Forest_Fires_KNN.ipynb

{
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area\n",
       "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0\n",
       "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0\n",
       "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0\n",
       "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0\n",
       "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74075460-dd5c-4b72-bfd6-02cd1c46e49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['area'] = np.log(data['area'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b56278ab-9952-458f-86e9-be619ec8cbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(517, 13)"
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "data.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da6344c6-533d-43ef-85d2-2e67bf395803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 517 entries, 0 to 516\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   X       517 non-null    int64  \n",
      " 1   Y       517 non-null    int64  \n",
      " 2   month   517 non-null    object \n",
      " 3   day     517 non-null    object \n",
      " 4   FFMC    517 non-null    float64\n",
      " 5   DMC     517 non-null    float64\n",
      " 6   DC      517 non-null    float64\n",
      " 7   ISI     517 non-null    float64\n",
      " 8   temp    517 non-null    float64\n",
      " 9   RH      517 non-null    int64  \n",
      " 10  wind    517 non-null    float64\n",
      " 11  rain    517 non-null    float64\n",
      " 12  area    517 non-null    float64\n",
      "dtypes: float64(8), int64(3), object(2)\n",
      "memory usage: 52.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e7a0630-11c1-4dc3-8643-e24edd34ae6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
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       "      <th>rain</th>\n",
       "      <th>area</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.669246</td>\n",
       "      <td>4.299807</td>\n",
       "      <td>90.644681</td>\n",
       "      <td>110.872340</td>\n",
       "      <td>547.940039</td>\n",
       "      <td>9.021663</td>\n",
       "      <td>18.889168</td>\n",
       "      <td>44.288201</td>\n",
       "      <td>4.017602</td>\n",
       "      <td>0.021663</td>\n",
       "      <td>1.111026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.313778</td>\n",
       "      <td>1.229900</td>\n",
       "      <td>5.520111</td>\n",
       "      <td>64.046482</td>\n",
       "      <td>248.066192</td>\n",
       "      <td>4.559477</td>\n",
       "      <td>5.806625</td>\n",
       "      <td>16.317469</td>\n",
       "      <td>1.791653</td>\n",
       "      <td>0.295959</td>\n",
       "      <td>1.398436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>437.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>91.600000</td>\n",
       "      <td>108.300000</td>\n",
       "      <td>664.200000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>142.400000</td>\n",
       "      <td>713.900000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.024193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>96.200000</td>\n",
       "      <td>291.300000</td>\n",
       "      <td>860.600000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.995620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                X           Y        FFMC         DMC          DC         ISI  \\\n",
       "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000   \n",
       "mean     4.669246    4.299807   90.644681  110.872340  547.940039    9.021663   \n",
       "std      2.313778    1.229900    5.520111   64.046482  248.066192    4.559477   \n",
       "min      1.000000    2.000000   18.700000    1.100000    7.900000    0.000000   \n",
       "25%      3.000000    4.000000   90.200000   68.600000  437.700000    6.500000   \n",
       "50%      4.000000    4.000000   91.600000  108.300000  664.200000    8.400000   \n",
       "75%      7.000000    5.000000   92.900000  142.400000  713.900000   10.800000   \n",
       "max      9.000000    9.000000   96.200000  291.300000  860.600000   56.100000   \n",
       "\n",
       "             temp          RH        wind        rain        area  \n",
       "count  517.000000  517.000000  517.000000  517.000000  517.000000  \n",
       "mean    18.889168   44.288201    4.017602    0.021663    1.111026  \n",
       "std      5.806625   16.317469    1.791653    0.295959    1.398436  \n",
       "min      2.200000   15.000000    0.400000    0.000000    0.000000  \n",
       "25%     15.500000   33.000000    2.700000    0.000000    0.000000  \n",
       "50%     19.300000   42.000000    4.000000    0.000000    0.418710  \n",
       "75%     22.800000   53.000000    4.900000    0.000000    2.024193  \n",
       "max     33.300000  100.000000    9.400000    6.400000    6.995620  "
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "data.describe()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44fe8f5e-69ac-4cbf-8b1b-1cc455c2f484",
   "metadata": {},
   "outputs": [],
   "source": [
    "features= data.iloc[:, : -1]\n",
    "predictions= data.iloc[:, -1]"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "id": "61bfa237-bf98-4688-a05c-8e74043a2fa6",
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
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       "      <th>rain</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain\n",
       "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0\n",
       "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0\n",
       "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0\n",
       "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2\n",
       "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2554cd57-83a1-4b3c-bc09-999970bcf17a",
   "metadata": {},
   "outputs": [
    {
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       "0    0.0\n",
       "1    0.0\n",
       "2    0.0\n",
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       "4    0.0\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd8a885f-eaa5-4ae6-b429-429f3f90e63f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          X         Y      FFMC       DMC        DC       ISI      temp  \\\n",
      "0  1.008313  0.569860 -0.805959 -1.323326 -1.830477 -0.860946 -1.842640   \n",
      "1  1.008313 -0.244001 -0.008102 -1.179541  0.488891 -0.509688 -0.153278   \n",
      "2  1.008313 -0.244001 -0.008102 -1.049822  0.560715 -0.509688 -0.739383   \n",
      "3  1.440925  1.383722  0.191362 -1.212361 -1.898266 -0.004756 -1.825402   \n",
      "4  1.440925  1.383722 -0.243833 -0.931043 -1.798600  0.126966 -1.291012   \n",
      "\n",
      "         RH      wind      rain  ...  month_may  month_nov  month_oct  \\\n",
      "0  0.411724  1.498614 -0.073268  ...  -0.062318  -0.044023  -0.172860   \n",
      "1 -0.692456 -1.741756 -0.073268  ...  -0.062318  -0.044023   5.785038   \n",
      "2 -0.692456 -1.518282 -0.073268  ...  -0.062318  -0.044023   5.785038   \n",
      "3  3.233519 -0.009834  0.603155  ...  -0.062318  -0.044023  -0.172860   \n",
      "4  3.356206 -1.238940 -0.073268  ...  -0.062318  -0.044023  -0.172860   \n",
      "\n",
      "   month_sep   day_mon   day_sat   day_sun   day_thu   day_tue   day_wed  \n",
      "0  -0.706081 -0.408709 -0.440449 -0.474467 -0.365748 -0.375873 -0.341512  \n",
      "1  -0.706081 -0.408709 -0.440449 -0.474467 -0.365748  2.660475 -0.341512  \n",
      "2  -0.706081 -0.408709  2.270410 -0.474467 -0.365748 -0.375873 -0.341512  \n",
      "3  -0.706081 -0.408709 -0.440449 -0.474467 -0.365748 -0.375873 -0.341512  \n",
      "4  -0.706081 -0.408709 -0.440449  2.107630 -0.365748 -0.375873 -0.341512  \n",
      "\n",
      "[5 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "df_encoded = pd.get_dummies(data, columns=['month', 'day'], drop_first=True)\n",
    "\n",
    "# Now, you can proceed with standard scaling the entire dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(df_encoded)\n",
    "\n",
    "# Convert the scaled features back to a DataFrame\n",
    "features_scaled = pd.DataFrame(features_scaled, columns=df_encoded.columns)\n",
    "\n",
    "# Check the resulting DataFrame\n",
    "print(features_scaled.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d7adb07-2340-4058-8123-5cf8ff227757",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_scaled, predictions, test_size=0.2, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5593fd05-61ec-4575-8431-89adb3ff1fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>...</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_mon</th>\n",
       "      <th>day_sat</th>\n",
       "      <th>day_sun</th>\n",
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       "      <th>day_tue</th>\n",
       "      <th>day_wed</th>\n",
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       "      <th>329</th>\n",
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       "      <td>-1.060516</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>1.416268</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>2.270410</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>1.465568</td>\n",
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       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>-0.289524</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>0.046297</td>\n",
       "      <td>0.244241</td>\n",
       "      <td>0.559101</td>\n",
       "      <td>-0.443828</td>\n",
       "      <td>-0.204993</td>\n",
       "      <td>-0.324396</td>\n",
       "      <td>-1.015466</td>\n",
       "      <td>-0.073268</td>\n",
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       "      <td>-0.044023</td>\n",
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       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>1.403643</td>\n",
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       "      <th>272</th>\n",
       "      <td>-1.154748</td>\n",
       "      <td>0.569860</td>\n",
       "      <td>0.263895</td>\n",
       "      <td>0.652153</td>\n",
       "      <td>0.444908</td>\n",
       "      <td>1.158784</td>\n",
       "      <td>0.225966</td>\n",
       "      <td>0.166351</td>\n",
       "      <td>-0.009834</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>2.660475</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>1.408545</td>\n",
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       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>-0.722136</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>0.989220</td>\n",
       "      <td>1.097573</td>\n",
       "      <td>0.497365</td>\n",
       "      <td>1.158784</td>\n",
       "      <td>2.311811</td>\n",
       "      <td>-1.060516</td>\n",
       "      <td>-1.015466</td>\n",
       "      <td>-0.073268</td>\n",
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       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>2.660475</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>2.752386</td>\n",
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       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.143088</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>-0.697161</td>\n",
       "      <td>-1.488991</td>\n",
       "      <td>-2.016091</td>\n",
       "      <td>-1.124389</td>\n",
       "      <td>-1.118628</td>\n",
       "      <td>0.534411</td>\n",
       "      <td>-1.015466</td>\n",
       "      <td>-0.073268</td>\n",
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       "      <td>-0.706081</td>\n",
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       "      <td>-0.365748</td>\n",
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       "      <th>71</th>\n",
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       "      <td>-0.447083</td>\n",
       "      <td>-0.233308</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>1.416268</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>-0.289524</td>\n",
       "      <td>0.569860</td>\n",
       "      <td>0.136963</td>\n",
       "      <td>-1.252996</td>\n",
       "      <td>-1.911178</td>\n",
       "      <td>-0.334060</td>\n",
       "      <td>-0.118801</td>\n",
       "      <td>-0.937830</td>\n",
       "      <td>-0.512650</td>\n",
       "      <td>-0.073268</td>\n",
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       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
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       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>2.734120</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>-1.154748</td>\n",
       "      <td>-1.871724</td>\n",
       "      <td>0.263895</td>\n",
       "      <td>0.652153</td>\n",
       "      <td>0.444908</td>\n",
       "      <td>1.158784</td>\n",
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       "      <td>0.718441</td>\n",
       "      <td>-0.512650</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>2.660475</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.418710</td>\n",
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       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-1.154748</td>\n",
       "      <td>0.569860</td>\n",
       "      <td>0.028164</td>\n",
       "      <td>-0.409042</td>\n",
       "      <td>-0.691372</td>\n",
       "      <td>-0.751178</td>\n",
       "      <td>0.846548</td>\n",
       "      <td>0.411724</td>\n",
