dealt_wih_outlierw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47359986",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a0696e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_index(i):\n",
    "    \n",
    "    country_index = pd.read_csv(\"country_index.csv\")\n",
    "    pred_country_index = pd.read_csv(\"submission_country_index.csv\")\n",
    "    \n",
    "    first_index = country_index.loc[i][1]\n",
    "    last_index = country_index.loc[i][2]\n",
    "    tile_value = country_index.loc[i][3]\n",
    "    code = country_index.loc[i][0]\n",
    "    print(code)\n",
    "    first_index_sub = pred_country_index.loc[i][1]\n",
    "    last_index_sub = pred_country_index.loc[i][2]\n",
    "           \n",
    "    return first_index, last_index, first_index_sub, last_index_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd021e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AL\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SNEH\\anaconda3\\envs\\myenv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3072: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "first_index, last_index, first_index_sub, last_index_sub = find_index(0)\n",
    "df1 = pd.read_csv(\"gee_features.csv\", \n",
    "                  skiprows=range(1, 1+first_index), \n",
    "                  nrows=(last_index - first_index + 1))\n",
    "df_train = pd.read_csv(\"training_label.csv\")\n",
    "df_sub = pd.read_csv(\"sample submission (1).csv\")\n",
    "df1 = df1.drop(['new_ind', 'index', 'ADM1DHS', 'ADM1FIPS', 'ADM1FIPSNA', 'ADM1NAME', 'ADM1SALBCO', 'ADM1SALBNA', 'ADM1SALNA', 'ALT_DEM', 'ALT_GPS', 'CCFIPS', 'DATUM', 'DHSCC', 'DHSCLUST', 'DHSREGCO', 'DHSREGNA', 'DHSYEAR', 'F21', 'F22', 'F23', 'LATNUM', 'LONGNUM', 'SOURCE', 'URBAN_RURA', 'ZONECO', 'ZONENA','key1','key2','key3'], axis = 1)\n",
    "df1 = df1.dropna(axis=1)\n",
    "df1 = df1.sort_index(axis=1)\n",
    "\n",
    "x12 = list(df1.columns)\n",
    "x12.remove(\"DHSID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cf63f5f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1@NASA/JPL/global_forest_canopy_height_2005</th>\n",
       "      <th>1_max@NASA/JPL/global_forest_canopy_height_2005</th>\n",
       "      <th>1_min@NASA/JPL/global_forest_canopy_height_2005</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_kurtosis@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_max_max@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_max_min@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_mean@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_median@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_min_max@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
       "      <th>AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_min_min@MODIS/061/MOD08_M3&amp;timestamped</th>\n",
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       "      <th>wind_speed_min_min@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&amp;timestamped</th>\n",
       "      <th>wind_speed_skew@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&amp;timestamped</th>\n",
       "      <th>wind_speed_stdDev@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&amp;timestamped</th>\n",
       "      <th>wth@MERIT/Hydro/v1_0_1</th>\n",
       "      <th>wth@MERIT/Hydro_reduced/v1_0_1</th>\n",
       "      <th>wth_max@MERIT/Hydro/v1_0_1</th>\n",
       "      <th>wth_max@MERIT/Hydro_reduced/v1_0_1</th>\n",
       "      <th>wth_min@MERIT/Hydro/v1_0_1</th>\n",
       "      <th>wth_min@MERIT/Hydro_reduced/v1_0_1</th>\n",
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       "      <th>0</th>\n",
       "      <td>5.311582</td>\n",
       "      <td>14</td>\n",
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       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
       "      <td>38.605247</td>\n",
       "      <td>60.359634</td>\n",
       "      <td>74.737500</td>\n",
       "      <td>74.737500</td>\n",
       "      <td>13.50</td>\n",
       "      <td>30.647140</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.459806</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>361</td>\n",
       "      <td>300</td>\n",
       "      <td>179.06818</td>\n",
       "      <td>191.5</td>\n",
       "      <td>78</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
       "      <td>27.702340</td>\n",
       "      <td>50.348183</td>\n",
       "      <td>38.890730</td>\n",
       "      <td>74.317700</td>\n",
       "      <td>11.25</td>\n",
       "      <td>36.577080</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.515421</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
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       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
       "      <td>-4.649163</td>\n",
       "      <td>-9.699539</td>\n",
       "      <td>-0.550000</td>\n",
       "      <td>-0.550000</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.550000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.444454</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
       "      <td>22.168653</td>\n",
       "      <td>46.490746</td>\n",
       "      <td>68.748215</td>\n",
       "      <td>68.748215</td>\n",
       "      <td>11.25</td>\n",
       "      <td>30.804241</td>\n",
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       "      <th>4</th>\n",
       "      <td>7.780849</td>\n",
       "      <td>15</td>\n",
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       "      <td>-3.0</td>\n",
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       "      <td>361</td>\n",
       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
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       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
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       "      <td>-0.988471</td>\n",
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       "      <td>-0.550000</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.550000</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>3.219099</td>\n",
       "      <td>12</td>\n",
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       "      <td>-3.0</td>\n",
       "      <td>361</td>\n",
       "      <td>361</td>\n",
       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
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       "      <td>361</td>\n",
