Implement_KNN.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Implement KNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPQHHeUhXtcbawFz4JGx3hy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Augusta02/Machine-Learning-Projects/blob/main/Implement_KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "H3fHGlXf73Sd",
        "outputId": "b1541a44-ff0f-4275-ef77-719f68dd2b0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7d139c8f-0e18-4139-a018-e2898b02c0ad\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7d139c8f-0e18-4139-a018-e2898b02c0ad\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving iris.data.txt to iris.data.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import io\n",
        "\n",
        "lines = pd.read_csv(io.BytesIO(uploaded['iris.data.txt']))\n",
        "# for row in lines :\n",
        "print(lines)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G94FRBjF8Fw4",
        "outputId": "04885565-a111-4f9c-e52b-2efd42a26e16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     5.1  3.5  1.4  0.2     Iris-setosa\n",
            "0    4.9  3.0  1.4  0.2     Iris-setosa\n",
            "1    4.7  3.2  1.3  0.2     Iris-setosa\n",
            "2    4.6  3.1  1.5  0.2     Iris-setosa\n",
            "3    5.0  3.6  1.4  0.2     Iris-setosa\n",
            "4    5.4  3.9  1.7  0.4     Iris-setosa\n",
            "..   ...  ...  ...  ...             ...\n",
            "144  6.7  3.0  5.2  2.3  Iris-virginica\n",
            "145  6.3  2.5  5.0  1.9  Iris-virginica\n",
            "146  6.5  3.0  5.2  2.0  Iris-virginica\n",
            "147  6.2  3.4  5.4  2.3  Iris-virginica\n",
            "148  5.9  3.0  5.1  1.8  Iris-virginica\n",
            "\n",
            "[149 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "def loadDataset(filename, split, trainingSet=[] , testSet=[]):\n",
        "    with open(filename, 'r') as csvfile:\n",
        "        lines = csv.reader(csvfile)\n",
        "        dataset = list(lines)\n",
        "        for x in range(len(dataset)):\n",
        "            for y in range(4):\n",
        "                dataset[x][y] = float(dataset[x][y])\n",
        "            if random.random() < split:\n",
        "                trainingSet.append(dataset[x])\n",
        "            else: \n",
        "                testSet.append(dataset[x])"
      ],
      "metadata": {
        "id": "L_UDdwGw96rF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainingSet=[]\n",
        "testSet=[]\n",
        "loadDataset('/content/iris.data.txt', 0.66, trainingSet, testSet)\n",
        "print ('Train: ' + repr(len(trainingSet)))\n",
        "print ('Test: ' + repr(len(testSet)) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3oF-Jyk-BCx",
        "outputId": "22b57d06-6759-4f15-fbf3-16509300c36a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: 101\n",
            "Test: 49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "def euclideanDistance(instance1, instance2, length):\n",
        "    d=0\n",
        "    for x in range(length):\n",
        "        d=d+(instance1[x]-instance2[x])**2\n",
        "    return math.sqrt(d)"
      ],
      "metadata": {
        "id": "VgjZWi2U-JXW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = [2, 2, 2, 'a']\n",
        "data2 = [4, 4, 4, 'b']\n",
        "distance = euclideanDistance(data1, data2, 3)\n",
        "print ('Distance: ' + repr(distance))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Ci_mwZN-PGz",
        "outputId": "52eedc9c-543b-48ae-883d-110739c58a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distance: 3.4641016151377544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import operator\n",
        "def getNeighbors(trainingSet, testInstance, k):\n",
        "    distances = []\n",
        "    length = len(testInstance)-1\n",
        "    for x in range(len(trainingSet)):\n",
        "        dist = euclideanDistance(testInstance, trainingSet[x], length)\n",
        "        distances.append((trainingSet[x], dist))\n",
        "        distances.sort(key=operator.itemgetter(1))\n",
        "        neighbors = []\n",
        "    for x in range(k):\n",
        "        neighbors.append(distances[x][0])\n",
        "    return neighbors\n",
        "trainSet = [[2, 2, 2, 'a'], [4, 4, 4, 'b']]\n",
        "testInstance = [5, 5, 5]\n",
        "k = 1\n",
        "neighbors = getNeighbors(trainSet, testInstance, 1)\n",
        "print(neighbors)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_b_04fy-YK_",
        "outputId": "6a90c706-f965-4073-c7d8-0b5e37107060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4, 4, 4, 'b']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import operator\n",
        "def getResponse(neighbors):\n",
        "    classVotes = {}\n",
        "    for x in range(len(neighbors)):\n",
        "        response = neighbors[x][ -1 ] #complete with appropriate number\n",
        "        if response in classVotes:\n",
        "            classVotes[response]=classVotes[response]+1\n",
        "        else:\n",
        "            classVotes={response:1}\n",
        "    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)\n",
        "    return sortedVotes[0][0]\n",
        "neighbors = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n",
        "response = getResponse(neighbors)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntmxMKAI-XlI",
        "outputId": "59364ee3-b382-445d-fee8-54e34c6903e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def accuracy (testSet,predictions):\n",
        "    correct=0\n",
        "    for x in range (len(testSet)):\n",
        "        if (testSet[x][-1]==predictions[x]):\n",
        "            correct=correct+1\n",
        "    return (correct/float(len(testSet))) * 100.0\n",
        "testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n",
        "predictions = ['a', 'a', 'a']\n",
        "accuracy = accuracy(testSet, predictions)\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg6aVUtG-jMD",
        "outputId": "17061d74-5efa-4d75-9a1f-97fdc96ba2f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66.66666666666666\n"
          ]
        }
      ]
    }
  ]
}