linear_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "caa044dd-1af1-40b4-96c4-a77377ad5a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: scikit-learn in c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (0.24.2)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.2.1; however, version 21.1.1 is available.\n",
      "You should consider upgrading via the 'c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn) (1.20.3)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn) (1.0.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from scikit-learn) (1.6.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69d1019f-1848-4fa5-b02e-8609f71da829",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83a39ba7-d038-4b4c-b8a3-c9124c60929b",
   "metadata": {
    "tags": []
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Salary_Data.csv\")\n",
    "data.head(10)\n",
    "# data.info()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da9c41f1-c570-49b8-82c2-954b7b72eabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.1,  1.3,  1.5,  2. ,  2.2,  2.9,  3. ,  3.2,  3.2,  3.7,  3.9,\n",
       "        4. ,  4. ,  4.1,  4.5,  4.9,  5.1,  5.3,  5.9,  6. ,  6.8,  7.1,\n",
       "        7.9,  8.2,  8.7,  9. ,  9.5,  9.6, 10.3, 10.5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_x = data.iloc[:,0].values\n",
    "real_y = data.iloc[:,1].values\n",
    "real_x\n",
    "# real_x = real_x.reshape(-1,1)\n",
    "real_x\n",
    "#real_y = real_y.reshape(-1,1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d81d5031-707e-4e1c-8a08-5a79d1ec4f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b92cac8-20b3-480d-8da1-556d192fe99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_x, testing_x, training_y, testing_y = train_test_split(real_x, real_y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71f87fbf-8480-4f66-a4d4-9945276fc037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.5, 10.3,  4.1,  3.9,  9.5,  8.7,  9.6,  4. ,  5.3])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_x\n",
    "testing_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6ada8051-cdd2-406c-bc48-8d16b704b50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d3324302-abeb-412d-86a7-96473e8ed48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin = LinearRegression()\n",
    "lin.fit(training_x, training_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fb98c828-ed11-45fc-abe8-eac2a6441404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 37731.]\n",
      " [122391.]\n",
      " [ 57081.]\n",
      " [ 63218.]\n",
      " [116969.]\n",
      " [109431.]\n",
      " [112635.]\n",
      " [ 55794.]\n",
      " [ 83088.]]\n",
      "[[ 40817.78327049]\n",
      " [123188.08258899]\n",
      " [ 65154.46261459]\n",
      " [ 63282.41035735]\n",
      " [115699.87356004]\n",
      " [108211.66453108]\n",
      " [116635.89968866]\n",
      " [ 64218.43648597]\n",
      " [ 76386.77615802]]\n"
     ]
    }
   ],
   "source": [
    "pred_y = lin.predict(testing_x)\n",
    "print(testing_y)\n",
    "print(pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dba6683e-6808-43fa-82f9-85db71c2f95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([109431.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_y[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e246687d-52cd-4de5-9b04-84f45a3e40a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([108211.66453108])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7005e3c4-b807-4e32-894b-eb32b31e8800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9360.26128619]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d8dff207-80f4-4d6b-ba13-6d917914ccb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26777.3913412])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "06ac5092-c816-4a08-a7a9-5d7a25bde54e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123188.082588957"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#y=b0+b1*x\n",
    "9360.26128619*10.3+26777.3913412"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e887b4cc-bc87-4faa-8a75-133dde03291b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(training_x, training_y, color=\"green\")\n",
    "plt.plot(training_x, lin.predict(training_x), color=\"blue\")\n",
    "plt.title(\"salary vs. exp. training graph\")\n",
    "plt.xlabel(\"Exp.\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "836fc3d4-67e9-49c4-9b30-1ee1442fa506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(testing_x, testing_y, color=\"green\")\n",
    "plt.plot(training_x, lin.predict(training_x), color=\"blue\")\n",
    "plt.title(\"salary vs. exp. training graph\")\n",
    "plt.xlabel(\"Exp.\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1523e35b-d8ec-45d3-b89d-6eaee60ba919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 5\n",
    "menMeans = (20, 35, 30, 35, -27)\n",
    "womenMeans = (25, 32, 34, 20, -25)\n",
    "menStd = (2, 3, 4, 1, 2)\n",
    "womenStd = (3, 5, 2, 3, 3)\n",
    "ind = np.arange(N)    # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "ig, ax = plt.subplots()\n",
    "\n",
    "p1 = ax.bar(ind, menMeans, width, yerr=menStd, label='Men')\n",
    "p2 = ax.bar(ind, womenMeans, width,\n",
    "            bottom=menMeans, yerr=womenStd, label='Women')\n",
    "\n",
    "ax.axhline(0, color='grey', linewidth=0.8)\n",
    "ax.set_ylabel('Scores')\n",
    "ax.set_title('Scores by group and gender')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))\n",
    "ax.legend()\n",
    "\n",
    "# Label with label_type 'center' instead of the default 'edge'\n",
    "ax.bar_label(p1, label_type='center')\n",
    "ax.bar_label(p2, label_type='center')\n",
    "ax.bar_label(p2) \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2fec39a8-f8e5-4fd1-9872-3693ef07337d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a3ce112a-0b0f-4444-b014-554551923d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"model_pickle\", \"wb\") as file:\n",
    "    pickle.dump(lin, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e97b67a4-fe73-44d9-8814-33cd7664c8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"model_pickle\", \"rb\")as file:\n",
    "    m = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c84d93d9-cff6-41bc-ab87-1678dd5e56db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9360.26128619]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e404c32f-3533-4230-8dfb-3f1f7a2ce98e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[73578.69777217]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.predict([[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2fbac78c-9c65-4dc3-a848-2b38422a0e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26777.3913412])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66ae55e2-282d-47c1-93de-4594105d9a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9747fdd2-2cbf-47ca-b44b-a57569c7fd66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_joblib']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lin, 'model_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f068b060-7dd1-4787-9d1d-a8f58ec162da",
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = joblib.load('model_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f1f8193-bb90-4589-89d2-7acb2bb1c3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9360.26128619]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f6473cc2-9305-40ee-857d-892f6b097fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26777.3913412])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "36bccef3-94b0-4540-b107-1cf80de20a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[37073.67875601]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.predict([[1.1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dd60621-cfb4-48d6-bc7e-51578b72ca47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bafb244-1399-47f8-a62a-eb119e2469c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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