Simple Linear Regression on Boston Housing Dataset.ipynb

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   "cell_type": "code",
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   "id": "f4d983f1",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "import matplotlib.pyplot as plt        # for graphical representation of the data\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "from sklearn import linear_model       # required model import from sklearn library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc603031",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the dataset from sklearn datasets repository\n",
    "boston_dataset =  load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdbf3ec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "# We print the value of the boston_dataset to understand what it contains. \n",
    "\n",
    "print(boston_dataset.keys())\n",
    "\n",
    "# data: contains the information for various houses\n",
    "# target: prices of the house\n",
    "# feature_names: names of the features\n",
    "# DESCR: describes the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb855a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To know more about the features use boston_dataset.DESCR The description of all the features is given below.\n",
    "print(boston_dataset.DESCR) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d736bf49",
   "metadata": {},
   "outputs": [
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       "      <th>TAX</th>\n",
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       "      <td>54.2</td>\n",
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       "      <td>222.0</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
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       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
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   "source": [
    "# Put the data into pandas DataFrames\n",
    "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
    "boston.head()\n",
    "\n",
    "# The prices of the house indicated by the variable MEDV is our target variable and the remaining are the feature variables,\n",
    "# based on which we will predict the value of a house."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1f85362",
   "metadata": {},
   "outputs": [
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       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
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       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
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       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see that the target value MEDV is missing from the data.\n",
    "# We create a new column of target values and add it to the dataframe.\n",
    "\n",
    "boston['MEDV'] = boston_dataset.target\n",
    "boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4df5c123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we create a correlation matrix that measures the linear relationships between the variables.\n",
    "# The correlation matrix can be formed by using the corr function from the pandas dataframe library.\n",
    "# We will use the heatmap function from the seaborn library to plot the correlation matrix.\n",
    "\n",
    "\n",
    "correlation_matrix = boston.corr().round(2)\n",
    "\n",
    "# annot = True to print the values inside the square\n",
    "sns.heatmap(data=correlation_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6071b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After analyzing the heatmap, I choose RM (average number of rooms per dwelling), \n",
    "# as the Independent Variable because the correlation was 0.7 with MEDV (prices). MEDV is our dependent (target) variable.\n",
    "\n",
    "# create linear regression\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(boston[['RM']], boston.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8fbf5ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a553818610>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "%matplotlib inline\n",
    "plt.xlabel(\"RM (average number of rooms per dwelling)\")\n",
    "plt.ylabel(\"Prices (MEDV-Median value of owner-occupied homes in $1000's)\")\n",
    "plt.scatter(boston.RM , boston.MEDV , color='red' , marker='+')\n",
    "plt.plot(boston.RM , reg.predict(boston[['RM']]),color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ec5b3a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25.17574577, 23.77402099, 30.72803225, 29.02593787, 30.38215211,\n",
       "       23.85593997, 20.05125842, 21.50759586, 16.5833549 , 19.97844155,\n",
       "       23.3735282 , 20.02395209, 18.93169901, 19.47782555, 20.81583557,\n",
       "       18.43108302, 19.35039603, 19.85101202, 14.99048582, 17.45715736,\n",
       "       16.02812625, 19.6234593 , 21.23453259, 18.23993873, 19.25027283,\n",
       "       16.29208741, 18.23993873, 20.36983223, 24.44757706, 26.07685456,\n",
       "       17.32972783, 20.59738496, 19.48692766, 17.22050253, 20.81583557,\n",
       "       19.33219181, 18.49479778, 18.57671676, 19.63256141, 25.35778795,\n",
