KNN_Example.ipynb

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  "metadata": {
    "colab": {
      "name": "KNN-Example.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMalht3rNuVGsewAn7LaOfg"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRpVQTpydL7F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import neighbors, metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOeuT0sLwqzX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data\",\n",
        "                   names=[\"buying\",\"maint\",\"doors\",\"persons\",\"lug_boot\",\"safetly\",\"class\"],\n",
        "                   header=None)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AfdTPBXxLzl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f4d2d007-4d40-4725-fe36-283200103b7c"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>buying</th>\n",
              "      <th>maint</th>\n",
              "      <th>doors</th>\n",
              "      <th>persons</th>\n",
              "      <th>lug_boot</th>\n",
              "      <th>safetly</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "      <td>low</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "      <td>med</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>small</td>\n",
              "      <td>high</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>med</td>\n",
              "      <td>low</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>med</td>\n",
              "      <td>med</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  buying  maint doors persons lug_boot safetly  class\n",
              "0  vhigh  vhigh     2       2    small     low  unacc\n",
              "1  vhigh  vhigh     2       2    small     med  unacc\n",
              "2  vhigh  vhigh     2       2    small    high  unacc\n",
              "3  vhigh  vhigh     2       2      med     low  unacc\n",
              "4  vhigh  vhigh     2       2      med     med  unacc"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gK1exlrVzA3z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = data[[\"buying\",\"maint\",\"safetly\"]].values\n",
        "y = data[[\"class\"]]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qbl75_LIzRNs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#converting the data\n",
        "le = LabelEncoder()     "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pga0JI7r0xTs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(len(x[0])):\n",
        "  x[:,i]  = le.fit_transform(x[:,i])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTlM98Y02x4m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "28bf58f5-4e5f-4363-8b4d-dd0b0badd902"
      },
      "source": [
        "x"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3, 3, 1],\n",
              "       [3, 3, 2],\n",
              "       [3, 3, 0],\n",
              "       ...,\n",
              "       [1, 1, 1],\n",
              "       [1, 1, 2],\n",
              "       [1, 1, 0]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHPfP7Dd20g5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_mapping = {\n",
        "    \"unacc\" : 0,\n",
        "    \"acc\":1,\n",
        "    \"good\":2,\n",
        "    \"vgood\":3\n",
        "}"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWkBuEcH3ND7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "016e7ce0-5c27-4785-ed90-5f4762fae1be"
      },
      "source": [
        "y[\"class\"] = y[\"class\"].map(label_mapping)\n",
        "y"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1727</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1728 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      class\n",
              "0         0\n",
              "1         0\n",
              "2         0\n",
              "3         0\n",
              "4         0\n",
              "...     ...\n",
              "1723      2\n",
              "1724      3\n",
              "1725      0\n",
              "1726      2\n",
              "1727      3\n",
              "\n",
              "[1728 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCMmhOBg3u8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "knm = neighbors.KNeighborsClassifier(n_neighbors=25,weights=\"uniform\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tASwqM33_nP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=.2)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dc82oYt54aRA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "38573e0e-d081-4fa7-de65-fbe173de5e08"
      },
      "source": [
        "knm.fit(X_train,y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=25, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QO9iahoR4q71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = knm.predict(X_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHaKdBbe4xtW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accuracy = metrics.accuracy_score(y_test,prediction)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DgyCLdB5WcI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a14da39a-1a1a-484d-8b2a-bebeee37b58d"
      },
      "source": [
        "print(accuracy)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7572254335260116\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAWk2THS8KZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1402fbaf-285b-4af5-b068-a2b4dd7c8737"
      },
      "source": [
        "x.shape\n",
        "x[20:21,:].shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGypMX-G5o2X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5aa391a9-97c4-4281-e31e-2883761e04cd"
      },
      "source": [
        "##some testing...\n",
        "print(\"y actual values :\",y.loc[20,\"class\"])\n",
        "print(\"y predicted values:\",knm.predict(x[20:21,:]))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y actual values : 0\n",
            "y predicted values: [0]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}