KNN MNSIT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recognising Handwritten Digits on MNIST Dataset using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 785)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('mnist_train.csv')\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 784) (42000,)\n"
     ]
    }
   ],
   "source": [
    "X = data[:,1:]\n",
    "Y = data[:,0]\n",
    "\n",
    "print(X.shape, Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33600\n"
     ]
    }
   ],
   "source": [
    "split = int(0.8*X.shape[0])\n",
    "print(split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33600, 784) (33600,)\n",
      "(8400, 784) (8400,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X[:split,:]\n",
    "Y_train = Y[:split]\n",
    "\n",
    "X_test = X[split:,:]\n",
    "Y_test = Y[split:]\n",
    "\n",
    "print(X_train.shape,Y_train.shape)\n",
    "print(X_test.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize some samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "def drawImg(sample):\n",
    "    img = sample.reshape((28,28))\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.show()\n",
    "    \n",
    "drawImg(X_train[3])\n",
    "print(Y_train[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(x1,x2):\n",
    "    return np.sqrt(sum((x1-x2)**2))\n",
    "\n",
    "def knn(X,Y,queryPoint,k=5):\n",
    "     \n",
    "    vals = []\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    for i in range(m):\n",
    "        d = dist(queryPoint,X[i])\n",
    "        vals.append((d,Y[i]))\n",
    "    \n",
    "    vals = sorted(vals)\n",
    "    \n",
    "    #Nearest/First K points\n",
    "    vals = vals[:k]\n",
    "    vals = np.array(vals)\n",
    "    \n",
    "    #print(vals)\n",
    "    \n",
    "    new_vals = np.unique(vals[:,1], return_counts=True)\n",
    "    #print(new_vals)\n",
    "    \n",
    "    index = new_vals[1].argmax()\n",
    "    pred = new_vals[0][index]\n",
    "    \n",
    "    return pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "pred = knn(X_train, Y_train, X_test[0])\n",
    "print(int(pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0H9DVwLUzZ1FT5qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "drawImg(X_test[0])\n",
    "print(Y_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}