Week-01/week1_solution.ipynb

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        "# COMP0189: Applied Artificial Intelligence\n",
        "## Week 1 (Data Preprocessing)\n",
        "\n",
        "### After this week you will be able to ...\n",
        "- load various datasets from sklearn\n",
        "- know the importance of data scaling and preprocessing\n",
        "- know the sensitivity between various learning algorithms\n",
        "- split the dataset into train and test set\n",
        "- know what will happen if you apply different preprocessing steps to train and test set\n",
        "- know how to encode categorical features to ordinal and one-hot representations and how these affect model performance\n",
        "- know how to deal with missing data\n",
        "\n",
        "### Acknowledgements\n",
        "- https://github.com/UCLAIS/Machine-Learning-Tutorials\n",
        "- https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/\n",
        "- https://scikit-learn.org/stable/\n",
        "- https://archive.ics.uci.edu/ml/datasets/adult"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a28f1108",
      "metadata": {
        "id": "a28f1108"
      },
      "source": [
        "## Introduction to Scikit-learn\n",
        "\n",
        "Why do we use sklearn??\n",
        "\n",
        "1. Example Datasets\n",
        "    - sklearn.datasets : Provides example datasets\n",
        "\n",
        "2. Feature Engineering  \n",
        "    - sklearn.preprocessing : Variable functions as to data preprocessing\n",
        "    - sklearn.feature_selection : Help selecting primary components in datasets\n",
        "    - sklearn.feature_extraction : Vectorised feature extraction\n",
        "    - sklearn.decomposition : Algorithms regarding Dimensionality Reduction\n",
        "\n",
        "3. Data split and Parameter Tuning  \n",
        "    - sklearn.model_selection : 'Train Test Split' for cross validation, Parameter tuning with GridSearch\n",
        "\n",
        "4. Evaluation  \n",
        "    - sklearn.metrics : accuracy score, ROC curve, F1 score, etc.\n",
        "\n",
        "5. ML Algorithms\n",
        "    - sklearn.ensemble : Ensemble, etc.\n",
        "    - sklearn.linear_model : Linear Regression, Logistic Regression, etc.\n",
        "    - sklearn.naive_bayes : Gaussian Naive Bayes classification, etc.\n",
        "    - sklearn.neighbors : Nearest Centroid classification, etc.\n",
        "    - sklearn.svm : Support Vector Machine\n",
        "    - sklearn.tree : DecisionTreeClassifier, etc.\n",
        "    - sklearn.cluster : Clustering (Unsupervised Learning)\n",
        "\n",
        "6. Utilities  \n",
        "    - sklearn.pipeline: pipeline of (feature engineering -> ML Algorithms -> Prediction)\n",
        "\n",
        "7. Train and Predict  \n",
        "    - fit()\n",
        "    - predict()\n",
        "\n",
        "8. and more..."
      ]
    },
    {
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        "outputId": "cdffe6bb-5e7e-4f44-9e36-68db5dc7ff34"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-learn==1.1.3\n",
            "  Downloading scikit_learn-1.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.5/30.5 MB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.1.3) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.1.3) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.1.3) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.1.3) (3.2.0)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 0.18.0 requires scikit-learn>=1.2.2, but you have scikit-learn 1.1.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed scikit-learn-1.1.3\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-learn==1.1.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "7e0eca34",
      "metadata": {
        "id": "7e0eca34"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_boston\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b5fdd50",
      "metadata": {
        "id": "6b5fdd50"
      },
      "source": [
        "**1. Boston House Price Dataset**\n",
        "\n",
        "Let's first take a look at the Boston House Price dataset. This Dataset is deprecated as of version 1.2, but we will use this for educational purpose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "d8783fee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8783fee",
        "outputId": "ad97b798-f506-46a3-ed1f-39cd6f7ba8e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _boston_dataset:\n",
            "\n",
            "Boston house prices dataset\n",
            "---------------------------\n",
            "\n",
            "**Data Set Characteristics:**  \n",
            "\n",
            "    :Number of Instances: 506 \n",
            "\n",
            "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
            "\n",
            "    :Attribute Information (in order):\n",
            "        - CRIM     per capita crime rate by town\n",
            "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
            "        - INDUS    proportion of non-retail business acres per town\n",
            "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
            "        - NOX      nitric oxides concentration (parts per 10 million)\n",
            "        - RM       average number of rooms per dwelling\n",
            "        - AGE      proportion of owner-occupied units built prior to 1940\n",
            "        - DIS      weighted distances to five Boston employment centres\n",
            "        - RAD      index of accessibility to radial highways\n",
            "        - TAX      full-value property-tax rate per $10,000\n",
            "        - PTRATIO  pupil-teacher ratio by town\n",
            "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
            "        - LSTAT    % lower status of the population\n",
            "        - MEDV     Median value of owner-occupied homes in $1000's\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
            "\n",
            "This is a copy of UCI ML housing dataset.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
            "\n",
            "\n",
            "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
            "\n",
            "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            "prices and the demand for clean air', J. Environ. Economics & Management,\n",
            "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            "pages 244-261 of the latter.\n",
            "\n",
            "The Boston house-price data has been used in many machine learning papers that address regression\n",
            "problems.   \n",
            "     \n",
            ".. topic:: References\n",
            "\n",
            "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
            "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "boston = load_boston()\n",
        "print(boston.DESCR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "111eb500",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "111eb500",
        "outputId": "2e4fb045-85a9-422e-ea3a-3b2bdf34880d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "boston.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "52fffb8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52fffb8b",
        "outputId": "adfb000c-492d-4eee-e577-8a4f0ff5fb56"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
              "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
              " 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "boston.feature_names, len(boston.feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2004f854",
      "metadata": {
        "id": "2004f854"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X, y = boston.data, boston.target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "eefdb9d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "eefdb9d4",
        "outputId": "0eb70448-833b-490b-a3ab-c8c8240daf4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(3, 5, figsize=(20, 10))\n",
        "for i, ax in enumerate(axes.ravel()):\n",
        "    if i > 12:\n",
        "        ax.set_visible(False)\n",
        "        continue\n",
        "    ax.plot(X[:, i], y, 'o', alpha=.5)\n",
        "    ax.set_title(\"{}: {}\".format(i, boston.feature_names[i]))\n",
        "    ax.set_ylabel(\"PRICE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e58cc370",
      "metadata": {
        "id": "e58cc370"
      },
      "source": [
        "See how our data are spread in different ranges. 3rd feature (CHAS) is even in binary. Most of the algorithms perform poorly on these various input spaces."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e949cac4",
      "metadata": {
        "id": "e949cac4"
      },
      "source": [
        "**2. Wine Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "936d1584",
      "metadata": {
        "id": "936d1584"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_wine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "17250624",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17250624",
        "outputId": "59d914a1-6f3d-4b28-8c2b-c3ef0034c63d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _wine_dataset:\n",
            "\n",
            "Wine recognition dataset\n",
            "------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 178\n",
            "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            " \t\t- Alcohol\n",
            " \t\t- Malic acid\n",
            " \t\t- Ash\n",
            "\t\t- Alcalinity of ash  \n",
            " \t\t- Magnesium\n",
            "\t\t- Total phenols\n",
            " \t\t- Flavanoids\n",
            " \t\t- Nonflavanoid phenols\n",
            " \t\t- Proanthocyanins\n",
            "\t\t- Color intensity\n",
            " \t\t- Hue\n",
            " \t\t- OD280/OD315 of diluted wines\n",
            " \t\t- Proline\n",
            "\n",
            "    - class:\n",
            "            - class_0\n",
            "            - class_1\n",
            "            - class_2\n",
            "\t\t\n",
            "    :Summary Statistics:\n",
            "    \n",
            "    ============================= ==== ===== ======= =====\n",
            "                                   Min   Max   Mean     SD\n",