       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.460847</td>\n",
       "      <td>-0.786067</td>\n",
       "      <td>-0.988471</td>\n",
       "      <td>-4.649163</td>\n",
       "      <td>67.193890</td>\n",
       "      <td>-0.550000</td>\n",
       "      <td>69.389275</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>64.876550</td>\n",
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       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>1.908488</td>\n",
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       "      <td>78</td>\n",
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       "      <td>-0.550000</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.550000</td>\n",
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       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>10.564707</td>\n",
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       "      <td>-3.0</td>\n",
       "      <td>361</td>\n",
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       "      <td>220.33333</td>\n",
       "      <td>246.5</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
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       "      <td>-0.460847</td>\n",
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       "      <td>52.813744</td>\n",
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       "      <td>52.813744</td>\n",
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       "    <tr>\n",
       "      <th>1164</th>\n",
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       "      <td>-0.550000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "      1@NASA/JPL/global_forest_canopy_height_2005  \\\n",
       "0                                        5.311582   \n",
       "1                                        9.459806   \n",
       "2                                       13.515421   \n",
       "3                                        7.444454   \n",
       "4                                        7.780849   \n",
       "...                                           ...   \n",
       "1160                                     3.219099   \n",
       "1161                                     1.822328   \n",
       "1162                                     1.908488   \n",
       "1163                                    10.564707   \n",
       "1164                                     7.441435   \n",
       "\n",
       "      1_max@NASA/JPL/global_forest_canopy_height_2005  \\\n",
       "0                                                  14   \n",
       "1                                                  17   \n",
       "2                                                  15   \n",
       "3                                                  13   \n",
       "4                                                  15   \n",
       "...                                               ...   \n",
       "1160                                               12   \n",
       "1161                                                9   \n",
       "1162                                               12   \n",
       "1163                                               20   \n",
       "1164                                               15   \n",
       "\n",
       "      1_min@NASA/JPL/global_forest_canopy_height_2005  \\\n",
       "0                                                   0   \n",
       "1                                                   0   \n",
       "2                                                  12   \n",
       "3                                                   0   \n",
       "4                                                   0   \n",
       "...                                               ...   \n",
       "1160                                                0   \n",
       "1161                                                0   \n",
       "1162                                                0   \n",
       "1163                                                0   \n",
       "1164                                                0   \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_kurtosis@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                  -3.0                                          \n",
       "1                                                  -3.0                                          \n",
       "2                                                  -3.0                                          \n",
       "3                                                  -3.0                                          \n",
       "4                                                  -3.0                                          \n",
       "...                                                 ...                                          \n",
       "1160                                               -3.0                                          \n",
       "1161                                               -3.0                                          \n",
       "1162                                               -3.0                                          \n",
       "1163                                               -3.0                                          \n",
       "1164                                               -3.0                                          \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_max_max@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                   361                                         \n",
       "1                                                   361                                         \n",
       "2                                                   361                                         \n",
       "3                                                   361                                         \n",
       "4                                                   361                                         \n",
       "...                                                 ...                                         \n",
       "1160                                                361                                         \n",
       "1161                                                361                                         \n",
