PSC_KNN_Assignment_ML.ipynb

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  "metadata": {
    "colab": {
      "name": "PSC_KNN_Assignment_ML.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/locdinh209/psc-knn-python/blob/master/PSC_KNN_Assignment_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO4hKEHEXROs"
      },
      "source": [
        "# Mount to Storage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Axn-AwMWXJym",
        "outputId": "03cad806-3dbb-4b3a-923f-b2ac965e31bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YseDpfVvDxSx"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5YmjhLkFMne"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3eDT-I6EJvm"
      },
      "source": [
        "# Import Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mD1BiGXrGoKG"
      },
      "source": [
        "   1. Sample code number            id number\n",
        "   2. Clump Thickness               1 - 10\n",
        "   3. Uniformity of Cell Size       1 - 10\n",
        "   4. Uniformity of Cell Shape      1 - 10\n",
        "   5. Marginal Adhesion             1 - 10\n",
        "   6. Single Epithelial Cell Size   1 - 10\n",
        "   7. Bare Nuclei                   1 - 10\n",
        "   8. Bland Chromatin               1 - 10\n",
        "   9. Normal Nucleoli               1 - 10\n",
        "   10. Mitoses                       1 - 10\n",
        "   11. Class:                        (2 for benign, 4 for malignant)car/car.names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OquvV-UFMA2"
      },
      "source": [
        "dataset = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', \n",
        "          names=['X1','X2','X3','X4','X5','X6','X7','X8','X9','X10','Y'])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUfTd0MwFUIX",
        "outputId": "b0036a79-c227-40bb-8c81-ffff9ef52a2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          X1  X2  X3  X4  X5  X6  X7  X8  X9  X10  Y\n",
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          "metadata": {
            "tags": []
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          "execution_count": 11
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iX0pVbTRZ0d",
        "outputId": "f6bb7948-73e1-4980-a2ba-c5f872585a59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 699 entries, 0 to 698\n",
            "Data columns (total 11 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   X1      699 non-null    int64 \n",
            " 1   X2      699 non-null    int64 \n",
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            " 9   X10     699 non-null    int64 \n",
            " 10  Y       699 non-null    int64 \n",
            "dtypes: int64(10), object(1)\n",
            "memory usage: 60.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DA7tu1THRuBb"
      },
      "source": [
        "dataset['Y'] = dataset['Y'].apply(lambda x: 0 if x == 2 else 1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG_3nzTtdyDC"
      },
      "source": [
        "dataset = dataset[dataset['X7'] != '?']"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7fH22S1eJln"
      },
      "source": [
        "input = dataset.drop(labels=['Y'],axis=1)\n",
        "output = dataset['Y']"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQqTND4OD1DR"
      },
      "source": [
        "# Step 1: PSC - Prototype selection based on Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zva92FS-D1Fx"
      },
      "source": [
        "# Step 2: KNN - k-Neareast-Neighbours"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZpHr5otiX37"
      },
      "source": [
        "## Generating Model for K=5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ss0YsULXd0nH"
      },
      "source": [
        "# Import train_test_split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(input, output, test_size=0.3, random_state=7)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwsjhf7bfXXl"
      },
      "source": [
        "#Import knearest neighbors Classifier model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Create KNN Classifier\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "#Train the model using the training sets\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = knn.predict(X_test)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiawT-6ViNc0",
        "outputId": "9a68c78a-fec9-4f23-f842-b1d283e266a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.624390243902439\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6M2SSL0iV14"
      },
      "source": [
        "## Re-generating Model for K=7"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8FMPoDgiU_K"
      },
      "source": [
        "#Import knearest neighbors Classifier model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Create KNN Classifier\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "\n",
        "#Train the model using the training sets\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "#Predict the response for test dataset\n",
        "y_pred = knn.predict(X_test)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dn8q5qJXicnz",
        "outputId": "748c3bb1-45e9-44ad-e83a-6fbcc61129b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.6487804878048781\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPrWOgPaEOcA"
      },
      "source": [
        "# Inference k-NN model"
      ]
    }
  ]
}