            "    ============================= ==== ===== ======= =====\n",
            "    Alcohol:                      11.0  14.8    13.0   0.8\n",
            "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
            "    Ash:                          1.36  3.23    2.36  0.27\n",
            "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
            "    Magnesium:                    70.0 162.0    99.7  14.3\n",
            "    Total Phenols:                0.98  3.88    2.29  0.63\n",
            "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
            "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
            "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
            "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
            "    Hue:                          0.48  1.71    0.96  0.23\n",
            "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
            "    Proline:                       278  1680     746   315\n",
            "    ============================= ==== ===== ======= =====\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "This is a copy of UCI ML Wine recognition datasets.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
            "\n",
            "The data is the results of a chemical analysis of wines grown in the same\n",
            "region in Italy by three different cultivators. There are thirteen different\n",
            "measurements taken for different constituents found in the three types of\n",
            "wine.\n",
            "\n",
            "Original Owners: \n",
            "\n",
            "Forina, M. et al, PARVUS - \n",
            "An Extendible Package for Data Exploration, Classification and Correlation. \n",
            "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
            "Via Brigata Salerno, 16147 Genoa, Italy.\n",
            "\n",
            "Citation:\n",
            "\n",
            "Lichman, M. (2013). UCI Machine Learning Repository\n",
            "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
            "School of Information and Computer Science. \n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  Comparison of Classifiers in High Dimensional Settings, \n",
            "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Technometrics). \n",
            "\n",
            "  The data was used with many others for comparing various \n",
            "  classifiers. The classes are separable, though only RDA \n",
            "  has achieved 100% correct classification. \n",
            "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
            "  (All results using the leave-one-out technique) \n",
            "\n",
            "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
            "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
            "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
            "  Mathematics and Statistics, James Cook University of North Queensland. \n",
            "  (Also submitted to Journal of Chemometrics).\n",
            "\n"
          ]
        }
      ],
      "source": [
        "wine = load_wine()\n",
        "print(wine.DESCR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "9e30564c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e30564c",
        "outputId": "9c88dc09-5383-43a5-c4b7-87d969bc31c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "wine.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "0d81aa2b",
      "metadata": {
        "id": "0d81aa2b"
      },
      "outputs": [],
      "source": [
        "wine_X = wine.data\n",
        "wine_labels = wine.target\n",
        "wine_feature_names = wine.feature_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "d0d26d44",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0d26d44",
        "outputId": "dade2564-c1e0-4d54-881d-c4c6e976f56c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "wine_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "c7d5e1c5",
      "metadata": {
        "colab": {
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          "height": 439
        },
        "id": "c7d5e1c5",
        "outputId": "6367f325-6c9f-4530-94d6-4776dcc4b764"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
              "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
              "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
              "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
              "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
              "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
              "..       ...         ...   ...                ...        ...            ...   \n",
              "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
              "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
              "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
              "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
              "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
              "\n",
              "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0          3.06                  0.28             2.29             5.64  1.04   \n",
              "1          2.76                  0.26             1.28             4.38  1.05   \n",
              "2          3.24                  0.30             2.81             5.68  1.03   \n",
              "3          3.49                  0.24             2.18             7.80  0.86   \n",
              "4          2.69                  0.39             1.82             4.32  1.04   \n",
              "..          ...                   ...              ...              ...   ...   \n",
              "173        0.61                  0.52             1.06             7.70  0.64   \n",
              "174        0.75                  0.43             1.41             7.30  0.70   \n",
              "175        0.69                  0.43             1.35            10.20  0.59   \n",
              "176        0.68                  0.53             1.46             9.30  0.60   \n",
              "177        0.76                  0.56             1.35             9.20  0.61   \n",
              "\n",
              "     od280/od315_of_diluted_wines  proline  \n",
              "0                            3.92   1065.0  \n",
              "1                            3.40   1050.0  \n",
              "2                            3.17   1185.0  \n",
              "3                            3.45   1480.0  \n",
              "4                            2.93    735.0  \n",
              "..                            ...      ...  \n",
              "173                          1.74    740.0  \n",
              "174                          1.56    750.0  \n",
              "175                          1.56    835.0  \n",
              "176                          1.62    840.0  \n",
              "177                          1.60    560.0  \n",
              "\n",
              "[178 rows x 13 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity_of_ash</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
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              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127.0</td>\n",
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              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
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              "      <td>3.92</td>\n",
              "      <td>1065.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
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              "      <td>1185.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>14.37</td>\n",
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              "      <td>16.8</td>\n",
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              "      <td>3.85</td>\n",
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              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735.0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.06</td>\n",
              "      <td>7.70</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.74</td>\n",
              "      <td>740.0</td>\n",
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              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.41</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.56</td>\n",
              "      <td>750.0</td>\n",
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              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>1.59</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.35</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.56</td>\n",
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              "      <td>2.37</td>\n",
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              "      <td>1.65</td>\n",
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              "      <td>1.46</td>\n",
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              "      <td>0.60</td>\n",
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              "      <td>840.0</td>\n",
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              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>9.20</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.60</td>\n",
              "      <td>560.0</td>\n",
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              "      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-76e76032-fc9a-4087-9efe-5f55bf4a86ce 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-76e76032-fc9a-4087-9efe-5f55bf4a86ce');\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",
              "\n",
              "\n",
              "<div id=\"df-da73c091-db16-44b1-82d9-df2330de3a6b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da73c091-db16-44b1-82d9-df2330de3a6b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da73c091-db16-44b1-82d9-df2330de3a6b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "pd.DataFrame(wine_X, columns=wine_feature_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "c2dcb7a8",
      "metadata": {
        "id": "c2dcb7a8"
      },
      "outputs": [],
      "source": [
        "def visualise_wine(X, labels=None, column_indices=(0,1), set_labels=False):\n",
        "    \"\"\"\n",
        "    @param: X        --> Data\n",
