SVM_Mnist_digit classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "14fe005e",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
      "     ---------------------------------------- 0.0/293.3 kB ? eta -:--:--\n",
      "     - -------------------------------------- 10.2/293.3 kB ? eta -:--:--\n",
      "     --------- --------------------------- 71.7/293.3 kB 991.0 kB/s eta 0:00:01\n",
      "     -------------------------------------- 293.3/293.3 kB 2.6 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from seaborn) (1.24.2)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from seaborn) (2.0.1)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.40.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\amit.parmar\\anaconda3\\envs\\ml_env\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.12.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca415385",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt \n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a9dd73b",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07b0d63b",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2760f21cf10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(digits.images[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14ba0581",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  7.,  8., 13., 16., 15.,  1.,  0.,  0.,  7.,  7.,  4.,\n",
       "       11., 12.,  0.,  0.,  0.,  0.,  0.,  8., 13.,  1.,  0.,  0.,  4.,\n",
       "        8.,  8., 15., 15.,  6.,  0.,  0.,  2., 11., 15., 15.,  4.,  0.,\n",
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     },
     "execution_count": 12,
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   "source": [
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       "    <tr>\n",
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       "<p>1797 rows × 64 columns</p>\n",
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       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba782bf3",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "df['target']=digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af788d6f",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</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>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1     2     3     4     5    6    7    8    9  ...   55   56   57   \n",
       "0   0.0  0.0   5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  \\\n",
       "1   0.0  0.0   0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2   0.0  0.0   0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3   0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0  0.0   \n",
       "4   0.0  0.0   0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "..  ...  ...   ...   ...   ...   ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9   0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "0   0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9   0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  0.0  0.0  1.0   \n",
       "\n",
       "     58    59    60    61   62   63  target  \n",
       "0   6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
       "1   0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
       "2   0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
       "3   7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
       "4   0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
       "..  ...   ...   ...   ...  ...  ...     ...  \n",
       "9   2.0  14.0  15.0   9.0  0.0  0.0       9  \n",
       "0   6.0  16.0  14.0   6.0  0.0  0.0       0  \n",
       "8   2.0   9.0  13.0   6.0  0.0  0.0       8  \n",
       "9   5.0  12.0  16.0  12.0  0.0  0.0       9  \n",
       "8   8.0  12.0  14.0  12.0  1.0  0.0       8  \n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "223f588a",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a28180b",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "X=df.drop('target',axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc0c82ed",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb3123e9",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b36acdaf",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(kernel='rbf')\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1045eb1c",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b671aecb",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27  0  0  0  1  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 37  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 36  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 41  0  0  0  2  0]\n",
      " [ 0  0  0  0  0 34  1  0  0  0]\n",
      " [ 0  0  0  0  0  0 41  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 20  0  0]\n",
      " [ 0  1  0  0  0  0  0  0 38  0]\n",
      " [ 0  0  0  0  0  0  0  0  1 41]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)\n",
    "print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7d9bf197",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'truth')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True)\n",
    "plt.xlabel(\"predicted\")\n",
    "plt.ylabel(\"truth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "60fe8be2",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 9, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(digits.data[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97e54569",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "09473929",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'reshape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5012\\1124305118.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\amit.parmar\\Anaconda3\\envs\\ML_env\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5985\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5986\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5987\u001b[0m         ):\n\u001b[0;32m   5988\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5989\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'reshape'"
     ]
    }
   ],
   "source": [
    "X_test[1].reshape(-1, 1)\n",
    "model.predict(X_test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2de73361",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8ce4719e",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "with open('model_pickle','wb') as f:\n",
    "    pickle.dump(model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2ad119fe",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "with open('model_pickle','rb') as f:\n",
    "    mp=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b62a9a81",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8780fcd7",
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": []
  }
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