mieszkania5/model_regresji_liniowej.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from statistics import mean,median\n",
    "import re\n",
    "import numpy as np\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wczytanie datasetów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = pd.read_csv(\"./train/train.tsv\", sep = \"\\t\", header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>309000.0</td>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>390 zł</td>\n",
       "      <td>spółdzielcze własnościowe</td>\n",
       "      <td>7113</td>\n",
       "      <td>https://www.otodom.pl/oferta/niezalezny-uklad-...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.44</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gazowe</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Polecamy na sprzedaż dwupokojowe mieszkanie p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, meble, piwnica, g...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1 rows × 26 columns</p>\n",
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      "text/plain": [
       "         0                1       2                          3     4   \\\n",
       "0  309000.0  do zamieszkania  390 zł  spółdzielcze własnościowe  7113   \n",
       "\n",
       "                                                  5  6   7      8       9   \\\n",
       "0  https://www.otodom.pl/oferta/niezalezny-uklad-...  2 NaN  43.44  wtórny   \n",
       "\n",
       "   ...  16      17          18  19  20   21     22  \\\n",
       "0  ... NaN  gazowe  plastikowe NaN NaN  NaN  cegła   \n",
       "\n",
       "                                                  23  24  \\\n",
       "0   Polecamy na sprzedaż dwupokojowe mieszkanie p... NaN   \n",
       "\n",
       "                                                  25  \n",
       "0  telewizja kablowa, internet, meble, piwnica, g...  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wczytywanie danych testowych i preprocessing jak na treningu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMN 15:\n",
      "Value counts before changes:\n",
      " 15\n",
      " 1     569\n",
      " 2     527\n",
      " 0     452\n",
      " 4     357\n",
      " 3     321\n",
      " 5     117\n",
      " 6      51\n",
      " 7      42\n",
      " 8      32\n",
      " 10     29\n",
      " 11     24\n",
      " 9      21\n",
      "-1       5\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 15\n",
      " 1     569\n",
      " 2     527\n",
      " 0     452\n",
      " 4     357\n",
      " 3     321\n",
      " 5     117\n",
      " 6      51\n",
      " 7      42\n",
      " 8      32\n",
      " 10     29\n",
      " 11     24\n",
      " 9      21\n",
      "-1       5\n",
      "Name: count, dtype: int64\n",
      "COLUMN 8:\n",
      "0        43.44\n",
      "1        42.60\n",
      "2        44.30\n",
      "3        88.00\n",
      "4        77.00\n",
      "         ...  \n",
      "2542     94.00\n",
      "2543     53.50\n",
      "2544     55.25\n",
      "2545     62.00\n",
      "2546    392.00\n",
      "Name: 8, Length: 2547, dtype: float64\n",
      "COLUMN 6:\n",
      "Value counts before changes:\n",
      " 6\n",
      "2     1014\n",
      "3      878\n",
      "4      293\n",
      "1      271\n",
      "5       64\n",
      "6       13\n",
      "7        7\n",
      "10       6\n",
      "9        1\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 6\n",
      "2     1014\n",
      "3      878\n",
      "4      293\n",
      "1      271\n",
      "5       64\n",
      "6       13\n",
      "7        7\n",
      "10       6\n",
      "9        1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Preprocessing column 15:\n",
    "print(\"COLUMN 15:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",train_dataset[15].value_counts())\n",
    "\n",
    "# Replace string to int or NaN:\n",
    "train_dataset[15] = train_dataset[15].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
    "train_dataset[15] = train_dataset[15].apply(float)\n",
    "\n",
    "# Fill Nans with median:\n",
    "train_dataset[15].fillna(train_dataset[15].median(), inplace=True)\n",
    "train_dataset[15]= train_dataset[15].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",train_dataset[15].value_counts())\n",
    "\n",
    "# Preprocessing column 8:\n",
    "print(\"COLUMN 8:\")\n",
    "# Replace strings containing space to NaN:\n",
    "train_dataset[8] = train_dataset[8].replace(' ', np.nan, regex=True)\n",
    "\n",
    "# Fill Nans with median:\n",