       "      <td>-1.238940</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>2.270410</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>-1.154748</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>0.753489</td>\n",
       "      <td>-0.040203</td>\n",
       "      <td>0.400119</td>\n",
       "      <td>1.751531</td>\n",
       "      <td>0.208728</td>\n",
       "      <td>-0.263053</td>\n",
       "      <td>-0.009834</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.17286</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>2.660475</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X         Y      FFMC       DMC        DC       ISI      temp  \\\n",
       "329 -0.289524 -1.057862  0.282028 -0.133975  0.821382 -0.136477  0.794833   \n",
       "173 -0.289524 -0.244001  0.046297  0.244241  0.559101 -0.443828 -0.204993   \n",
       "272 -1.154748  0.569860  0.263895  0.652153  0.444908  1.158784  0.225966   \n",
       "497 -0.722136 -0.244001  0.989220  1.097573  0.497365  1.158784  2.311811   \n",
       "182  0.143088 -0.244001 -0.697161 -1.488991 -2.016091 -1.124389 -1.118628   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "71  -0.289524  0.569860  0.662824 -0.402790  0.582505  1.510041 -0.204993   \n",
       "106 -0.289524  0.569860  0.136963 -1.252996 -1.911178 -0.334060 -0.118801   \n",
       "270 -1.154748 -1.871724  0.263895  0.652153  0.444908  1.158784  0.501780   \n",
       "435 -1.154748  0.569860  0.028164 -0.409042 -0.691372 -0.751178  0.846548   \n",
       "102 -1.154748 -0.244001  0.753489 -0.040203  0.400119  1.751531  0.208728   \n",
       "\n",
       "           RH      wind      rain  ...  month_nov  month_oct  month_sep  \\\n",
       "329 -1.060516 -0.009834 -0.073268  ...  -0.044023   -0.17286   1.416268   \n",
       "173 -0.324396 -1.015466 -0.073268  ...  -0.044023   -0.17286   1.416268   \n",
       "272  0.166351 -0.009834 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "497 -1.060516 -1.015466 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "182  0.534411 -1.015466 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "..        ...       ...       ...  ...        ...        ...        ...   \n",
       "71  -0.447083 -0.233308 -0.073268  ...  -0.044023   -0.17286   1.416268   \n",
       "106 -0.937830 -0.512650 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "270  0.718441 -0.512650 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "435  0.411724 -1.238940 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "102 -0.263053 -0.009834 -0.073268  ...  -0.044023   -0.17286  -0.706081   \n",
       "\n",
       "      day_mon   day_sat   day_sun   day_thu   day_tue   day_wed  target_column  \n",
       "329 -0.408709  2.270410 -0.474467 -0.365748 -0.375873 -0.341512       1.465568  \n",
       "173  2.446730 -0.440449 -0.474467 -0.365748 -0.375873 -0.341512       1.403643  \n",
       "272 -0.408709 -0.440449 -0.474467 -0.365748  2.660475 -0.341512       1.408545  \n",
       "497 -0.408709 -0.440449 -0.474467 -0.365748  2.660475 -0.341512       2.752386  \n",
       "182 -0.408709 -0.440449  2.107630 -0.365748 -0.375873 -0.341512       1.998774  \n",
       "..        ...       ...       ...       ...       ...       ...            ...  \n",
       "71  -0.408709 -0.440449 -0.474467 -0.365748 -0.375873 -0.341512       0.000000  \n",
       "106 -0.408709 -0.440449 -0.474467  2.734120 -0.375873 -0.341512       0.000000  \n",
       "270 -0.408709 -0.440449 -0.474467 -0.365748  2.660475 -0.341512       0.418710  \n",
       "435 -0.408709  2.270410 -0.474467 -0.365748 -0.375873 -0.341512       0.000000  \n",
       "102 -0.408709 -0.440449 -0.474467 -0.365748  2.660475 -0.341512       0.000000  \n",
       "\n",
       "[413 rows x 29 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Train_data = pd.concat([X_train, y_train], axis='columns')\n",
    "Train_data.columns = list(X_train.columns) + ['target_column']\n",
    "Train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "19fac4aa-8b90-44d0-ac7d-15d18512c4cc",
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>...</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_mon</th>\n",
       "      <th>day_sat</th>\n",
       "      <th>day_sun</th>\n",
       "      <th>day_thu</th>\n",
       "      <th>day_tue</th>\n",
       "      <th>day_wed</th>\n",
       "      <th>target_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.003560</td>\n",
       "      <td>0.018090</td>\n",
       "      <td>-0.020176</td>\n",
       "      <td>0.020417</td>\n",
       "      <td>0.007558</td>\n",
       "      <td>-0.054563</td>\n",
       "      <td>-0.000345</td>\n",
       "      <td>0.016334</td>\n",
       "      <td>-0.011457</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.014677</td>\n",
       "      <td>0.023637</td>\n",
       "      <td>0.033780</td>\n",
       "      <td>-0.000673</td>\n",
       "      <td>0.006941</td>\n",
       "      <td>-0.012979</td>\n",
       "      <td>0.006428</td>\n",
       "      <td>-0.016921</td>\n",
       "      <td>1.103601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.992508</td>\n",
       "      <td>0.983923</td>\n",
       "      <td>1.079508</td>\n",
       "      <td>1.028594</td>\n",
       "      <td>1.005602</td>\n",
       "      <td>0.855746</td>\n",
       "      <td>0.998170</td>\n",
       "      <td>1.017687</td>\n",
       "      <td>0.996337</td>\n",
       "      <td>1.088099</td>\n",
       "      <td>...</td>\n",
       "      <td>1.119929</td>\n",
       "      <td>1.041528</td>\n",
       "      <td>1.009304</td>\n",
       "      <td>1.034551</td>\n",
       "      <td>1.000596</td>\n",
       "      <td>1.006848</td>\n",
       "      <td>0.985619</td>\n",
       "      <td>1.008517</td>\n",
       "      <td>0.978910</td>\n",
       "      <td>1.376300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.587360</td>\n",
       "      <td>-1.871724</td>\n",
       "      <td>-13.045818</td>\n",
       "      <td>-1.715608</td>\n",
       "      <td>-2.179108</td>\n",
       "      <td>-1.980578</td>\n",
       "      <td>-2.876943</td>\n",
       "      <td>-1.796637</td>\n",
       "      <td>-1.741756</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.172860</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.722136</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>-0.080635</td>\n",
       "      <td>-0.626282</td>\n",
       "      <td>-0.444828</td>\n",
       "      <td>-0.575549</td>\n",
       "      <td>-0.584238</td>\n",
       "      <td>-0.753800</td>\n",
       "      <td>-0.736124</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.172860</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.289524</td>\n",
       "      <td>-0.244001</td>\n",
       "      <td>0.173229</td>\n",
       "      <td>-0.038640</td>\n",
       "      <td>0.470330</td>\n",
       "      <td>-0.136477</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>-0.140366</td>\n",
       "      <td>-0.009834</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.172860</td>\n",
       "      <td>-0.706081</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>0.431782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.008313</td>\n",
       "      <td>0.569860</td>\n",
       "      <td>0.408960</td>\n",
       "      <td>0.492739</td>\n",
       "      <td>0.674505</td>\n",
       "      <td>0.346501</td>\n",
       "      <td>0.674164</td>\n",
       "      <td>0.534411</td>\n",
       "      <td>0.772325</td>\n",
       "      <td>-0.073268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044023</td>\n",
       "      <td>-0.172860</td>\n",
       "      <td>1.416268</td>\n",
       "      <td>-0.408709</td>\n",
       "      <td>-0.440449</td>\n",
       "      <td>-0.474467</td>\n",
       "      <td>-0.365748</td>\n",
       "      <td>-0.375873</td>\n",
       "      <td>-0.341512</td>\n",
       "      <td>2.024193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.873537</td>\n",
       "      <td>3.825306</td>\n",
       "      <td>1.007353</td>\n",
       "      <td>2.819865</td>\n",
       "      <td>1.261610</td>\n",
       "      <td>3.002885</td>\n",
       "      <td>2.484195</td>\n",
       "      <td>3.417549</td>\n",
       "      <td>3.007063</td>\n",
       "      <td>21.572284</td>\n",
       "      <td>...</td>\n",
       "      <td>22.715633</td>\n",
       "      <td>5.785038</td>\n",
       "      <td>1.416268</td>\n",
       "      <td>2.446730</td>\n",
       "      <td>2.270410</td>\n",
       "      <td>2.107630</td>\n",
       "      <td>2.734120</td>\n",
       "      <td>2.660475</td>\n",
       "      <td>2.928152</td>\n",
       "      <td>6.616440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                X           Y        FFMC         DMC          DC         ISI  \\\n",
       "count  413.000000  413.000000  413.000000  413.000000  413.000000  413.000000   \n",
       "mean    -0.003560    0.018090   -0.020176    0.020417    0.007558   -0.054563   \n",
       "std      0.992508    0.983923    1.079508    1.028594    1.005602    0.855746   \n",
       "min     -1.587360   -1.871724  -13.045818   -1.715608   -2.179108   -1.980578   \n",
       "25%     -0.722136   -0.244001   -0.080635   -0.626282   -0.444828   -0.575549   \n",
       "50%     -0.289524   -0.244001    0.173229   -0.038640    0.470330   -0.136477   \n",
       "75%      1.008313    0.569860    0.408960    0.492739    0.674505    0.346501   \n",
       "max      1.873537    3.825306    1.007353    2.819865    1.261610    3.002885   \n",
       "\n",
       "             temp          RH        wind        rain  ...   month_nov  \\\n",
       "count  413.000000  413.000000  413.000000  413.000000  ...  413.000000   \n",
       "mean    -0.000345    0.016334   -0.011457    0.000434  ...    0.011086   \n",
       "std      0.998170    1.017687    0.996337    1.088099  ...    1.119929   \n",
       "min     -2.876943   -1.796637   -1.741756   -0.073268  ...   -0.044023   \n",
       "25%     -0.584238   -0.753800   -0.736124   -0.073268  ...   -0.044023   \n",
       "50%      0.036344   -0.140366   -0.009834   -0.073268  ...   -0.044023   \n",
       "75%      0.674164    0.534411    0.772325   -0.073268  ...   -0.044023   \n",
       "max      2.484195    3.417549    3.007063   21.572284  ...   22.715633   \n",
       "\n",
       "        month_oct   month_sep     day_mon     day_sat     day_sun     day_thu  \\\n",
       "count  413.000000  413.000000  413.000000  413.000000  413.000000  413.000000   \n",
       "mean     0.014677    0.023637    0.033780   -0.000673    0.006941   -0.012979   \n",
       "std      1.041528    1.009304    1.034551    1.000596    1.006848    0.985619   \n",
       "min     -0.172860   -0.706081   -0.408709   -0.440449   -0.474467   -0.365748   \n",
       "25%     -0.172860   -0.706081   -0.408709   -0.440449   -0.474467   -0.365748   \n",
       "50%     -0.172860   -0.706081   -0.408709   -0.440449   -0.474467   -0.365748   \n",
       "75%     -0.172860    1.416268   -0.408709   -0.440449   -0.474467   -0.365748   \n",
       "max      5.785038    1.416268    2.446730    2.270410    2.107630    2.734120   \n",
       "\n",
       "          day_tue     day_wed  target_column  \n",
       "count  413.000000  413.000000     413.000000  \n",
       "mean     0.006428   -0.016921       1.103601  \n",
       "std      1.008517    0.978910       1.376300  \n",
       "min     -0.375873   -0.341512       0.000000  \n",
       "25%     -0.375873   -0.341512       0.000000  \n",
       "50%     -0.375873   -0.341512       0.431782  \n",
       "75%     -0.375873   -0.341512       2.024193  \n",
       "max      2.660475    2.928152       6.616440  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_data.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3e43ec4-731b-4721-867e-5ef2ac5d6c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.boxplot(data= X_train, orient=\"h\").set(xlabel= \"range\", ylabel=\" features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d58133a4-fccd-4c27-9b74-915f04bb9c47",
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>...</th>\n",
       "      <th>month_nov</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_mon</th>\n",
       "      <th>day_sat</th>\n",
       "      <th>day_sun</th>\n",
       "      <th>day_thu</th>\n",
       "      <th>day_tue</th>\n",
       "      <th>day_wed</th>\n",
       "      <th>target_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.529508</td>\n",
       "      <td>-0.037199</td>\n",
       "      <td>-0.034402</td>\n",
       "      <td>-0.099985</td>\n",
       "      <td>-0.039236</td>\n",
       "      <td>-0.069575</td>\n",
       "      <td>0.131535</td>\n",
       "      <td>0.032861</td>\n",
       "      <td>0.068566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028788</td>\n",
       "      <td>0.075084</td>\n",
       "      <td>-0.128728</td>\n",
       "      <td>0.057510</td>\n",
       "      <td>0.010639</td>\n",
       "      <td>-0.032282</td>\n",
       "      <td>-0.026845</td>\n",
       "      <td>0.021109</td>\n",
       "      <td>-0.014494</td>\n",
       "      <td>0.077387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.529508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038022</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>-0.085578</td>\n",
       "      <td>0.015593</td>\n",
       "      <td>0.011271</td>\n",
       "      <td>0.045170</td>\n",
       "      <td>-0.015438</td>\n",
       "      <td>0.036829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053940</td>\n",
