Image_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a81d7c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.read_excel(\"files/Pumpkin_Seeds_Dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7feadeb",
   "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>Area</th>\n",
       "      <th>Perimeter</th>\n",
       "      <th>Major_Axis_Length</th>\n",
       "      <th>Minor_Axis_Length</th>\n",
       "      <th>Convex_Area</th>\n",
       "      <th>Equiv_Diameter</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>Extent</th>\n",
       "      <th>Roundness</th>\n",
       "      <th>Aspect_Ration</th>\n",
       "      <th>Compactness</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56276</td>\n",
       "      <td>888.242</td>\n",
       "      <td>326.1485</td>\n",
       "      <td>220.2388</td>\n",
       "      <td>56831</td>\n",
       "      <td>267.6805</td>\n",
       "      <td>0.7376</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>0.7453</td>\n",
       "      <td>0.8963</td>\n",
       "      <td>1.4809</td>\n",
       "      <td>0.8207</td>\n",
       "      <td>Çerçevelik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76631</td>\n",
       "      <td>1068.146</td>\n",
       "      <td>417.1932</td>\n",
       "      <td>234.2289</td>\n",
       "      <td>77280</td>\n",
       "      <td>312.3614</td>\n",
       "      <td>0.8275</td>\n",
       "      <td>0.9916</td>\n",
       "      <td>0.7151</td>\n",
       "      <td>0.8440</td>\n",
       "      <td>1.7811</td>\n",
       "      <td>0.7487</td>\n",
       "      <td>Çerçevelik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71623</td>\n",
       "      <td>1082.987</td>\n",
       "      <td>435.8328</td>\n",
       "      <td>211.0457</td>\n",
       "      <td>72663</td>\n",
       "      <td>301.9822</td>\n",
       "      <td>0.8749</td>\n",
       "      <td>0.9857</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.7674</td>\n",
       "      <td>2.0651</td>\n",
       "      <td>0.6929</td>\n",
       "      <td>Çerçevelik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66458</td>\n",
       "      <td>992.051</td>\n",
       "      <td>381.5638</td>\n",
       "      <td>222.5322</td>\n",
       "      <td>67118</td>\n",
       "      <td>290.8899</td>\n",
       "      <td>0.8123</td>\n",
       "      <td>0.9902</td>\n",
       "      <td>0.7396</td>\n",
       "      <td>0.8486</td>\n",
       "      <td>1.7146</td>\n",
       "      <td>0.7624</td>\n",
       "      <td>Çerçevelik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66107</td>\n",
       "      <td>998.146</td>\n",
       "      <td>383.8883</td>\n",
       "      <td>220.4545</td>\n",
       "      <td>67117</td>\n",
       "      <td>290.1207</td>\n",
       "      <td>0.8187</td>\n",
       "      <td>0.9850</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>0.8338</td>\n",
       "      <td>1.7413</td>\n",
       "      <td>0.7557</td>\n",
       "      <td>Çerçevelik</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Area  Perimeter  Major_Axis_Length  Minor_Axis_Length  Convex_Area  \\\n",
       "0  56276    888.242           326.1485           220.2388        56831   \n",
       "1  76631   1068.146           417.1932           234.2289        77280   \n",
       "2  71623   1082.987           435.8328           211.0457        72663   \n",
       "3  66458    992.051           381.5638           222.5322        67118   \n",
       "4  66107    998.146           383.8883           220.4545        67117   \n",
       "\n",
       "   Equiv_Diameter  Eccentricity  Solidity  Extent  Roundness  Aspect_Ration  \\\n",
       "0        267.6805        0.7376    0.9902  0.7453     0.8963         1.4809   \n",
       "1        312.3614        0.8275    0.9916  0.7151     0.8440         1.7811   \n",
       "2        301.9822        0.8749    0.9857  0.7400     0.7674         2.0651   \n",
       "3        290.8899        0.8123    0.9902  0.7396     0.8486         1.7146   \n",
       "4        290.1207        0.8187    0.9850  0.6752     0.8338         1.7413   \n",
       "\n",
       "   Compactness       Class  \n",
       "0       0.8207  Çerçevelik  \n",
       "1       0.7487  Çerçevelik  \n",
       "2       0.6929  Çerçevelik  \n",
       "3       0.7624  Çerçevelik  \n",