    "train_dataset[8] = train_dataset[8].apply(float)\n",
    "train_dataset[8].fillna(train_dataset[8].median(), inplace=True)\n",
    "\n",
    "print(train_dataset[8])\n",
    "\n",
    "# Preprocessing column 6:\n",
    "print(\"COLUMN 6:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",train_dataset[6].value_counts())\n",
    "\n",
    "# Change string to 10:\n",
    "train_dataset[6] = train_dataset[6].replace({\"więcej niż 10\": 10})\n",
    "train_dataset[6] = train_dataset[6].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",train_dataset[6].value_counts())\n",
    "\n",
    "train_dataset[10].fillna(train_dataset[10].median(), inplace=True)\n",
    "train_dataset[10] = train_dataset[10].apply(float)\n",
    "\n",
    "train_dataset = train_dataset[[0,6,8,10,15]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = pd.read_csv(\"./dev-0/in.tsv\", sep= \"\\t\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMN 15:\n",
      "Value counts before changes:\n",
      " 14\n",
      " 1     108\n",
      " 2      89\n",
      " 0      82\n",
      " 4      65\n",
      " 3      54\n",
      " 5      22\n",
      " 6      12\n",
      " 7       9\n",
      " 11      9\n",
      " 10      5\n",
      " 8       3\n",
      "-1       2\n",
      " 9       2\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 14\n",
      " 1     108\n",
      " 2      89\n",
      " 0      82\n",
      " 4      65\n",
      " 3      54\n",
      " 5      22\n",
      " 6      12\n",
      " 7       9\n",
      " 11      9\n",
      " 10      5\n",
      " 8       3\n",
      "-1       2\n",
      " 9       2\n",
      "Name: count, dtype: int64\n",
      "COLUMN 8:\n",
      "0      59.10\n",
      "1      38.00\n",
      "2      63.84\n",
      "3      50.00\n",
      "4      65.62\n",
      "       ...  \n",
      "457    72.78\n",
      "458    51.23\n",
      "459    54.16\n",
      "460    90.10\n",
      "461    71.90\n",
      "Name: 7, Length: 462, dtype: float64\n",
      "COLUMN 6:\n",
      "Value counts before changes:\n",
      " 5\n",
      "2    196\n",
      "3    152\n",
      "1     51\n",
      "4     50\n",
      "5      9\n",
      "6      4\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 5\n",
      "2    196\n",
      "3    152\n",
      "1     51\n",
      "4     50\n",
      "5      9\n",
      "6      4\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Preprocessing column 15:\n",
    "print(\"COLUMN 15:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",test_dataset[14].value_counts())\n",
    "\n",
    "# Replace string to int or NaN:\n",
    "test_dataset[14] = test_dataset[14].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
    "test_dataset[14] = test_dataset[14].apply(float)\n",
    "\n",
    "# Fill Nans with median:\n",
    "test_dataset[14].fillna(test_dataset[14].median(), inplace=True)\n",
    "test_dataset[14]= test_dataset[14].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",test_dataset[14].value_counts())\n",
    "\n",
    "# Preprocessing column 8:\n",
    "print(\"COLUMN 8:\")\n",
    "# Replace strings containing space to NaN:\n",
    "test_dataset[7] = test_dataset[7].replace(' ', np.nan, regex=True)\n",
    "\n",
    "# Fill Nans with median:\n",
    "test_dataset[7] = test_dataset[7].apply(float)\n",
    "test_dataset[7].fillna(test_dataset[7].median(), inplace=True)\n",
    "\n",
    "print(test_dataset[7])\n",
    "\n",
    "# Preprocessing column 6:\n",
    "print(\"COLUMN 6:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",test_dataset[5].value_counts())\n",
    "\n",
    "# Change string to 10:\n",
    "test_dataset[5] = test_dataset[5].replace({\"więcej niż 10\": 10})\n",
    "test_dataset[5] = test_dataset[5].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",test_dataset[5].value_counts())\n",
    "\n",
    "test_dataset[9].fillna(test_dataset[9].median(), inplace=True)\n",
    "test_dataset[9] = test_dataset[9].apply(float)\n",
    "\n",
    "test_dataset = test_dataset[[5,7,9,14]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>5</th>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>59.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
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       "   5     7    9   14\n",
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   ],
   "source": [
    "test_dataset.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_dataset.drop(0,axis=1)\n",
    "y_train = train_dataset[[0]]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "trans_data = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg.fit(X_train, y_train)\n",