        "    @param: lables   --> Default is set to None, but if you've got your result of labels from clustering,\n",
        "                         you can input according labels in a list format.\n",
        "    @param: column_indices --> column indices of dataset X to be selected for plotting.\n",
        "                                 two-element tuple if you want 2D graph,\n",
        "                                 three-element tuple if you want 3D graph.\n",
        "    \"\"\"\n",
        "    assert type(column_indices) is tuple\n",
        "\n",
        "    if len(column_indices)==2:  # 2D\n",
        "        first_col, second_col = column_indices[0], column_indices[1]\n",
        "\n",
        "        if set_labels:\n",
        "            plt.xlabel(wine_feature_names[first_col])\n",
        "            plt.ylabel(wine_feature_names[second_col])\n",
        "\n",
        "        plt.scatter(X[:, first_col], X[:, second_col], c=labels)\n",
        "\n",
        "    elif len(column_indices)==3:  # 3D\n",
        "        first_col, second_col, third_col = column_indices[0], column_indices[1], column_indices[2]\n",
        "        fig = plt.figure()\n",
        "        plt.clf()\n",
        "        ax = fig.add_subplot(projection='3d')\n",
        "\n",
        "        plt.cla()\n",
        "\n",
        "        if set_labels:\n",
        "            ax.set_xlabel(wine_feature_names[first_col])\n",
        "            ax.set_ylabel(wine_feature_names[second_col])\n",
        "            ax.set_zlabel(wine_feature_names[third_col])\n",
        "\n",
        "        ax.scatter(X[:, first_col], X[:, second_col], X[:, third_col], c=labels)\n",
        "\n",
        "    else:\n",
        "        raise RuntimeError(\"Your dimension should be either set to \\\"2d\\\" or \\\"3d\\\"\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "c36356fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "c36356fb",
        "outputId": "43e22541-ad72-4d03-d8fc-58b3714a0ff2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualise_wine(wine_X, labels=wine_labels, column_indices=(8, 10), set_labels=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "754a3fd1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "754a3fd1",
        "outputId": "56e5c875-8f47-4409-ff80-015cae40eba9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# try out different col_in_X and get some feeling of how the data is shaped.\n",
        "visualise_wine(wine_X, labels=wine_labels, column_indices=(8, 10, 12), set_labels=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b99a2680",
      "metadata": {
        "id": "b99a2680"
      },
      "source": [
        "We will closely look into details of many functions in scikit-learn (fit, predict, PCA, metrics, etc.) in the following practicals as we learn more in lectures.  \n",
        "For now, it is good to be familiar with datasets and the main takeaways we demonstrate."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cee6973e",
      "metadata": {
        "id": "cee6973e"
      },
      "source": [
        "## Exercise 1: Impact of feature scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e5dede2",
      "metadata": {
        "id": "8e5dede2"
      },
      "source": [
        "Normalization scales each input variable separately to the range 0-1.  \n",
        "Standardization scales each input variable separately by subtracting the mean (centering) and dividing each of them by the standard deviation to shift the distribution to have a mean of zero and a standard deviation of one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "18994d2f",
      "metadata": {
        "id": "18994d2f"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c96b1bba",
      "metadata": {
        "id": "c96b1bba"
      },
      "source": [
        "#### Examaple usage of sklearn.preprocessing.StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "4ce7c672",
      "metadata": {
        "id": "4ce7c672"
      },
      "outputs": [],
      "source": [
        "# Example\n",
        "unscaled_data = np.asarray([[100, 0.001],\n",
        " [8, 0.05],\n",
        " [50, 0.005],\n",
        " [88, 0.07],\n",
        " [4, 0.1]])\n",
        "# define standard scaler\n",
        "scaler = StandardScaler()\n",
        "# transform data\n",
        "scaled_data = scaler.fit_transform(unscaled_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "4c79b31b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "4c79b31b",
        "outputId": "0ce2666a-686d-49a6-b698-b1fa13c9d735"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': '0'}>, <Axes: title={'center': '1'}>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pd.DataFrame(unscaled_data).hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "54b7ceb7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "54b7ceb7",
        "outputId": "22def577-f499-4ddd-9dc8-2ef799fb9d7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': '0'}>, <Axes: title={'center': '1'}>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "pd.DataFrame(scaled_data).hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "1244e3e6",
      "metadata": {
        "id": "1244e3e6"
      },
      "outputs": [],
      "source": [
        "del scaled_data, unscaled_data, scaler"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe24e99f",
      "metadata": {
        "id": "fe24e99f"
      },
      "source": [
        "**Questions**  \n",
        "- Try using different scaling methods, such as MinMaxScaler and Normalisation. Do you see the difference in the histogram?\n",
        "- Experiment the effects of different feature scaling methods on various ML algorithms e.g. KNN, SVM, Decision-Tree."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "465fa5bd",
      "metadata": {
        "id": "465fa5bd"
      },
      "source": [
        "### Scaling Vs. Unscaling the Wine Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "ffdfa4ba",
      "metadata": {
        "id": "ffdfa4ba"
      },
      "outputs": [],
      "source": [
        "RANDOM_STATE = 42\n",
        "# We are using the wind dataset again\n",
        "features, target = load_wine(return_X_y=True)\n",
        "\n",
        "# Make a train/test split using 30% test size\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    features, target, test_size=0.30, random_state=RANDOM_STATE\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define scalers and models\n",
        "scalers = {\n",
        "    'StandardScaler': StandardScaler(),\n",
        "    'MinMaxScaler': MinMaxScaler(),\n",
        "    'Normalizer': Normalizer()\n",
        "}\n",
        "\n",
        "models = {\n",
        "    'KNeighborsClassifier': KNeighborsClassifier(),\n",
        "    'SVC': SVC(random_state=RANDOM_STATE),\n",
        "    'DecisionTreeClassifier': DecisionTreeClassifier(random_state=RANDOM_STATE)\n",
        "}\n",
        "\n",
        "# Store results\n",
        "results = {}\n",
        "\n",
        "# Iterate over each scaler\n",
        "for scaler_name, scaler in scalers.items():\n",
        "    scaled_X_train = scaler.fit_transform(X_train)\n",
        "    scaled_X_test = scaler.transform(X_test)\n",
        "\n",
        "    # Iterate over each model\n",
        "    for model_name, model in models.items():\n",
        "        key = f'{scaler_name}_{model_name}'\n",
        "\n",
        "        # Fit and predict with unscaled and scaled data\n",
        "        model.fit(X_train, y_train)\n",
        "        unscaled_y_hat = model.predict(X_test)\n",
        "        unscaled_acc = accuracy_score(y_test, unscaled_y_hat)\n",
        "\n",
        "        model.fit(scaled_X_train, y_train)\n",
        "        scaled_y_hat = model.predict(scaled_X_test)\n",
        "        scaled_acc = accuracy_score(y_test, scaled_y_hat)\n",
        "\n",
        "        # Store results\n",
        "        results[key] = {\n",
        "            'Unscaled Accuracy': unscaled_acc,\n",
        "            'Scaled Accuracy': scaled_acc\n",
        "        }\n",
        "\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "jF8ckAVlYkk2",
        "outputId": "8f99578e-4db3-409e-f84d-6dc07763318f"
      },
      "id": "jF8ckAVlYkk2",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   StandardScaler_KNeighborsClassifier  StandardScaler_SVC  \\\n",
              "Unscaled Accuracy                             0.740741            0.759259   \n",
              "Scaled Accuracy                               0.962963            0.981481   \n",
              "\n",
              "                   StandardScaler_DecisionTreeClassifier  \\\n",
              "Unscaled Accuracy                               0.962963   \n",
              "Scaled Accuracy                                 0.962963   \n",
              "\n",
              "                   MinMaxScaler_KNeighborsClassifier  MinMaxScaler_SVC  \\\n",
              "Unscaled Accuracy                           0.740741          0.759259   \n",
              "Scaled Accuracy                             0.944444          0.981481   \n",
              "\n",
              "                   MinMaxScaler_DecisionTreeClassifier  \\\n",
              "Unscaled Accuracy                             0.962963   \n",
              "Scaled Accuracy                               0.962963   \n",
              "\n",
              "                   Normalizer_KNeighborsClassifier  Normalizer_SVC  \\\n",
              "Unscaled Accuracy                         0.740741        0.759259   \n",
              "Scaled Accuracy                           0.759259        0.462963   \n",
              "\n",
              "                   Normalizer_DecisionTreeClassifier  \n",
              "Unscaled Accuracy                           0.962963  \n",
              "Scaled Accuracy                             0.962963  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StandardScaler_KNeighborsClassifier</th>\n",
              "      <th>StandardScaler_SVC</th>\n",
              "      <th>StandardScaler_DecisionTreeClassifier</th>\n",
              "      <th>MinMaxScaler_KNeighborsClassifier</th>\n",
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              "  </thead>\n",