       "      <td>-0.013626</td>\n",
       "      <td>-0.129728</td>\n",
       "      <td>-0.008922</td>\n",
       "      <td>-0.003134</td>\n",
       "      <td>0.016493</td>\n",
       "      <td>0.024401</td>\n",
       "      <td>0.007581</td>\n",
       "      <td>0.032165</td>\n",
       "      <td>0.051651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFMC</th>\n",
       "      <td>-0.037199</td>\n",
       "      <td>-0.038022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.369869</td>\n",
       "      <td>0.318263</td>\n",
       "      <td>0.580653</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>-0.295827</td>\n",
       "      <td>-0.027535</td>\n",
       "      <td>0.055683</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.091419</td>\n",
       "      <td>-0.004978</td>\n",
       "      <td>0.064004</td>\n",
       "      <td>-0.046969</td>\n",
       "      <td>-0.005787</td>\n",
       "      <td>-0.095015</td>\n",
       "      <td>0.093901</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.071671</td>\n",
       "      <td>0.060374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMC</th>\n",
       "      <td>-0.034402</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.369869</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.683103</td>\n",
       "      <td>0.363387</td>\n",
       "      <td>0.442685</td>\n",
       "      <td>0.097735</td>\n",
       "      <td>-0.078025</td>\n",
       "      <td>0.067075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.081827</td>\n",
       "      <td>-0.193795</td>\n",
       "      <td>0.127005</td>\n",
       "      <td>-0.122874</td>\n",
       "      <td>0.023023</td>\n",
       "      <td>0.031309</td>\n",
       "      <td>0.085160</td>\n",
       "      <td>-0.021440</td>\n",
       "      <td>0.014507</td>\n",
       "      <td>0.094123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>-0.099985</td>\n",
       "      <td>-0.085578</td>\n",
       "      <td>0.318263</td>\n",
       "      <td>0.683103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.292579</td>\n",
       "      <td>0.475584</td>\n",
       "      <td>-0.019278</td>\n",
       "      <td>-0.192572</td>\n",
       "      <td>0.028957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.087704</td>\n",
       "      <td>0.095649</td>\n",
       "      <td>0.543043</td>\n",
       "      <td>-0.052414</td>\n",
       "      <td>-0.029183</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.021267</td>\n",
       "      <td>0.025663</td>\n",
       "      <td>0.048440</td>\n",
       "      <td>0.065856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISI</th>\n",
       "      <td>-0.039236</td>\n",
       "      <td>0.015593</td>\n",
       "      <td>0.580653</td>\n",
       "      <td>0.363387</td>\n",
       "      <td>0.292579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427872</td>\n",
       "      <td>-0.132449</td>\n",
       "      <td>0.123776</td>\n",
       "      <td>0.092557</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>-0.066788</td>\n",
       "      <td>-0.085348</td>\n",
       "      <td>-0.145362</td>\n",
       "      <td>-0.008940</td>\n",
       "      <td>-0.042845</td>\n",
       "      <td>0.038508</td>\n",
       "      <td>0.081545</td>\n",
       "      <td>0.085068</td>\n",
       "      <td>-0.004163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>-0.069575</td>\n",
       "      <td>0.011271</td>\n",
       "      <td>0.413136</td>\n",
       "      <td>0.442685</td>\n",
       "      <td>0.475584</td>\n",
       "      <td>0.427872</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.546717</td>\n",
       "      <td>-0.222612</td>\n",
       "      <td>0.073452</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>-0.057388</td>\n",
       "      <td>0.081376</td>\n",
       "      <td>-0.122960</td>\n",
       "      <td>0.054352</td>\n",
       "      <td>-0.005173</td>\n",
       "      <td>0.087147</td>\n",
       "      <td>0.043120</td>\n",
       "      <td>0.051280</td>\n",
       "      <td>0.038279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>0.131535</td>\n",
       "      <td>0.045170</td>\n",
       "      <td>-0.295827</td>\n",
       "      <td>0.097735</td>\n",
       "      <td>-0.019278</td>\n",
       "      <td>-0.132449</td>\n",
       "      <td>-0.546717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103825</td>\n",
       "      <td>0.087408</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040301</td>\n",
       "      <td>-0.072985</td>\n",
       "      <td>-0.049142</td>\n",
       "      <td>-0.006657</td>\n",
       "      <td>-0.048417</td>\n",
       "      <td>0.163702</td>\n",
       "      <td>-0.141751</td>\n",
       "      <td>0.000514</td>\n",
       "      <td>-0.077569</td>\n",
       "      <td>-0.023247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind</th>\n",
       "      <td>0.032861</td>\n",
       "      <td>-0.015438</td>\n",
       "      <td>-0.027535</td>\n",
       "      <td>-0.078025</td>\n",
       "      <td>-0.192572</td>\n",
       "      <td>0.123776</td>\n",
       "      <td>-0.222612</td>\n",
       "      <td>0.103825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013910</td>\n",
       "      <td>-0.031626</td>\n",
       "      <td>-0.202016</td>\n",
       "      <td>-0.052644</td>\n",
       "      <td>-0.055330</td>\n",
       "      <td>0.026261</td>\n",
       "      <td>-0.056775</td>\n",
       "      <td>0.037498</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.030784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain</th>\n",
       "      <td>0.068566</td>\n",
       "      <td>0.036829</td>\n",
       "      <td>0.055683</td>\n",
       "      <td>0.067075</td>\n",
       "      <td>0.028957</td>\n",
       "      <td>0.092557</td>\n",
       "      <td>0.073452</td>\n",
       "      <td>0.087408</td>\n",
       "      <td>0.047322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003341</td>\n",
       "      <td>-0.012226</td>\n",
       "      <td>-0.049091</td>\n",
       "      <td>-0.029041</td>\n",
       "      <td>-0.029843</td>\n",
       "      <td>-0.013117</td>\n",
       "      <td>-0.024302</td>\n",
       "      <td>0.146887</td>\n",
       "      <td>-0.017475</td>\n",
       "      <td>0.032201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.077387</td>\n",
       "      <td>0.051651</td>\n",
       "      <td>0.060374</td>\n",
       "      <td>0.094123</td>\n",
       "      <td>0.065856</td>\n",
       "      <td>-0.004163</td>\n",
       "      <td>0.038279</td>\n",
       "      <td>-0.023247</td>\n",
       "      <td>0.030784</td>\n",
       "      <td>0.032201</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039553</td>\n",
       "      <td>-0.033078</td>\n",
       "      <td>0.081522</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.040246</td>\n",
       "      <td>-0.044578</td>\n",
       "      <td>-0.027405</td>\n",
       "      <td>0.036006</td>\n",
       "      <td>-0.003074</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_aug</th>\n",
       "      <td>-0.019069</td>\n",
       "      <td>0.033530</td>\n",
       "      <td>0.231256</td>\n",
       "      <td>0.481493</td>\n",
       "      <td>0.261773</td>\n",
       "      <td>0.419722</td>\n",
       "      <td>0.341218</td>\n",
       "      <td>0.054137</td>\n",
       "      <td>0.054295</td>\n",
       "      <td>0.088334</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035280</td>\n",
       "      <td>-0.129099</td>\n",
       "      <td>-0.518373</td>\n",
       "      <td>-0.108769</td>\n",
       "      <td>0.031748</td>\n",
       "      <td>0.051199</td>\n",
       "      <td>0.081627</td>\n",
       "      <td>0.021168</td>\n",
       "      <td>0.053057</td>\n",
       "      <td>-0.040145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_dec</th>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.068171</td>\n",
       "      <td>-0.115374</td>\n",
       "      <td>-0.158594</td>\n",
       "      <td>-0.096670</td>\n",
       "      <td>-0.148857</td>\n",
       "      <td>-0.305894</td>\n",
       "      <td>-0.033373</td>\n",
       "      <td>0.260363</td>\n",
       "      <td>-0.008234</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005982</td>\n",
       "      <td>-0.021889</td>\n",
       "      <td>-0.087890</td>\n",
       "      <td>0.115769</td>\n",
       "      <td>-0.053429</td>\n",
       "      <td>-0.006165</td>\n",
       "      <td>-0.043510</td>\n",
       "      <td>-0.046081</td>\n",
       "      <td>0.027364</td>\n",
       "      <td>0.131885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_feb</th>\n",
       "      <td>0.041931</td>\n",
       "      <td>-0.007824</td>\n",
       "      <td>-0.274449</td>\n",
       "      <td>-0.333139</td>\n",
       "      <td>-0.422209</td>\n",
       "      <td>-0.298573</td>\n",
       "      <td>-0.343619</td>\n",
       "      <td>0.150316</td>\n",
       "      <td>-0.005644</td>\n",
       "      <td>-0.014477</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010517</td>\n",
       "      <td>-0.038484</td>\n",
       "      <td>-0.154524</td>\n",
       "      <td>0.006904</td>\n",
       "      <td>0.002570</td>\n",
       "      <td>0.019612</td>\n",
       "      <td>-0.039153</td>\n",
       "      <td>-0.009521</td>\n",
       "      <td>-0.031209</td>\n",
       "      <td>-0.001444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_jan</th>\n",
       "      <td>-0.014212</td>\n",
       "      <td>0.027661</td>\n",
       "      <td>-0.595184</td>\n",
       "      <td>-0.083251</td>\n",
       "      <td>-0.074898</td>\n",
       "      <td>-0.111018</td>\n",
       "      <td>-0.116596</td>\n",
       "      <td>0.164853</td>\n",
       "      <td>-0.085663</td>\n",
       "      <td>-0.003341</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002427</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>-0.021097</td>\n",
       "      <td>-0.021680</td>\n",
       "      <td>0.102914</td>\n",
       "      <td>-0.017655</td>\n",
       "      <td>-0.018698</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>-0.039553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_jul</th>\n",
       "      <td>0.057196</td>\n",
       "      <td>0.083666</td>\n",
       "      <td>0.032083</td>\n",
       "      <td>-0.004394</td>\n",
       "      <td>-0.110347</td>\n",
       "      <td>0.032809</td>\n",
       "      <td>0.142998</td>\n",
       "      <td>0.015967</td>\n",
       "      <td>-0.026152</td>\n",
       "      <td>-0.012739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013539</td>\n",
       "      <td>-0.049542</td>\n",
       "      <td>-0.198927</td>\n",
       "      <td>-0.012935</td>\n",
       "      <td>0.033298</td>\n",
       "      <td>-0.034229</td>\n",
       "      <td>-0.008959</td>\n",
       "      <td>0.067089</td>\n",
       "      <td>0.003837</td>\n",
       "      <td>0.003782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_jun</th>\n",
       "      <td>0.099292</td>\n",
       "      <td>0.078251</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.053568</td>\n",
       "      <td>-0.181192</td>\n",
       "      <td>-0.010866</td>\n",
       "      <td>0.047877</td>\n",
       "      <td>0.017401</td>\n",
       "      <td>0.020536</td>\n",
       "      <td>-0.012226</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>-0.032500</td>\n",
       "      <td>-0.130497</td>\n",
       "      <td>0.037765</td>\n",
       "      <td>-0.004099</td>\n",
       "      <td>-0.015088</td>\n",
       "      <td>0.022733</td>\n",
       "      <td>-0.068421</td>\n",
       "      <td>-0.013475</td>\n",
       "      <td>-0.028963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_mar</th>\n",
       "      <td>0.028798</td>\n",
       "      <td>0.036321</td>\n",
       "      <td>-0.062249</td>\n",
       "      <td>-0.400696</td>\n",
       "      <td>-0.643037</td>\n",
       "      <td>-0.147681</td>\n",
       "      <td>-0.334468</td>\n",
       "      <td>-0.106502</td>\n",
       "      <td>0.178652</td>\n",
       "      <td>-0.017832</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016576</td>\n",
       "      <td>-0.060657</td>\n",
       "      <td>-0.243555</td>\n",
       "      <td>0.055157</td>\n",
       "      <td>0.047512</td>\n",
       "      <td>-0.037653</td>\n",
       "      <td>0.005558</td>\n",
       "      <td>-0.031106</td>\n",
       "      <td>-0.058121</td>\n",
       "      <td>-0.063953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_may</th>\n",
       "      <td>-0.014212</td>\n",
       "      <td>-0.053940</td>\n",
       "      <td>-0.007734</td>\n",
       "      <td>-0.065039</td>\n",
       "      <td>-0.094236</td>\n",
       "      <td>-0.038888</td>\n",
       "      <td>-0.007557</td>\n",
       "      <td>-0.013542</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>-0.003341</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002427</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>-0.021097</td>\n",
       "      <td>-0.021680</td>\n",
       "      <td>-0.023585</td>\n",
       "      <td>-0.017655</td>\n",
       "      <td>-0.018698</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>0.092187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_nov</th>\n",
       "      <td>0.028788</td>\n",
       "      <td>-0.053940</td>\n",
       "      <td>-0.091419</td>\n",
