Task1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "05d8291d-9c49-4b4e-ad3d-ef55b5c577d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_validate, KFold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.linear_model import LinearRegression, ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import joblib\n",
    "\n",
    "import mlflow\n",
    "\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "01ba0ec6-a989-4523-9f56-470de810d298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: cycler>=0.10 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from matplotlib<4->mlflow) (0.11.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from requests<3,>=2.17.3->mlflow) (2023.7.22)\n",
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      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from requests<3,>=2.17.3->mlflow) (3.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from scikit-learn<2->mlflow) (1.1.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from scikit-learn<2->mlflow) (2.2.0)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from sqlalchemy<3,>=1.4.0->mlflow) (2.0.1)\n",
      "Requirement already satisfied: smmap<5,>=3.0.1 in c:\\users\\mustafaj\\appdata\\local\\miniconda3\\envs\\azure_ml_cli\\lib\\site-packages (from gitdb<5,>=4.0.1->gitpython<4,>=2.1.0->mlflow) (4.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#%pip install missingno\n",
    "#%pip install xgboost\n",
    "#%pip install mlflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa57b675-077a-4469-98f3-6a950053e69d",
   "metadata": {},
   "source": [
    "1) Separate the data from the analysis.\n",
    "2) Be able to automate our analysis. \n",
    "3) We want our analysis to be reproducible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b984d6",
   "metadata": {},
   "source": [
    "### EDA - Explore properties of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "57c42a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1436, 10)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y</th>\n",
       "      <th>Feature 1</th>\n",
       "      <th>Feature 2</th>\n",
       "      <th>Feature 3</th>\n",
       "      <th>Feature 4</th>\n",
       "      <th>Feature 5</th>\n",
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       "      <th>Feature 8</th>\n",
       "      <th>Feature 9</th>\n",
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       "      <th>0</th>\n",
       "      <td>13500</td>\n",
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       "      <td>8450</td>\n",
       "      <td>80</td>\n",
       "      <td>23000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>7500</td>\n",
       "      <td>69</td>\n",
       "      <td>20544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
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       "      <td>1300</td>\n",
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       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>10845</td>\n",
       "      <td>72</td>\n",
       "      <td>19000</td>\n",
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       "      <td>86</td>\n",
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       "      <td>8500</td>\n",
       "      <td>71</td>\n",
       "      <td>17016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
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       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
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       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>7250</td>\n",
       "      <td>70</td>\n",
       "      <td>16916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>1015</td>\n",
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       "</table>\n",
       "<p>1435 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Y  Feature 1  Feature 2  Feature 3  Feature 4  Feature 5  Feature 6  \\\n",
       "0     13500         23      46986        1.0         90          1          0   \n",
       "1     13750         23      72937        1.0         90          1          0   \n",
       "2     13950         24      41711        1.0         90          1          0   \n",
       "3     14950         26      48000        1.0         90          0          0   \n",
       "4     13750         30      38500        1.0         90          0          0   \n",
       "...     ...        ...        ...        ...        ...        ...        ...   \n",
       "1430   8450         80      23000        NaN         86          0          0   \n",
       "1431   7500         69      20544        NaN         86          1          0   \n",
       "1432  10845         72      19000        NaN         86          0          0   \n",
       "1433   8500         71      17016        NaN         86          0          0   \n",
       "1434   7250         70      16916        NaN         86          1          0   \n",
       "\n",
       "      Feature 7  Feature 8  Feature 9  \n",
       "0          2000          3       1165  \n",
       "1          2000          3       1165  \n",
       "2          2000          3       1165  \n",
       "3          2000          3       1165  \n",
       "4          2000          3       1170  \n",
       "...         ...        ...        ...  \n",
