  0, 40],\n",
              "       [41,  3,  0, 40],\n",
              "       [34,  2,  0, 45],\n",
              "       [38,  8,  0, 20],\n",
              "       [74,  1,  0, 40],\n",
              "       [68,  9,  0, 60],\n",
              "       [41, 12,  0, 40],\n",
              "       [45, 15,  0, 35],\n",
              "       [38,  1,  0, 45],\n",
              "       [52,  3,  0, 20],\n",
              "       [32,  5,  0, 40],\n",
              "       [51,  5,  0, 55],\n",
              "       [46,  9,  0, 40],\n",
              "       [45,  9,  0, 76],\n",
              "       [57,  2,  0, 40],\n",
              "       [22,  5,  0, 50],\n",
              "       [23, 11,  0, 40],\n",
              "       [25,  6,  0, 34],\n",
              "       [32, 15,  0, 40],\n",
              "       [26, 13,  0, 40],\n",
              "       [28, 13,  0, 54],\n",
              "       [32,  6,  0, 21],\n",
              "       [32,  6,  0, 40],\n",
              "       [38, 15,  0, 44],\n",
              "       [26,  6,  0, 40],\n",
              "       [23, 15,  0, 23],\n",
              "       [32,  6,  0, 40],\n",
              "       [32,  6,  0, 40],\n",
              "       [26, 14,  0, 87],\n",
              "       [32,  6,  0, 40],\n",
              "       [32, 15,  0, 40],\n",
              "       [27,  6,  0, 43],\n",
              "       [32, 15,  0, 40],\n",
              "       [29,  6,  0, 40],\n",
              "       [32,  6,  0, 26],\n",
              "       [32, 15,  0, 40],\n",
              "       [32,  6,  0, 40],\n",
              "       [32, 11,  0, 56],\n",
              "       [32,  6,  0, 40],\n",
              "       [65,  6,  0, 40],\n",
              "       [32, 16,  0, 40],\n",
              "       [34,  6,  0, 40],\n",
              "       [32,  6,  0, 40],\n",
              "       [54, 17,  0, 43],\n",
              "       [32,  6,  0, 40],\n",
              "       [32, 16,  0, 40],\n",
              "       [23,  6,  0, 65],\n",
              "       [32,  6,  0, 40],\n",
              "       [54, 17,  0, 40],\n",
              "       [32,  6,  0, 21],\n",
              "       [37,  6,  0, 40],\n",
              "       [32, 15,  0, 40],\n",
              "       [49,  6,  0, 78],\n",
              "       [21,  6,  0, 40],\n",
              "       [32,  1,  0, 40],\n",
              "       [56,  5,  0, 54],\n",
              "       [32,  1,  0, 40],\n",
              "       [32, 14,  0, 40],\n",
              "       [43,  6,  0, 32],\n",
              "       [32,  6,  0, 40],\n",
              "       [56,  6,  0, 54],\n",
              "       [32, 15,  0, 40],\n",
              "       [28,  6,  0, 32],\n",
              "       [38, 16,  0, 40],\n",
              "       [32,  6,  0, 65],\n",
              "       [29,  6,  0, 40],\n",
              "       [32, 15,  0, 40],\n",
              "       [54,  6,  0, 32],\n",
              "       [38,  6,  0, 40],\n",
              "       [45,  6,  0, 40],\n",
              "       [32, 16,  0, 34],\n",
              "       [27,  6,  0, 40],\n",
              "       [26, 17,  0, 27],\n",
              "       [32,  6,  0, 40],\n",
              "       [49,  6,  0, 40],\n",
              "       [27, 17,  0, 58],\n",
              "       [39,  6,  0, 40],\n",
              "       [43,  1,  0, 24],\n",
              "       [32,  5,  0, 40],\n",
              "       [26, 15,  0, 40],\n",
              "       [36,  6,  0, 39],\n",
              "       [32,  6,  0, 40],\n",
              "       [32, 61,  0, 29],\n",
              "       [43,  1,  0, 40],\n",
              "       [32,  6,  0, 23],\n",
              "       [23, 61,  0, 40],\n",
              "       [32,  6,  0, 45],\n",
              "       [45,  6,  0, 40],\n",
              "       [39, 63,  0, 32],\n",
              "       [32,  6,  0, 40],\n",
              "       [24, 12,  0, 43],\n",
              "       [32,  6,  0, 40],\n",
              "       [43,  6,  0, 43],\n",
              "       [32, 12,  0, 40],\n",
              "       [54,  6,  0, 65],\n",
              "       [27, 12,  0, 40]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = dataset.iloc[:, -1].values\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiIJ5Brqjh8v",
        "outputId": "68bc52a2-3129-492a-c6dc-0f054d73d7fd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Splitting Dataset into Train and Test***"
      ],
      "metadata": {
        "id": "TVwSkuvZjrkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2Ko6tEHjya3",
