K_Nearest_Neighbor_(KNN)_on__breast_cancer_dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/josenikhid97/Credit-card-fraud-detection-using-machine-learning/blob/main/K_Nearest_Neighbor_(KNN)_on__breast_cancer_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOq8HaeeKxj8"
      },
      "source": [
        "# K Nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H6UBBxGyKxkA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1-Bv89ZKxkD"
      },
      "source": [
        "# loading and seeing the description"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8m3yV3MgKxkD",
        "outputId": "dc4be16b-23f3-48b7-8f9e-19c2bf590723"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry \n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
            "        13 is Radius SE, field 23 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ],
      "source": [
        "cancer=load_breast_cancer()\n",
        "print(cancer.DESCR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y4_RH6eKKxkF",
        "outputId": "82c02015-2a28-480c-dd9e-b1883f214488"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
            " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
            " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
            " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
            " 'smoothness error' 'compactness error' 'concavity error'\n",
            " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
            " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
            " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
            " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
            "['malignant' 'benign']\n"
          ]
        }
      ],
      "source": [
        "print(cancer.feature_names)\n",
        "print(cancer.target_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nn7XmJdGKxkG"
      },
      "source": [
        "# Splitting Datasets into Train & Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "36yAh585KxkG"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test =train_test_split(cancer.data,cancer.target, stratify=cancer.target, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waB4ThwjKxkH"
      },
      "source": [
        "# Finding the best k in KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pbOj1Zd5KxkH",
        "outputId": "3f8bc955-2484-4ff4-fc28-8e113cb28527"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x14dc1743b70>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "k_range = range(1, 11)\n",
        "score1=[]\n",
        "score2=[]\n",
        "for k in k_range:\n",
        "    knn=KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train,y_train)\n",
        "    score1.append(knn.score(X_train,y_train))\n",
        "    score2.append(knn.score(X_test,y_test))\n",
        "    \n",
        "%matplotlib inline\n",
        "plt.plot(k_range,score1,label= 'Accuracy on training set')\n",
        "plt.plot(k_range,score2,label= 'Accuracy on testing set')\n",
        "plt.xlabel('Value of K in KNN')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FWaMwfDKxkJ"
      },
      "source": [
        "### Here n in KNN is 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H4gpSFz9KxkJ",
        "outputId": "a3d572c5-d468-441b-903e-30247bf04f14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of KNN n=7 on the testing dataset is :0.944\n"
          ]
        }
      ],
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=7)\n",
        "knn.fit(X_train,y_train)\n",
        "print('Accuracy of KNN n=7 on the testing dataset is :{:.3f}'.format(knn.score(X_test,y_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yZQCXglmKxkK"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "colab": {
      "provenance": [],
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