       "      <td>-0.081827</td>\n",
       "      <td>-0.087704</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>-0.060373</td>\n",
       "      <td>-0.040301</td>\n",
       "      <td>0.013910</td>\n",
       "      <td>-0.003341</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>-0.021097</td>\n",
       "      <td>-0.021680</td>\n",
       "      <td>-0.023585</td>\n",
       "      <td>-0.017655</td>\n",
       "      <td>0.129809</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>-0.039553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_oct</th>\n",
       "      <td>0.075084</td>\n",
       "      <td>-0.013626</td>\n",
       "      <td>-0.004978</td>\n",
       "      <td>-0.193795</td>\n",
       "      <td>0.095649</td>\n",
       "      <td>-0.066788</td>\n",
       "      <td>-0.057388</td>\n",
       "      <td>-0.072985</td>\n",
       "      <td>-0.031626</td>\n",
       "      <td>-0.012226</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130497</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>-0.004099</td>\n",
       "      <td>0.020519</td>\n",
       "      <td>-0.064603</td>\n",
       "      <td>0.015182</td>\n",
       "      <td>-0.013475</td>\n",
       "      <td>-0.033078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_sep</th>\n",
       "      <td>-0.128728</td>\n",
       "      <td>-0.129728</td>\n",
       "      <td>0.064004</td>\n",
       "      <td>0.127005</td>\n",
       "      <td>0.543043</td>\n",
       "      <td>-0.085348</td>\n",
       "      <td>0.081376</td>\n",
       "      <td>-0.049142</td>\n",
       "      <td>-0.202016</td>\n",
       "      <td>-0.049091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035662</td>\n",
       "      <td>-0.130497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>-0.055812</td>\n",
       "      <td>-0.032390</td>\n",
       "      <td>-0.034669</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>-0.001651</td>\n",
       "      <td>0.081522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_mon</th>\n",
       "      <td>0.057510</td>\n",
       "      <td>-0.008922</td>\n",
       "      <td>-0.046969</td>\n",
       "      <td>-0.122874</td>\n",
       "      <td>-0.052414</td>\n",
       "      <td>-0.145362</td>\n",
       "      <td>-0.122960</td>\n",
       "      <td>-0.006657</td>\n",
       "      <td>-0.052644</td>\n",
       "      <td>-0.029041</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021097</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188442</td>\n",
       "      <td>-0.205000</td>\n",
       "      <td>-0.153457</td>\n",
       "      <td>-0.162527</td>\n",
       "      <td>-0.142167</td>\n",
       "      <td>0.013357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_sat</th>\n",
       "      <td>0.010639</td>\n",
       "      <td>-0.003134</td>\n",
       "      <td>-0.005787</td>\n",
       "      <td>0.023023</td>\n",
       "      <td>-0.029183</td>\n",
       "      <td>-0.008940</td>\n",
       "      <td>0.054352</td>\n",
       "      <td>-0.048417</td>\n",
       "      <td>-0.055330</td>\n",
       "      <td>-0.029843</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021680</td>\n",
       "      <td>-0.004099</td>\n",
       "      <td>-0.055812</td>\n",
       "      <td>-0.188442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.210657</td>\n",
       "      <td>-0.157691</td>\n",
       "      <td>-0.167012</td>\n",
       "      <td>-0.146090</td>\n",
       "      <td>0.040246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_sun</th>\n",
       "      <td>-0.032282</td>\n",
       "      <td>0.016493</td>\n",
       "      <td>-0.095015</td>\n",
       "      <td>0.031309</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>-0.042845</td>\n",
       "      <td>-0.005173</td>\n",
       "      <td>0.163702</td>\n",
       "      <td>0.026261</td>\n",
       "      <td>-0.013117</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023585</td>\n",
       "      <td>0.020519</td>\n",
       "      <td>-0.032390</td>\n",
       "      <td>-0.205000</td>\n",
       "      <td>-0.210657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.171547</td>\n",
       "      <td>-0.181687</td>\n",
       "      <td>-0.158926</td>\n",
       "      <td>-0.044578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_thu</th>\n",
       "      <td>-0.026845</td>\n",
       "      <td>0.024401</td>\n",
       "      <td>0.093901</td>\n",
       "      <td>0.085160</td>\n",
       "      <td>0.021267</td>\n",
       "      <td>0.038508</td>\n",
       "      <td>0.087147</td>\n",
       "      <td>-0.141751</td>\n",
       "      <td>-0.056775</td>\n",
       "      <td>-0.024302</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017655</td>\n",
       "      <td>-0.064603</td>\n",
       "      <td>-0.034669</td>\n",
       "      <td>-0.153457</td>\n",
       "      <td>-0.157691</td>\n",
       "      <td>-0.171547</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136005</td>\n",
       "      <td>-0.118968</td>\n",
       "      <td>-0.027405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_tue</th>\n",
       "      <td>0.021109</td>\n",
       "      <td>0.007581</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>-0.021440</td>\n",
       "      <td>0.025663</td>\n",
       "      <td>0.081545</td>\n",
       "      <td>0.043120</td>\n",
       "      <td>0.000514</td>\n",
       "      <td>0.037498</td>\n",
       "      <td>0.146887</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129809</td>\n",
       "      <td>0.015182</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>-0.162527</td>\n",
       "      <td>-0.167012</td>\n",
       "      <td>-0.181687</td>\n",
       "      <td>-0.136005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.125999</td>\n",
       "      <td>0.036006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_wed</th>\n",
       "      <td>-0.014494</td>\n",
       "      <td>0.032165</td>\n",
       "      <td>0.071671</td>\n",
       "      <td>0.014507</td>\n",
       "      <td>0.048440</td>\n",
       "      <td>0.085068</td>\n",
       "      <td>0.051280</td>\n",
       "      <td>-0.077569</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>-0.017475</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016356</td>\n",
       "      <td>-0.013475</td>\n",
       "      <td>-0.001651</td>\n",
       "      <td>-0.142167</td>\n",
       "      <td>-0.146090</td>\n",
       "      <td>-0.158926</td>\n",
       "      <td>-0.118968</td>\n",
       "      <td>-0.125999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_column</th>\n",
       "      <td>0.077387</td>\n",
       "      <td>0.051651</td>\n",
       "      <td>0.060374</td>\n",
       "      <td>0.094123</td>\n",
       "      <td>0.065856</td>\n",
       "      <td>-0.004163</td>\n",
       "      <td>0.038279</td>\n",
       "      <td>-0.023247</td>\n",
       "      <td>0.030784</td>\n",
       "      <td>0.032201</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039553</td>\n",
       "      <td>-0.033078</td>\n",
       "      <td>0.081522</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.040246</td>\n",
       "      <td>-0.044578</td>\n",
       "      <td>-0.027405</td>\n",
       "      <td>0.036006</td>\n",
       "      <td>-0.003074</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      X         Y      FFMC       DMC        DC       ISI  \\\n",
       "X              1.000000  0.529508 -0.037199 -0.034402 -0.099985 -0.039236   \n",
       "Y              0.529508  1.000000 -0.038022  0.029582 -0.085578  0.015593   \n",
       "FFMC          -0.037199 -0.038022  1.000000  0.369869  0.318263  0.580653   \n",
       "DMC           -0.034402  0.029582  0.369869  1.000000  0.683103  0.363387   \n",
       "DC            -0.099985 -0.085578  0.318263  0.683103  1.000000  0.292579   \n",
       "ISI           -0.039236  0.015593  0.580653  0.363387  0.292579  1.000000   \n",
       "temp          -0.069575  0.011271  0.413136  0.442685  0.475584  0.427872   \n",
       "RH             0.131535  0.045170 -0.295827  0.097735 -0.019278 -0.132449   \n",
       "wind           0.032861 -0.015438 -0.027535 -0.078025 -0.192572  0.123776   \n",
       "rain           0.068566  0.036829  0.055683  0.067075  0.028957  0.092557   \n",
       "area           0.077387  0.051651  0.060374  0.094123  0.065856 -0.004163   \n",
       "month_aug     -0.019069  0.033530  0.231256  0.481493  0.261773  0.419722   \n",
       "month_dec      0.000299  0.068171 -0.115374 -0.158594 -0.096670 -0.148857   \n",
       "month_feb      0.041931 -0.007824 -0.274449 -0.333139 -0.422209 -0.298573   \n",
       "month_jan     -0.014212  0.027661 -0.595184 -0.083251 -0.074898 -0.111018   \n",
       "month_jul      0.057196  0.083666  0.032083 -0.004394 -0.110347  0.032809   \n",
       "month_jun      0.099292  0.078251 -0.060252 -0.053568 -0.181192 -0.010866   \n",
       "month_mar      0.028798  0.036321 -0.062249 -0.400696 -0.643037 -0.147681   \n",
       "month_may     -0.014212 -0.053940 -0.007734 -0.065039 -0.094236 -0.038888   \n",
       "month_nov      0.028788 -0.053940 -0.091419 -0.081827 -0.087704 -0.097098   \n",
       "month_oct      0.075084 -0.013626 -0.004978 -0.193795  0.095649 -0.066788   \n",
       "month_sep     -0.128728 -0.129728  0.064004  0.127005  0.543043 -0.085348   \n",
       "day_mon        0.057510 -0.008922 -0.046969 -0.122874 -0.052414 -0.145362   \n",
       "day_sat        0.010639 -0.003134 -0.005787  0.023023 -0.029183 -0.008940   \n",
       "day_sun       -0.032282  0.016493 -0.095015  0.031309  0.001044 -0.042845   \n",
       "day_thu       -0.026845  0.024401  0.093901  0.085160  0.021267  0.038508   \n",
       "day_tue        0.021109  0.007581  0.000936 -0.021440  0.025663  0.081545   \n",
       "day_wed       -0.014494  0.032165  0.071671  0.014507  0.048440  0.085068   \n",
       "target_column  0.077387  0.051651  0.060374  0.094123  0.065856 -0.004163   \n",
       "\n",
       "                   temp        RH      wind      rain  ...  month_nov  \\\n",
       "X             -0.069575  0.131535  0.032861  0.068566  ...   0.028788   \n",
       "Y              0.011271  0.045170 -0.015438  0.036829  ...  -0.053940   \n",
       "FFMC           0.413136 -0.295827 -0.027535  0.055683  ...  -0.091419   \n",
       "DMC            0.442685  0.097735 -0.078025  0.067075  ...  -0.081827   \n",
       "DC             0.475584 -0.019278 -0.192572  0.028957  ...  -0.087704   \n",
       "ISI            0.427872 -0.132449  0.123776  0.092557  ...  -0.097098   \n",
       "temp           1.000000 -0.546717 -0.222612  0.073452  ...  -0.060373   \n",
       "RH            -0.546717  1.000000  0.103825  0.087408  ...  -0.040301   \n",
       "wind          -0.222612  0.103825  1.000000  0.047322  ...   0.013910   \n",
       "rain           0.073452  0.087408  0.047322  1.000000  ...  -0.003341   \n",
       "area           0.038279 -0.023247  0.030784  0.032201  ...  -0.039553   \n",
       "month_aug      0.341218  0.054137  0.054295  0.088334  ...  -0.035280   \n",
       "month_dec     -0.305894 -0.033373  0.260363 -0.008234  ...  -0.005982   \n",
       "month_feb     -0.343619  0.150316 -0.005644 -0.014477  ...  -0.010517   \n",
       "month_jan     -0.116596  0.164853 -0.085663 -0.003341  ...  -0.002427   \n",
       "month_jul      0.142998  0.015967 -0.026152 -0.012739  ...  -0.013539   \n",
       "month_jun      0.047877  0.017401  0.020536 -0.012226  ...  -0.008882   \n",
       "month_mar     -0.334468 -0.106502  0.178652 -0.017832  ...  -0.016576   \n",
       "month_may     -0.007557 -0.013542  0.000080 -0.003341  ...  -0.002427   \n",
       "month_nov     -0.060373 -0.040301  0.013910 -0.003341  ...   1.000000   \n",
       "month_oct     -0.057388 -0.072985 -0.031626 -0.012226  ...  -0.008882   \n",
       "month_sep      0.081376 -0.049142 -0.202016 -0.049091  ...  -0.035662   \n",
       "day_mon       -0.122960 -0.006657 -0.052644 -0.029041  ...  -0.021097   \n",
       "day_sat        0.054352 -0.048417 -0.055330 -0.029843  ...  -0.021680   \n",
       "day_sun       -0.005173  0.163702  0.026261 -0.013117  ...  -0.023585   \n",
       "day_thu        0.087147 -0.141751 -0.056775 -0.024302  ...  -0.017655   \n",
       "day_tue        0.043120  0.000514  0.037498  0.146887  ...   0.129809   \n",
       "day_wed        0.051280 -0.077569  0.000996 -0.017475  ...  -0.016356   \n",
       "target_column  0.038279 -0.023247  0.030784  0.032201  ...  -0.039553   \n",
       "\n",
       "               month_oct  month_sep   day_mon   day_sat   day_sun   day_thu  \\\n",
       "X               0.075084  -0.128728  0.057510  0.010639 -0.032282 -0.026845   \n",
       "Y              -0.013626  -0.129728 -0.008922 -0.003134  0.016493  0.024401   \n",
       "FFMC           -0.004978   0.064004 -0.046969 -0.005787 -0.095015  0.093901   \n",
       "DMC            -0.193795   0.127005 -0.122874  0.023023  0.031309  0.085160   \n",
       "DC              0.095649   0.543043 -0.052414 -0.029183  0.001044  0.021267   \n",
       "ISI            -0.066788  -0.085348 -0.145362 -0.008940 -0.042845  0.038508   \n",
       "temp           -0.057388   0.081376 -0.122960  0.054352 -0.005173  0.087147   \n",
       "RH             -0.072985  -0.049142 -0.006657 -0.048417  0.163702 -0.141751   \n",
       "wind           -0.031626  -0.202016 -0.052644 -0.055330  0.026261 -0.056775   \n",