        "outputId": "086c39d5-cf95-420c-ff17-1ee14a5596f5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.21.6)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.7.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Scikit Learn\n",
        "from sklearn.model_selection import train_test_split #sklearn - split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\n"
      ],
      "metadata": {
        "id": "OYsidNySj5fq"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Feature Scaling***"
      ],
      "metadata": {
        "id": "yv8kR8mekZx6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcBsP8-XkeUa",
        "outputId": "4eec4b26-6406-4111-d14e-e98a03f4396d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.39726507,  0.68988101,  0.        , -0.05075766],\n",
              "       [ 0.07184581, -0.14191035,  0.        , -1.92879113],\n",
              "       [-0.7881908 ,  0.27398533,  0.        , -0.05075766],\n",
              "       [-0.86637594,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507,  0.48193317,  0.        , -0.05075766],\n",
              "       [ 0.46277154, -0.86972779,  0.        , -1.55318444],\n",
              "       [-0.39726507, -0.34985819,  0.        ,  0.41875071],\n",
              "       [-0.6318205 , -0.34985819,  0.        , -0.05075766],\n",
              "       [ 1.32280815,  0.79385493,  0.        , -0.05075766],\n",
              "       [-0.86637594, -0.34985819,  0.        , -0.05075766],\n",
              "       [-1.17911653, -0.45383211,  0.        ,  0.88825907],\n",
              "       [-1.25730167, -0.34985819,  0.        , -0.05075766],\n",
              "       [ 1.47917844, -0.34985819,  0.        ,  1.26386577],\n",
              "       [-0.7881908 , -0.34985819,  0.        , -0.05075766],\n",
              "       [ 3.51199223, -0.45383211,  0.        , -2.11659448],\n",
              "       [ 0.46277154, -0.34985819,  0.        , -0.80197105],\n",
              "       [ 2.18284475, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [ 0.30640125, -0.66177995,  0.        , -0.05075766],\n",
              "       [ 0.69732698, -0.03793643,  0.        , -0.05075766],\n",
              "       [ 1.55736358, -0.76575387,  0.        , -0.05075766],\n",
              "       [-0.39726507,  0.17001141,  0.        ,  1.45166911],\n",
              "       [-0.39726507,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.24089477, -0.76575387,  0.        ,  0.41875071],\n",
              "       [ 0.61914183, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.86972779,  0.        , -0.05075766],\n",
              "       [ 4.1374734 , -0.14191035,  0.        , -0.05075766],\n",
              "       [-0.7881908 , -0.34985819,  0.        ,  0.23094736],\n",
              "       [-1.10093138,  0.58590709,  0.        , -1.64708611],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [ 0.07184581, -0.86972779,  0.        ,  0.41875071],\n",
              "       [-0.39726507,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.71000565,  0.37795925,  0.        ,  1.26386577],\n",
              "       [-0.39726507, -0.34985819,  0.        , -1.83488946],\n",
              "       [ 0.61914183,  0.58590709,  0.        , -0.52026603],\n",
              "       [-0.86637594,  0.48193317,  0.        ,  4.36262099],\n",
              "       [ 0.07184581,  0.68988101,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.86972779,  0.        , -0.05075766],\n",
              "       [ 0.15003096, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507,  5.36870741,  0.        , -1.08367607],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [ 1.08825271, -0.45383211,  0.        ,  1.35776744],\n",
              "       [-1.10093138,  5.36870741,  0.        , -0.05075766],\n",
              "       [-1.10093138,  0.17001141,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-1.10093138, -0.34985819,  0.        ,  2.29678417],\n",
              "       [-0.39726507,  0.27398533,  0.        , -0.05075766],\n",
              "       [ 1.32280815, -0.34985819,  0.        ,  2.29678417],\n",
              "       [-0.39726507,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.94456109, -0.34985819,  0.        , -0.6141677 ],\n",
              "       [-1.02274623,  0.27398533,  0.        ,  0.23094736],\n",
              "       [ 0.61914183, -0.34985819,  0.        , -0.05075766],\n",
              "       [ 0.93188242, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -1.36538109],\n",