       "1162                                                361                                         \n",
       "1163                                                361                                         \n",
       "1164                                                361                                         \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_max_min@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                   361                                         \n",
       "1                                                   300                                         \n",
       "2                                                   361                                         \n",
       "3                                                   361                                         \n",
       "4                                                   361                                         \n",
       "...                                                 ...                                         \n",
       "1160                                                361                                         \n",
       "1161                                                361                                         \n",
       "1162                                                361                                         \n",
       "1163                                                361                                         \n",
       "1164                                                361                                         \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_mean@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                             220.33333                                      \n",
       "1                                             179.06818                                      \n",
       "2                                             220.33333                                      \n",
       "3                                             220.33333                                      \n",
       "4                                             220.33333                                      \n",
       "...                                                 ...                                      \n",
       "1160                                          220.33333                                      \n",
       "1161                                          220.33333                                      \n",
       "1162                                          220.33333                                      \n",
       "1163                                          220.33333                                      \n",
       "1164                                          220.33333                                      \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_median@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                 246.5                                        \n",
       "1                                                 191.5                                        \n",
       "2                                                 246.5                                        \n",
       "3                                                 246.5                                        \n",
       "4                                                 246.5                                        \n",
       "...                                                 ...                                        \n",
       "1160                                              246.5                                        \n",
       "1161                                              246.5                                        \n",
       "1162                                              246.5                                        \n",
       "1163                                              246.5                                        \n",
       "1164                                              246.5                                        \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_min_max@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                    78                                         \n",
       "1                                                    78                                         \n",
       "2                                                    78                                         \n",
       "3                                                    78                                         \n",
       "4                                                    78                                         \n",
       "...                                                 ...                                         \n",
       "1160                                                 78                                         \n",
       "1161                                                 78                                         \n",
       "1162                                                 78                                         \n",
       "1163                                                 78                                         \n",
       "1164                                                 78                                         \n",
       "\n",
       "      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean_min_min@MODIS/061/MOD08_M3&timestamped  \\\n",
       "0                                                    78                                         \n",
       "1                                                    68                                         \n",
       "2                                                    78                                         \n",
       "3                                                    78                                         \n",
       "4                                                    78                                         \n",
       "...                                                 ...                                         \n",
       "1160                                                 78                                         \n",
       "1161                                                 78                                         \n",
       "1162                                                 78                                         \n",