    "results = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.metrics import r2_score\n",
    "# pickle.dump(reg, open(\"model.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
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       "       [ 656199.3017448 ],\n",
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       "       [ 316359.12444587],\n",
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       "       [ 301726.6340505 ],\n",
       "       [ 239057.85565444],\n",
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       "       [ 325824.388963  ],\n",
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       "       [ 624904.92801633],\n",
       "       [ 421648.73037838],\n",
       "       [ 290610.1806833 ],\n",
       "       [ 288601.57055477],\n",
       "       [ 504376.11184616],\n",
       "       [ 246333.26344267],\n",
       "       [ 390474.87202575],\n",
       "       [ 676486.10550918],\n",
       "       [ 217145.59058386],\n",
       "       [ 253652.38012152],\n",
       "       [ 874149.26336545],\n",
       "       [ 241282.41631858],\n",
       "       [ 300762.91295673],\n",
       "       [ 364474.75217731],\n",
       "       [ 819789.82291398],\n",
       "       [ 227915.8527324 ],\n",
       "       [ 333171.00574275],\n",
       "       [ 406022.1093964 ],\n",
       "       [ 424620.79041928],\n",
       "       [ 433925.27065205],\n",
       "       [ 369693.34834663],\n",
       "       [ 659209.432679  ],\n",
       "       [ 377446.15042634],\n",
       "       [ 489887.75954764],\n",
       "       [ 215155.31778014],\n",
       "       [ 369953.55013564],\n",
       "       [ 541589.4962969 ],\n",
       "       [ 270941.83369892],\n",
       "       [ 342407.81015845],\n",
       "       [ 259234.77608678],\n",
       "       [ 433939.99000574],\n",
       "       [ 277310.70667081],\n",
       "       [ 675738.22998601],\n",
       "       [ 376222.07869798],\n",
       "       [ 381358.95913228],\n",
       "       [ 366112.32656763],\n",
       "       [ 311543.39889473],\n",
       "       [ 236978.24663136],\n",
       "       [ 321370.9939401 ],\n",
       "       [ 412013.97636694],\n",
       "       [ 321355.16969248],\n",
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       "       [ 382338.23329227],\n",
       "       [ 581803.00342058],\n",
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       "       [ 316897.22979356],\n",
       "       [ 352344.72600475],\n",
       "       [ 256796.27651754],\n",
       "       [ 453674.42663487],\n",
       "       [ 321897.63353148],\n",
       "       [ 307478.51951008],\n",
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       "       [ 443134.68009882],\n",
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       "       [ 476949.14679571],\n",
       "       [ 286093.7277434 ],\n",
       "       [ 585186.00373445],\n",
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       "       [ 318649.50357336],\n",
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       "       [ 316360.32253217],\n",
       "       [ 318678.94228073],\n",
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       "       [ 911134.08274977],\n",
       "       [ 427920.19747611],\n",
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       "       [1170092.55572165],\n",
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       "       [ 490663.22836409],\n",
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       "       [ 333969.41784446],\n",
       "       [ 282679.25692334],\n",
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       "       [ 538215.56556676],\n",
       "       [ 226111.82057552],\n",
       "       [ 352457.85116153],\n",
       "       [-161201.02176254],\n",
       "       [ 302303.81022042],\n",
       "       [ 323007.37024413],\n",
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       "       [1060592.82741372],\n",
       "       [ 248909.9801574 ],\n",
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       "       [ 528632.81740156],\n",
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       "       [ 390314.04386082],\n",
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       "       [ 349637.21447153],\n",
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       "       [ 301125.47078581],\n",
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       "       [ 305130.09668038],\n",
       "       [ 278844.04749905],\n",