       "       29.26259271, 26.95065703, 21.48028953, 21.86257811, 20.57007863,\n",
       "       17.04756245, 17.99418179, 20.21509638, 14.47166561, 16.31939374,\n",
       "       19.60525508, 20.98877564, 24.5932108 , 19.92382889, 18.9225969 ,\n",
       "       31.31056723, 23.42814085, 27.36935404, 21.26183891, 19.27757916,\n",
       "       17.58458688, 19.63256141, 24.09259481, 26.87784015, 29.99076143,\n",
       "       22.58164472, 18.0032839 , 18.83157581, 16.24657686, 18.89529058,\n",
       "       23.73761256, 19.58705086, 20.53367019, 22.17204981, 22.42690886,\n",
       "       22.54523628, 22.48152152, 21.21632837, 22.05372239, 18.79516738,\n",
       "       26.55926634, 25.57623857, 22.69087002, 21.46208531, 23.4827535 ,\n",
       "       25.67636177, 20.07856475, 21.0433883 , 29.10785685, 29.7632087 ,\n",
       "       23.73761256, 23.62838725, 23.96516528, 21.86257811, 22.20845825,\n",
       "       25.63085122, 21.42567687, 38.77429659, 36.50787146, 32.83061943,\n",
       "       26.55926634, 27.05078022, 23.62838725, 21.18902204, 21.46208531,\n",
       "       18.58581887, 18.44928724, 21.09800095, 24.25643277, 22.02641607,\n",
       "       21.71694436, 26.45004103, 19.15014963, 20.77942714, 22.25396879,\n",
       "       19.28668126, 21.54400429, 20.1331774 , 18.77696316, 17.49356579,\n",
       "       18.75875894, 19.97844155, 19.58705086, 18.63132942, 18.84067792,\n",
       "       19.81460358, 16.41951693, 17.14768565, 23.86504208, 16.63796755,\n",
       "       24.11079902, 22.90932064, 23.32801765, 18.32185771, 17.73022063,\n",
       "       22.99123962, 19.41411079, 24.07439059, 18.64043153, 21.31645157,\n",
       "       21.52580007, 11.0128642 , 14.50807405, 15.09971113,  9.95701956,\n",
       "       21.12530728, 16.55604857, 10.16636806, 12.5329164 , 16.27388319,\n",
       "       21.05249041, 14.51717616, 10.94914944, 17.2933194 , 21.11620517,\n",
       "       21.32555368, 13.31569777, 28.52532188, 20.5427723 , 24.58410869,\n",
       "       22.21756036, 33.49507338, 36.34403349, 41.55954194, 18.6131252 ,\n",
       "       20.86134612, 37.50000134, 18.82247371, 22.84560588, 23.60108092,\n",
       "       18.80426949, 18.84978003, 16.04633047, 23.72851045, 18.65863574,\n",
       "       24.91178461, 20.12407529, 22.80919744, 27.76984683, 28.86209991,\n",
       "       36.00725546, 21.2527368 , 30.45496898, 25.06652047, 16.33759795,\n",
       "       21.33465578, 36.60799466, 27.05988233, 25.0028057 , 30.72803225,\n",
       "       28.59813875, 26.66849165, 30.66431749, 27.2237203 , 25.43970694,\n",
       "       37.00848745, 31.65644737, 30.01806775, 31.53811995, 28.81658937,\n",
       "       30.2729268 , 21.41657477, 34.59642857, 36.80824105, 38.45572278,\n",
       "       18.94990323, 22.90932064, 17.96687546, 20.52456809, 13.97104962,\n",
       "       19.57794875, 14.51717616, 18.18532608, 23.35532398, 14.58999303,\n",
       "       21.59861695, 18.9225969 , 25.78558708, 19.49602977, 23.33711976,\n",
       "       28.59813875, 21.43477898, 27.94278691, 25.56713646, 40.56741206,\n",
       "       44.74528008, 38.51033543, 30.52778586, 35.28818885, 24.96639727,\n",
       "       19.76909304, 32.79421099, 41.2136618 , 40.39447199, 26.55016423,\n",
       "       20.72481448, 25.68546388, 32.30269711, 24.32014753, 25.45791115,\n",
       "       28.10662487, 20.80673346, 23.20058813, 23.51916194, 16.23747476,\n",
       "       16.34670006, 20.92506088, 21.99910974, 23.8832463 , 26.47734736,\n",
       "       24.37476018, 23.92875684, 28.65275141, 40.5036973 , 20.92506088,\n",
       "       18.8133716 , 33.17649957, 44.5541358 , 32.07514438, 27.60600887,\n",
       "       30.89187022, 33.77723876, 41.76889045, 32.02053173, 30.91917654,\n",
       "       15.93710516, 29.17157162, 40.84957744, 33.32213331, 19.21386439,\n",
       "       18.63132942, 22.12653927, 24.83896774, 35.3336994 , 26.84143172,\n",
       "       27.71523418, 31.47440519, 27.46037513, 24.32924964, 27.3329456 ,\n",
       "       36.50787146, 28.7528746 , 34.91500238, 37.44538868, 29.84512768,\n",
       "       24.06528848, 22.03551818, 21.84437389, 22.80919744, 25.08472469,\n",
       "       27.77894894, 30.39125422, 25.67636177, 21.09800095, 20.02395209,\n",
       "       26.113263  , 24.93909094, 18.03059022, 23.08226071, 29.41732856,\n",
       "       27.86997003, 25.31227741, 24.44757706, 28.88030413, 31.19223981,\n",
       "       25.54893224, 32.86702786, 27.66972364, 25.72187231, 19.68717406,\n",
       "       10.59416719, 21.05249041, 20.15138162, 22.3631941 , 25.1029289 ,\n",
       "       17.25691096, 19.15925174, 17.95777335, 23.41903874, 20.97057143,\n",
       "       23.81953154, 23.36442609, 20.31521958, 17.28421729, 23.71940834,\n",
       "       23.86504208, 22.78189111, 20.69750816, 18.74055473, 22.9730354 ,\n",
       "       21.2527368 , 17.26601307, 20.22419849, 22.81829955, 22.76368689,\n",
       "       20.27881114, 18.74965683, 18.98631167, 20.47905754, 19.80550148,\n",
       "       19.65076562, 31.23775036, 24.85717196, 26.27710096, 27.89727636,\n",
       "       20.06946264, 19.01361799, 24.63872134, 25.72187231, 28.48891344,\n",
       "       24.40206651, 25.21215421, 18.88618847, 26.56836845, 16.87462238,\n",