       "1163                                                 78                                         \n",
       "1164                                                 78                                         \n",
       "\n",
       "      ...  wind_speed_min_max@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&timestamped  \\\n",
       "0     ...                                          -0.460847               \n",
       "1     ...                                          -0.460847               \n",
       "2     ...                                          -0.460847               \n",
       "3     ...                                          -0.460847               \n",
       "4     ...                                          -0.460847               \n",
       "...   ...                                                ...               \n",
       "1160  ...                                          -0.460847               \n",
       "1161  ...                                          -0.460847               \n",
       "1162  ...                                          -0.460847               \n",
       "1163  ...                                          -0.460847               \n",
       "1164  ...                                          -0.460847               \n",
       "\n",
       "      wind_speed_min_min@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&timestamped  \\\n",
       "0                                             -0.460847               \n",
       "1                                             -0.460847               \n",
       "2                                             -0.460847               \n",
       "3                                             -0.460847               \n",
       "4                                             -0.460847               \n",
       "...                                                 ...               \n",
       "1160                                          -0.460847               \n",
       "1161                                          -0.460847               \n",
       "1162                                          -0.460847               \n",
       "1163                                          -0.460847               \n",
       "1164                                          -0.460847               \n",
       "\n",
       "      wind_speed_skew@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&timestamped  \\\n",
       "0                                             -0.786067            \n",
       "1                                             -0.786067            \n",
       "2                                             -0.786067            \n",
       "3                                             -0.786067            \n",
       "4                                             -0.786067            \n",
       "...                                                 ...            \n",
       "1160                                          -0.786067            \n",
       "1161                                          -0.786067            \n",
       "1162                                          -0.786067            \n",
       "1163                                          -0.786067            \n",
       "1164                                          -0.786067            \n",
       "\n",
       "      wind_speed_stdDev@NOAA/CDR/ATMOS_NEAR_SURFACE/V2&timestamped  \\\n",
       "0                                             -0.988471              \n",
       "1                                             -0.988471              \n",
       "2                                             -0.988471              \n",
       "3                                             -0.988471              \n",
       "4                                             -0.988471              \n",
       "...                                                 ...              \n",
       "1160                                          -0.988471              \n",
       "1161                                          -0.988471              \n",
       "1162                                          -0.988471              \n",
       "1163                                          -0.988471              \n",
       "1164                                          -0.988471              \n",
       "\n",
       "      wth@MERIT/Hydro/v1_0_1  wth@MERIT/Hydro_reduced/v1_0_1  \\\n",
       "0                  38.605247                       60.359634   \n",
       "1                  27.702340                       50.348183   \n",
       "2                  -4.649163                       -9.699539   \n",
       "3                  22.168653                       46.490746   \n",
       "4                  -4.649163                       -9.699539   \n",
       "...                      ...                             ...   \n",
       "1160               -4.649163                      262.926420   \n",
       "1161               -4.649163                       67.193890   \n",
       "1162               -4.649163                       -9.699539   \n",
       "1163               52.813744                       52.813744   \n",
       "1164               -4.649163                       -9.699539   \n",
       "\n",
       "      wth_max@MERIT/Hydro/v1_0_1  wth_max@MERIT/Hydro_reduced/v1_0_1  \\\n",
       "0                      74.737500                           74.737500   \n",
       "1                      38.890730                           74.317700   \n",
       "2                      -0.550000                           -0.550000   \n",
       "3                      68.748215                           68.748215   \n",
       "4                      -0.550000                           -0.550000   \n",
       "...                          ...                                 ...   \n",
       "1160                   -0.550000                          262.926420   \n",
       "1161                   -0.550000                           69.389275   \n",
       "1162                   -0.550000                           -0.550000   \n",
       "1163                   -0.550000                           52.813744   \n",
       "1164                   -0.550000                           -0.550000   \n",
       "\n",
       "      wth_min@MERIT/Hydro/v1_0_1  wth_min@MERIT/Hydro_reduced/v1_0_1  \n",