       "1430       1300          3       1015  \n",
       "1431       1300          3       1025  \n",
       "1432       1300          3       1015  \n",
       "1433       1300          3       1015  \n",
       "1434       1300          3       1015  \n",
       "\n",
       "[1435 rows x 10 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./data.csv\", sep=\";\")\n",
    "print(df.shape)\n",
    "df.head(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "d637d943",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Feature 2</th>\n",
       "      <th>Feature 3</th>\n",
       "      <th>Feature 4</th>\n",
       "      <th>Feature 5</th>\n",
       "      <th>Feature 6</th>\n",
       "      <th>Feature 7</th>\n",
       "      <th>Feature 8</th>\n",
       "      <th>Feature 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10730.824513</td>\n",
       "      <td>55.947075</td>\n",
       "      <td>68533.259749</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>101.502089</td>\n",
       "      <td>0.674791</td>\n",
       "      <td>0.055710</td>\n",
       "      <td>1566.827994</td>\n",
       "      <td>4.033426</td>\n",
       "      <td>1072.45961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3626.964585</td>\n",
       "      <td>18.599988</td>\n",
       "      <td>37506.448872</td>\n",
       "      <td>0.379493</td>\n",
       "      <td>14.981080</td>\n",
       "      <td>0.468616</td>\n",
       "      <td>0.229441</td>\n",
       "      <td>187.182436</td>\n",
       "      <td>0.952677</td>\n",
       "      <td>52.64112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4350.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8450.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1040.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9900.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>63389.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1070.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11950.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>87020.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1085.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32500.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>243000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1615.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Y    Feature 1      Feature 2   Feature 3    Feature 4  \\\n",
       "count   1436.000000  1436.000000    1436.000000  414.000000  1436.000000   \n",
       "mean   10730.824513    55.947075   68533.259749    0.173913   101.502089   \n",
       "std     3626.964585    18.599988   37506.448872    0.379493    14.981080   \n",
       "min     4350.000000     1.000000       1.000000    0.000000    69.000000   \n",
       "25%     8450.000000    44.000000   43000.000000    0.000000    90.000000   \n",
       "50%     9900.000000    61.000000   63389.500000    0.000000   110.000000   \n",
       "75%    11950.000000    70.000000   87020.750000    0.000000   110.000000   \n",
       "max    32500.000000    80.000000  243000.000000    1.000000   192.000000   \n",
       "\n",
       "         Feature 5    Feature 6    Feature 7    Feature 8   Feature 9  \n",
       "count  1436.000000  1436.000000  1436.000000  1436.000000  1436.00000  \n",
       "mean      0.674791     0.055710  1566.827994     4.033426  1072.45961  \n",
       "std       0.468616     0.229441   187.182436     0.952677    52.64112  \n",
       "min       0.000000     0.000000  1300.000000     2.000000  1000.00000  \n",
       "25%       0.000000     0.000000  1400.000000     3.000000  1040.00000  \n",
       "50%       1.000000     0.000000  1600.000000     4.000000  1070.00000  \n",
       "75%       1.000000     0.000000  1600.000000     5.000000  1085.00000  \n",
       "max       1.000000     1.000000  2000.000000     5.000000  1615.00000  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "946f6da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1436 entries, 0 to 1435\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Y          1436 non-null   int64  \n",
      " 1   Feature 1  1436 non-null   int64  \n",
      " 2   Feature 2  1436 non-null   int64  \n",
      " 3   Feature 3  414 non-null    float64\n",
      " 4   Feature 4  1436 non-null   int64  \n",
      " 5   Feature 5  1436 non-null   int64  \n",
      " 6   Feature 6  1436 non-null   int64  \n",
      " 7   Feature 7  1436 non-null   int64  \n",
      " 8   Feature 8  1436 non-null   int64  \n",
      " 9   Feature 9  1436 non-null   int64  \n",
      "dtypes: float64(1), int64(9)\n",
      "memory usage: 112.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e4db46ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create labels for the correlation matrix\n",
    "df_corr = df.corr(method=\"pearson\")\n",
    "labels = np.where(np.abs(df_corr)>0.75, \"S\",\n",
    "                  np.where(np.abs(df_corr)>0.5, \"M\",\n",
    "                           np.where(np.abs(df_corr)>0.25, \"W\", \"\")))\n",
    "\n",
    "# Plot correlation matrix\n",
    "plt.figure(figsize=(15, 15))\n",
    "sns.heatmap(df_corr, mask=np.eye(len(df_corr)), square=True,\n",
    "            center=0, annot=labels, fmt='', linewidths=.5,\n",