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              "      <th>Unscaled Accuracy</th>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.759259</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.759259</td>\n",
              "      <td>0.962963</td>\n",
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              "      <td>0.759259</td>\n",
              "      <td>0.962963</td>\n",
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              "      <th>Scaled Accuracy</th>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981481</td>\n",
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              "      <td>0.944444</td>\n",
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              "    .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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8da35ad5-eb93-4331-8e41-37b3918a3366 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-8da35ad5-eb93-4331-8e41-37b3918a3366');\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",
              "\n",
              "\n",
              "<div id=\"df-95e21a57-c892-45e6-885c-e2c024bd43ac\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95e21a57-c892-45e6-885c-e2c024bd43ac')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-95e21a57-c892-45e6-885c-e2c024bd43ac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91b715b4",
      "metadata": {
        "id": "91b715b4"
      },
      "source": [
        "## Exercise 2: Impact of different preprocessing strategy in train and test data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5caf828b",
      "metadata": {
        "id": "5caf828b"
      },
      "source": [
        "Do you see the difference in accuracy?  \n",
        "**Question**  \n",
        "Now, notice that I also scaled the test set.   \n",
        "Using the same code, see what happens if you don't scale the test data and predict based on the unscaled data."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Store results\n",
        "results = {}\n",
        "\n",
        "# Iterate over each scaler\n",
        "for scaler_name, scaler in scalers.items():\n",
        "    scaled_X_train = scaler.fit_transform(X_train)\n",
        "    scaled_X_test = scaler.transform(X_test)\n",
        "\n",
        "    # Iterate over each model\n",
        "    for model_name, model in models.items():\n",
        "        key = f'{scaler_name}_{model_name}'\n",
        "\n",
        "        # Fit with scaled data\n",
        "        model.fit(scaled_X_train, y_train)\n",
        "\n",
        "        # Predict with unscaled test data\n",
        "        unscaled_y_hat = model.predict(X_test)\n",
        "        unscaled_acc = accuracy_score(y_test, unscaled_y_hat)\n",
        "\n",
        "        # Predict with scaled test data\n",
        "        scaled_y_hat = model.predict(scaled_X_test)\n",
        "        scaled_acc = accuracy_score(y_test, scaled_y_hat)\n",
        "\n",
        "        # Store results\n",
        "        results[key] = {\n",
        "            'Accuracy with Unscaled Test Data': unscaled_acc,\n",
        "            'Accuracy with Scaled Test Data': scaled_acc\n",
        "        }\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df\n",
        "\n",
        "# Display results\n",
        "#for key, value in results.items():\n",
        "#    print(key, value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "VURv48wAZ6k9",
        "outputId": "ba7d0f37-74cd-421b-e7a3-b314b89520f3"
      },
      "id": "VURv48wAZ6k9",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  StandardScaler_KNeighborsClassifier  \\\n",
              "Accuracy with Unscaled Test Data                             0.351852   \n",
              "Accuracy with Scaled Test Data                               0.962963   \n",
              "\n",
              "                                  StandardScaler_SVC  \\\n",
              "Accuracy with Unscaled Test Data            0.388889   \n",
              "Accuracy with Scaled Test Data              0.981481   \n",
              "\n",
              "                                  StandardScaler_DecisionTreeClassifier  \\\n",
              "Accuracy with Unscaled Test Data                               0.351852   \n",
              "Accuracy with Scaled Test Data                                 0.962963   \n",
              "\n",
              "                                  MinMaxScaler_KNeighborsClassifier  \\\n",
              "Accuracy with Unscaled Test Data                           0.351852   \n",
              "Accuracy with Scaled Test Data                             0.944444   \n",
              "\n",
              "                                  MinMaxScaler_SVC  \\\n",
              "Accuracy with Unscaled Test Data          0.388889   \n",
              "Accuracy with Scaled Test Data            0.981481   \n",
              "\n",
              "                                  MinMaxScaler_DecisionTreeClassifier  \\\n",
              "Accuracy with Unscaled Test Data                             0.351852   \n",
              "Accuracy with Scaled Test Data                               0.962963   \n",
              "\n",
              "                                  Normalizer_KNeighborsClassifier  \\\n",
              "Accuracy with Unscaled Test Data                         0.759259   \n",
              "Accuracy with Scaled Test Data                           0.759259   \n",
              "\n",
              "                                  Normalizer_SVC  \\\n",
              "Accuracy with Unscaled Test Data        0.388889   \n",
              "Accuracy with Scaled Test Data          0.462963   \n",
              "\n",
              "                                  Normalizer_DecisionTreeClassifier  \n",
              "Accuracy with Unscaled Test Data                           0.388889  \n",
              "Accuracy with Scaled Test Data                             0.962963  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef9e4702-db3e-43f0-9c59-a7cde88aae57\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StandardScaler_KNeighborsClassifier</th>\n",
              "      <th>StandardScaler_SVC</th>\n",
              "      <th>StandardScaler_DecisionTreeClassifier</th>\n",
              "      <th>MinMaxScaler_KNeighborsClassifier</th>\n",
              "      <th>MinMaxScaler_SVC</th>\n",
              "      <th>MinMaxScaler_DecisionTreeClassifier</th>\n",
              "      <th>Normalizer_KNeighborsClassifier</th>\n",
              "      <th>Normalizer_SVC</th>\n",
              "      <th>Normalizer_DecisionTreeClassifier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy with Unscaled Test Data</th>\n",
              "      <td>0.351852</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.351852</td>\n",
              "      <td>0.351852</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.351852</td>\n",
              "      <td>0.759259</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.388889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy with Scaled Test Data</th>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981481</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.944444</td>\n",
              "      <td>0.981481</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.759259</td>\n",
              "      <td>0.462963</td>\n",
              "      <td>0.962963</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef9e4702-db3e-43f0-9c59-a7cde88aae57')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef9e4702-db3e-43f0-9c59-a7cde88aae57 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-ef9e4702-db3e-43f0-9c59-a7cde88aae57');\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",
              "\n",
              "\n",
              "<div id=\"df-19e04109-87fc-4f15-a102-a860e8fe3502\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19e04109-87fc-4f15-a102-a860e8fe3502')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-19e04109-87fc-4f15-a102-a860e8fe3502 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "232ba604",
      "metadata": {
        "id": "232ba604"
      },
      "source": [
        "## Now we move on the next session which is about categorial features and data imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "18a61ed9",
      "metadata": {
        "id": "18a61ed9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import svm\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.feature_selection import mutual_info_classif"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "d6977889",
      "metadata": {
        "id": "d6977889"
      },
      "outputs": [],
      "source": [
        "# Open the csv file and skim through it. It does not have column names\n",
        "# so we will allocate names to each column\n",
        "\n",
        "# Naming the Columns\n",
        "names = ['age','workclass','fnlwgt','education','education-num',\n",
        "        'marital-status','occupation','relationship','race','sex',\n",
        "        'capital-gain','capital-loss','hours-per-week','native-country',\n",
        "        'y']\n",
        "\n",
        "# Load dataset with specifying ' ?' as missing values\n",
        "df = pd.read_csv('/content/adult.data', delimiter=',', names=names, na_values=' ?')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyRpMIOErkHo",
        "outputId": "733a866f-cb15-413f-ce49-838bda4ed4ea"
      },
      "id": "AyRpMIOErkHo",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "y                    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the 15th row of the DataFrame - notice NaN\n",
        "row_15 = df.iloc[14]\n",
        "print(row_15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "An_vzHiIroYf",
        "outputId": "8515661b-028b-425f-fcae-94f3891007ea"
      },
      "id": "An_vzHiIroYf",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                                40\n",
            "workclass                     Private\n",
            "fnlwgt                         121772\n",
            "education                   Assoc-voc\n",
            "education-num                      11\n",
            "marital-status     Married-civ-spouse\n",
            "occupation               Craft-repair\n",
            "relationship                  Husband\n",
            "race               Asian-Pac-Islander\n",
            "sex                              Male\n",