       "rain           -0.012226  -0.049091 -0.029041 -0.029843 -0.013117 -0.024302   \n",
       "area           -0.033078   0.081522  0.013357  0.040246 -0.044578 -0.027405   \n",
       "month_aug      -0.129099  -0.518373 -0.108769  0.031748  0.051199  0.081627   \n",
       "month_dec      -0.021889  -0.087890  0.115769 -0.053429 -0.006165 -0.043510   \n",
       "month_feb      -0.038484  -0.154524  0.006904  0.002570  0.019612 -0.039153   \n",
       "month_jan      -0.008882  -0.035662 -0.021097 -0.021680  0.102914 -0.017655   \n",
       "month_jul      -0.049542  -0.198927 -0.012935  0.033298 -0.034229 -0.008959   \n",
       "month_jun      -0.032500  -0.130497  0.037765 -0.004099 -0.015088  0.022733   \n",
       "month_mar      -0.060657  -0.243555  0.055157  0.047512 -0.037653  0.005558   \n",
       "month_may      -0.008882  -0.035662 -0.021097 -0.021680 -0.023585 -0.017655   \n",
       "month_nov      -0.008882  -0.035662 -0.021097 -0.021680 -0.023585 -0.017655   \n",
       "month_oct       1.000000  -0.130497  0.076087 -0.004099  0.020519 -0.064603   \n",
       "month_sep      -0.130497   1.000000  0.014019 -0.055812 -0.032390 -0.034669   \n",
       "day_mon         0.076087   0.014019  1.000000 -0.188442 -0.205000 -0.153457   \n",
       "day_sat        -0.004099  -0.055812 -0.188442  1.000000 -0.210657 -0.157691   \n",
       "day_sun         0.020519  -0.032390 -0.205000 -0.210657  1.000000 -0.171547   \n",
       "day_thu        -0.064603  -0.034669 -0.153457 -0.157691 -0.171547  1.000000   \n",
       "day_tue         0.015182   0.001860 -0.162527 -0.167012 -0.181687 -0.136005   \n",
       "day_wed        -0.013475  -0.001651 -0.142167 -0.146090 -0.158926 -0.118968   \n",
       "target_column  -0.033078   0.081522  0.013357  0.040246 -0.044578 -0.027405   \n",
       "\n",
       "                day_tue   day_wed  target_column  \n",
       "X              0.021109 -0.014494       0.077387  \n",
       "Y              0.007581  0.032165       0.051651  \n",
       "FFMC           0.000936  0.071671       0.060374  \n",
       "DMC           -0.021440  0.014507       0.094123  \n",
       "DC             0.025663  0.048440       0.065856  \n",
       "ISI            0.081545  0.085068      -0.004163  \n",
       "temp           0.043120  0.051280       0.038279  \n",
       "RH             0.000514 -0.077569      -0.023247  \n",
       "wind           0.037498  0.000996       0.030784  \n",
       "rain           0.146887 -0.017475       0.032201  \n",
       "area           0.036006 -0.003074       1.000000  \n",
       "month_aug      0.021168  0.053057      -0.040145  \n",
       "month_dec     -0.046081  0.027364       0.131885  \n",
       "month_feb     -0.009521 -0.031209      -0.001444  \n",
       "month_jan     -0.018698 -0.016356      -0.039553  \n",
       "month_jul      0.067089  0.003837       0.003782  \n",
       "month_jun     -0.068421 -0.013475      -0.028963  \n",
       "month_mar     -0.031106 -0.058121      -0.063953  \n",
       "month_may     -0.018698 -0.016356       0.092187  \n",
       "month_nov      0.129809 -0.016356      -0.039553  \n",
       "month_oct      0.015182 -0.013475      -0.033078  \n",
       "month_sep      0.001860 -0.001651       0.081522  \n",
       "day_mon       -0.162527 -0.142167       0.013357  \n",
       "day_sat       -0.167012 -0.146090       0.040246  \n",
       "day_sun       -0.181687 -0.158926      -0.044578  \n",
       "day_thu       -0.136005 -0.118968      -0.027405  \n",
       "day_tue        1.000000 -0.125999       0.036006  \n",
       "day_wed       -0.125999  1.000000      -0.003074  \n",
       "target_column  0.036006 -0.003074       1.000000  \n",
       "\n",
       "[29 rows x 29 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat= Train_data.corr(method= \"pearson\")\n",
    "corr_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1df33555-eee1-424c-941a-c7920f7a327d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(corr_mat)\n",
    "plt.title(\"Burning Area\")\n",
    "plt.xticks(range(len(corr_mat.columns)),corr_mat.columns, rotation=90)\n",
    "plt.yticks(range(len(corr_mat.columns)),corr_mat.columns)\n",
    "# plt.figure(figsize=[15,15])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af1529a-62bd-45ec-865b-b6fce7e20a29",
   "metadata": {},
   "source": [
    "This shows that only the area variable is solely affecting the Target_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b4991c05-4b60-4f82-9c33-4a0ca9492d10",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 6 is out of bounds for axis 0 with size 6",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m axs \u001b[38;5;241m=\u001b[39m axs\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, col \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(Train_data\u001b[38;5;241m.\u001b[39mcolumns[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]):\n\u001b[1;32m----> 5\u001b[0m     \u001b[43maxs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mhist(Train_data[col], bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m)\n\u001b[0;32m      6\u001b[0m     axs[i]\u001b[38;5;241m.\u001b[39mset_title(col)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n",
      "\u001b[1;31mIndexError\u001b[0m: index 6 is out of bounds for axis 0 with size 6"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(15, 8))\n",
    "axs = axs.flatten()\n",
    "\n",
    "for i, col in enumerate(Train_data.columns[:-2]):\n",
    "    axs[i].hist(Train_data[col], bins=200)\n",
    "    axs[i].set_title(col)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bb6d6c94-ead7-4590-af04-b68b38411499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X                  9\n",
       "Y                  7\n",
       "FFMC             102\n",
       "DMC              194\n",
       "DC               197\n",
       "ISI              108\n",
       "temp             180\n",
       "RH                73\n",
       "wind              20\n",
       "rain               6\n",
       "area             205\n",
       "month_aug          2\n",
       "month_dec          2\n",
       "month_feb          2\n",
       "month_jan          2\n",
       "month_jul          2\n",
       "month_jun          2\n",
       "month_mar          2\n",
       "month_may          2\n",
       "month_nov          2\n",
       "month_oct          2\n",
       "month_sep          2\n",
       "day_mon            2\n",
       "day_sat            2\n",
       "day_sun            2\n",
       "day_thu            2\n",
       "day_tue            2\n",
       "day_wed            2\n",
       "target_column    205\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values= Train_data.nunique()\n",
    "unique_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "19f83ce4-836e-4ef2-929b-16117cd07fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axs = axs.flatten()\n",
    "\n",
    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(Train_data[[\"area\"]]), ax=axs[0])\n",
    "axs[0].set_title(\"Boxplots of area\")\n",
    "\n",
    "sns.histplot(data=Train_data[[\"area\"]], ax=axs[1], bins=20, kde=True)\n",
    "axs[1].set(xlabel='bins(=20) of values')\n",
    "axs[1].set_title(\"Histograms of area\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6101276e-96b0-4665-ba59-3bb4afa47dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define a function to remove outliers using the IQR method\n",
    "def remove_outliers_iqr(data, column):\n",
    "    q1 = data[column].quantile(0.25)\n",
    "    q3 = data[column].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower_bound = q1 - 1.5 * iqr\n",
    "    upper_bound = q3 + 1.5 * iqr\n",
    "    return data[(data[column] >= lower_bound) & (data[column] <= upper_bound)]\n",
    "\n",
    "# Remove outliers from the 'area' column\n",
    "Train_data_cleaned = remove_outliers_iqr(Train_data, 'area')\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axs = axs.flatten()\n",
    "\n",
    "# Boxplot without outliers\n",
    "sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(Train_data_cleaned[[\"area\"]]), ax=axs[0])\n",
    "axs[0].set_title(\"Boxplot of area without outliers\")\n",
    "\n",
    "# Histogram without outliers\n",
    "sns.histplot(data=Train_data_cleaned[[\"area\"]], ax=axs[1], bins=20, kde=True)\n",
    "axs[1].set(xlabel='bins(=20) of values')\n",
    "axs[1].set_title(\"Histogram of area without outliers\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b26b1bcd-eb94-43d0-9bc2-faaa9e6109b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "model =KNN= KNeighborsRegressor(n_neighbors=7)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred_train= model.predict(X_train)\n",
    "y_pred= model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e4775a5f-1ef3-4df4-a6c4-d875b186785b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation metrics: Train Data\n",
      "Mean absolute error:  0.6999458146851528\n",
      "Mean squared error:  0.8288236288205831\n",
      "R-squared score:  0.5613794950811204\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "mse_train = mean_squared_error(y_train, y_pred_train)\n",
    "mae_train = mean_absolute_error(y_train, y_pred_train)\n",
    "r2_train = r2_score(y_train, y_pred_train)\n",
    "\n",
    "# Print the evaluation metrics on train data\n",
    "print(\"Evaluation metrics: Train Data\")\n",
    "print(\"Mean absolute error: \", mae_train)\n",
    "print(\"Mean squared error: \", mse_train)\n",
    "print(\"R-squared score: \", r2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "040d8911-afab-473e-8048-5aa70fdf71e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation metrics: Test Data\n",
      "Mean absolute error:  0.7794494180553057\n",
      "Mean squared error:  1.0331181868268304\n",
      "R-squared score:  0.5299439237693196\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "# Print the evaluation metrics on test data\n",
    "print(\"Evaluation metrics: Test Data\")\n",
    "print(\"Mean absolute error: \", mae)\n",
    "print(\"Mean squared error: \", mse)\n",
    "print(\"R-squared score: \", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "492eab51-55ab-4c6e-b23c-406e0be60d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UtPoLEgQBW7ZsQd++fZ+6T82aNTFixAjMmDFDt2337t3o0aMHMjMzYWpqWuQ5c+bMwdy5c4tsX7duHczMzCTpHQCScoC5EUZQCCLmN82HsVKyoYmIiCq9zMxMDBw4ECkpKbCysnrmvhVq5uZFzJgxA5MnT9Z9nZqaCjc3N3Tu3Pm535zSEEURS6+E4l56LhzrNEVLH3vJxn6cRqNBUFAQOnXqBJVKxRqswRqsUalrlFUd1pC/RsGRl5KoUOHGyckJCQkJhbYlJCTAysqq2FkbAFCr1VCr1UW2q1Qqyf9i/d1tsP/CXZyLS0e7Oi6Sjv0kffTPGqzBGqxRUWuUVR3WkK9GacaqUNe5admyJYKDgwttCwoKQsuWLWXqqLCGbtYAgMiYFJk7ISIiqrxkDTfp6emIjIxEZGQkgIenekdGRuLWrVsAHh5SGjp0qG7/9957Dzdu3MC0adNw6dIlLF26FBs3bsSkSZPkaL8IfzcbAEBkTDLKyVImIiKiSkfWcHPq1Cn4+/vD398fADB58mT4+/tj1qxZAIC4uDhd0AEALy8v7Nq1C0FBQfDz88OCBQvw888/l4vTwAGgvosVFIKIe+m5uJOcJXc7RERElZKsa246dOjwzBmO4q4+3KFDB5w+fVqPXb04E5USrmZATAYQcSsZ1WylOxuLiIiISqZCrbmpCDwtH4a107ceyNwJERFR5cRwIzFPi4fhJuJWsryNEBERVVIMNxIrmLm5EJuCbE2+zN0QERFVPgw3EquqBqqaG0OTL+LfWJ4STkREVNYYbiQmCID/o+vdnOahKSIiojLHcKMHDR9d7yaCi4qJiIjKHMONHjTkzA0REZFsGG70oIGrFZQKAXEp2YhL4cX8iIiIyhLDjR6YGRuhtpMlACAiOlneZoiIiCoZhhs98Xe3AcCL+REREZU1hhs9aeRuC4CLiomIiMoaw42e+D8KN+djU5GTx4v5ERERlRWGGz3xrGoGWzMVcvO0uBCbKnc7RERElQbDjZ4IgqCbveEp4URERGWH4UaPGj1aVMx1N0RERGWH4UaPOHNDRERU9hhu9MjPzQaCANxJzsLd1Gy52yEiIqoUGG70yEJthFqOjy7mx9kbIiKiMsFwo2f/HZriuhsiIqKywHCjZ/9dqThZ1j6IiIgqC4YbPSu4UvHZO8nQ5Gtl7oaIiMjwMdzoWXU7c1ibqpCt0eJSXJrc7RARERk8hhs9UygENHSzAcDr3RAREZUFhpsy0IiLiomIiMoMw00Z8NddqThZ1j6IiIgqA4abMtDQ/eHF/G4lZSIxPUfudoiIiAwaw00ZsDJRwcfeAgBPCSciItI3hpsyUrDuhouKiYiI9Ivhpoz8dzE/hhsiIiJ9YrgpI408Hs7cnIlJQR4v5kdERKQ3DDdlxMfeApZqI2Rp8nE5gRfzIyIi0heGmzKiUAhoyFPCiYiI9I7hpgz5P7pSMdfdEBER6Q/DTRny9yi4UnGyvI0QEREZMIabMlQwcxOVmIEHGbnyNkNERGSgGG7KkI2ZMarbmwMATsfw0BQREZE+MNyUMX83HpoiIiLSJ4abMtbIwwYAr1RMRESkLww3Zaxg5uZMTArytaLM3RARERkehpsyVsvJEubGSqTn5OHqXV7Mj4iISGoMN2VMqRDgp7veTbKsvRARERkihhsZFNxEMyKa626IiIikxnAjg0buj86YikmWtxEiIiIDxHAjg4aPDktdu5uOlEyNvM0QEREZGIYbGVS1UMOzqhkAIPJ2srzNEBERGRiGG5n4Pzo0xXU3RERE0mK4kUmjgkXFvJgfERGRpBhuZFIwcxMZkwwtL+ZHREQkGdnDzZIlS+Dp6QkTExM0b94cJ06ceOq+Go0G8+bNg7e3N0xMTODn54e9e/eWYbfSqe1kCROVAmnZebh+L13udoiIiAyGrOFmw4YNmDx5MmbPno2IiAj4+fmhS5cuuHv3brH7f/LJJ/jxxx/x/fff48KFC3jvvffQr18/nD59uow7f3lGSgV8q9kA4MX8iIiIpCRruFm4cCFGjx6NESNGoG7duli+fDnMzMywatWqYvdfu3YtPv74Y3Tv3h3Vq1fHmDFj0L17dyxYsKCMO5dGwfVuuO6GiIhIOkZyFc7NzUV4eDhmzJih26ZQKBAQEIBjx44V+5ycnByYmJgU2mZqaorDhw8/tU5OTg5ycnJ0X6empgJ4eIhLo5H2GjMF45V0XF8XSwAPz5gq6XNKW+NFsAZrsAZrVJQaZVWHNeSvUZrxBFEUZVnNGhsbC1dXVxw9ehQtW7bUbZ82bRpCQ0MRFhZW5DkDBw7EmTNnsHXrVnh7eyM4OBh9+vRBfn5+oQDzuDlz5mDu3LlFtq9btw5mZmbSvaAXkJoLzAw3ggARXzTNh6lsUZOIiKh8y8zMxMCBA5GSkgIrK6tn7luhfpx+9913GD16NGrXrg1BEODt7Y0RI0Y89TAWAMyYMQOTJ0/WfZ2amgo3Nzd07tz5ud+c0tJoNAgKCkKnTp2gUqlK9Jwfrx/C7eRsONVrjtbeVfVSo7RYgzVYgzUqSo2yqsMa8tcoOPJSErKFGzs7OyiVSiQkJBTanpCQACcnp2KfY29vj61btyI7Oxv379+Hi4sLpk+fjurVqz+1jlqthlqtLrJdpVLp7S+2NGM38qiC28mxOHsnDR1qF/+6X7bGi2IN1mAN1qgoNcqqDmvIV6M0Y8m2oNjY2BiNGzdGcHCwbptWq0VwcHChw1TFMTExgaurK/Ly8vDXX3+hT58++m5XbwruEH6ai4qJiIgkIethqcmTJ2PYsGFo0qQJmjVrhm+//RYZGRkYMWIEAGDo0KFwdXXFF198AQAICwvDnTt30LBhQ9y5cwdz5syBVqvFtGnT5HwZL+XxO4SLoghBEGTuiIiIqGKTNdwEBgbi3r17mDVrFuLj49GwYUPs3bsXjo6OAIBbt25Bofhvcik7OxuffPIJbty4AQsLC3Tv3h1r166FjY2NTK/g5dVxtoLaSIHkTA2iEjNQ3d5C7paIiIgqNNkXFI8bNw7jxo0r9rGQkJBCX7dv3x4XLlwog67KjrGRAg1crXEq+gEibiUz3BAREb0k2W+/QEAjj0eHprjuhoiI6KUx3JQD/m42AIAI3oaBiIjopTHclAMFMzeX41ORkZMnczdEREQVG8NNOeBoZQIXaxNoReDM7WS52yEiIqrQGG7KCX/duptkeRshIiKq4BhuyomCdTdcVExERPRyGG7KiUaPzdzIdC9TIiIig8BwU07Uc7GCsVKB+xm5uJWUKXc7REREFRbDTTmhNlKinuvDu5RH8NAUERHRC2O4KUf83biomIiI6GUx3JQjjTxsAHDmhoiI6GUw