multivariate_model[2].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1318949b-bba4-4288-b665-db7c8e8b79d4",
   "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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area   Price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "data = [[2600, 550000], [3000, 565000], [3200, 610000],[3600, 680000],[4000, 725000]]\n",
    "df = pd.DataFrame(data, columns = ['Area', 'Price'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49f3fc65-ee60-4f1e-8531-a8c381420666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b03dd71c90>"
      ]
     },
     "execution_count": 7,
     "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": [
    "%matplotlib inline\n",
    "plt.scatter(df.Area,df.Price,color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82dddc64-ab1e-45ea-a650-fc7e0c213565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['Area']],df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14b5a3d6-94d9-4b35-96a6-5eeb8fc087c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[['Area']])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7bf866ae-bfca-4515-bf76-f13e46659432",
   "metadata": {},
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a9348ab6-3a3b-4d3b-b4aa-b4be236db245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0bb74506-f2cb-441e-b5aa-cbc9b486d463",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'joblib' from 'sklearn.externals' (C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\externals\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexternals\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m joblib\n\u001b[0;32m      2\u001b[0m joblib\u001b[38;5;241m.\u001b[39mdump(reg,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m)    \n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'joblib' from 'sklearn.externals' (C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\externals\\__init__.py)"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open('model_pickle', 'wb') as f:\n",
    "    pickle.dump(model,f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "932af72f-a487-4201-b639-f49622144a20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[5000].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_pickle\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m      2\u001b[0m     mp \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m5000\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_base.py:386\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    373\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    374\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[0;32m    375\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    384\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 386\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_base.py:369\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    366\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decision_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    367\u001b[0m     check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 369\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    370\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:605\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    603\u001b[0m         out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    604\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 605\u001b[0m     out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[0;32m    607\u001b[0m     out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\validation.py:938\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    936\u001b[0m     \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[0;32m    937\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 938\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    939\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    940\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    941\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    942\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    943\u001b[0m         )\n\u001b[0;32m    945\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(array\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    946\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    947\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    948\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    949\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[5000].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
     ]
    }
   ],
   "source": [
    "with open('model_pickle', 'rb') as f:\n",
    "    mp = pickle.load(f)\n",
    "w = \n",
    "mp.predict([5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "516a16ae-3ea7-45ad-9256-d4791951bc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "      <th></th>\n",
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       "      <th>income</th>\n",
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       "      <th>45</th>\n",
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       "      <td>35175.18898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2016</td>\n",
       "      <td>34229.19363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year       income\n",
       "45  2015  35175.18898\n",
       "46  2016  34229.19363"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"canada_per_capita_income.csv\")\n",
    "df.rename(columns={'per capita income (US$)': 'income'}, inplace=True)\n",
    "df.columns\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f471cfd3-63b6-4735-aaec-816097f2e7e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b03dd66a10>"
      ]
     },
     "execution_count": 31,
     "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.scatter(df.year,df.income,color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad8cb9c8-e955-4092-8396-cb683387c0bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['year'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m reg \u001b[38;5;241m=\u001b[39m linear_model\u001b[38;5;241m.\u001b[39mLinearRegression()\n\u001b[1;32m----> 2\u001b[0m reg\u001b[38;5;241m.\u001b[39mfit(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myear\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m,df\u001b[38;5;241m.\u001b[39mincome)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:3902\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3900\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m   3901\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 3902\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcolumns\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m   3904\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m   3905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6114\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   6111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   6112\u001b[0m     keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6114\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6116\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m   6117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m   6118\u001b[0m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6175\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   6173\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[0;32m   6174\u001b[0m         key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 6175\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6177\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m   6178\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['year'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['year']],df.income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8bc3e74-9ad4-4186-88e2-8339e8e1fdaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.predict([5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8036765-4163-464b-a591-3c770ba9974f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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