            "capital-gain                        0\n",
            "capital-loss                        0\n",
            "hours-per-week                     40\n",
            "native-country                    NaN\n",
            "y                                >50K\n",
            "Name: 14, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "99897885",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99897885",
        "outputId": "a2c723f4-9885-4d1c-bdb0-d4654d22e59e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32561"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "len(df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for now we will drop the rows with NA values\n",
        "\n",
        "df = df.dropna()\n",
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7jFzTUYwCe8",
        "outputId": "bc728dfd-ad77-415a-bd10-70dbeaeac153"
      },
      "id": "L7jFzTUYwCe8",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30162"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "94f0773d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94f0773d",
        "outputId": "1e2925c9-3e5b-4a2c-a941-6ce7128fd154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' White', ' Black', ' Asian-Pac-Islander', ' Amer-Indian-Eskimo',\n",
              "       ' Other'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# TASK 1: Get the unique values in the race column\n",
        "df['race'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "c95cd314",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c95cd314",
        "outputId": "ce452cc8-d76a-477b-b00e-c191ce7cb39a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' <=50K', ' >50K'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# TASK 2: Get the unique values in the 'y' column\n",
        "df['y'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "245ce71b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "245ce71b",
        "outputId": "64b0c2de-ec27-44b1-ed04-eea5cdfde55c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Race')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# TODO: Get the popluation count by race\n",
        "counts = df['race'].value_counts()\n",
        "labels = counts.index\n",
        "\n",
        "# Plot pie chart\n",
        "plt.pie(counts, startangle=90)\n",
        "plt.legend(labels, loc=2,fontsize=8)\n",
        "plt.title(\"Race\",size=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "81c51abd",
      "metadata": {
        "id": "81c51abd"
      },
      "outputs": [],
      "source": [
        "# TASK 3\n",
        "# We see redundant space prefix in the values. Remove them.\n",
        "df['race'] = df['race'].apply(lambda x: x.strip())\n",
        "df['y'] = df['y'].apply(lambda x: x.strip())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "71a747d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71a747d2",
        "outputId": "7d54333a-74ad-4ed1-f3b2-3e97a8906f72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['White', 'Black', 'Asian-Pac-Islander', 'Amer-Indian-Eskimo',\n",
              "        'Other'], dtype=object),\n",
              " array(['<=50K', '>50K'], dtype=object),\n",
              " array([' Adm-clerical', ' Exec-managerial', ' Handlers-cleaners',\n",
              "        ' Prof-specialty', ' Other-service', ' Sales', ' Transport-moving',\n",
              "        ' Farming-fishing', ' Machine-op-inspct', ' Tech-support',\n",
              "        ' Craft-repair', ' Protective-serv', ' Armed-Forces',\n",
              "        ' Priv-house-serv'], dtype=object))"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df['race'].unique(), df['y'].unique(), df['occupation'].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ded9523",
      "metadata": {
        "id": "8ded9523"
      },
      "source": [
        "Hmmm it's not just the race and y column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "a575e39b",
      "metadata": {
        "id": "a575e39b"
      },
      "outputs": [],
      "source": [
        "# Let's try to apply this to all the string-valued columns\n",
        "for col_name in df.columns:\n",
        "    if df[col_name].dtype == object:  # Checking for object type (string in pandas)\n",
        "        df[col_name] = df[col_name].apply(lambda x: x.strip() if isinstance(x, str) else x)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "78e919df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78e919df",
        "outputId": "3cd57066-eb9d-46f3-e469-3265f3595c94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['State-gov' 'Self-emp-not-inc' 'Private' 'Federal-gov' 'Local-gov'\n",
            " 'Self-emp-inc' 'Without-pay']\n",
            "['Bachelors' 'HS-grad' '11th' 'Masters' '9th' 'Some-college' 'Assoc-acdm'\n",
            " '7th-8th' 'Doctorate' 'Assoc-voc' 'Prof-school' '5th-6th' '10th'\n",
            " 'Preschool' '12th' '1st-4th']\n",
            "['Never-married' 'Married-civ-spouse' 'Divorced' 'Married-spouse-absent'\n",
            " 'Separated' 'Married-AF-spouse' 'Widowed']\n",
            "['Adm-clerical' 'Exec-managerial' 'Handlers-cleaners' 'Prof-specialty'\n",
            " 'Other-service' 'Sales' 'Transport-moving' 'Farming-fishing'\n",
            " 'Machine-op-inspct' 'Tech-support' 'Craft-repair' 'Protective-serv'\n",
            " 'Armed-Forces' 'Priv-house-serv']\n",
            "['Not-in-family' 'Husband' 'Wife' 'Own-child' 'Unmarried' 'Other-relative']\n",
            "['White' 'Black' 'Asian-Pac-Islander' 'Amer-Indian-Eskimo' 'Other']\n",
            "['Male' 'Female']\n",
            "['United-States' 'Cuba' 'Jamaica' 'India' 'Mexico' 'Puerto-Rico'\n",
            " 'Honduras' 'England' 'Canada' 'Germany' 'Iran' 'Philippines' 'Poland'\n",
            " 'Columbia' 'Cambodia' 'Thailand' 'Ecuador' 'Laos' 'Taiwan' 'Haiti'\n",
            " 'Portugal' 'Dominican-Republic' 'El-Salvador' 'France' 'Guatemala'\n",
            " 'Italy' 'China' 'South' 'Japan' 'Yugoslavia' 'Peru'\n",
            " 'Outlying-US(Guam-USVI-etc)' 'Scotland' 'Trinadad&Tobago' 'Greece'\n",
            " 'Nicaragua' 'Vietnam' 'Hong' 'Ireland' 'Hungary' 'Holand-Netherlands']\n",
            "['<=50K' '>50K']\n"
          ]
        }
      ],
      "source": [
        "for col_name in df.columns:\n",
        "    if not 'int' in str(df[col_name].dtype):\n",
        "        print(df[col_name].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b51257e6",
      "metadata": {
        "id": "b51257e6"
      },
      "source": [
        "All done!  \n",
        "Now let's specifically look into the 'race' and 'y' columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "7ec1b341",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "7ec1b341",
        "outputId": "b493b048-dd81-4d88-8408-18164f63b6e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    race      y\n",
              "0  White  <=50K\n",
              "1  White  <=50K\n",
              "2  White  <=50K\n",
              "3  Black  <=50K\n",
              "4  Black  <=50K\n",
              "5  White  <=50K\n",
              "6  Black  <=50K\n",
              "7  White   >50K\n",
              "8  White   >50K\n",
              "9  White   >50K"
            ],
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df[['race', 'y']].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "116f91d9",
      "metadata": {
        "id": "116f91d9"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, LabelEncoder\n",
        "\n",
        "# TASK 4: Convert features and target to binary numerical values using\n",
        "# Ordinal, One-hot, LabelEncoding as appropriate."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df is your DataFrame\n",
        "\n",
        "# Ordinal Encoding for 'education'\n",
        "# Define the order of education categories\n",
        "education_order = ['Preschool', '1st-4th', '5th-6th', '7th-8th', '9th', '10th', '11th', '12th', 'HS-grad', 'Some-college', 'Assoc-voc', 'Assoc-acdm', 'Prof-school', 'Bachelors', 'Masters', 'Doctorate']\n",
        "# Initialize the OrdinalEncoder with the specified categories\n",
        "ordinal_encoder = OrdinalEncoder(categories=[education_order])\n",
        "# Apply the OrdinalEncoder to the 'education' column\n",
        "df['education_encoded'] = ordinal_encoder.fit_transform(df[['education']])\n",
        "\n",
        "# OneHotEncoding for nominal features without an implied order\n",
        "# Including the previously missed nominal columns\n",
        "nominal_columns = ['workclass', 'occupation', 'native-country', 'marital-status', 'relationship', 'race', 'sex']\n",
        "onehot_encoder = OneHotEncoder(sparse=False)\n",
        "onehot_encoded_columns = onehot_encoder.fit_transform(df[nominal_columns])\n",
        "column_names = onehot_encoder.get_feature_names_out(nominal_columns)\n",
        "df_onehot_encoded = pd.DataFrame(onehot_encoded_columns, columns=column_names)\n",
        "\n",
        "# Integrate these new columns back into the original dataframe\n",
        "df = df.reset_index(drop=True)  # Reset index to align with the new onehot encoded DataFrame\n",
        "df = pd.concat([df, df_onehot_encoded], axis=1)\n",
        "\n",
        "# Optionally, remove the categorical columns if no longer needed\n",
        "df.drop(columns=nominal_columns + ['education'], inplace=True)\n",
        "\n",
        "# Label Encoding for the target variable\n",
        "label_encoder = LabelEncoder()\n",
        "df['y_encoded'] = label_encoder.fit_transform(df['y'])\n",
        "\n",
        "# Remove the original 'y' column if no longer needed\n",
        "df.drop(columns=['y'], inplace=True)\n",
        "\n",
        "# Display the first few rows of the modified DataFrame\n",
        "df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "r1hzAqc8eW_V",
        "outputId": "2755a606-ef67-4078-f717-bca396ce81cc"
      },
      "id": "r1hzAqc8eW_V",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
              "0   39   77516             13          2174             0              40   \n",
              "1   50   83311             13             0             0              13   \n",
              "2   38  215646              9             0             0              40   \n",
              "3   53  234721              7             0             0              40   \n",