    "            cmap=\"vlag\", cbar_kws={\"shrink\": 0.8});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7c481efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(lw=0, marker='.', subplots=True, layout=(-1,4), figsize=(15,10), markersize=2);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "2f472e8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=25, figsize=(15,7), layout=(-1,5), edgecolor=\"black\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d5d16fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df[[\"Feature 1\", \"Y\"]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5378d155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Feature 1',y='Y', data=df, size=10, alpha=.5, marker='+');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acdd9c5",
   "metadata": {},
   "source": [
    "### Structure of numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5e30eb0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 3       2\n",
      "Feature 5       2\n",
      "Feature 6       2\n",
      "Feature 8       4\n",
      "Feature 4      12\n",
      "Feature 7      12\n",
      "Feature 9      59\n",
      "Feature 1      77\n",
      "Y             236\n",
      "Feature 2    1263\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_values = df.select_dtypes(include=\"number\").nunique().sort_values()\n",
    "\n",
    "print(unique_values)\n",
    "unique_values.plot.bar(logy=True, title=\"Unique values per feature\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160b6451",
   "metadata": {},
   "source": [
    "### Quality of features\n",
    "\n",
    "- Duplicates, missing values, outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b5a22930",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y               0\n",
      "Feature 1       0\n",
      "Feature 2       0\n",
      "Feature 3    1022\n",
      "Feature 4       0\n",
      "Feature 5       0\n",
      "Feature 6       0\n",
      "Feature 7       0\n",
      "Feature 8       0\n",
      "Feature 9       0\n",
      "dtype: int64\n",
      "Y            0.000000\n",
      "Feature 1    0.000000\n",
      "Feature 2    0.000000\n",
      "Feature 4    0.000000\n",
      "Feature 5    0.000000\n",
      "Feature 6    0.000000\n",
      "Feature 7    0.000000\n",
      "Feature 8    0.000000\n",
      "Feature 9    0.000000\n",
      "Feature 3    0.711699\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Missing values\n",
    "missing_values = df.isna().sum()\n",
    "ratio_missing_values = df.isna().mean().sort_values()\n",
    "print(missing_values)\n",
    "print(ratio_missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4f176cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df, labels=True, sort=\"descending\",figsize=(10,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8454aa",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f6e9e834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1436, 10)\n",
      "(1435, 9)\n"
     ]
    }
   ],
   "source": [
    "# Drop column feature 3\n",
    "print(df.shape)\n",
    "df = df.drop('Feature 3', axis=1)\n",
    "# Drop duplicates\n",
    "df = df.drop_duplicates(keep='first')\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "048b371a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1383, 9)\n"
     ]
    }
   ],
   "source": [
    "# Filter outliers using 0.5, 99 percentile\n",
    "df = df[df['Y'] > np.percentile(df['Y'], q=0.5)]\n",
    "df = df[df['Y'] <= np.percentile(df['Y'], q=99)]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b849480a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f8c90a4e",
   "metadata": {},
   "source": [
    "### Build Data Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "47bfeeb3-aaa8-4ac5-a433-2dd40ea46095",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(filename):\n",
    "    assert os.path.exists(filename), print(\"Dataset does not exist!\")\n",
    "    delimeter = \";\"\n",
    "    dataf = pd.read_csv(filename, sep=delimeter)\n",
    "    dataf.columns = dataf.columns.str.lower()\n",
    "    dataf.columns = dataf.columns.str.replace(\" \", \"_\")\n",
    "    return dataf \n",
    "\n",
    "def select_best_features(dataf, num_features=4):\n",
    "\n",
    "    dataf = clean_dataset(dataf)\n",
    "    X = dataf.drop(\"y\", axis=1)\n",
    "    y = dataf.y\n",
    "    X_train, _ , y_train, _ = train_test_split(X, y, test_size=0.25, random_state=42, shuffle=True)\n",
    "    select = SelectKBest(k=num_features)\n",
    "    selected_features = select.fit(X_train, y_train)\n",
    "    ix_selected = selected_features.get_support(indices=True)\n",
    "    col_selected = [X.columns[i] for i in ix_selected]\n",
    "\n",
    "    return col_selected\n",
    "\n",
    "def start_pipeline(dataf):\n",
    "    print(\"Building Pipeline\")\n",
    "    print(\"-----------------------------------------------------------------\")\n",
    "    return dataf.copy()\n",
    "    \n",
    "def clean_dataset(dataf):\n",
    "\n",
    "    # drop feature with missing values\n",
    "    dataf.drop(\"feature_3\", axis=1, inplace=True)\n",
    "    dataf = dataf.drop_duplicates(keep='first')\n",
    "\n",
    "    # select best features + target\n",
    "    #cols = [\"y\",\"feature_1\",\"feature_2\", \"feature_4\",\"feature_9\"]\n",
    "    #dataf = dataf[cols]\n",
    "    return dataf\n",
    "\n",
    "def remove_outliers(dataf):\n",