              "       [-0.39726507, -0.45383211,  0.        , -0.05075766],\n",
              "       [ 1.32280815,  0.79385493,  0.        ,  0.23094736],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.71000565, -0.34985819,  0.        , -0.80197105],\n",
              "       [ 1.47917844, -0.45383211,  0.        ,  1.26386577],\n",
              "       [ 1.16643785, -0.66177995,  0.        , -1.92879113],\n",
              "       [ 1.32280815, -0.34985819,  0.        , -0.80197105],\n",
              "       [-0.39726507, -0.34985819,  0.        , -1.64708611],\n",
              "       [-0.6318205 , -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.39726507,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.08452448, -0.34985819,  0.        , -0.14465933],\n",
              "       [ 0.30640125,  0.27398533,  0.        , -0.05075766],\n",
              "       [ 0.46277154, -0.34985819,  0.        ,  0.23094736],\n",
              "       [-0.39726507, -0.34985819,  0.        ,  2.29678417],\n",
              "       [-0.39726507,  0.58590709,  0.        , -0.05075766],\n",
              "       [-0.39726507, -0.34985819,  0.        , -0.05075766],\n",
              "       [-0.24089477, -0.34985819,  0.        , -0.05075766]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Finding the Best K-Value***"
      ],
      "metadata": {
        "id": "6LylpDTAk1TL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "error = []\n",
        "from sklearn.neighbors import KNeighborsClassifier #Algorithm\n",
        "import matplotlib.pyplot as plt #Data Visulaization\n",
        "\n",
        "#Calculating error for K values between 1 and 40\n",
        "for i in range(1, 40):\n",
        "  model = KNeighborsClassifier(n_neighbors=i)\n",
        "  model.fit(X_train, y_train)\n",
        "  pred_i = model.predict(X_test)\n",
        "  error.append(np.mean(pred_i != y_test))\n",
        "  \n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=10)\n",
        "plt.title('Error Rate K value')\n",
        "plt.xlabel('K Value')\n",
        "plt.ylabel('Mean Error')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "UWEexi37k8Wx",
        "outputId": "47f1d71a-6c93-4309-9aba-f012495879aa"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Mean Error')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Training***"
      ],
      "metadata": {
        "id": "lxQ2gwr5qERI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier(n_neighbors= 30, metric = 'minkowski', p=2)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auj1x8r6qHdC",
        "outputId": "c4a5c944-7196-4514-d006-761860ea3be0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=30)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Predicting Test Data***"
      ],
      "metadata": {
        "id": "bvePfuYrq-mC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "hgUME0OprGn7"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Evaluating Model - CONFUSION MATRIX***"
      ],
      "metadata": {
        "id": "iPL5UWWBrQP9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test,y_pred)*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QubCCBTMrIEz",
        "outputId": "c63793f8-e792-4c8d-dc6d-9999412ccba8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of the Model: 64.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Predicting new data***"
      ],
      "metadata": {
        "id": "xet0AHNjr1bs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age = (int)(input(\"Enter New Employee's age: \"))\n",
        "edu = (int)(input(\"Enter New Employee's Education: \"))\n",
        "cg = int(input(\"ENter new employee's Capital Gain: \"))\n",
        "wh = int(input(\"Enter new Employee's Hour's per week\"))\n",
        "newEmp = [[age,edu,cg,wh]]\n",
        "result = model.predict(sc.transform(newEmp))\n",
        "print(result)\n",
        "\n",
        "if result == 1:\n",
        "  print(\"Employee might got salary above 50K\")\n",
        "else:\n",
        "  print(\"Customer might not got salary above 50K\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1trnxWHr8NK",
        "outputId": "fa412915-7c06-4eb9-90ed-b89b54c242ae"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter New Employee's age: 32\n",
            "Enter New Employee's Education: 12\n",
            "ENter new employee's Capital Gain: 0\n",
            "Enter new Employee's Hour's per week32\n",
            "[0]\n",
            "Customer might not got salary above 50K\n"
          ]
        }
      ]
    }
  ]
}