              "4   28  338409             13             0             0              40   \n",
              "5   37  284582             14             0             0              40   \n",
              "6   49  160187              5             0             0              16   \n",
              "7   52  209642              9             0             0              45   \n",
              "8   31   45781             14         14084             0              50   \n",
              "9   42  159449             13          5178             0              40   \n",
              "\n",
              "   education_encoded  workclass_Federal-gov  workclass_Local-gov  \\\n",
              "0               13.0                    0.0                  0.0   \n",
              "1               13.0                    0.0                  0.0   \n",
              "2                8.0                    0.0                  0.0   \n",
              "3                6.0                    0.0                  0.0   \n",
              "4               13.0                    0.0                  0.0   \n",
              "5               14.0                    0.0                  0.0   \n",
              "6                4.0                    0.0                  0.0   \n",
              "7                8.0                    0.0                  0.0   \n",
              "8               14.0                    0.0                  0.0   \n",
              "9               13.0                    0.0                  0.0   \n",
              "\n",
              "   workclass_Private  ...  relationship_Unmarried  relationship_Wife  \\\n",
              "0                0.0  ...                     0.0                0.0   \n",
              "1                0.0  ...                     0.0                0.0   \n",
              "2                1.0  ...                     0.0                0.0   \n",
              "3                1.0  ...                     0.0                0.0   \n",
              "4                1.0  ...                     0.0                1.0   \n",
              "5                1.0  ...                     0.0                1.0   \n",
              "6                1.0  ...                     0.0                0.0   \n",
              "7                0.0  ...                     0.0                0.0   \n",
              "8                1.0  ...                     0.0                0.0   \n",
              "9                1.0  ...                     0.0                0.0   \n",
              "\n",
              "   race_Amer-Indian-Eskimo  race_Asian-Pac-Islander  race_Black  race_Other  \\\n",
              "0                      0.0                      0.0         0.0         0.0   \n",
              "1                      0.0                      0.0         0.0         0.0   \n",
              "2                      0.0                      0.0         0.0         0.0   \n",
              "3                      0.0                      0.0         1.0         0.0   \n",
              "4                      0.0                      0.0         1.0         0.0   \n",
              "5                      0.0                      0.0         0.0         0.0   \n",
              "6                      0.0                      0.0         1.0         0.0   \n",
              "7                      0.0                      0.0         0.0         0.0   \n",
              "8                      0.0                      0.0         0.0         0.0   \n",
              "9                      0.0                      0.0         0.0         0.0   \n",
              "\n",
              "   race_White  sex_Female  sex_Male  y_encoded  \n",
              "0         1.0         0.0       1.0          0  \n",
              "1         1.0         0.0       1.0          0  \n",
              "2         1.0         0.0       1.0          0  \n",
              "3         0.0         0.0       1.0          0  \n",
              "4         0.0         1.0       0.0          0  \n",
              "5         1.0         1.0       0.0          0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "897db437",
      "metadata": {
        "id": "897db437"
      },
      "source": [
        "### Dealing with Missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "25831b63",
      "metadata": {
        "id": "25831b63"
      },
      "source": [
        "#### In processing the data earlier, we did not take account of the missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "47bf7cbc",
      "metadata": {
        "id": "47bf7cbc"
      },
      "outputs": [],
      "source": [
        "# Re-Load dataset with specifying ' ?' as missing values\n",
        "df = pd.read_csv('/content/adult.data', delimiter=',', names=names, na_values=' ?')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "d6a0c2dd",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d6a0c2dd",
        "outputId": "1695e3b4-9b18-4c20-e6b2-7944e2d536f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "y                    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# TASK 7 Create 3 datasets using different methods for dealing with missing data:\n",
        "# A: Drop missing values, B: KNN imputation, C: Most frequest imputation\n",
        "\n",
        "for col_name in df.columns:\n",
        "    if df[col_name].dtype == object:  # Checking for object type (string in pandas)\n",
        "        df[col_name] = df[col_name].apply(lambda x: x.strip() if isinstance(x, str) else x)\n",
        "\n",
        "# Check for missing values\n",
        "print(df.isnull().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# first conduct encoding for features without missing values\n",
        "\n",
        "# Ordinal Encoding for 'education'\n",
        "# Define the order of education categories\n",
        "education_order = ['Preschool', '1st-4th', '5th-6th', '7th-8th', '9th', '10th', '11th', '12th', 'HS-grad', 'Some-college', 'Assoc-voc', 'Assoc-acdm', 'Prof-school', 'Bachelors', 'Masters', 'Doctorate']\n",
        "# Initialize the OrdinalEncoder with the specified categories\n",
        "ordinal_encoder = OrdinalEncoder(categories=[education_order])\n",
        "# Apply the OrdinalEncoder to the 'education' column\n",
        "df['education_encoded'] = ordinal_encoder.fit_transform(df[['education']])\n",
        "\n",
        "# OneHotEncoding for nominal features without missing values and without an implied order\n",
        "nominal_columns_without_missing = ['marital-status', 'relationship', 'race', 'sex']\n",
        "onehot_encoder = OneHotEncoder(sparse=False)\n",
        "onehot_encoded_columns = onehot_encoder.fit_transform(df[nominal_columns_without_missing])\n",
        "column_names = onehot_encoder.get_feature_names_out(nominal_columns_without_missing)\n",
        "df_onehot_encoded = pd.DataFrame(onehot_encoded_columns, columns=column_names)\n",
        "\n",
        "# Integrate these new columns back into the original dataframe\n",
        "df = df.reset_index(drop=True)  # Reset index to align with the new onehot encoded DataFrame\n",
        "df = pd.concat([df, df_onehot_encoded], axis=1)\n",
        "\n",
        "# Optionally, remove the original categorical columns if no longer needed\n",
        "df.drop(columns=nominal_columns_without_missing + ['education'], inplace=True)\n",
        "\n",
        "# Label Encoding for the target variable\n",
        "label_encoder = LabelEncoder()\n",
        "df['y_encoded'] = label_encoder.fit_transform(df['y'])\n",
        "\n",
        "# Remove the original 'y' column if no longer needed\n",
        "df.drop(columns=['y'], inplace=True)\n",
        "\n",
        "# Display the first few rows of the modified DataFrame\n",
        "df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "_OjRTt683uBW",
        "outputId": "4bb3ae1d-cd59-4146-ed20-9e5cc278154f"
      },
      "id": "_OjRTt683uBW",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age         workclass  fnlwgt  education-num         occupation  \\\n",
              "0   39         State-gov   77516             13       Adm-clerical   \n",
              "1   50  Self-emp-not-inc   83311             13    Exec-managerial   \n",
              "2   38           Private  215646              9  Handlers-cleaners   \n",
              "3   53           Private  234721              7  Handlers-cleaners   \n",
              "4   28           Private  338409             13     Prof-specialty   \n",
              "5   37           Private  284582             14    Exec-managerial   \n",
              "6   49           Private  160187              5      Other-service   \n",
              "7   52  Self-emp-not-inc  209642              9    Exec-managerial   \n",
              "8   31           Private   45781             14     Prof-specialty   \n",
              "9   42           Private  159449             13    Exec-managerial   \n",
              "\n",
              "   capital-gain  capital-loss  hours-per-week native-country  \\\n",
              "0          2174             0              40  United-States   \n",
              "1             0             0              13  United-States   \n",
              "2             0             0              40  United-States   \n",
              "3             0             0              40  United-States   \n",
              "4             0             0              40           Cuba   \n",
              "5             0             0              40  United-States   \n",
              "6             0             0              16        Jamaica   \n",
              "7             0             0              45  United-States   \n",
              "8         14084             0              50  United-States   \n",
              "9          5178             0              40  United-States   \n",
              "\n",
              "   education_encoded  ...  relationship_Unmarried  relationship_Wife  \\\n",
              "0               13.0  ...                     0.0                0.0   \n",
              "1               13.0  ...                     0.0                0.0   \n",
              "2                8.0  ...                     0.0                0.0   \n",
              "3                6.0  ...                     0.0                0.0   \n",