       "       19.35949814, 21.87168021, 23.53736616, 21.09800095, 20.96146932,\n",
       "       23.56467249, 22.22666246, 14.13488758, 18.14891764, 45.24589608,\n",
       "       -2.25801069, 10.5031461 ,  0.49082622, 10.56686086, 26.15877354,\n",
       "       29.18977584, 21.90808865, 18.80426949,  9.98432589,  2.99390619,\n",
       "       31.8931022 , 25.84930184, 27.16910764, 23.40083452, 21.97180341,\n",
       "       28.7528746 , 24.90268251, 15.71865454, 15.5730208 ,  5.08739125,\n",
       "       13.36120832,  7.6723902 , 10.83992413,  9.74767105, 14.38974663,\n",
       "       17.32972783, 20.40624067, 11.16760005, 21.69874014, 18.9134948 ,\n",
       "       24.22912644, 23.62838725, 17.63919954, 14.9631795 , 18.59492098,\n",
       "       19.82370569, 23.06405649, 23.61928514, 14.01656016, 15.673144  ,\n",
       "       17.05666456,  2.99390619, 16.37400639, 16.45592537, 27.69702996,\n",
       "       17.73022063, 25.92211871,  7.45393959, 12.25075102,  6.46180971,\n",
       "       23.89234841, 27.05988233, 13.60696526, 19.55064242, 27.44217091,\n",
       "       23.6829999 , 19.99664576, 16.73809075, 20.87955034, 15.9826157 ,\n",
       "       18.99541378, 18.45838935, 21.78065912, 21.69874014, 23.40083452,\n",
       "       23.10956704, 27.52408989, 23.81042943, 23.91055263, 21.83527178,\n",
       "       25.66725966, 24.13810535, 21.32555368, 19.35039603, 16.54694646,\n",
       "       18.28544928, 23.63748936, 21.93539498, 24.35655597, 18.6131252 ,\n",
       "       24.11990113, 23.04585227, 22.22666246, 21.62592327, 23.73761256,\n",
       "       26.75951274, 25.90391449, 22.64535948, 32.62127092, 26.56836845,\n",
       "       24.72064033, 19.7235825 , 19.35949814, 22.68176791, 20.67930394,\n",
       "       26.32261151, 23.36442609, 22.82740166, 24.61141502, 21.84437389,\n",
       "       17.74842485, 19.50513188, 19.96933944, 19.26847705, 17.32972783,\n",
       "       21.46208531, 22.02641607, 23.91965474, 28.86209991, 14.72652466,\n",
       "       21.41657477, 24.34745386, 13.60696526, 21.62592327, 22.02641607,\n",
       "       22.14474348, 26.76861485, 29.59937074, 17.77573117, 18.76786105,\n",
       "       22.78189111, 20.97967353, 19.07733276, 14.97228161, 14.60819725,\n",
       "       11.68642026, 19.78729726, 19.78729726, 17.27511518, 19.26847705,\n",
       "       16.93833715, 14.38974663, 18.06699866, 20.11497318, 16.01902414,\n",
       "       20.18779005, 25.33958374, 21.03428619, 28.82569148, 27.16910764,\n",
       "       20.21509638])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(boston[['RM']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a21f1575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.10210898])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = reg.coef_\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9d471b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-34.67062077643857"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_inter = reg.intercept_\n",
    "y_inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17a4e4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22720.60183217])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM = 2500\n",
    "price = coef * RM + y_inter\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43130bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   rooms\n",
       "0     25\n",
       "1     10\n",
       "2     15\n",
       "3      8\n",
       "4     12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rooms_prices_prediction = pd.read_csv(\"D:/rooms-prices-to-predict.csv\")\n",
    "rooms_prices_prediction.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11aa25b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([192.88210375,  56.35046904, 101.86101394,  38.14625107,\n",
       "        74.554687  ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_prices = reg.predict(rooms_prices_prediction)\n",
    "predicted_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4464b90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms_prices_prediction['price'] = predicted_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42b00488",
   "metadata": {},
   "outputs": [],
   "source": [
    "rooms_prices_prediction.to_csv(\"D:/predicted-prices-of-rooms.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14d033c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rooms</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>192.882104</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>56.350469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>101.861014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>38.146251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>74.554687</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   rooms       price\n",
       "0     25  192.882104\n",
       "1     10   56.350469\n",
       "2     15  101.861014\n",
       "3      8   38.146251\n",
       "4     12   74.554687"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_prices_with_rooms = pd.read_csv(\"D:/predicted-prices-of-rooms.csv\")\n",
    "predicted_prices_with_rooms.head(5)"
   ]
  }
 ],
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