Supervised learning - Classsification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import KNeighborsClassifier from sklearn.neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Create arrays for the features and the response variable\n",
    "y = df['party'].values\n",
    "X = df.drop('party', axis=1).values\n",
    "\n",
    "# Create a k-NN classifier with 6 neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "knn.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import KNeighborsClassifier from sklearn.neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "\n",
    "# Create arrays for the features and the response variable\n",
    "y = df['party'].values\n",
    "X = df.drop('party', axis=1).values\n",
    "\n",
    "# Create a k-NN classifier with 6 neighbors: knn\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "knn.fit(X, y)\n",
    "\n",
    "\n",
    "\n",
    "# Predict the labels for the training data X\n",
    "y_pred = knn.predict(X)\n",
    "\n",
    "# Predict and print the label for the new data point X_new\n",
    "new_prediction = knn.predict(X_new)\n",
    "print(\"Prediction: {}\".format(new_prediction))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])\n",
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "(1797, 8, 8)\n",
      "(1797, 64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the digits dataset: digits\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Print the keys and DESCR of the dataset\n",
    "print(digits.keys())\n",
    "print(digits.DESCR)\n",
    "\n",
    "# Print the shape of the images and data keys\n",
    "print(digits.images.shape)\n",
    "print(digits.data.shape)\n",
    "\n",
    "# Display digit 1010\n",
    "plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9833333333333333\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create feature and target arrays\n",
    "y = digits.target\n",
    "X = digits.data\n",
    "\n",
    "# Split into training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Create a k-NN classifier with 7 neighbors: knn\n",
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Print the accuracy\n",
    "print(knn.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(type(np.arange(9)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup arrays to store train and test accuracies\n",
    "neighbors = np.arange(1, 9)\n",
    "train_accuracy = np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "# Loop over different values of k\n",
    "for i, k in enumerate(neighbors):\n",
    "    # Setup a k-NN Classifier with k neighbors: knn\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "\n",
    "    # Fit the classifier to the training data\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    #Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "\n",
    "    #Compute accuracy on the testing set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)\n",
    "\n",
    "# Generate plot\n",
    "plt.title('k-NN: Varying Number of Neighbors')\n",
    "plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')\n",
    "plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()\n"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}