              "4               13.0  ...                     0.0                1.0   \n",
              "5               14.0  ...                     0.0                1.0   \n",
              "6                4.0  ...                     0.0                0.0   \n",
              "7                8.0  ...                     0.0                0.0   \n",
              "8               14.0  ...                     0.0                0.0   \n",
              "9               13.0  ...                     0.0                0.0   \n",
              "\n",
              "   race_Amer-Indian-Eskimo  race_Asian-Pac-Islander  race_Black  race_Other  \\\n",
              "0                      0.0                      0.0         0.0         0.0   \n",
              "1                      0.0                      0.0         0.0         0.0   \n",
              "2                      0.0                      0.0         0.0         0.0   \n",
              "3                      0.0                      0.0         1.0         0.0   \n",
              "4                      0.0                      0.0         1.0         0.0   \n",
              "5                      0.0                      0.0         0.0         0.0   \n",
              "6                      0.0                      0.0         1.0         0.0   \n",
              "7                      0.0                      0.0         0.0         0.0   \n",
              "8                      0.0                      0.0         0.0         0.0   \n",
              "9                      0.0                      0.0         0.0         0.0   \n",
              "\n",
              "   race_White  sex_Female  sex_Male  y_encoded  \n",
              "0         1.0         0.0       1.0          0  \n",
              "1         1.0         0.0       1.0          0  \n",
              "2         1.0         0.0       1.0          0  \n",
              "3         0.0         0.0       1.0          0  \n",
              "4         0.0         1.0       0.0          0  \n",
              "5         1.0         1.0       0.0          0  \n",
              "6         0.0         1.0       0.0          0  \n",
              "7         1.0         0.0       1.0          1  \n",
              "8         1.0         1.0       0.0          1  \n",
              "9         1.0         0.0       1.0          1  \n",
              "\n",
              "[10 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6239598-a55a-4857-819c-e88539c36e71\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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-num</th>\n",
              "      <th>occupation</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>education_encoded</th>\n",
              "      <th>...</th>\n",
              "      <th>relationship_Unmarried</th>\n",
              "      <th>relationship_Wife</th>\n",
              "      <th>race_Amer-Indian-Eskimo</th>\n",
              "      <th>race_Asian-Pac-Islander</th>\n",
              "      <th>race_Black</th>\n",
              "      <th>race_Other</th>\n",
              "      <th>race_White</th>\n",
              "      <th>sex_Female</th>\n",
              "      <th>sex_Male</th>\n",
              "      <th>y_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>13</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>13.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>13</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>13.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>9</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>7</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>13</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>13.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>37</td>\n",
              "      <td>Private</td>\n",
              "      <td>284582</td>\n",
              "      <td>14</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>49</td>\n",
              "      <td>Private</td>\n",
              "      <td>160187</td>\n",
              "      <td>5</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>209642</td>\n",
              "      <td>9</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>8.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>31</td>\n",
              "      <td>Private</td>\n",
              "      <td>45781</td>\n",
              "      <td>14</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>14084</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>42</td>\n",
              "      <td>Private</td>\n",
              "      <td>159449</td>\n",
              "      <td>13</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>5178</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>13.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 31 columns</p>\n",
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              "\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",
              "\n",
              "\n",
              "<div id=\"df-a688b59e-6803-40f0-9875-b01dce6a7f88\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a688b59e-6803-40f0-9875-b01dce6a7f88')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a688b59e-6803-40f0-9875-b01dce6a7f88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsdn-Zkk4krB",
        "outputId": "86634b35-cc12-40b5-8148-d3f1bada891d"
      },
      "id": "vsdn-Zkk4krB",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                                        0\n",
            "workclass                               1836\n",
            "fnlwgt                                     0\n",
            "education-num                              0\n",
            "occupation                              1843\n",
            "capital-gain                               0\n",
            "capital-loss                               0\n",
            "hours-per-week                             0\n",
            "native-country                           583\n",
            "education_encoded                          0\n",
            "marital-status_Divorced                    0\n",
            "marital-status_Married-AF-spouse           0\n",
            "marital-status_Married-civ-spouse          0\n",
            "marital-status_Married-spouse-absent       0\n",
            "marital-status_Never-married               0\n",
            "marital-status_Separated                   0\n",
            "marital-status_Widowed                     0\n",
            "relationship_Husband                       0\n",
            "relationship_Not-in-family                 0\n",
            "relationship_Other-relative                0\n",
            "relationship_Own-child                     0\n",
            "relationship_Unmarried                     0\n",
            "relationship_Wife                          0\n",
            "race_Amer-Indian-Eskimo                    0\n",
            "race_Asian-Pac-Islander                    0\n",
            "race_Black                                 0\n",
            "race_Other                                 0\n",
            "race_White                                 0\n",
            "sex_Female                                 0\n",
            "sex_Male                                   0\n",
            "y_encoded                                  0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "# A: Dataset with dropped missing values\n",
        "df_dropna = df.dropna()\n",
        "\n",
        "print(df_dropna.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ulc8JBkg6YF",
        "outputId": "c29d708b-ea0e-4795-9586-6afadf766129"
      },
      "id": "7ulc8JBkg6YF",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                                     0\n",
            "workclass                               0\n",
            "fnlwgt                                  0\n",
            "education-num                           0\n",
            "occupation                              0\n",
            "capital-gain                            0\n",
            "capital-loss                            0\n",
            "hours-per-week                          0\n",
            "native-country                          0\n",
            "education_encoded                       0\n",
            "marital-status_Divorced                 0\n",
            "marital-status_Married-AF-spouse        0\n",
            "marital-status_Married-civ-spouse       0\n",
            "marital-status_Married-spouse-absent    0\n",
            "marital-status_Never-married            0\n",
            "marital-status_Separated                0\n",
            "marital-status_Widowed                  0\n",
            "relationship_Husband                    0\n",
            "relationship_Not-in-family              0\n",
            "relationship_Other-relative             0\n",
            "relationship_Own-child                  0\n",
            "relationship_Unmarried                  0\n",
            "relationship_Wife                       0\n",
            "race_Amer-Indian-Eskimo                 0\n",
            "race_Asian-Pac-Islander                 0\n",
            "race_Black                              0\n",
            "race_Other                              0\n",
            "race_White                              0\n",
            "sex_Female                              0\n",
            "sex_Male                                0\n",
            "y_encoded                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import KNNImputer\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "df_knn_imputed = df.copy()\n",
        "\n",
        "# Temporarily encode categorical columns with missing values\n",
        "temp_encoder = OrdinalEncoder()\n",
        "columns_with_missing_values = ['workclass', 'occupation', 'native-country']\n",
        "df_temp = df[columns_with_missing_values].copy()\n",
        "df_temp_encoded = temp_encoder.fit_transform(df_temp)\n",
        "\n",
        "# Apply KNN imputer\n",
        "knn_imputer = KNNImputer(n_neighbors=5)\n",
        "imputed_data = knn_imputer.fit_transform(df_temp_encoded)\n",
        "\n",
        "# Decode the categorical columns back to original categories\n",
        "imputed_data_decoded = temp_encoder.inverse_transform(imputed_data)\n",