       "       [ 302727.32114364],\n",
       "       [ 310444.76356463],\n",
       "       [ 430134.21928882],\n",
       "       [ 309671.80782539],\n",
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       "       [ 351846.05585305],\n",
       "       [ 704587.65260372],\n",
       "       [ 584703.8865779 ],\n",
       "       [ 293576.41296521],\n",
       "       [ 252313.71396867],\n",
       "       [ 718524.74266092],\n",
       "       [ 253189.43400231],\n",
       "       [ 302168.8466723 ],\n",
       "       [ 288735.60719799],\n",
       "       [ 498483.16389807],\n",
       "       [ 457172.49071198],\n",
       "       [ 333178.1247804 ],\n",
       "       [ 704558.21389635],\n",
       "       [ 468988.05558085],\n",
       "       [ 417037.85398029],\n",
       "       [ 245048.7518395 ],\n",
       "       [ 423113.73966605],\n",
       "       [ 354931.43920809],\n",
       "       [ 720067.89359305],\n",
       "       [ 749936.43382107],\n",
       "       [ 551719.98192466],\n",
       "       [ 441294.46634331],\n",
       "       [ 556690.74879784],\n",
       "       [ 592416.24176006],\n",
       "       [ 422202.56678133],\n",
       "       [ 318693.39045302],\n",
       "       [ 403430.30895423],\n",
       "       [ 604406.85270084],\n",
       "       [ 255244.59836449],\n",
       "       [ 374597.01387348],\n",
       "       [ 308150.766466  ],\n",
       "       [ 604268.00000018],\n",
       "       [ 616438.10869654],\n",
       "       [ 993142.48503895],\n",
       "       [ 395901.52706491],\n",
       "       [ 434344.3298916 ],\n",
       "       [ 485469.25484471],\n",
       "       [ 627355.43570469],\n",
       "       [ 310876.80170877],\n",
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       "       [ 447061.07115658],\n",
       "       [ 335585.89436361],\n",
       "       [ 327421.4232634 ],\n",
       "       [ 373803.94825239],\n",
       "       [ 373789.2288987 ],\n",
       "       [ 348443.52012806],\n",
       "       [ 372500.73495064],\n",
       "       [ 556630.4021154 ],\n",
       "       [ 553112.80262718],\n",
       "       [ 400867.67603802],\n",
       "       [ 569468.03490261],\n",
       "       [ 444493.62218294],\n",
       "       [ 514644.81461941],\n",
       "       [ 702403.53883714],\n",
       "       [ 608248.81678902],\n",
       "       [1162131.46450679],\n",
       "       [ 314668.1242534 ],\n",
       "       [ 344572.38851392],\n",
       "       [ 641419.64152483],\n",
       "       [ 406890.71039238],\n",
       "       [ 330256.77930708],\n",
       "       [ 406762.86588192],\n",
       "       [ 453663.96080749],\n",
       "       [1656917.6302292 ],\n",
       "       [ 449408.85843111],\n",
       "       [ 258707.30278287],\n",
       "       [ 345745.18314357],\n",
       "       [ 310109.76692089],\n",
       "       [ 424505.72665604],\n",
       "       [ 310095.0475672 ],\n",
       "       [ 401125.77300418],\n",
       "       [ 457988.13524458],\n",
       "       [ 550575.62607344],\n",
       "       [ 523303.70234502],\n",
       "       [ 506600.1301475 ],\n",
       "       [ 379014.11039314],\n",
       "       [ 352767.4964078 ],\n",
       "       [ 644515.6686963 ],\n",
       "       [ 537987.4698391 ]])"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>373000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>365000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>369000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483791.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>655544.02</td>\n",
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       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>471397.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>309958.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>699000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>850000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0    373000.00\n",
       "1    299000.00\n",
       "2    365000.00\n",
       "3    369000.00\n",
       "4    483791.00\n",
       "..         ...\n",
       "457  655544.02\n",
       "458  471397.97\n",
       "459  309958.00\n",
       "460  699000.00\n",
       "461  850000.00\n",
       "\n",
       "[462 rows x 1 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = pd.read_csv(\"./dev-0/expected.tsv\", header=None)\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6393762535622007"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71559.96181964973"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predykcja dla zbioru testowego"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_test_dataset = pd.read_csv(\"./test-A/in.tsv\", sep= \"\\t\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMN 15:\n",
