11 Machine Learning - Image Classification Multiclass.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de0ae5e4",
   "metadata": {},
   "source": [
    "#### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f89ef56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# letterdata.csv\n",
    "# location: https://mitu.co.in/dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1a37dc",
   "metadata": {},
   "source": [
    "#### Import all libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "00e6f79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2453a48",
   "metadata": {},
   "source": [
    "#### Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f54dd294",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('letterdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3038684c",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>letter</th>\n",
       "      <th>xbox</th>\n",
       "      <th>ybox</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
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      ],
      "text/plain": [
       "      letter  xbox  ybox  width  height  onpix  xbar  ybar  x2bar  y2bar  \\\n",
       "0          T     2     8      3       5      1     8    13      0      6   \n",
       "1          I     5    12      3       7      2    10     5      5      4   \n",
       "2          D     4    11      6       8      6    10     6      2      6   \n",
       "3          N     7    11      6       6      3     5     9      4      6   \n",
       "4          G     2     1      3       1      1     8     6      6      6   \n",
       "...      ...   ...   ...    ...     ...    ...   ...   ...    ...    ...   \n",
       "19995      D     2     2      3       3      2     7     7      7      6   \n",
       "19996      C     7    10      8       8      4     4     8      6      9   \n",
       "19997      T     6     9      6       7      5     6    11      3      7   \n",
       "19998      S     2     3      4       2      1     8     7      2      6   \n",
       "19999      A     4     9      6       6      2     9     5      3      1   \n",
       "\n",
       "       xybar  x2ybar  xy2bar  xedge  xedgey  yedge  yedgex  \n",
       "0          6      10       8      0       8      0       8  \n",
       "1         13       3       9      2       8      4      10  \n",
       "2         10       3       7      3       7      3       9  \n",
       "3          4       4      10      6      10      2       8  \n",
       "4          6       5       9      1       7      5      10  \n",
       "...      ...     ...     ...    ...     ...    ...     ...  \n",
       "19995      6       6       4      2       8      3       7  \n",
       "19996     12       9      13      2       9      3       7  \n",
       "19997     11       9       5      2      12      2       4  \n",
       "19998     10       6       8      1       9      5       8  \n",
       "19999      8       1       8      2       7      2       8  \n",
       "\n",
       "[20000 rows x 17 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a0763b6",
   "metadata": {},
   "source": [
    "#### Separate the input and output data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fb39da4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('letter', axis = 1)\n",
    "y = df['letter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "70d1f800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.countplot(y = y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "37ddbd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "U    813\n",
       "D    805\n",
       "P    803\n",
       "T    796\n",
       "M    792\n",
       "A    789\n",
       "X    787\n",
       "Y    786\n",
       "N    783\n",
       "Q    783\n",
       "F    775\n",
       "G    773\n",
       "E    768\n",
       "B    766\n",
       "V    764\n",
       "L    761\n",
       "R    758\n",
       "I    755\n",
       "O    753\n",
       "W    752\n",
       "S    748\n",
       "J    747\n",
       "K    739\n",
       "C    736\n",
       "H    734\n",
       "Z    734\n",
       "Name: letter, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9d9e8e",
   "metadata": {},
   "source": [
    "#### Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3fffeab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "06dc3047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16000, 16)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4726c459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 16)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e796c21",
   "metadata": {},
   "source": [
    "#### Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a63c963b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c80645f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e69096e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b004d2c9",
   "metadata": {},
   "source": [
    "#### Performance Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d2e30a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f6f908b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6cfe270e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f48255dae50>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6f683248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96775"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ba283ffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.99      1.00      1.00       147\n",
      "           B       0.94      0.96      0.95       158\n",
      "           C       0.97      0.99      0.98       156\n",
      "           D       0.93      0.97      0.95       171\n",
      "           E       0.97      0.94      0.96       145\n",
      "           F       0.95      0.95      0.95       167\n",
      "           G       0.95      0.95      0.95       182\n",
      "           H       0.94      0.90      0.92       123\n",
      "           I       0.97      0.93      0.95       127\n",
      "           J       0.97      0.96      0.97       159\n",
      "           K       0.96      0.93      0.94       143\n",
      "           L       0.98      0.98      0.98       159\n",
      "           M       0.99      0.97      0.98       173\n",
      "           N       0.96      0.99      0.97       134\n",
      "           O       0.98      0.96      0.97       142\n",
      "           P       0.98      0.96      0.97       165\n",
      "           Q       0.93      0.99      0.96       145\n",
      "           R       0.92      0.97      0.94       149\n",
      "           S       0.99      0.97      0.98       154\n",
      "           T       0.99      0.98      0.99       177\n",
      "           U       0.99      0.98      0.98       160\n",
      "           V       0.96      0.97      0.96       153\n",
      "           W       0.98      0.97      0.98       141\n",
      "           X       0.98      0.98      0.98       173\n",
      "           Y       0.99      0.99      0.99       154\n",
      "           Z       1.00      0.99      1.00       143\n",
      "\n",
      "    accuracy                           0.97      4000\n",
      "   macro avg       0.97      0.97      0.97      4000\n",
      "weighted avg       0.97      0.97      0.97      4000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eb451f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = pd.concat([x.iloc[67:68,:], x.iloc[767:768,:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c334d732",
   "metadata": {},
   "outputs": [
    {
     "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>xbox</th>\n",
       "      <th>ybox</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>onpix</th>\n",
       "      <th>xbar</th>\n",
       "      <th>ybar</th>\n",
       "      <th>x2bar</th>\n",
       "      <th>y2bar</th>\n",
       "      <th>xybar</th>\n",
       "      <th>x2ybar</th>\n",
       "      <th>xy2bar</th>\n",
       "      <th>xedge</th>\n",
       "      <th>xedgey</th>\n",
       "      <th>yedge</th>\n",
       "      <th>yedgex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     xbox  ybox  width  height  onpix  xbar  ybar  x2bar  y2bar  xybar  \\\n",
       "67      2     3      3       2      2     7     7      5      7      7   \n",
       "767     3     9      5       6      3     5     4      2      8      6   \n",
       "\n",
       "     x2ybar  xy2bar  xedge  xedgey  yedge  yedgex  \n",
       "67        6       8      2       8      5      10  \n",
       "767       1      10      0       6      3       7  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a1a9e37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['E', 'L'], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "811e6d67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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