       "0                          13.50                           30.647140  \n",
       "1                          11.25                           36.577080  \n",
       "2                          -0.55                           -0.550000  \n",
       "3                          11.25                           30.804241  \n",
       "4                          -0.55                           -0.550000  \n",
       "...                          ...                                 ...  \n",
       "1160                       -0.55                          262.926420  \n",
       "1161                       -0.55                           64.876550  \n",
       "1162                       -0.55                           -0.550000  \n",
       "1163                       -0.55                           52.813744  \n",
       "1164                       -0.55                           -0.550000  \n",
       "\n",
       "[1165 rows x 11319 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05f42b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info = {\n",
    "    'name' : x12,\n",
    "    'len' : [],\n",
    "    'element' : [],\n",
    "    'min' : [],\n",
    "    'max' : [],\n",
    "    'top_value_count' : [],\n",
    "}\n",
    "for i in x12:\n",
    "    w1 = list(set(df1[i]))\n",
    "    df_info['max'].append(max(w1))\n",
    "    df_info['min'].append(min(w1))\n",
    "    df_info['element'].append(len(w1))\n",
    "    df_info['len'].append(len(list(df1[i])))\n",
    "    df_info['top_value_count'].append(df1[i].value_counts().values[0])\n",
    "    \n",
    "df_info = pd.DataFrame(df_info)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53ce641b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2cad251",
   "metadata": {},
   "outputs": [],
   "source": [
    "qaw = list(df_info[(df_info['max'] == 65535.000000) & (df_info['element'] > 60)]['name'])\n",
    "for i in qaw:\n",
    "    sq = np.array(list(df1[i]))\n",
    "    sq[sq == 65535.000000] = np.mean(sq[sq != 65535.000000])\n",
    "    df1[i] = sq  \n",
    "qaw = list(df_info[(df_info['max'] == 255.000000) & (df_info['element'] > 60)]['name'])\n",
    "for i in qaw:\n",
    "    sq = np.array(list(df1[i]))\n",
    "    sq[sq == 255.000000] = np.mean(sq[sq != 255.000000])\n",
    "    df1[i] = sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b91c3652",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f9e010c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_info = {\n",
    "    'name' : x12,\n",
    "    'len' : [],\n",
    "    'element' : [],\n",
    "    'min' : [],\n",
    "    'max' : [],\n",
    "    'top_value_count' : [],\n",
    "}\n",
    "for i in x12:\n",
    "    w1 = list(set(df1[i]))\n",
    "    df_info['max'].append(max(w1))\n",
    "    df_info['min'].append(min(w1))\n",
    "    df_info['element'].append(len(w1))\n",
    "    df_info['len'].append(len(list(df1[i])))\n",
    "    df_info['top_value_count'].append(df1[i].value_counts().values[0])\n",
    "\n",
    "df_info = pd.DataFrame(df_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "905f242d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_1 = list(df_info[(df_info['element'] >= 775)]['name'])\n",
    "cat_2 = list(df_info[(df_info['element'] < 60) & (df_info['element'] != 1)]['name'])\n",
    "cat_3 = list(df_info[(df_info['element'] < 775) & (df_info['element'] >= 60)]['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ac94ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e7efc9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "249fe7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "sa1 = list(set(df1[x12[6400]]))\n",
    "#sa1 = [0, 1, 2, 3, 4, 5, 6, 3554, 55]\n",
    "sa1.sort()\n",
    "x_values = list(range(len(sa1)))\n",
    "x = np.array(x_values)\n",
    "y = sa1\n",
    "x = x.reshape(-1, 1)\n",
    "poly = PolynomialFeatures(degree=4)\n",
    "x_poly = poly.fit_transform(x)\n",
    "model = LinearRegression()\n",
    "model.fit(x_poly, y)\n",
    "y_pred = model.predict(x_poly)\n",
    "# Plot the original data points and the polynomial regression line\n",
    "plt.scatter(x, y, label='Data')\n",
    "plt.plot(x, y_pred, color='red', label='Polynomial Regression')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Polynomial Regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7c184a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example continuous data\n",
    "data = np.random.normal(loc=0, scale=1, size=1000)\n",
    "\n",
    "# Plotting PDF\n",
    "sns.histplot(df1[x12[6400]], kde=True)\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Probability Density Function')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ede7705",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example data\n",
    "#data = [1, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, 5]\n",
    "\n",
    "# Plotting histogram\n",
    "plt.hist(df1[x12[6400]], bins=5, edgecolor='black')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution Histogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0730088",
   "metadata": {},
   "outputs": [],
   "source": [
    "def updatearray_reg(X,deg,check):\n",
    "    yset = list(set(X))\n",
    "    yset.sort()\n",
    "    X = np.array(X).reshape(-1)\n",
    "    xset = np.arange(len(yset)).reshape(-1,1)\n",
    "    poly = PolynomialFeatures(degree=deg)\n",
    "    x_poly = poly.fit_transform(xset)\n",
    "    model = LinearRegression()\n",
    "    model.fit(x_poly, yset)\n",
    "    y_pred = model.predict(x_poly)\n",
    "    for i in range(len(yset)):\n",
    "        X[X == yset[i]] = y_pred[i]\n",
    "        \n",
    "    if check > 0:\n",
    "        mean = np.mean(X)\n",
    "        std = np.std(X)\n",
    "        X = (X - mean) / std\n",
    "        X = X*check\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e29434c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [0,0,1,2,3,4,99,567,33,45]\n",
    "\n",
    "b =[0,99,2,3454]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3602e7f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = updatearray_reg(df1[x12[6400]],4,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db9f9ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example continuous data\n",
    "data = np.random.normal(loc=0, scale=1, size=1000)\n",
    "\n",