3JQj/o/uEH4xLg1Zufkyd0NERFQxMdyUIy7WJnC0UiNfK+IsL+ZHRET0QhhuyhFBEP5bdxOTLG8zREREFRTDTTmjW3cTzXU3REREL4LhppwpWHdzOoYX8yMiInoRDDflTANXaxgpBNxLy8HtB1lyt0NERFThMNyUMyYqJeq5PLyYH9fdEBERlR7DTTlUcGiK626IiIhKj+GmHPJ3twHAmRsiIqIXwXBTDjV6NHNzITYF2RpezI+IiKg0GG7KoWq2prCzUEOTL+Lf2BS52yEiIqpQGG7KIUEQdIemIqKTZe2FiIioomG4Kaca6a53w0XFREREpcFwU05x5oaIiOjFMNyUU77VrKFUCIhPzUZcCi/mR0REVFIMN+WUmbERajtZAuDsDRERUWkw3JRjBetuIm5x3Q0REVFJMdyUY7qL+THcEBERlRjDTTlWMHNz/k4qcvJ4MT8iIqKSYLgpxzyqmqGKuTFy87W4EJsqdztEREQVAsNNOSYIAvzdbAAAEbeSZe2FiIioomC4Kee47oaIiKh0GG7KOd2VijlzQ0REVCIMN+Wcr5sNFAJwJzkLCanZcrdDRERU7jHclHMWaiPUdHx4MT8emiIiIno+hpsKoJEHD00RERGVFMNNBfDfGVOcuSEiInoehpsKoGDm5uztFGjytTJ3Q0REVL4x3FQAXlXNYW2qQk6eFpfi0+Ruh4iIqFxjuKkAFApBd72byJgUeZshIiIq5xhuKgh/t0eLimOS5W2EiIionGO4qSAaedgA4MwNERHR8zDcVBB+bjYQBCDmQRbSNHJ3Q0REVH4x3FQQViYq1HCwAADcTBNk7oaIiKj8YripQArW3TDcEBERPR3DTQVSsO7mZjrDDRER0dMw3FQgjT2qAACi0oC7aTkyd0NERFQ+MdxUID4OFmjsboN8UcDvYTFyt0NERFQuyR5ulixZAk9PT5iYmKB58+Y4ceLEM/f/9ttvUatWLZiamsLNzQ2TJk1CdnZ2GXUrv+GtPAAAf5yMQbYmX+ZuiIiIyh9Zw82GDRswefJkzJ49GxEREfDz80OXLl1w9+7dYvdft24dpk+fjtmzZ+PixYtYuXIlNmzYgI8//riMO5dPpzoOqKIW8SBTg62n78jdDhERUbkja7hZuHAhRo8ejREjRqBu3bpYvnw5zMzMsGrVqmL3P3r0KFq3bo2BAwfC09MTnTt3xltvvfXc2R5DolQIaOf08OaZq45EQRRFmTsiIiIqX4zkKpybm4vw8HDMmDFDt02hUCAgIADHjh0r9jmtWrXCb7/9hhMnTqBZs2a4ceMGdu/ejSFDhjy1Tk5ODnJy/lt8m5qaCgDQaDTQaKS9Gl7BeFKP+2SNFg4i9scpcSUhHQcvxaOtj53kNR7/Ux9YgzVYgzUqUh3WkL9GacYTRJl+9Y+NjYWrqyuOHj2Kli1b6rZPmzYNoaGhCAsLK/Z5ixcvxocffghRFJGXl4f33nsPy5Yte2qdOXPmYO7cuUW2r1u3DmZmZi//QmSyOUqB0HgF6tho8V4drdztEBER6VVmZiYGDhyIlJQUWFlZPXPfF565yc3NRVRUFLy9vWFkVDYTQCEhIfj888+xdOlSNG/eHNeuXcOECRPw6aefYubMmcU+Z8aMGZg8ebLu69TUVLi5uaFz587P/eaUlkajQVBQEDp16gSVSiXp2E/WmPFGKxxachwXkxWo2aQNfB5dvVjKGmXxOliDNViDNSpCHdaQv0bBkZeSKHUqyczMxAcffIA1a9YAAK5cuYLq1avjgw8+gKurK6ZPn16icezs7KBUKpGQkFBoe0JCApycnIp9zsyZMzFkyBCMGjUKANCgQQNkZGTgnXfewf/+9z8oFEWXEKnVaqjV6iLbVSqV3v5i9Tl2AW9HK3Sq44j9FxKw9sRtfN6vgeQ1yuJ1sAZrsAZrVKQ6rCFfjdKMVeoFxTNmzMCZM2cQEhICExMT3faAgABs2LChxOMYGxujcePGCA4O1m3TarUIDg4udJjqcZmZmUUCjFKpBIBKubD27TZeAIDNEbfxICNX5m6IiIjKh1KHm61bt+KHH35AmzZtIAj/3QagXr16uH79eqnGmjx5MlasWIE1a9bg4sWLGDNmDDIyMjBixAgAwNChQwstOO7VqxeWLVuG9evXIyoqCkFBQZg5cyZ69eqlCzmVSTOvKqjnYoVsjRbrTtySux0iIqJyodSHpe7duwcHB4ci2zMyMgqFnZIIDAzEvXv3MGvWLMTHx6Nhw4bYu3cvHB0dAQC3bt0qNFPzySefQBAEfPLJJ7hz5w7s7e3Rq1cvfPbZZ6V9GQZBEAS83cYLkzeewa/HbmJ02+owNpL9uoxERESyKvVPwiZNmmDXrl26rwsCzc8///zUw0nPMm7cOERHRyMnJwdhYWFo3ry57rGQkBCsXr1a97WRkRFmz56Na9euISsrC7du3cKSJUtgY2NT6rqGoqevC+wt1UhIzcHuc3Fyt0NERCS7Us/cfP755+jWrRsuXLiAvLw8fPfdd7hw4QKOHj2K0NBQffRIz2BspMDQFh5YEHQFq45EoU9Dl1LPoBERERmSUs/ctGnTBmfOnEFeXh4aNGiA/fv3w8HBAceOHUPjxo310SM9x6AWHlAbKXD2dgpORT+Qux0iIiJZlWrmRqPR4N1338XMmTOxYsUKffVEpVTF3BivNXLFHydisPKfKDT1rCJ3S0RERLIp1cyNSqXCX3/9pa9e6CWMbP3wtPD9F+IRk5QpczdERETyKfVhqb59+2Lr1q16aIVeRg1HS7StYQetCKw+elPudoiIiGRT6gXFNWrUwLx583DkyBE0btwY5ubmhR4fP368ZM1R6bzdxgv/XE3EhpMxmBhQA5Ym+r8iKBERUXlT6nCzcuVK2NjYIDw8HOHh4YUeEwSB4UZG7Wvaw8fBAtfupuPPU7cx8tEVjImIiCqTUoebqKgoffRBEhAEASNae+J/W87jl6NRGNbKE0oFTwsnIqLK5aUuZyuKYqW8p1N59pp/NdiYqRCTlIWgCwnPfwIREZGBeaFw8+uvv6JBgwYwNTWFqakpfH19sXbtWql7oxdgaqzEoObuAIBVhznLRkRElU+pw83ChQsxZswYdO/eHRs3bsTGjRvRtWtXvPfee1i0aJE+eqRSGtLCE0YKASduJuHc7RS52yEiIipTpV5z8/3332PZsmUYOnSoblvv3r1Rr149zJkzB5MmTZK0QSo9J2sT9PR1xtbIWKw6EoVFgQ3lbomIiKjMlHrmJi4uDq1atSqyvVWrVoiL440by4u321QHAOw4E4uE1GyZuyEiIio7pQ43Pj4+2LhxY5HtGzZsQI0aNSRpil5eg2rWaOZZBXlaEWuPRcvdDhERUZkp9WGpuXPnIjAwEIcOHULr1q0BAEeOHEFwcHCxoYfkM7KNJ07cTMLvYdEY96oPTFRKuVsiIiLSu1LP3Lz++usICwuDnZ0dtm7diq1bt8LOzg4nTpxAv3799NEjvaBOdZ3gVsUUDzI12BxxR+52iIiIykSpZ24AoHHjxvjtt9+k7oUkplQIGN7KC5/uvIBVR6LwVjM3CAIv6kdERIat1DM3u3fvxr59+4ps37dvH/bs2SNJUySd/k2qwUJthGt303HoaqLc7RAREeldqcPN9OnTkZ+fX2S7KIqYPn26JE2RdCxNVOjfxA0AsJIX9SMiokqg1OHm6tWrqFu3bpHttWvXxrVr1yRpiqQ1orUnFAJw6Mo9XE1Ik7sdIiIivSp1uLG2tsaNGzeKbL927RrMzc0laYqk5VbFDJ3qOgIAVh25KW8zREREelbqcNOnTx9MnDgR169f1227du0apkyZgt69e0vaHEmn4KJ+myNuIykjV+ZuiIiI9KfU4earr76Cubk5ateuDS8vL3h5eaFOnTqoWrUqvvnmG330SBJo6mmLBq7WyMnTYl0YL+pHRESGq9SngltbW+Po0aMICgrCmTNndHcFb9eunT76I4kIgoCRbTwxacMZ/HosGu+084ax0QvdFJ6IiKhce6Hr3AiCgM6dO6Nz584AgOTkZCl7Ij3p0cAFX+y+hLtpOdh1Lhb9/KvJ3RIREZHkSv2r+/z587Fhwwbd1/3790fVqlXh6uqKM2fOSNocScvYSIFhrTwBPDwtXBRFeRsiIiLSg1KHm+XLl8PN7eF1U4KCghAUFIQ9e/agW7dumDp1quQNkrQGNnOH2kiB83dScfLmA7nbISIiklypw018fLwu3OzcuRP9+/dH586dMW3aNJw8eVLyBklatubGeK3Rw8NRKw8XPaWfiIiooit1uLG1tUVMTAwAYO/evQgICADw8ArFxV25mMqft9t4AgD2X0jArfuZ8jZDREQksVKHm9deew0DBw5Ep06dcP/+fXTr1g0AcPr0afj4+EjeIEnPx8ES7WvaQxSB1Udvyt0OERGRpEodbhYtWoRx48ahbt26CAoKgoWFBQAgLi4O77//vuQNkn6MbOMFANh4KgZp2RqZuyEiIpJOqU8FV6lU+PDDD4tsnzRpkiQNUdloV8MONRwscPVuOjacjMGottXlbomIiEgSvIpbJfXwon4PZ29WH72JfC1PCyciIsPAcFOJ9fN3ha2ZCrcfZCHoQrzc7RAREUmC4aYSM1EpMai5B4CHF/UjIiIyBAw3ldyQlh5QKQWcvPkAZ28ny90OERHRS2O4qeQcrUzQ09cFALCKszdERGQAShVuli5dioCAAPTv3x/BwcGFHktMTET16jzjpiJ6+9HC4p1n4xCfki1zN0RERC+nxOFm8eLFmDp1KmrXrg21Wo3u3bvjiy++0D2en5+P6OhovTRJ+lXf1RrNvKogTyvi12M35W6HiIjopZT4Ojc//vgjVqxYgYEDBwIAxowZg759+yIrKwvz5s3TW4NUNt5u44UTUUlYd+IW3mvrKXc7REREL6zE4SYqKgqtWrXSfd2qVSscOHAAAQEB0Gg0mDhxoj76ozISUMcR7lXMcCspE1vPxMJa7oaIiIheUIkPS9nZ2elumFmgfv36OHDgAH755RdMmzZN8uao7CgVAoa38gQArD56C7ymHxERVVQlDjdt2rTB5s2bi2yvW7cugoODsWfPHkkbo7LXv6kbLNVGuJGYgcvJgtztEBERvZASh5vp06fD19e32Mfq1auHAwcOYNasWZI1RmXPQm2E/k3dAAAH4gSIIqdviIio4inxmhtfX9+nhhvg4SGq+vXrS9IUyWd4K0+sOXoTV1IU2BIZi8BmnnK3REREVCqSXcRv8+bNzww/VDG4VTHDhFe9AQBzd15CVGKGzB0RERGVTqnCzY8//og33ngDAwcORFhYGADgwIED8Pf3x5AhQ9C6dWu9NElla3RbL/hYaZGZm48J608jN08rd0tEREQlVuJw8+WXX+KDDz7AzZs3sX37drz66qv4/PPPMWjQIAQGBuL27dtYtmyZPnulMqJUCBjio4W1qRHO3k7BwqArcrdERERUYiUON7/88gtWrFiBU6dOYc+ePcjKysLRo0dx7do1TJ8+Hba2ti/cxJIlS+Dp6QkTExM0b94cJ06ceOq+HTp0gCAIRT569OjxwvWpKBs18FmfegCAHw9dx5FriTJ3REREVDIlDje3bt3Cq6++CgBo27YtVCoV5s6dC3Nz85dqYMOGDZg8eTJmz56NiIgI+Pn5oUuXLrh7926x+2/evBlxcXG6j/Pnz0OpVOLNN998qT6oqC71HPFWM3eIIjB5YySSMnLlbomIiOi5ShxucnJyYGJiovva2NgYVapUeekGFi5ciNGjR2PEiBGoW7culi9fDjMzM6xatarY/atUqQInJyfdR1BQEMzMzBhu9GRmzzrwtjdHQmoOpm06y9PDiYio3CvxqeAAMHPmTJiZmQEAcnNz8X//93+wti58of6FCxeWeLzc3FyEh4djxowZum0KhQIBAQE4duxYicZYuXIlBgwY8NQZpJycHOTk5Oi+Tk1NBQBoNBpoNJoS91oSBeNJPa6cNVQqFRa+2QBv/BiGvy8mYM3RKAxq5iZpDX1hDdZgDcOvUVZ1WEP+GqUZTxBL+Kt4wVqXZw4mCDhw4ECJi8fGxsLV1RVHjx5Fy5YtddunTZuG0NBQ3RlZT3PixAk0b94cYWFhaNasWbH7zJkzB3Pnzi2yfd26dbqgRs8XEidgy00lVIKIKb75cOa3joiIylBmZiYGDhyIlJQUWFlZPXPfEs/chISEvGxfklu5ciUaNGjw1GADADNmzMDkyZN1X6empsLNzQ2dO3d+7jentDQaDYKCgtCpUyeoVCpJx5a7RletiPu/ReDQ1fvYEm+Dv95tDrVKKWkNqbEGa7CG4dcoqzqsIX+NgiMvJVGqw1JSs7Ozg1KpREJCQqHtCQkJcHJyeuZzMzIysH79esybN++Z+6nVaqjV6iLbVSqV3v5i9Tm2nDUW9PdHt+8O4XJCOr75+zrm9K4neQ19YA3WYA3Dr1FWdVhDvhqlGUuyKxS/CGNjYzRu3BjBwcG6bVqtFsHBwYUOUxXnzz//RE5ODgYPHqzvNukRe0s1vn7DDwCw+uhNHLiU8JxnEBERlT1Zww0ATJ48GStWrMCaNWtw8eJFjBkzBhkZGRgxYgQAYOjQoYUWHBdYuXIl+vbti6pVq5Z1y5XaK7UdMKK1JwDgwz/P4m5qtrwNERERPUHWw1IAEBgYiHv37mHWrFmIj49Hw4YNsXfvXjg6OgJ4eH0dhaJwBrt8+TIOHz6M/fv3y9FypTe9W20cv5GEi3GpmPLnGawZ0QwKxbMXmxMREZUV2cMNAIwbNw7jxo0r9rHiFjLXqlWL11uRkdpIicUDGqLXD4fxz9VErDoShVFtq8vdFhEREYAXDDfJyck4ceIE7t69C6228E0Vhw4dKkljVL7VcLTEzJ518b8t5zF/7yW0qF4V9V2tn/9EIiIiPSt1uNmxYwcGDRqE9PR0WFlZFbr2jSAIDDeVyMBm7gi9fA/7LyRg/PrT2PlBG5gZl4vJQCIiqsRKvaB4ypQpGDlyJNLT05GcnIwHDx7oPpKSkvTRI5VTgiBg/uu+cLRS48a9DMzbcUHuloiIiEofbu7cuYPx48fz6r4EALA1N8aiwIYQBGD9yRjsPhcnd0tERFTJlTrcdOnSBadOndJHL1RBtfK2w5j23gCA6X+dRWxylswdERFRZVbqBRI9evTA1KlTceHCBTRo0KDIFQN79+4tWXNUcUzqVBNHriXizO0UTNwQiT9Gt4CSp4cTEZEMSh1uRo8eDQDF3vZAEATk5+e/fFdU4aiUCnw3wB89Fv+DE1FJWHrwGj7oWEPutoiIqBIq9WEprVb71A8Gm8rN084c8/rUBwB8G3wV4dEPZO6IiIgqI9lvv0CG5bVGrujt54J8rYgJ608jNVsjd0tERFTJlOiw1OLFi/HOO+/AxMQEixcvfua+48ePl6QxqpgEQcD/9auPiFsPcPtBFmZtPY9vB/jL3RYREVUiJQo3ixYtwqBBg2BiYoJFixY9dT9BEBhuCFYmKnw3wB/9fzyGrZGxaF/LHv38q8ndFhERVRIlCjdRUVHFfk70NI09bDGhYw0sDLqCT7acRyN3W3hUNZe7LSIiqgS45ob0ZuwrPmjmWQUZufkYvz4Smnzt859ERET0kl7oRkC3b9/G9u3bcevWLeTm5hZ6bOHChZI0RhWfUiFg0YCG6PrtIZyJSca3f1/B1C615W6LiIgMXKnDTXBwMHr37o3q1avj0qVLqF+/Pm7evAlRFNGoUSN99EgVmKuNKb58zRdj10Vgach1tPGxR0vvqnK3RUREBqzUh6VmzJiBDz/8EOfOnYOJiQn++usvxMTEoH379njzzTf10SNVcD18nRHYxA2iCEzaEInkzNznP4mIiOgFlTrcXLx4EUOHDgUAGBkZISsrCxYWFpg3bx7mz58veYNkGGb1qovqduaIT83G9L/OQRRFuVsiIiIDVepwY25urltn4+zsjOvXr+seS0xMlK4zMijmaiN8N8AfKqWAvf/GY8OpO3K3REREBqrU4aZFixY4fPgwAKB79+6YMmUKPvvsM4wcORItWrSQvEEyHA2qWWNql1oAgM/2XEJ8pswNERGRQSr1guKFCxciPT0dADB37lykp6djw4YNqFGjBs+Uouca1aY6Dl1JxOFrifjlihLdUrLhbqd6/hOJiIhKqFQzN/n5+bh9+zbc3d0BPDxEtXz5cpw9exZ//fUXPDw89NIkGQ6FQsDC/n6wszBGfJaA15YfR8Qt3mCTiIikU6pwo1Qq0blzZzx4wB9G9OIcrEyw8Z1mcDYTkZieiwE/Hsem8Ntyt0VERAai1Gtu6tevjxs3buijF6pE3GzNMLF+PjrVcUBuvhYf/nkGn+26gHwtz6IiIqKXU+pw83//93/48MMPsXPnTsTFxSE1NbXQB1FJmSiBHwb4YfyrPgCAFf9EYeTqk0jJ0sjcGRERVWQlDjfz5s1DRkYGunfvjjNnzqB3796oVq0abG1tYWtrCxsbG9ja2uqzVzJACoWAyZ1r4YeB/jBRKRB65R76LT2CG/fS5W6NiIgqqBKfLTV37ly89957OHjwoD77oUqqp68LPKuaY/Svp3DjXgb6LDmCJQMboV1Ne7lbIyKiCqbE4abgirLt27fXWzNUudV3tcb2cW3w7tpTiLiVjOG/nMD/etTFyNaeEARB7vaIiKiCKNWaG/6AIX2zt1Tjj3da4I3G1aAVgU93XsC0TWeRk5cvd2tERFRBlOoifjVr1nxuwElKSnqphojURkp8/YYv6jhb4bNdF/Bn+G3cSMzA8sGNYW+plrs9IiIq50oVbubOnQtra2t99UKkIwgC3m7jBR8HC4xbF4Hw6Afo/cNhrBjaBPVd+W+QiIierlThZsCAAXBwcNBXL0RFtK9pj21jW2PUo4XGbyw/im/e9ENPXxe5WyMionKqxGtuuN6G5FLd3gJb3m+N9jXtka3RYty601i4/zK0vOAfEREVo8ThpuBsKSI5WJuqsGp4U4xu6wUAWHzgGt77LRwZOXkyd0ZEROVNicONVqvlISmSlVIh4H896uKbN/1grFRg/4UEvL7sKGKSMuVujYiIypFS336BSG5vNK6G9e+2gL2lGpfi09BnyREcv3Ff7raIiKicYLihCqmRuy22j2uNBq7WSMrIxeCfw/B7WLTcbRERUTnAcEMVlrO1KTa+2xK9/FyQpxXxvy3nMWvbeWjytXK3RkREMmK4oQrN1FiJxQMaYmqXWgCAX49FY+jKE3iQkStzZ0REJBeGG6rwBEHA2Fd88NOQxjA3VuLYjfvoveQwribwzuJERJURww0ZjM71nLD5/dZwq2KKmKQsvPlTGM4l8fpMRESVDcMNGZRaTpbYPrYNWlSvgozcfPx8WYmJG8/ibmq23K0REVEZYbghg2Nrboy1bzfH2609IEDErnPx6LggFGuO3kQ+r2pMRGTwGG7IIKmUCkzvWgtTGuTD19UKaTl5mL39X/RdcgRnbyfL3R4REekRww0ZNDcLYOM7zfFp3/qwNDHCuTsp6LPkCGZtO4+ULI3c7RERkR4w3JDBUyoEDGnhgeAp7dG3oQtE8eEp4x0XhGJb5B3eN42IyMAw3FCl4WBpgm8H+GPdqOaobmeOxPQcTFgficErw3D9Hk8bJyIyFAw3VOm08rHDnoltMaVTTaiNFDhy7T66ffsPFu6/jGxNvtztERHRS2K4oUpJbaTEBx1rIGhSe3SoZY/cfC0WH7iGLt8eQsjlu3K3R0REL4Hhhio196pm+GV4Uywb1AiOVmpE38/E8F9OYuzvEYhP4bVxiIgqItnDzZIlS+Dp6QkTExM0b94cJ06ceOb+ycnJGDt2LJydnaFWq1GzZk3s3r27jLolQyQIAro1cEbwlA54u40XFAKw61wcAhaGYtXhKOTxRpxERBWKrOFmw4YNmDx5MmbPno2IiAj4+fmhS5cuuHu3+MMCubm56NSpE27evIlNmzbh8uXLWLFiBVxdXcu4czJEFmojzOxZFzs+aAN/dxuk5+Rh3s4L6P3DEZy+9UDu9oiIqIRkDTcLFy7E6NGjMWLECNStWxfLly+HmZkZVq1aVez+q1atQlJSErZu3YrWrVvD09MT7du3h5+fXxl3Toasnos1/nqvFT7v1wDWpipciEvFa8uO4n9bziElk9fGISIq74zkKpybm4vw8HDMmDFDt02hUCAgIADHjh0r9jnbt29Hy5YtMXbsWGzbtg329vYYOHAgPvroIyiVymKfk5OTg5ycHN3XqampAACNRgONRtofVAXjST0ua8hT481Gzni1ZhXM33cFWyLj8HvYLew9H4/pXWuij58zBEGoEK+DNVjDkGuUVR3WkL9GacYTRJmuYBYbGwtXV1ccPXoULVu21G2fNm0aQkNDERYWVuQ5tWvXxs2bNzFo0CC8//77uHbtGt5//32MHz8es2fPLrbOnDlzMHfu3CLb161bBzMzM+leEBm0aynAxiglErIe3mXcx0qL/tW1cDSVuTEiokoiMzMTAwcOREpKCqysrJ65b4UKNzVr1kR2djaioqJ0MzULFy7E119/jbi4uGLrFDdz4+bmhsTExOd+c0pLo9EgKCgInTp1gkqlknRs1pC/Rm6eFquO3MSS0BvI1mihUgoY0dIdNXKvo0fXivM6WIM1DKlGWdVhDflrpKamws7OrkThRrbDUnZ2dlAqlUhISCi0PSEhAU5OTsU+x9nZGSqVqtAhqDp16iA+Ph65ubkwNjYu8hy1Wg21Wl1ku0ql0ttfrD7HZg35aqhUwAcBtdC3kRvmbP8XwZfu4qfD0bBTK1GrSTZ83fU7E1iRvleswRplXaOs6rCGfDVKM5ZsC4qNjY3RuHFjBAcH67ZptVoEBwcXmsl5XOvWrXHt2jVotf+dmnvlyhU4OzsXG2yI9MGtihl+HtYEPw5pDCcrNRJzBPRfEYatp+/I3RoREUHms6UmT56MFStWYM2aNbh48SLGjBmDjIwMjBgxAgAwdOjQQguOx4wZg6SkJEyYMAFXrlzBrl278Pnnn2Ps2LFyvQSqpARBQJd6TtgxthVqW2uRrdFi4oZIzN3xLzS8Lg4RkaxkOywFAIGBgbh37x5mzZqF+Ph4NGzYEHv37oWjoyMA4NatW1Ao/stfbm5u2LdvHyZNmgRfX1+4urpiwoQJ+Oijj+R6CVTJ2Zip8G4dLa6ovbEsNAq/HLmJf2NTsWRgI9hbFj0cSkRE+idruAGAcePGYdy4ccU+FhISUmRby5Ytcfz4cT13RVRyCgGYHFADDd2rYMrGMzgRlYSe3/+DZYMbo5G7rdztERFVOrLffoHIUHSp54StY1vD294cCak5GPDjcawLuyV3W0RElQ7DDZGEfBwssG1cG3St54TcfC0+3nIO0/86i2xNvtytERFVGgw3RBKzUBth2eBGmNa1FhQCsP5kDAJ/PIbY5Cy5WyMiqhQYboj0QBAEvN/BB6tHNIONmQpnbqeg1/eHcez6fblbIyIyeAw3RHrUrqY9doxrg7rOVrifkYvBK8Pw8z83INOFwYmIKgWGGyI9c6tihr/GtMJr/q7I14r4v10XMX59JDJz8+RujYjIIDHcEJUBU2MlFvT3w9ze9WCkELDjTCxeW3oU0fcz5G6NiMjgMNwQlRFBEDCslSfWjW4BOws1LsWnodf3h3Hw0l25WyMiMigMN0RlrJlXFewa3waN3G2Qmp2HkWtOYnHwVWi1XIdDRCQFhhsiGThamWD9Oy0xuIU7RBFYGHQF76wNR2q2Ru7WiIgqPIYbIpkYGynwf30b4Ks3fGFspMDfFxPQ54cjuJKQJndrREQVGsMNkcz6N3HDpvdawsXaBFGJGei75Ah2nY2Tuy0iogqL4YaoHPCtZoMdH7RBK++qyMzNx9h1Efhiz0Xk5Wvlbo2IqMJhuCEqJ6paqPHryGZ4t111AMCPoTcw/JeTSMrIlbkzIqKKheGGqBwxUiowo3sd/DDQH2bGShy+loh+y44jJl3uzoiIKg6GG6JyqKevC7a83xqeVc0Qm5KNb88r8XvYLd62gYioBBhuiMqpWk6W2DauDTrWtkeeKGDOzkt477dwJGfyMBUR0bMw3BCVY9amKiwb2BD9PPOhUgrY928Cun/3D07dTJK7NSKicovhhqicEwQBHZxFbBzdXHeYKvCn4/jhwFXk86rGRERFMNwQVRD1Xa2wc3xb9Ht0d/Fv9l/B4J/DkJCaLXdrRETlCsMNUQVioTbCosCGWPCmH8yMlTh24z66ffcPb75JRPQYhhuiCuj1xtWw44M2qOtshaSMXIxYfRL/t/MCcvN40T8iIoYbogrK294CW8a2wvBWngCAnw9H4Y3lR3EzMUPexoiIZMZwQ1SBqY2UmNO7HlYMbQIbMxXO3k5Bz+8PY1vkHblbIyKSDcMNkQHoVNcReya0RTPPKkjPycOE9ZGY+ucZZObmyd0aEVGZY7ghMhDO1qZYN7o5JnSsAYUA/Bl+Gz2/P4wLsalyt0ZEVKYYbogMiJFSgUmdamLd6BZwsjLBjXsZ6Lv0CH49dpO3biCiSoPhhsgAtaheFbsntEXH2g7IzdNi1rZ/8e5a3rqBiCoHhhsiA1XF3Bg/D2uC2b3qwlipwP4LD2/dcJK3biAiA8dwQ2TABEHAiNZe2Px+K3jZmT+8dcOPx7A4mLduICLDxXBDVAnUd7XGjg/a4DV/V2hFYGHQFQz6+Thv3UBEBonhhqiSsFAbYeFjt244fiMJ3b77BwcuJcjdGhGRpBhuiCqZ1xtXw84P2qCey8NbN4xcfQqf77kM3rmBiAwFww1RJVTd3gKb32+FEa09AQC/HI3GovNKnI5JlrUvIiIpMNwQVVJqIyVm96qHn4c2ga2ZCrczBPT/6QQmrD+N2OQsudsjInphDDdElVxAXUfsHNcKze21EARgW2QsXl0QgoVBV3j7BiKqkBhuiAgOlmoM9NFi87st0MyzCrI1WiwOvopXvgnB5ojb0PK0cSKqQBhuiEinvqsVNrzbAksHNUI1W1MkpOZg8sYz6Lf0CMKjefE/IqoYGG6IqBBBENC9gTP+ntweH3WtDXNjJc7cTsHry45h3LoI3H6QKXeLRETPxHBDRMUyUSkxpoM3Dk7tgAFN3SAIwM6zcXh1QSi+2XcZGTlcj0NE5RPDDRE9k4OlCb583Rc7P2iDFtWrIDdPix8OXkOHb0Kw8VQM1+MQUbnDcENEJVLPxRp/jG6BH4c0hkdVM9xLy8G0TWfRe8lhhN24L3d7REQ6DDdEVGKCIKBLPSfsn9QOH3evDUu1Ec7fSUXgT8fx/u/hiEniehwikh/DDRGVmtpIiXfaPVyPM6i5OxQCsPtcPDouCMWXey4hLVsjd4tEVIkx3BDRC7OzUOOzfg2we0JbtPGxQ26+FstDr+OVb0Kw/sQt5HM9DhHJgOGGiF5abScrrH27GVYOawIvO3Mkpudi+uZz6Pn9YRy9nih3e0RUyTDcEJEkBEFAxzqO2DexHWb2rAsrEyNcjEvFwBVheOfXU4i+z/U4RFQ2jORugIgMi7GRAm+38UI/f1d8+/cV/B52C/svJODg5btoaqfA3WPRcLAyhb2FGnaWalQ1N4atmTEUCkHu1onIQJSLcLNkyRJ8/fXXiI+Ph5+fH77//ns0a9as2H1Xr16NESNGFNqmVquRnZ1dFq0SUQlVMTfGvD71MaSFBz7ddRGHrtzD0QQFju6+XGRfpUJAFXNjVDU3hr2lGnYWD0NPQfixs1TD3kKNqhbGqGquhrERJ52J6OlkDzcbNmzA5MmTsXz5cjRv3hzffvstunTpgsuXL8PBwaHY51hZWeHy5f/+gxQE/sZHVF7VcLTEryOb4cCFOKzefwoWds5IytQgMT0Xiek5SM7UIF8r4l5aDu6l5eBSfNpzx7Q2VaGqhTHsLP4LPXYWatiYKpGQIqCbyIXMRJWZ7OFm4cKFGD16tG42Zvny5di1axdWrVqF6dOnF/scQRDg5ORUlm0S0UtqW8MOaVe16N7dDyqVSrddk69FUkYu7qXl4H5GLhLTcnA/I+dh+EnLQeKjbYnpDx/P14pIydIgJUuDG/cyiqmkROSaCMx/wxfVbM3K7gUSUbkha7jJzc1FeHg4ZsyYodumUCgQEBCAY8eOPfV56enp8PDwgFarRaNGjfD555+jXr16xe6bk5ODnJwc3depqakAAI1GA41G2mtxFIwn9biswRqGXqOKqRJVTM0APDuMaLUiUrI1uJ+e+zAI6f7Mwf30XNxLy8bhq4k4cv0+uiw6hKldauKtJtUkXc8j9/eKNeSpwxry1yjNeIIoyjd/GxsbC1dXVxw9ehQtW7bUbZ82bRpCQ0MRFhZW5DnHjh3D1atX4evri5SUFHzzzTc4dOgQ/v33X1SrVq3I/nPmzMHcuXOLbF+3bh3MzPhbHZGhuZsF/HFdiRtpDwONj5UWb3lrYWcic2NE9FIyMzMxcOBApKSkwMrK6pn7Vrhw8ySNRoM6dergrbfewqefflrk8eJmbtzc3JCYmPjcb05paTQaBAUFoVOnToWm3VmDNVijbGt0DAjAhoh4fLP/CrI0WpiqFJjcqQaGNHeH8iVncQzte1XRa5RVHdaQv0Zqairs7OxKFG5kPSxlZ2cHpVKJhISEQtsTEhJKvKZGpVLB398f165dK/ZxtVoNtVpd7PP09Rerz7FZgzVY4/nUxsZ4u603OtV1xkd/ncWxG/fx2e7L2PvvXXz1hi+87S1euoahfK8MpUZZ1WEN+WqUZixZz6c0NjZG48aNERwcrNum1WoRHBxcaCbnWfLz83Hu3Dk4Ozvrq00iqqDcq5rh91HN8Vm/+rBQGyE8+gG6ffcPlodeR16+Vu72iEhPZL9YxOTJk7FixQqsWbMGFy9exJgxY5CRkaE7e2ro0KGFFhzPmzcP+/fvx40bNxAREYHBgwcjOjoao0aNkuslEFE5plAIGNTcA/smtUO7mvbIzdPiyz2X8Pqyo7hcgtPOiajikf1U8MDAQNy7dw+zZs1CfHw8GjZsiL1798LR0REAcOvWLSgU/2WwBw8eYPTo0YiPj4etrS0aN26Mo0ePom7dunK9BCKqAFxtTLFmRFNsCr+NeTsv4MztFPT8/h+Mf7UG3uvgDZVS9t/1iEgisocbABg3bhzGjRtX7GMhISGFvl60aBEWLVpUBl0RkaERBAFvNnFDu5r2+N+Wc/j74l0sCLqCPefj8fWbvqjnYi13i0QkAf6qQkSVjqOVCVYMbYJvAxvCxkyFC3Gp6PPDESzcfxk5eflyt0dEL4nhhogqJUEQ0NffFUGT2qNbfSfkaUUsPnANvb4/jDMxyXK3R0QvgeGGiCo1e0s1lg1ujKWDGqGquTGuJKSj39Ij+GLPRWRrOItDVBEx3BARAejewBlBk9ujt58LtCLwY+gNdF/8D8Kjk+RujYhKieGGiOiRKubGWPyWP34a0hgOlmrcuJeBN5Yfw7wdF5CVy1kcooqC4YaI6Amd6zkhaFJ7vN6oGkQRWHUkCl2/O4Rj1+/L3RoRlQDDDRFRMazNVFjQ3w+/jGgKZ2sTRN/PxFsrjmPOjovI5iQOUbnGcENE9Ayv1HLAvknt8FYzdwDA7ydi8HmkErO2X8DW03cQk5QJGe8/TETFKBcX8SMiKs+sTFT44rUG6OnrjI82ncHt5Gz8cfI2/jh5GwDgZGWCxp62aOJhi6aeVVDbyRJGvOIxkWwYboiISqi1jx12f9Aa327YD9hVR3hMCv69k4L41GzsOhuHXWfjAADmxkr4u9uisYctmnjawt/dFhZq/ndLVFb4biMiKgVTYyX8qoro3q0WVCoVsnLzERmTjPDoJJyKfoDw6AdIy87D4WuJOHwtEQCgEIA6zlZo6lkFjR/N7jhZm8j8SogMF8MNEdFLMDVWoqV3VbT0rgoA0GpFXLmbhpM3HyD8ZhJO3nyAO8lZ+Dc2Ff/GpmL10ZsAHt7Is6mnLRp7VkETD1vUdLSEUiHI+EqIDAfDDRGRhBQKAbWdrFDbyQpDWngAAOJTsnEqOgmnbj7AqegkXIhNxZ3kLNyJzMLWyFgAgKWJERq526Kppy0aVrMCL6tD9OIYboiI9MzJ2gQ9fV3Q09cFAJCek4fIW8m6wHP61sNDWaFX7iH0yj0AgFJQYm/qafTxr4aAOg4wM+Z/10QlxXcLEVEZs1AboU0NO7SpYQcAyMvX4lJ8Gk7dTMLJ6Ac4dTMJCak5CL50D8GX7sHMWIlOdR3R288FbWvYw9iIZ2IRPQvDDRGRzIyUCtR3tUZ9V2sMb+2F3NxcrPxrD1Kta2Dn+XjEJGVhW2QstkXGwsZMhW71ndDLzwXNvapynQ5RMRhuiIjKGUEQ4GIGjOpUA9O61UFkTDK2n4nFzrNxuJeWgz9OxOCPEzFwsFSjp68Lejd0gV81awgCgw4RwHBDRFSuCYIAf/eH18r5pEddhN24j+1nYrH7XBzupuVg1ZEorDoSBY+qZujt54Lefi6o4Wgpd9tEsmK4ISKqIJQKAa187NDKxw5z+9TDoSuJ2H4mFn9fSED0/Ux8f+Aavj9wDbWdLNG7oQt6+brArYqZ3G0TlTmGGyKiCkht9HCRcae6jsjMzUPQhQTsOBOL0Cv3cCk+DZf2XsZXey+jkbsNevu5oIevC+wt1XK3TVQmGG6IiCo4M2Mj9Gnoij4NXZGcmYu95+Ox/Uwsjt24j4hbyYi4lYx5Oy+glbcdevu5oEt9J5iV8n9/URSRpclHalYeUrM1SM3SIC37v89TdZ8/ti1Lg8QkJY7nXcArtR3R2scO5rwNBZUB/isjIjIgNmbGGNDMHQOaueNuajZ2no3DtjOxOBOTrLslxCdbz6Ndjapw0AjIOR2LDI0WqVl5SMvWFAooT4aXfO2L3P1c0N1kVKUU0NSzCtrXtEeHWg6o6WjBRdCkFww3REQGysHKBCPbeGFkGy9E38/AjjOx2H4mFlcS0vH3pXsAlFh3/XypxjRSCLAyVcHSxAhWJipYmRrBUv3wz4df//eYuUrAiVPh0Nh64tC1+4i+n4mj1+/j6PX7+GLPJbhYm6B9LXu0r2mP1j52sDRR6ecbQZUOww0RUSXgUdUc416tgXGv1sCl+FRsjbiNoNM34OJoB2sz4ycCiwpWj4UXKxMVLB99bqpSlni2RaPRICdKRPfudaBSqRCVmIGQy3cRcvkejt+4j9iUbN1p7UYKAY09bNG+lj061HRAHWdLzurQC2O4ISKqZGo7WWFKpxqoo7mK7t0bQ6UqmxkTLztzeNl5YURrL2Rr8nH8xn2EXH54y4moxAyERSUhLCoJX+29DEcrNdrXtEf7mg5oU8MO1qac1aGSY7ghIqIyZ6JSokMtB3So5QAAiL6fgdAr9xBy+R6OXk9EQmoONp66jY2nbkOpENDI3Ua3VqeusxUUvDIzPQPDDRERyc6jqjmGtjTH0JaeyNbk4+TNJIRcvoeQy3dx/V4GTt58gJM3H+Cb/VdgZ6FGu5p26FDLAe1q2MFcxaBDhTHcEBFRuWKiUqJtDXu0rWGPmT3rIiYps9CsTmJ6DjZH3MHmiDtQCEADV2uocxS49PdVuNiaw9nKBE7WJnC2NkEVc2Ou3amEGG6IiKhcc6tihsEtPDC4hQdy8vIRfvMBQq48nNW5kpCOM7dTAChwIjSqyHONlQo4WqvhbGWqCzwFfzpamcDZ2hT2lmregNTAMNwQEVGFoTZS6m5B8XH3OriTnIVj1+4h5EQkbJw9kZCWi/iUbMSlZCMxPQe5+VrEJGUhJinrqWMqFQIcLNVwsjaB02OzPk7WpnCyevi5ramyDF+lNLTahxdezMzNR1ZuPjJy85CWlYOYdOD6vQxYmalhqlLC1FgJtZHCoGa4GG6IiKjCcrUxRR8/Z6junNadcl4gN0+Lu2nZurBT8GdCajbiUrIQn5KNhLQc5GtFxD167FmUghKfnD4AE5USJioFTIyU/32uUkJt9N/nxT6uUsLEqODxx/Z99DwFtIjPBM7eTkGuVkBmbl6hYFLweWZuvu6xxz/Pys1HpiYPmTmPAo0m/ymvxAjfnDtSaIsg4GHQeRR2TFVKmBk/7NPU+LHPH203VSlhYqyEWcH+xka65xsrRCQ8PUuWCYYbIiIySMZGClSzNUM126ffPDRfK+J+eo4u3MSnZCEuNRsJBV+nPvwzN0+LfFFAWnYe0rLz9Ni1EXAmTPJRTVVKmKsfhqu0zCxAqUJWrha5+VoAgChCF5aQ8fL13M2VGPHyw7wwhhsiIqq0lAoBDlYmcLAygZ9b8fuIoojE1Czs2heEVm3bI09UIDsvH9mafORotMjW5D/6+tHnj23TPf5oe1bB53la5Dy2/eG++RDEfFibm8JcbaSbITEzVsJMbQSzJz5/OKNiBHN1wX5GMFM/2kf13+cmRkrdqfMajQa7d+9G9+5doFKpkJevRXaeFpm5ecjO1T46jJWn67NgRkj3uebRx6Ptus8f256ZmwdbKRLSS2C4ISIiegZBEGBjpoKt+uGFCPV10cP/gke7MruwopFSAQulAhYS3tC04HXISSFrdSIiIiKJMdwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkU6e5xXkGIoggASE1NlXxsjUaDzMxMpKam6u129azBGqzBGqxR9nVYQ/4aBT+3C36OP0ulCzdpaWkAADc3N5k7ISIiotJKS0uDtbX1M/cRxJJEIAOi1WoRGxsLS0tLCIIg6dipqalwc3NDTEwMrKysJB2bNViDNViDNeSrwxry1xBFEWlpaXBxcYFC8exVNZVu5kahUKBatWp6rWFlZaXXNzJrsAZrsAZryFOHNeSt8bwZmwJcUExEREQGheGGiIiIDArDjYTUajVmz54NtVrNGqzBGqzBGmVQo6zqsEb5qvE8lW5BMRERERk2ztwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDjQQOHTqEXr16wcXFBYIgYOvWrZKO/8UXX6Bp06awtLSEg4MD+vbti8uXL0taAwCWLVsGX19f3YWXWrZsiT179khep8CXX34JQRAwceJEScedM2cOBEEo9FG7dm1JawDAnTt3MHjwYFStWhWmpqZo0KABTp06Jdn4np6eRV6HIAgYO3asZDXy8/Mxc+ZMeHl5wdTUFN7e3vj0009LdO+W0khLS8PEiRPh4eEBU1NTtGrVCidPnnzh8Z73nhNFEbNmzYKzszNMTU0REBCAq1evSlpj8+bN6Ny5M6pWrQpBEBAZGSnp69BoNPjoo4/QoEEDmJubw8XFBUOHDkVsbKykr2POnDmoXbs2zM3NYWtri4CAAISFhUla43HvvfceBEHAt99+K2mN4cOHF3mvdO3aVfLXcfHiRfTu3RvW1tYwNzdH06ZNcevWLclqFPeeFwQBX3/9tWQ10tPTMW7cOFSrVg2mpqaoW7culi9fXuLxS1IjISEBw4cPh4uLC8zMzNC1a9dSvwdfBsONBDIyMuDn54clS5boZfzQ0FCMHTsWx48fR1BQEDQaDTp37oyMjAxJ61SrVg1ffvklwsPDcerUKbz66qvo06cP/v33X0nrAMDJkyfx448/wtfXV/KxAaBevXqIi4vTfRw+fFjS8R88eIDWrVtDpVJhz549uHDhAhYsWABbW1vJapw8ebLQawgKCgIAvPnmm5LVmD9/PpYtW4YffvgBFy9exPz58/HVV1/h+++/l6wGAIwaNQpBQUFYu3Ytzp07h86dOyMgIAB37tx5ofGe95776quvsHjxYixfvhxhYWEwNzdHly5dkJ2dLVmNjIwMtGnTBvPnz3+h1/C8GpmZmYiIiMDMmTMRERGBzZs34/Lly+jdu7dkNQCgZs2a+OGHH3Du3DkcPnwYnp6e6Ny5M+7duydZjQJbtmzB8ePH4eLiUqrXUNIaXbt2LfSe+eOPPyStcf36dbRp0wa1a9dGSEgIzp49i5kzZ8LExESyGo/3HxcXh1WrVkEQBLz++uuS1Zg8eTL27t2L3377DRcvXsTEiRMxbtw4bN++XZIaoiiib9++uHHjBrZt24bTp0/Dw8MDAQEBkv/ceiqRJAVA3LJli15r3L17VwQghoaG6rWOKIqira2t+PPPP0s6ZlpamlijRg0xKChIbN++vThhwgRJx589e7bo5+cn6ZhP+uijj8Q2bdrotcaTJkyYIHp7e4tarVayMXv06CGOHDmy0LbXXntNHDRokGQ1MjMzRaVSKe7cubPQ9kaNGon/+9//Xnr8J99zWq1WdHJyEr/++mvdtuTkZFGtVot//PGHJDUeFxUVJQIQT58+/UJjl6RGgRMnTogAxOjoaL3VSElJEQGIf//9t6Q1bt++Lbq6uornz58XPTw8xEWLFr3Q+E+rMWzYMLFPnz4vPGZJagQGBoqDBw/Wa40n9enTR3z11VclrVGvXj1x3rx5hba9zPvxyRqXL18WAYjnz5/XbcvPzxft7e3FFStWvFCN0uLMTQWUkpICAKhSpYreauTn52P9+vXIyMhAy5YtJR177Nix6NGjBwICAiQd93FXr16Fi4sLqlevjkGDBpVq2rgktm/fjiZNmuDNN9+Eg4MD/P39sWLFCklrPC43Nxe//fYbRo4cKekNX1u1aoXg4GBcuXIFAHDmzBkcPnwY3bp1k6xGXl4e8vPzi/x2a2pqKvmMGgBERUUhPj6+0L8va2trNG/eHMeOHZO8XllKSUmBIAiwsbHRy/i5ubn46aefYG1tDT8/P8nG1Wq1GDJkCKZOnYp69epJNu6TQkJC4ODggFq1amHMmDG4f/++ZGNrtVrs2rULNWvWRJcuXeDg4IDmzZtLvgzhcQkJCdi1axfefvttScdt1aoVtm/fjjt37kAURRw8eBBXrlxB586dJRk/JycHAAq95xUKBdRqtV7e88VhuKlgtFotJk6ciNatW6N+/fqSj3/u3DlYWFhArVbjvffew5YtW1C3bl3Jxl+/fj0iIiLwxRdfSDbmk5o3b47Vq1dj7969WLZsGaKiotC2bVukpaVJVuPGjRtYtmwZatSogX379mHMmDEYP3481qxZI1mNx23duhXJyckYPny4pONOnz4dAwYMQO3ataFSqeDv74+JEydi0KBBktWwtLREy5Yt8emnnyI2Nhb5+fn47bffcOzYMcTFxUlWp0B8fDwAwNHRsdB2R0dH3WMVUXZ2Nj766CO89dZbkt+McOfOnbCwsICJiQkWLVqEoKAg2NnZSTb+/PnzYWRkhPHjx0s25pO6du2KX3/9FcHBwZg/fz5CQ0PRrVs35OfnSzL+3bt3kZ6eji+//BJdu3bF/v370a9fP7z22msIDQ2VpMaT1qxZA0tLS7z22muSjvv999+jbt26qFatGoyNjdG1a1csWbIE7dq1k2T82rVrw93dHTNmzMCDBw+Qm5uL+fPn4/bt23p5zxen0t0VvKIbO3Yszp8/r7f0W6tWLURGRiIlJQWbNm3CsGHDEBoaKknAiYmJwYQJExAUFFSqY9Sl9fisg6+vL5o3bw4PDw9s3LhRst+AtFotmjRpgs8//xwA4O/vj/Pnz2P58uUYNmyYJDUet3LlSnTr1u2F1io8y8aNG/H7779j3bp1qFevHiIjIzFx4kS4uLhI+jrWrl2LkSNHwtXVFUqlEo0aNcJbb72F8PBwyWoYMo1Gg/79+0MURSxbtkzy8V955RVERkYiMTERK1asQP/+/REWFgYHB4eXHjs8PBzfffcdIiIiJJ11fNKAAQN0nzdo0AC+vr7w9vZGSEgIOnbs+NLja7VaAECfPn0wadIkAEDDhg1x9OhRLF++HO3bt3/pGk9atWoVBg0aJPn/l99//z2OHz+O7du3w8PDA4cOHcLYsWPh4uIiyYy6SqXC5s2b8fbbb6NKlSpQKpUICAhAt27dJD9Z4Wk4c1OBjBs3Djt37sTBgwdRrVo1vdQwNjaGj48PGjdujC+++AJ+fn747rvvJBk7PDwcd+/eRaNGjWBkZAQjIyOEhoZi8eLFMDIykuw3rCfZ2NigZs2auHbtmmRjOjs7Fwl8derUkfzwFwBER0fj77//xqhRoyQfe+rUqbrZmwYNGmDIkCGYNGmS5DNr3t7eCA0NRXp6OmJiYnDixAloNBpUr15d0joA4OTkBODhlP7jEhISdI9VJAXBJjo6GkFBQZLP2gCAubk5fHx80KJFC6xcuRJGRkZYuXKlJGP/888/uHv3Ltzd3XXv++joaEyZMgWenp6S1ChO9erVYWdnJ9n73s7ODkZGRmX2vv/nn39w+fJlyd/3WVlZ+Pjjj7Fw4UL06tULvr6+GDduHAIDA/HNN99IVqdx48aIjIxEcnIy4uLisHfvXty/f18v7/niMNxUAKIoYty4cdiyZQsOHDgALy+vMqut1Wp1x09fVseOHXHu3DlERkbqPpo0aYJBgwYhMjISSqVSkjpPSk9Px/Xr1+Hs7CzZmK1bty5yOv6VK1fg4eEhWY0Cv/zyCxwcHNCjRw/Jx87MzIRCUfi/AaVSqfstVWrm5uZwdnbGgwcPsG/fPvTp00fyGl5eXnByckJwcLBuW2pqKsLCwiRfP6ZvBcHm6tWr+Pvvv1G1atUyqSvl+37IkCE4e/Zsofe9i4sLpk6din379klSozi3b9/G/fv3JXvfGxsbo2nTpmX2vl+5ciUaN24s6don4OG/KY1GU2bve2tra9jb2+Pq1as4deqUXt7zxeFhKQmkp6cX+u0gKioKkZGRqFKlCtzd3V96/LFjx2LdunXYtm0bLC0tdesGrK2tYWpq+tLjF5gxYwa6desGd3d3pKWlYd26dQgJCZHsPyBLS8si64TMzc1RtWpVSdcPffjhh+jVqxc8PDwQGxuL2bNnQ6lU4q233pKsxqRJk9CqVSt8/vnn6N+/P06cOIGffvoJP/30k2Q1gIc/ZH755RcMGzYMRkbSv1179eqFzz77DO7u7qhXrx5Onz6NhQsXYuTIkZLW2bdvH0RRRK1atXDt2jVMnToVtWvXxogRI15ovOe95yZOnIj/+7//Q40aNeDl5YWZM2fCxcUFffv2laxGUlISbt26pbvuTMEPPScnpxLPED2rhrOzM9544w1ERERg586dyM/P1733q1SpAmNj45euUbVqVXz22Wfo3bs3nJ2dkZiYiCVLluDOnTuluuTA875XT4YylUoFJycn1KpVS5IaVapUwdy5c/H666/DyckJ169fx7Rp0+Dj44MuXbpI9jqmTp2KwMBAtGvXDq+88gr27t2LHTt2ICQkRLIawMMw/ueff2LBggUlHrc0Ndq3b4+pU6fC1NQUHh4eCA0Nxa+//oqFCxdKVuPPP/+Evb093N3dce7cOUyYMAF9+/aVbNHyc5XJOVkG7uDBgyKAIh/Dhg2TZPzixgYg/vLLL5KMX2DkyJGih4eHaGxsLNrb24sdO3YU9+/fL2mNJ+njVPDAwEDR2dlZNDY2Fl1dXcXAwEDx2rVrktYQRVHcsWOHWL9+fVGtVou1a9cWf/rpJ8lr7Nu3TwQgXr58WfKxRVEUU1NTxQkTJoju7u6iiYmJWL16dfF///ufmJOTI2mdDRs2iNWrVxeNjY1FJycncezYsWJycvILj/e895xWqxVnzpwpOjo6imq1WuzYsWOpv4fPq/HLL78U+/js2bMlqVFwinlxHwcPHpSkRlZWltivXz/RxcVFNDY2Fp2dncXevXuLJ06ckPR79aQXORX8WTUyMzPFzp07i/b29qJKpRI9PDzE0aNHi/Hx8ZK/jpUrV4o+Pj6iiYmJ6OfnJ27dulXyGj/++KNoamr6wu+R59WIi4sThw8fLrq4uIgmJiZirVq1xAULFpTqMhPPq/Hdd9+J1apVE1Uqleju7i5+8sknkv+/8iyCKJbR6h4iIiKiMsA1N0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RU4Q0fPrzIrRU2bdoEExOTF76EPRFVXLy3FBEZnJ9//hljx47F8uXLX/j+VURUcXHmhogMyldffYUPPvgA69evZ7AhqqQ4c0NEBuOjjz7C0qVLsXPnTnTs2FHudohIJgw3RGQQ9uzZg23btiE4OBivvvqq3O0QkYx4WIqIDIKvry88PT0xe/ZspKeny90OEcmI4YaIDIKrqytCQkJw584ddO3aFWlpaXK3REQyYbghIoPh4eGB0NBQxMfHM+AQVWIMN0RkUNzc3BASEoK7d++iS5cuSE1NlbslIipjDDdEZHCqVauGkJAQJCYmMuAQVUKCKIqi3E0QERERSYUzN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKD8v/4OzTfZdiQQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_values = list(range(1, 20)) #k ranges between 1 and 19\n",
    "model_best = KNeighborsRegressor() #Notice that the value of K is not inserted here\n",
    "r2_values, mse_values = [], []\n",
    "for k in k_values:\n",
    "  model_best.set_params(n_neighbors=k)\n",
    "  model_best.fit(X_train, y_train)\n",
    "  y_pred_current = model_best.predict(X_train)\n",
    "  r2_current = r2_score(y_train, y_pred_current)\n",
    "  r2_values.append(r2_current)\n",
    "  mse_current = mean_squared_error(y_train, y_pred_current)\n",
    "  mse_values.append(mse_current)\n",
    "\n",
    "\n",
    "# Plot the variation of R2 for various values of K\n",
    "plt.plot(k_values, r2_values)\n",
    "plt.title(\"Train R2 score vs K\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Train R2 score\")\n",
    "plt.xticks(k_values)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81860e8d-35f8-4acd-b509-6c8a5d7b17a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_values, mse_values)\n",
    "plt.title(\"Train MSE score vs K\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Train MSE score\")\n",
    "plt.xticks(k_values)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2580e423-1182-4a5d-bc44-27842c26c33f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.9591790271651378\n",
      "Test R2 score: 0.9558915581154972\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Define the Ridge regression model with regularization (alpha)\n",
    "model = Ridge(alpha=100)  # You can adjust the alpha value as needed\n",
    "\n",
    "# Train the Ridge model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model\n",
    "train_r2 = model.score(X_train, y_train)\n",
    "test_r2 = model.score(X_test, y_test)\n",
    "\n",
    "print(\"Train R2 score:\", train_r2)\n",
    "print(\"Test R2 score:\", test_r2)\n",
    "# k_best = k_values[np.argmax(r2_values)]\n",
    "# model_best.set_params(n_neighbors=k_best)\n",
    "# model_best.fit(X_train, y_train)\n",
    "# y_train_pred_best = model_best.predict(X_train)\n",
    "# y_test_pred_best = model_best.predict(X_test)\n",
    "# r2_best_train = r2_score(y_train, y_train_pred_best)\n",
    "# r2_best_test = r2_score(y_test, y_test_pred_best)\n",
    "# print(\"Best K: \", k_best)\n",
    "# print('Train R2 score: ', r2_best_train)\n",
    "# print(\"Test R2 score: \",r2_best_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a16bbb10-3191-4238-8a08-0be9b6e3d521",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=1, Train R2 score: 1.0000, Test R2 score: 0.4499\n",
      "K=2, Train R2 score: 0.8093, Test R2 score: 0.5059\n",
      "K=3, Train R2 score: 0.7137, Test R2 score: 0.5353\n",
      "K=4, Train R2 score: 0.6551, Test R2 score: 0.5292\n",
      "K=5, Train R2 score: 0.6206, Test R2 score: 0.5513\n",
      "K=6, Train R2 score: 0.5947, Test R2 score: 0.5434\n",