      "Value counts before changes:\n",
      " 14\n",
      "1           92\n",
      "parter      70\n",
      "3           68\n",
      "4           64\n",
      "2           61\n",
      "5           15\n",
      "6           11\n",
      "7            7\n",
      "10           5\n",
      "> 10         5\n",
      "9            4\n",
      "8            2\n",
      "suterena     1\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 14\n",
      " 1     92\n",
      " 2     74\n",
      " 0     70\n",
      " 3     68\n",
      " 4     64\n",
      " 5     15\n",
      " 6     11\n",
      " 7      7\n",
      " 10     5\n",
      " 11     5\n",
      " 9      4\n",
      " 8      2\n",
      "-1      1\n",
      "Name: count, dtype: int64\n",
      "COLUMN 8:\n",
      "0      61.99\n",
      "1      64.00\n",
      "2      51.15\n",
      "3      45.77\n",
      "4      44.36\n",
      "       ...  \n",
      "413    34.97\n",
      "414    49.06\n",
      "415    76.71\n",
      "416    72.63\n",
      "417    65.84\n",
      "Name: 7, Length: 418, dtype: float64\n",
      "COLUMN 6:\n",
      "Value counts before changes:\n",
      " 5\n",
      "2                175\n",
      "3                143\n",
      "4                 50\n",
      "1                 40\n",
      "5                  6\n",
      "6                  2\n",
      "więcej niż 10      1\n",
      "8                  1\n",
      "Name: count, dtype: int64\n",
      "Value counts after changes:\n",
      " 5\n",
      "2     175\n",
      "3     143\n",
      "4      50\n",
      "1      40\n",
      "5       6\n",
      "6       2\n",
      "10      1\n",
      "8       1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Preprocessing column 15:\n",
    "print(\"COLUMN 15:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",final_test_dataset[14].value_counts())\n",
    "\n",
    "# Replace string to int or NaN:\n",
    "final_test_dataset[14] = final_test_dataset[14].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
    "final_test_dataset[14] = final_test_dataset[14].apply(float)\n",
    "\n",
    "# Fill Nans with median:\n",
    "final_test_dataset[14].fillna(final_test_dataset[14].median(), inplace=True)\n",
    "final_test_dataset[14]= final_test_dataset[14].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",final_test_dataset[14].value_counts())\n",
    "\n",
    "# Preprocessing column 8:\n",
    "print(\"COLUMN 8:\")\n",
    "# Replace strings containing space to NaN:\n",
    "final_test_dataset[7] = final_test_dataset[7].replace(' ', np.nan, regex=True)\n",
    "\n",
    "# Fill Nans with median:\n",
    "final_test_dataset[7] = final_test_dataset[7].apply(float)\n",
    "final_test_dataset[7].fillna(final_test_dataset[7].median(), inplace=True)\n",
    "\n",
    "print(final_test_dataset[7])\n",
    "\n",
    "# Preprocessing column 6:\n",
    "print(\"COLUMN 6:\")\n",
    "# Count the occurrence of unique values in column before preprocessing:\n",
    "print(\"Value counts before changes:\\n\",final_test_dataset[5].value_counts())\n",
    "\n",
    "# Change string to 10:\n",
    "final_test_dataset[5] = final_test_dataset[5].replace({\"więcej niż 10\": 10})\n",
    "final_test_dataset[5] = final_test_dataset[5].apply(int)\n",
    "\n",
    "# Count the occurrence of unique values in column after preprocessing:\n",
    "print(\"Value counts after changes:\\n\",final_test_dataset[5].value_counts())\n",
    "\n",
    "final_test_dataset[9].fillna(final_test_dataset[9].median(), inplace=True)\n",
    "final_test_dataset[9] = final_test_dataset[9].apply(float)\n",
    "\n",
    "final_test_dataset = final_test_dataset[[5,7,9,14]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
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       "     5      7     9   14\n",
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     "execution_count": 319,
     "metadata": {},
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    "final_test_dataset"
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  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_results = reg.predict(final_test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(final_results).to_csv(\"./test-A/out.tsv\", sep='\\t', index=False, header=None)"
   ]
  }
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