KNN_with_Python_.ipynb

{
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  "metadata": {
    "colab": {
      "name": "KNN with Python .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyP5F/MUuMECwDqfqT/hWEMn",
      "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/hussain0048/Machine-Learning/blob/master/KNN_with_Python_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset**"
      ],
      "metadata": {
        "id": "2EE6EUa9CgX9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3xvG_5WCB0xW"
      },
      "outputs": [],
      "source": [
        "# read in the iris data\n",
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "\n",
        "# create X (features) and y (response)\n",
        "X = iris.data\n",
        "y = iris.target"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **KNN (K=5)**\n"
      ],
      "metadata": {
        "id": "edWE1NORCzEM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X, y)\n",
        "y_pred = knn.predict(X)\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "id": "zUWFag3LC29s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **KNN (K=1)**\n"
      ],
      "metadata": {
        "id": "1iiDPsmkDNcK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X, y)\n",
        "y_pred = knn.predict(X)\n",
        "print(metrics.accuracy_score(y, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-nSWOTvDR96",
        "outputId": "06264400-522e-44c8-c0c5-c05fe88f9052"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Evaluation procedure #2: Train/test split**\n"
      ],
      "metadata": {
        "id": "gZFVYcunDf16"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the shapes of X and y\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMlebVPgDq4j",
        "outputId": "7d4efdfd-3afa-474d-d494-ee916a0d887b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(150, 4)\n",
            "(150,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# STEP 1: split X and y into training and testing sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
      ],
      "metadata": {
        "id": "UbBF5af_F9fM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print the shapes of the new X objects\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ue1TOw_GDpY",
        "outputId": "3c8dfdc9-963e-462d-9222-055679529f83"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(90, 4)\n",
            "(60, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the shapes of the new y objects\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "DyrZNBH5GHwI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Repeat for KNN with K=5:**\n",
        "\n"
      ],
      "metadata": {
        "id": "WVrAozv6GO0Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1BXJmzJGRTR",
        "outputId": "f57550a9-6e05-40bd-c7cc-3d987cae7780"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Repeat for KNN with K=1**\n",
        "\n"
      ],
      "metadata": {
        "id": "BkVRVhTtGZHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)\n",
        "print(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "bCyJ_HhzGkuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Can we locate an even better value for K?**\n",
        "\n"
      ],
      "metadata": {
        "id": "jZlQPqiNGsJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# try K=1 through K=25 and record testing accuracy\n",
        "k_range = list(range(1, 26))\n",
        "scores = []\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "TnvErmrEGxDY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import Matplotlib (scientific plotting library)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# allow plots to appear within the notebook\n",
        "%matplotlib inline\n",
        "\n",
        "# plot the relationship between K and testing accuracy\n",
        "plt.plot(k_range, scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Testing Accuracy')"
      ],
      "metadata": {
        "id": "DzRYpsiAG86g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **References**\n"
      ],
      "metadata": {
        "id": "bqzF4sMlG_yX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1-Comparing Machine Learning models in scikit-learn](https://github.com/justmarkham/scikit-learn-videos/blob/master/05_model_evaluation.ipynb)"
      ],
      "metadata": {
        "id": "N_FzM6jhHFlR"
      }
    }
  ]
}