       "4       0.7557  Çerçevelik  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "793be86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "345d281c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2500 entries, 0 to 2499\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Area               2500 non-null   int64  \n",
      " 1   Perimeter          2500 non-null   float64\n",
      " 2   Major_Axis_Length  2500 non-null   float64\n",
      " 3   Minor_Axis_Length  2500 non-null   float64\n",
      " 4   Convex_Area        2500 non-null   int64  \n",
      " 5   Equiv_Diameter     2500 non-null   float64\n",
      " 6   Eccentricity       2500 non-null   float64\n",
      " 7   Solidity           2500 non-null   float64\n",
      " 8   Extent             2500 non-null   float64\n",
      " 9   Roundness          2500 non-null   float64\n",
      " 10  Aspect_Ration      2500 non-null   float64\n",
      " 11  Compactness        2500 non-null   float64\n",
      " 12  Class              2500 non-null   object \n",
      "dtypes: float64(10), int64(2), object(1)\n",
      "memory usage: 254.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49d8bdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area                 0\n",
       "Perimeter            0\n",
       "Major_Axis_Length    0\n",
       "Minor_Axis_Length    0\n",
       "Convex_Area          0\n",
       "Equiv_Diameter       0\n",
       "Eccentricity         0\n",
       "Solidity             0\n",
       "Extent               0\n",
       "Roundness            0\n",
       "Aspect_Ration        0\n",
       "Compactness          0\n",
       "Class                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ef74508",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cad4a91d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Class',axis='columns')\n",
    "Y = df.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ee352995",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_Scaled = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8986d930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.78470346, -2.21575484, -2.32022415, ...,  1.8737626 ,\n",
       "        -1.77506344,  2.19727996],\n",
       "       [-0.29478016, -0.56880361, -0.70091635, ...,  0.93837685,\n",
       "        -0.82486401,  0.84023019],\n",
       "       [-0.66135033, -0.43294002, -0.36939513, ..., -0.43161453,\n",
       "         0.07405883, -0.21148339],\n",
       "       ...,\n",
       "       [ 0.53695644,  0.7326892 ,  0.90028724, ..., -0.65517709,\n",
       "         0.76312751, -0.83346454],\n",
       "       [-0.04737457,  0.48215494,  0.8057821 , ..., -1.30619127,\n",
       "         1.29646729, -1.28581446],\n",
       "       [ 0.31297387,  0.27147071,  0.11193101, ...,  0.0316071 ,\n",
       "        -0.21587543,  0.1183551 ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57c54c9a",
   "metadata": {},
   "source": [
    "##### Now i transformed the datas into same features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bedf0d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_Scaled, Y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8ebf289",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb8f0027",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99d0a510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e98eaaf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.868"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7f2250c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "93f927b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[226,  25],\n",
       "       [ 41, 208]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test,y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "41b6870c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.722222222222214, 0.5, 'True')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.heatmap(cm,annot=True)\n",
    "plt.xlabel(\"predicted\")\n",
    "plt.ylabel(\"True\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "be31768e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Yes       0.85      0.90      0.87       251\n",
      "          No       0.89      0.84      0.86       249\n",
      "\n",
      "    accuracy                           0.87       500\n",
      "   macro avg       0.87      0.87      0.87       500\n",
      "weighted avg       0.87      0.87      0.87       500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target = [\"Yes\",\"No\"]\n",
    "cr = classification_report(Y_test,y_predicted,target_names=target)\n",
    "print(cr)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}