      "K=7, Train R2 score: 0.5614, Test R2 score: 0.5299\n",
      "K=8, Train R2 score: 0.5305, Test R2 score: 0.5314\n",
      "K=9, Train R2 score: 0.5065, Test R2 score: 0.5117\n",
      "K=10, Train R2 score: 0.5028, Test R2 score: 0.4933\n",
      "K=11, Train R2 score: 0.4751, Test R2 score: 0.4913\n",
      "K=12, Train R2 score: 0.4653, Test R2 score: 0.4915\n",
      "K=13, Train R2 score: 0.4507, Test R2 score: 0.4897\n",
      "K=14, Train R2 score: 0.4532, Test R2 score: 0.4886\n",
      "K=15, Train R2 score: 0.4413, Test R2 score: 0.4894\n",
      "K=16, Train R2 score: 0.4367, Test R2 score: 0.4734\n",
      "K=17, Train R2 score: 0.4352, Test R2 score: 0.4608\n",
      "K=18, Train R2 score: 0.4371, Test R2 score: 0.4484\n",
      "K=19, Train R2 score: 0.4354, Test R2 score: 0.4475\n",
      "K=20, Train R2 score: 0.4333, Test R2 score: 0.4441\n",
      "Best K: 5\n",
      "Train R2 score for best K: 0.6206\n",
      "Test R2 score for best K: 0.5513\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Define a range of K values to experiment with\n",
    "k_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,11,12,13,14,15,16,17,18,19,20]\n",
    "\n",
    "# Initialize variables to store the best K and R2 scores\n",
    "best_k = None\n",
    "best_train_r2 = -1.0\n",
    "best_test_r2 = -1.0\n",
    "\n",
    "# Iterate through different K values\n",
    "for k in k_values:\n",
    "    # Create a KNN regression model with the current K value\n",
    "    model = KNeighborsRegressor(n_neighbors=k)\n",
    "    \n",
    "    # Train the KNN model\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Make predictions on the training and test data\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate R-squared (R2) scores for training and test data\n",
    "    train_r2 = r2_score(y_train, y_train_pred)\n",
    "    test_r2 = r2_score(y_test, y_test_pred)\n",
    "    \n",
    "    # Print R2 scores for the current K value\n",
    "    print(f\"K={k}, Train R2 score: {train_r2:.4f}, Test R2 score: {test_r2:.4f}\")\n",
    "    \n",
    "    # Check if the current model has a better test R2 score\n",
    "    if test_r2 > best_test_r2:\n",
    "        best_k = k\n",
    "        best_train_r2 = train_r2\n",
    "        best_test_r2 = test_r2\n",
    "\n",
    "# Print the best K and corresponding R2 scores\n",
    "print(f\"Best K: {best_k}\")\n",
    "print(f\"Train R2 score for best K: {best_train_r2:.4f}\")\n",
    "print(f\"Test R2 score for best K: {best_test_r2:.4f}\")\n",
    "\n",
    "# Train the final KNN model with the best K\n",
    "final_model = KNeighborsRegressor(n_neighbors=best_k)\n",
    "final_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1f5af0af-1df4-4c6c-94a5-b691c3d25e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score: 0.9995225713881909\n",
      "Test R2 score: 0.9895512359557653\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Create a Random Forest Regressor model\n",
    "model = RandomForestRegressor(n_estimators=100, random_state=0)  # You can adjust the number of estimators as needed\n",
    "\n",
    "# Train the model on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on both the training and test sets\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate R-squared (R2) scores for training and test sets\n",
    "train_r2 = r2_score(y_train, y_train_pred)\n",
    "test_r2 = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(\"Train R2 score:\", train_r2)\n",
    "print(\"Test R2 score:\", test_r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e219e2d6-a9c2-4eed-8368-bf7a6209f80d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "\nAll the 100 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n20 fits failed with the following error:\nTraceback (most recent call last):\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_regression.py\", line 218, in fit\n    return self._fit(X, y)\n           ^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_base.py\", line 456, in _fit\n    X, y = self._validate_data(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 622, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 1146, in check_X_y\n    X = check_array(\n        ^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py\", line 2084, in __array__\n    arr = np.asarray(values, dtype=dtype)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'mar'\n\n--------------------------------------------------------------------------------\n80 fits failed with the following error:\nTraceback (most recent call last):\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_regression.py\", line 218, in fit\n    return self._fit(X, y)\n           ^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_base.py\", line 456, in _fit\n    X, y = self._validate_data(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 622, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 1146, in check_X_y\n    X = check_array(\n        ^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py\", line 2084, in __array__\n    arr = np.asarray(values, dtype=dtype)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'sep'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[67], line 23\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Perform grid search with cross-validation to find the best k\u001b[39;00m\n\u001b[0;32m     22\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(knn, param_grid, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 23\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# Get the best k value\u001b[39;00m\n\u001b[0;32m     26\u001b[0m best_k \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_params_[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_neighbors\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1145\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1147\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1148\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1150\u001b[0m     )\n\u001b[0;32m   1151\u001b[0m ):\n\u001b[1;32m-> 1152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_search.py:898\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    892\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m    893\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m    894\u001b[0m     )\n\u001b[0;32m    896\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m--> 898\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    900\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m    901\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m    902\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_search.py:1422\u001b[0m, in \u001b[0;36mGridSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[0;32m   1421\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1422\u001b[0m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_search.py:875\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m!=\u001b[39m n_candidates \u001b[38;5;241m*\u001b[39m n_splits:\n\u001b[0;32m    869\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    870\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcv.split and cv.get_n_splits returned \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    871\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minconsistent results. Expected \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    872\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplits, got \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(n_splits, \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m n_candidates)\n\u001b[0;32m    873\u001b[0m     )\n\u001b[1;32m--> 875\u001b[0m \u001b[43m_warn_or_raise_about_fit_failures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# For callable self.scoring, the return type is only know after\u001b[39;00m\n\u001b[0;32m    878\u001b[0m \u001b[38;5;66;03m# calling. If the return type is a dictionary, the error scores\u001b[39;00m\n\u001b[0;32m    879\u001b[0m \u001b[38;5;66;03m# can now be inserted with the correct key. The type checking\u001b[39;00m\n\u001b[0;32m    880\u001b[0m \u001b[38;5;66;03m# of out will be done in `_insert_error_scores`.\u001b[39;00m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscoring):\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py:414\u001b[0m, in \u001b[0;36m_warn_or_raise_about_fit_failures\u001b[1;34m(results, error_score)\u001b[0m\n\u001b[0;32m    407\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_failed_fits \u001b[38;5;241m==\u001b[39m num_fits:\n\u001b[0;32m    408\u001b[0m     all_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    409\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mAll the \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    410\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt is very likely that your model is misconfigured.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    411\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou can try to debug the error by setting error_score=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    412\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    413\u001b[0m     )\n\u001b[1;32m--> 414\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(all_fits_failed_message)\n\u001b[0;32m    416\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    417\u001b[0m     some_fits_failed_message \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    418\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mnum_failed_fits\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m fits failed out of a total of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_fits\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    419\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe score on these train-test partitions for these parameters\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    423\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBelow are more details about the failures:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mfit_errors_summary\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    424\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: \nAll the 100 fits failed.\nIt is very likely that your model is misconfigured.\nYou can try to debug the error by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n20 fits failed with the following error:\nTraceback (most recent call last):\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_regression.py\", line 218, in fit\n    return self._fit(X, y)\n           ^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_base.py\", line 456, in _fit\n    X, y = self._validate_data(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 622, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 1146, in check_X_y\n    X = check_array(\n        ^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py\", line 2084, in __array__\n    arr = np.asarray(values, dtype=dtype)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'mar'\n\n--------------------------------------------------------------------------------\n80 fits failed with the following error:\nTraceback (most recent call last):\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 1152, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_regression.py\", line 218, in fit\n    return self._fit(X, y)\n           ^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\neighbors\\_base.py\", line 456, in _fit\n    X, y = self._validate_data(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py\", line 622, in _validate_data\n    X, y = check_X_y(X, y, **check_params)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 1146, in check_X_y\n    X = check_array(\n        ^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\validation.py\", line 915, in check_array\n    array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\utils\\_array_api.py\", line 380, in _asarray_with_order\n    array = numpy.asarray(array, order=order, dtype=dtype)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"C:\\Users\\MAYANK PUVVALA\\AppData\\Roaming\\Python\\Python311\\site-packages\\pandas\\core\\generic.py\", line 2084, in __array__\n    arr = np.asarray(values, dtype=dtype)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: could not convert string to float: 'sep'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv')\n",
    "\n",
    "# Define features (X) and target (y)\n",
    "X = data.drop(columns=['area'])  # Exclude 'area' as a feature\n",
    "y = data['area']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create a KNN regressor\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "# Define a parameter grid for hyperparameter tuning (k values)\n",
    "param_grid = {'n_neighbors': range(1, 21)}  # Try k values from 1 to 20\n",
    "\n",
    "# Perform grid search with cross-validation to find the best k\n",
    "grid_search = GridSearchCV(knn, param_grid, cv=5, scoring='neg_mean_squared_error')\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best k value\n",
    "best_k = grid_search.best_params_['n_neighbors']\n",
    "\n",
    "# Create and fit the final KNN regressor with the best k\n",
    "best_knn = KNeighborsRegressor(n_neighbors=best_k)\n",
    "best_knn.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "best_accuracy = best_knn.score(X_test, y_test)\n",
    "\n",
    "print(\"Best K:\", best_k)\n",
    "print(\"Best Test R2 score:\", best_accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6acb715-daed-4867-8f44-ee656ed76178",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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