        "df_imputed_final = pd.DataFrame(imputed_data_decoded, columns=columns_with_missing_values)\n",
        "\n",
        "# Integrate the imputed columns back into the main DataFrame\n",
        "df_knn_imputed[columns_with_missing_values] = df_imputed_final\n",
        "\n",
        "print(df_knn_imputed.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2Crh-TB5MJG",
        "outputId": "0bbfd6b0-64af-4535-d40c-569f73e3e65c"
      },
      "id": "B2Crh-TB5MJG",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                                     0\n",
            "workclass                               0\n",
            "fnlwgt                                  0\n",
            "education-num                           0\n",
            "occupation                              0\n",
            "capital-gain                            0\n",
            "capital-loss                            0\n",
            "hours-per-week                          0\n",
            "native-country                          0\n",
            "education_encoded                       0\n",
            "marital-status_Divorced                 0\n",
            "marital-status_Married-AF-spouse        0\n",
            "marital-status_Married-civ-spouse       0\n",
            "marital-status_Married-spouse-absent    0\n",
            "marital-status_Never-married            0\n",
            "marital-status_Separated                0\n",
            "marital-status_Widowed                  0\n",
            "relationship_Husband                    0\n",
            "relationship_Not-in-family              0\n",
            "relationship_Other-relative             0\n",
            "relationship_Own-child                  0\n",
            "relationship_Unmarried                  0\n",
            "relationship_Wife                       0\n",
            "race_Amer-Indian-Eskimo                 0\n",
            "race_Asian-Pac-Islander                 0\n",
            "race_Black                              0\n",
            "race_Other                              0\n",
            "race_White                              0\n",
            "sex_Female                              0\n",
            "sex_Male                                0\n",
            "y_encoded                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "# Create an imputer object using the most frequent strategy\n",
        "mode_imputer = SimpleImputer(strategy='most_frequent')\n",
        "\n",
        "# Apply the imputer to the categorical columns with missing values\n",
        "df_mode_imputed = df.copy()\n",
        "df_mode_imputed[columns_with_missing_values] = mode_imputer.fit_transform(df_mode_imputed[columns_with_missing_values])\n",
        "\n",
        "print(df_mode_imputed.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxda87fO5PzL",
        "outputId": "2793d06c-b9a9-4075-af60-72ff6c2d62c9"
      },
      "id": "hxda87fO5PzL",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age                                     0\n",
            "workclass                               0\n",
            "fnlwgt                                  0\n",
            "education-num                           0\n",
            "occupation                              0\n",
            "capital-gain                            0\n",
            "capital-loss                            0\n",
            "hours-per-week                          0\n",
            "native-country                          0\n",
            "education_encoded                       0\n",
            "marital-status_Divorced                 0\n",
            "marital-status_Married-AF-spouse        0\n",
            "marital-status_Married-civ-spouse       0\n",
            "marital-status_Married-spouse-absent    0\n",
            "marital-status_Never-married            0\n",
            "marital-status_Separated                0\n",
            "marital-status_Widowed                  0\n",
            "relationship_Husband                    0\n",
            "relationship_Not-in-family              0\n",
            "relationship_Other-relative             0\n",
            "relationship_Own-child                  0\n",
            "relationship_Unmarried                  0\n",
            "relationship_Wife                       0\n",
            "race_Amer-Indian-Eskimo                 0\n",
            "race_Asian-Pac-Islander                 0\n",
            "race_Black                              0\n",
            "race_Other                              0\n",
            "race_White                              0\n",
            "sex_Female                              0\n",
            "sex_Male                                0\n",
            "y_encoded                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_onehot_encoding(df, columns):\n",
        "    # Perform One-Hot Encoding\n",
        "    encoded_data = onehot_encoder.fit_transform(df[columns])\n",
        "    column_names = onehot_encoder.get_feature_names_out(columns)\n",
        "    df_encoded = pd.DataFrame(encoded_data, columns=column_names)\n",
        "\n",
        "    # Reset indices to ensure alignment\n",
        "    df_reset = df.reset_index(drop=True)\n",
        "    df_encoded_reset = df_encoded.reset_index(drop=True)\n",
        "\n",
        "    # Drop original columns and concatenate the new One-Hot Encoded columns\n",
        "    return pd.concat([df_reset.drop(columns, axis=1), df_encoded_reset], axis=1)\n",
        "\n",
        "columns_to_encode = ['workclass', 'occupation', 'native-country']\n",
        "\n",
        "# Apply One-Hot Encoding\n",
        "df_dropna_encoded = apply_onehot_encoding(df_dropna, columns_to_encode)\n",
        "\n",
        "# Check for missing values after encoding\n",
        "print(df_dropna_encoded.isnull().sum().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIi-nJ2eA8mD",
        "outputId": "62358864-97d8-4904-b246-47d02f12b4da"
      },
      "id": "zIi-nJ2eA8mD",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# now apply one-hot encoding for the feautres which were imputed\n",
        "\n",
        "# Initialize One-Hot Encoder\n",
        "onehot_encoder = OneHotEncoder(sparse=False)\n",
        "\n",
        "# Columns to be One-Hot Encoded\n",
        "columns_to_encode = ['workclass', 'occupation', 'native-country']\n",
        "\n",
        "# Function to apply One-Hot Encoding to a DataFrame\n",
        "def apply_onehot_encoding(df, columns):\n",
        "    encoded_data = onehot_encoder.fit_transform(df[columns])\n",
        "    column_names = onehot_encoder.get_feature_names_out(columns)\n",
        "    df_encoded = pd.DataFrame(encoded_data, columns=column_names)\n",
        "\n",
        "    # Drop original columns and concatenate the new One-Hot Encoded columns\n",
        "    return pd.concat([df.drop(columns, axis=1), df_encoded], axis=1)\n",
        "\n",
        "# Apply One-Hot Encoding to each DataFrame\n",
        "\n",
        "df_knn_imputed_encoded = apply_onehot_encoding(df_knn_imputed, columns_to_encode)\n",
        "df_mode_imputed_encoded = apply_onehot_encoding(df_mode_imputed, columns_to_encode)\n",
        "\n",
        "\n",
        "print(df_knn_imputed_encoded.isnull().sum().sum())\n",
        "print(df_mode_imputed_encoded.isnull().sum().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9R_FZ5A6zOv",
        "outputId": "28b9a16b-f8a6-4af1-fc36-0c069d53c78d"
      },
      "id": "h9R_FZ5A6zOv",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72a28f3f",
      "metadata": {
        "id": "72a28f3f"
      },
      "source": [
        "### Now, train an SVM or KNN Classifier and check the metrics by using the function below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "24e991bc",
      "metadata": {
        "id": "24e991bc"
      },
      "outputs": [],
      "source": [
        "# TASK 8: Train an SVM Classifier on the differnt dataset to compare imputation method accuracy\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "ec6683e7",
      "metadata": {
        "id": "ec6683e7"
      },
      "outputs": [],
      "source": [
        "# For dataset A\n",
        "X_dropna = df_dropna_encoded.drop('y_encoded', axis=1)\n",
        "y_dropna = df_dropna_encoded['y_encoded']\n",
        "\n",
        "# For dataset B\n",
        "X_knn = df_knn_imputed_encoded.drop('y_encoded', axis=1)\n",
        "y_knn = df_knn_imputed_encoded['y_encoded']\n",
        "\n",
        "# For dataset C\n",
        "X_mode = df_mode_imputed_encoded.drop('y_encoded', axis=1)\n",
        "y_mode = df_mode_imputed_encoded['y_encoded']\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to train and evaluate SVM\n",
        "def train_evaluate_svm(X, y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "    clf = SVC()\n",
        "    clf.fit(X_train, y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    return accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "id": "fK63qLXb7x5b"
      },
      "id": "fK63qLXb7x5b",
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "00aabc2a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00aabc2a",
        "outputId": "bb2880bc-8163-4714-93c4-2702ed7d72a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy with dropped missing values: 0.7803072162669908\n",
            "Accuracy with KNN imputation: 0.7975227761285699\n",
            "Accuracy with mode imputation: 0.7975227761285699\n"
          ]
        }
      ],
      "source": [
        "# Train and evaluate on each dataset\n",
        "accuracy_dropna = train_evaluate_svm(X_dropna, y_dropna)\n",
        "accuracy_knn = train_evaluate_svm(X_knn, y_knn)\n",
        "accuracy_mode = train_evaluate_svm(X_mode, y_mode)\n",
        "\n",
        "# Print the accuracies\n",
        "print(f\"Accuracy with dropped missing values: {accuracy_dropna}\")\n",
        "print(f\"Accuracy with KNN imputation: {accuracy_knn}\")\n",
        "print(f\"Accuracy with mode imputation: {accuracy_mode}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hxg2qYlRitbJ"
      },
      "id": "hxg2qYlRitbJ",
      "execution_count": 59,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.13"
    },
    "colab": {
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    }
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  "nbformat_minor": 5
}