    "# Plotting PDF\n",
    "sns.histplot(df1[x12[6400]], kde=True)\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Probability Density Function')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01b6bf29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example continuous data\n",
    "data = np.random.normal(loc=0, scale=1, size=1000)\n",
    "\n",
    "# Plotting PDF\n",
    "sns.histplot(a1, kde=True)\n",
    "plt.xlabel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d48d73c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_cat(arr):\n",
    "    unique_values = np.unique(arr)\n",
    "    unique_values.sort()\n",
    "    converted_arr = np.searchsorted(unique_values, arr)\n",
    "    return converted_arr\n",
    "unique_cat([0,1,2,33,2,3,2,1,345,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5f190aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat1 = {}\n",
    "for i in cat_1:\n",
    "    df_cat1[i] = updatearray_reg(list(df1[i]),2,10)\n",
    "df_cat1 = pd.DataFrame(df_cat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5316e941",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5845e65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat2 = {}\n",
    "for i in cat_2:\n",
    "    df_cat2[i] = unique_cat(list(df1[i]))\n",
    "for i in cat_3:\n",
    "    df_cat2[i] = updatearray_reg(list(df1[i]),3,5)\n",
    "df_cat2 = pd.DataFrame(df_cat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99b6c0b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67e3e115",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.concat([df_cat1, df_cat2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a09d3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ab53cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "data = df_final.values\n",
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "pca = PCA()\n",
    "pca.fit(data)\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "cumulative_variance_ratio = np.cumsum(explained_variance_ratio)\n",
    "num_components = len(explained_variance_ratio)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, num_components + 1), cumulative_variance_ratio, marker='o', linestyle='-')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance Ratio')\n",
    "plt.title('Cumulative Explained Variance Ratio vs. Number of Components')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac6f99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_pca_with_standardization(X, j, n_components, apply_standardization=False):\n",
    "    back = len(X)\n",
    "    X = np.tile(X, (j, 1))\n",
    "    pca = PCA(n_components=n_components)\n",
    "    X_pca = pca.fit_transform(X)\n",
    "    if apply_standardization:\n",
    "        scaler = StandardScaler()\n",
    "        X_pca = scaler.fit_transform(X_pca)\n",
    "    return np.array(X_pca[:back])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d3a204e",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df_final = apply_pca_with_standardization(df_final,2, 1000, apply_standardization= True)\n",
    "transformed_df_final = pd.DataFrame(transformed_df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bd578c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_df_final['DHSID'] = df1['DHSID']\n",
    "merged_df = pd.merge(df_train, transformed_df_final, on='DHSID')\n",
    "merged_df_sub = pd.merge(df_sub, transformed_df_final, on='DHSID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9160dbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "779affda",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d6d688f",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df[336].corr(merged_df['Mean_BMI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b69c83a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_created_r2 = {\n",
    "    'feature' : list(range(0,1000)),\n",
    "    'Mean_BMI' : [merged_df[i].corr(merged_df['Mean_BMI']) for i in range(1000)] ,\n",
    "    'Median_BMI' : [merged_df[i].corr(merged_df['Median_BMI']) for i in range(1000)],\n",
    "    'Unmet_Need_Rate' : [merged_df[i].corr(merged_df['Unmet_Need_Rate']) for i in range(1000)],\n",
    "    'Under5_Mortality_Rate' : [merged_df[i].corr(merged_df['Under5_Mortality_Rate']) for i in range(1000)],\n",
    "    'Skilled_Birth_Attendant_Rate' : [merged_df[i].corr(merged_df['Skilled_Birth_Attendant_Rate']) for i in range(1000)],\n",
    "    'Stunted_Rate' : [merged_df[i].corr(merged_df['Stunted_Rate']) for i in range(1000)],\n",
    "}\n",
    "df_created_r2 = pd.DataFrame(df_created_r2)\n",
    "df_created_r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69c515e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_map = list(df_created_r2[(df_created_r2['Unmet_Need_Rate'] < -0.03) | (df_created_r2['Unmet_Need_Rate'] > 0.03)]['feature'])\n",
    "len(feature_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f09e4e97",
   "metadata": {},
   "outputs": [],
   "source": [
    "f2 = feature_map.copy()\n",
    "f2.append('Unmet_Need_Rate')\n",
    "print(f2)\n",
    "sd1 = merged_df[f2]\n",
    "x_sub = merged_df_sub[feature_map]\n",
    "sd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7413a44f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51670c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd2 = sd1.dropna(axis=0)\n",
    "sd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "317d6d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_value = sd2[feature_map]\n",
    "Y_value = sd2['Unmet_Need_Rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93dfbf70",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_value, Y_value, test_size=0.2, random_state=42)\n",
    "len(x_train), len(x_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "753c54ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x23 = list(df_final.columns)\n",
    "#x23.remove('DHSID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd5da1e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'Mean_BMI','Median_BMI','Unmet_Need_Rate','Under5_Mortality_Rate','Skilled_Birth_Attendant_Rate', 'Stunted_Rate']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f7a450f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = merged_df[x23+['Stunted_Rate']]\n",
    "#X = X.dropna(axis=0)\n",
    "#Y = X['Stunted_Rate'].values\n",
