Image Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification mnist dataset\n",
    "#### Train data URL: https://www.kaggle.com/oddrationale/mnist-in-csv?select=mnist_train.csv\n",
    "#### Test data URL: https://www.kaggle.com/oddrationale/mnist-in-csv?select=mnist_test.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries/APIs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and viewing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the training dataset\n",
    "train_data = pd.read_csv('mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>1x1</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
       "0      5    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "1      0    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "2      4    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "3      1    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "4      9    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "\n",
       "   28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
       "0      0      0      0      0      0      0      0      0  \n",
       "1      0      0      0      0      0      0      0      0  \n",
       "2      0      0      0      0      0      0      0      0  \n",
       "3      0      0      0      0      0      0      0      0  \n",
       "4      0      0      0      0      0      0      0      0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# viewing the dataset\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 785 entries, label to 28x28\n",
      "dtypes: int64(785)\n",
      "memory usage: 359.3 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# information about the dataset\n",
    "print(train_data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing an image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualizing the image data in the forth row\n",
    "img = train_data.iloc[4, 1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784,)\n"
     ]
    }
   ],
   "source": [
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f139f9b4370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OJEHYgSQIO5AEYQeS+H+ctitrvLo9awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reshaping the image data\n",
    "img = img.reshape(28, 28).astype('uint8')\n",
    "\n",
    "# plotting the img\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating the input variables and traget parameter\n",
    "train_x = train_data.iloc[:, 1:]\n",
    "train_y = train_data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1x1</th>\n",
       "      <th>1x2</th>\n",
       "      <th>1x3</th>\n",
       "      <th>1x4</th>\n",
       "      <th>1x5</th>\n",
       "      <th>1x6</th>\n",
       "      <th>1x7</th>\n",
       "      <th>1x8</th>\n",
       "      <th>1x9</th>\n",
       "      <th>1x10</th>\n",
       "      <th>...</th>\n",
       "      <th>28x19</th>\n",
       "      <th>28x20</th>\n",
       "      <th>28x21</th>\n",
       "      <th>28x22</th>\n",
       "      <th>28x23</th>\n",
       "      <th>28x24</th>\n",
       "      <th>28x25</th>\n",
       "      <th>28x26</th>\n",
       "      <th>28x27</th>\n",
       "      <th>28x28</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  1x10  ...  28x19  28x20  \\\n",
       "0    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
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       "3    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "4    0    0    0    0    0    0    0    0    0     0  ...      0      0   \n",
       "\n",
       "   28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
       "0      0      0      0      0      0      0      0      0  \n",
       "1      0      0      0      0      0      0      0      0  \n",
       "2      0      0      0      0      0      0      0      0  \n",
       "3      0      0      0      0      0      0      0      0  \n",
       "4      0      0      0      0      0      0      0      0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    0\n",
       "2    4\n",
       "3    1\n",
       "4    9\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using Random Forest Classifier for classifictaion\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the test data\n",
    "test_data = pd.read_csv('mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   label  1x1  1x2  1x3  1x4  1x5  1x6  1x7  1x8  1x9  ...  28x19  28x20  \\\n",
       "0      7    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
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       "4      4    0    0    0    0    0    0    0    0    0  ...      0      0   \n",
       "\n",
       "   28x21  28x22  28x23  28x24  28x25  28x26  28x27  28x28  \n",
       "0      0      0      0      0      0      0      0      0  \n",
       "1      0      0      0      0      0      0      0      0  \n",
       "2      0      0      0      0      0      0      0      0  \n",
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       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating the input variables and traget parameter\n",
    "test_x = test_data.iloc[:, 1:]\n",
    "test_y = test_data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting\n",
    "predictions = rfc.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9689\n"
     ]
    }
   ],
   "source": [
    "# priniting the accuracy\n",
    "print('Accuracy: ', accuracy_score(test_y, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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