fashion mnist.ipynb

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   "execution_count": 2,
   "id": "bcee72e0-c3c6-41ba-ab05-b5a43ee77c4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhavy\\AppData\\Local\\Temp\\ipykernel_29920\\4080736814.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a44056e4-4b68-4665-b042-c8da6b715fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df=pd.read_csv('C:\\\\Users\\\\bhavy\\\\Downloads\\\\fashion\\\\fashion-mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3888bb1-11f0-4a93-981f-6771f4f5597d",
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   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23b162c9-38f8-48b3-95a8-fc7eaef52426",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc6108bc-371f-4865-b66b-9d61af2a8336",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3684e037-95dc-4c78-855c-7f155cca0eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df.shape\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6b22c04-bd48-45e8-968a-2d77ab5bb9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df=df.sample(frac=0.3).reset_index(drop=True)\n",
    "df.shape"
   ]
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   "id": "9edc3762-7c91-4af1-8f67-6c696b739c63",
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eda490a2-27d8-41e4-b986-2a38403ca782",
   "metadata": {},
   "outputs": [],
   "source": [
    "LOOKUP={\n",
    "    0:\"T-shirt\",\n",
    "    1:\"Trouser\",\n",
    "    2:\"Pullover\",\n",
    "    3:\"Dress\",\n",
    "    4:\"Coat\",\n",
    "    5:\"Sandal\",\n",
    "    6:\"Shirt\",\n",
    "    7:\"Sneaker\",\n",
    "    8:\"Bag\",\n",
    "    9:\"Ankle Boot\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e485f0d-9194-4bce-a7ce-1dd54a1b19fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_image(features, actual_label):\n",
    "    print(\"Actual label: \",LOOKUP[actual_label])\n",
    "    plt.imshow(features.reshape(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2758eb64-7d0c-4b1e-af48-59d39e3dca5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[df.columns[1:]]\n",
    "Y=df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59277a78-22ef-4254-9bce-71d3cb254c53",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>60</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>123</td>\n",
       "      <td>111</td>\n",
       "      <td>43</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "      <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",
       "    </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",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
       "0       0       0       0       0       0       0       0       1       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0      60   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0        0  ...         9         0         0        11        56         4   \n",
       "1        0  ...         0         0         0         0         0         0   \n",
       "2      110  ...       123       111        43         0         0         0   \n",
       "3        0  ...         0         0         0         0         0         0   \n",
       "4        0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0         2         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6090fe55-7589-4de2-81fe-3678b33309da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    5\n",
       "2    0\n",
       "3    7\n",
       "4    7\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d0ece850-9381-4876-b561-eb7a26dcbc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[5].values[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fcc97eb-956f-4fae-8149-cb79a14f5aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.loc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "427337fd-c55a-4ef3-ae0f-5647e7bf812e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual label:  Sandal\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_image(X.loc[5].values,Y.loc[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb71e7f6-c912-4307-9531-6f0c381d4420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual label:  Trouser\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_image(X.loc[567].values,Y.loc[567])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dedd8a13-996e-4888-8a35-f018ebf2200b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual label:  Coat\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_image(X.loc[15088].values,Y.loc[15088])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8374870c-142e-40fa-9137-be3925c6a187",
   "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>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>pixel10</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "      <th>pixel784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.003922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.219608</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.007843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>...</td>\n",
       "      <td>0.482353</td>\n",
       "      <td>0.435294</td>\n",
       "      <td>0.168627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7    pixel8    pixel9  \\\n",
       "0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  0.003922  0.000000   \n",
       "1     0.0     0.0     0.0     0.0     0.0     0.0     0.0  0.000000  0.000000   \n",
       "2     0.0     0.0     0.0     0.0     0.0     0.0     0.0  0.000000  0.235294   \n",
       "3     0.0     0.0     0.0     0.0     0.0     0.0     0.0  0.000000  0.000000   \n",
       "4     0.0     0.0     0.0     0.0     0.0     0.0     0.0  0.000000  0.000000   \n",
       "\n",
       "    pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0  0.000000  ...  0.035294  0.000000  0.000000  0.043137  0.219608  0.015686   \n",
       "1  0.000000  ...  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2  0.431373  ...  0.482353  0.435294  0.168627  0.000000  0.000000  0.000000   \n",
       "3  0.000000  ...  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "4  0.000000  ...  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0  0.007843       0.0       0.0       0.0  \n",
       "1  0.000000       0.0       0.0       0.0  \n",
       "2  0.000000       0.0       0.0       0.0  \n",
       "3  0.000000       0.0       0.0       0.0  \n",
       "4  0.000000       0.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X/255\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba39776c-2839-4b61-a30f-efc6397ef10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "48d9dace-9539-4f70-a8f6-cbedea07715f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14400, 784), (14400,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6988253b-e1fc-4705-b483-dda892be6ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_classification(y_test,y_pred,avg_method=\"weighted\"):\n",
    "    acc=accuracy_score(y_test,y_pred,normalize=True)\n",
    "    num_acc=accuracy_score(y_test,y_pred,normalize=True)\n",
    "    prec=precision_score(y_test,y_pred,average=\"weighted\")\n",
    "    recall=recall_score(y_test,y_pred,average=\"weighted\")\n",
    "    print(\"Test data count: \",len(y_test))\n",
    "    print(\"accuracy_count\",num_acc)\n",
    "    print(\"accuracy_score\",acc)\n",
    "    print(\"precision_score\",prec)\n",
    "    print(\"recall_score\",recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28015e58-b58d-42ff-a911-2e3f313edc6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model=LogisticRegression(solver=\"sag\",multi_class=\"auto\",max_iter=10000).fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f4ade5-d392-4d8b-b460-87790b9f3dd4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa69a34d-f8cb-40c6-8d05-9c347054ea9e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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