    "#X = X[x23].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc22af2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_mean = np.mean(X, axis=0)  # Compute the mean along the rows (axis=0)\n",
    "#X_std = np.std(X, axis=0)    # Compute the standard deviation along the rows (axis=0)\n",
    "#X_standardized = (X - X_mean) / X_std\n",
    "\n",
    "# Standardize the 1D array y\n",
    "#y_mean = np.mean(Y)\n",
    "#y_std = np.std(Y)\n",
    "#y_standardized = (Y - y_mean) / y_std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94cadddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_standardized, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bf00e45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c084a334",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "mse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556451bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model = DecisionTreeRegressor()\n",
    "dt_model.fit(x_train, y_train)\n",
    "y_pred_dt = dt_model.predict(x_test)\n",
    "mse_dt = np.sqrt(mean_squared_error(y_test, y_pred_dt))\n",
    "print(\"Mean Squared Error:\", mse_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da089eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_model = Ridge(alpha=0.4)  # You can adjust the alpha (regularization strength)\n",
    "ridge_model.fit(x_train, y_train)\n",
    "y_pred_ridge = ridge_model.predict(x_test)\n",
    "mse_ridge = np.sqrt(mean_squared_error(y_test, y_pred_ridge))\n",
    "print(\"Mean Squared Error:\", mse_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "976a96be",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_model = Lasso(alpha=1, max_iter = 2000) # You can adjust the alpha (regularization strength)\n",
    "lasso_model.fit(x_train, y_train)\n",
    "y_pred_lasso = lasso_model.predict(x_test)\n",
    "mse_lasso = np.sqrt(mean_squared_error(y_test, y_pred_lasso))\n",
    "print(\"Mean Squared Error:\", mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a13d2a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_model = SVR(kernel='rbf', epsilon = 0.9)  # You can try different kernels, e.g., 'linear', 'poly', etc.\n",
    "svr_model.fit(x_train, y_train)\n",
    "y_pred_svr = svr_model.predict(x_test)\n",
    "mse_svr = np.sqrt(mean_squared_error(y_test, y_pred_svr))\n",
    "print(\"Mean Squared Error:\", mse_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d70ad08f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import mean_squared_error\n",
    "model = ElasticNet()\n",
    "\n",
    "param_grid = {\n",
    "    'alpha': [0.1, 0.15, 0.2, 0.25,0.3,0.35, 0.4,0.45, 0.5,0.55, 0.6,0.65, 0.7,0.75,0.8,0.85, 0.9],\n",
    "    'l1_ratio': [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "}\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='r2', cv=5)\n",
    "\n",
    "# Fit the GridSearchCV object to the data\n",
    "grid_search.fit(x_train, y_train)\n",
    "best_model = grid_search.best_estimator_\n",
    "best_params = grid_search.best_params_\n",
    "elastic_net_preds = best_model.predict(x_test)\n",
    "print(len(elastic_net_preds))\n",
    "accuracy = np.sqrt(mean_squared_error(y_test, elastic_net_preds))\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c7f3ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a7547d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(elastic_net_preds)\n",
    "elastic_net_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98e6b359",
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic_net_preds1 = best_model.predict(x_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5611e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "elastic_net_preds1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d247528",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model = RandomForestRegressor(max_depth = 5, n_estimators = 500)\n",
    "rf_model.fit(X_train, y_train)\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "mse_rf = np.sqrt(mean_squared_error(y_test, y_pred_rf))\n",
    "print(\"Mean Squared Error:\", mse_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e7b947",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "mse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6018cd4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GradientBoostingRegressor(n_estimators=200, learning_rate=0.1, max_depth=5, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "mse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "973f3c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GradientBoostingRegressor(n_estimators=200, learning_rate=0.05, max_depth=5, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ee348b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xgb_reg = xgb.XGBRegressor()\n",
    "xgb_reg.fit(X_train, y_train)  \n",
    "y_pred = xgb_reg.predict(X_test)\n",
    "mse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Mean Squared Error:\", mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c16b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "k_neighbors = 10  # You can adjust this value based on your preference\n",
    "\n",
    "model = KNeighborsRegressor(n_neighbors=k_neighbors)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate the root mean squared error (RMSE)\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc364ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import BayesianRidge\n",
    "model = BayesianRidge()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate the root mean squared error (RMSE)\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee2edbbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, SimpleRNN\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(units=2500, input_shape=(7712,)))\n",
    "model.add(Dense(units=1500, activation = 'relu'))\n",
    "model.add(Dense(units=1000, activation = 'relu'))\n",
    "model.add(Dense(units=500, activation = 'relu'))\n",
    "model.add(Dense(units=256, activation = 'relu'))\n",
    "model.add(Dense(units=64, activation = 'relu'))\n",
    "model.add(Dense(units=1, activation = 'linear'))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=100, batch_size= 32, validation_split=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d097b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc5006f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd9dbb32",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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