    "    dataf = dataf[dataf['y'] > np.percentile(dataf['y'], q=0.5)]\n",
    "    dataf = dataf[dataf['y'] <= np.percentile(dataf['y'], q=99)]\n",
    "    return dataf\n",
    "\n",
    "def standard_scale(dataf):\n",
    "    X = dataf.drop(\"y\", axis=1)\n",
    "    y = dataf.y\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "    X_scaled = pd.DataFrame(X_scaled) \n",
    "    return X, y \n",
    "    \n",
    "def split_data(dataf):\n",
    "    #X = dataf.drop(\"y\", axis=1)\n",
    "    #y = dataf.y\n",
    "    X, y = standard_scale(dataf)\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25, random_state=42, shuffle=True)\n",
    "    return X_train, X_val, y_train, y_val\n",
    "\n",
    "\n",
    "\n",
    "def build_pipeline(dataf):\n",
    "    pipeline = (dataf\n",
    "                .pipe(start_pipeline)\n",
    "                .pipe(clean_dataset)\n",
    "                .pipe(remove_outliers)\n",
    "                .pipe(split_data))\n",
    "    \n",
    "    return pipeline\n",
    "\n",
    "def evaluate_metric(regressor, X_train, X_val, y_train, y_val, error=\"\",  **kwargs):\n",
    "    model = regressor(**kwargs)\n",
    "    model.fit(X_train, y_train)\n",
    "    #joblib.dump(value=model, filename=\"./Models/random_forest.pkl\")\n",
    "    #mlflow.sklearn.log_model(model, \"random_forest_model\")\n",
    "    preds_val = model.predict(X_val)\n",
    "    if error == \"mae\":\n",
    "        mae = mean_absolute_error(y_val, preds_val)\n",
    "        return(mae)\n",
    "    else:\n",
    "        mse = mean_squared_error(y_val, preds_val)\n",
    "        return(mse)\n",
    "\n",
    "\n",
    "def save_model(pipeline, file_name):\n",
    "    joblib.dump(pipeline, file_name)\n",
    "\n",
    "def load_model(file_name):\n",
    "    return joblib.load(file_name)\n",
    "    \n",
    "# Utility functions\n",
    "def read_test_result(cv_dict, cv=5):\n",
    "    dic = dict()\n",
    "    for key in cv_dict.keys():\n",
    "        if key.startswith('test'):\n",
    "            dic['Average {}-fold '.format(cv)+key] = cv_dict[key].mean()\n",
    "            # print('Average {}-fold '.format(cv)+key)\n",
    "            # print(cv_dict[key].mean())\n",
    "    \n",
    "    print(pd.DataFrame(cv_dict).head(20))\n",
    "    return(dic)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11814758",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "bb91044e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building Pipeline\n",
      "-----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "df = load_data(filename=\"data.csv\")\n",
    "num_leaf_nodes = np.arange(10,200,10)\n",
    "regressor = RandomForestRegressor\n",
    "metric_name = \"mae\"\n",
    "X_train, X_val, y_train, y_val = build_pipeline(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "11a3ee81",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring_metrics = ('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')\n",
    "folds_5 = KFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844333b9",
   "metadata": {},
   "source": [
    "### LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "295ac259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 891.87\n",
      "-------------------------------------------------------\n",
      "Cross Validation\n",
      "   fit_time  score_time   test_r2  test_neg_mean_squared_error  \\\n",
      "0  0.003999    0.002995  0.850607                -1.993321e+06   \n",
      "1  0.008001    0.002997  0.848227                -1.810824e+06   \n",
      "2  0.002003    0.002998  0.871658                -1.896314e+06   \n",
      "3  0.003013    0.002998  0.864788                -1.776296e+06   \n",
      "4  0.009001    0.009001  0.854935                -1.755937e+06   \n",
      "\n",
      "   test_neg_mean_absolute_error  test_neg_root_mean_squared_error  \n",
      "0                  -1001.201747                      -1411.850209  \n",
      "1                  -1024.940111                      -1345.668588  \n",
      "2                  -1051.728162                      -1377.067127  \n",
      "3                   -952.007811                      -1332.777392  \n",
      "4                   -991.255465                      -1325.117781  \n"
     ]
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)\n",
    "lr_mae = evaluate_metric(LinearRegression, X_train, X_val, y_train, y_val, error=metric_name)\n",
    "print(f\"MAE: {round(lr_mae,2)}\")\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(\"Cross Validation\")\n",
    "lr_scores = cross_validate(regressor, X, y, cv=folds_5, scoring=scoring_metrics)\n",
    "lr_dict = read_test_result(lr_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4d4e06",
   "metadata": {},
   "source": [
    "### XgBoostRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "8903f675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 847.4\n",
      "-------------------------------------------------------\n",
      "Cross Validation\n",
      "   fit_time  score_time   test_r2  test_neg_mean_squared_error  \\\n",
      "0  0.164000    0.008022  0.910544                -1.193597e+06   \n",
      "1  0.188165    0.008011  0.889989                -1.312557e+06   \n",
      "2  0.145999    0.007002  0.889719                -1.629462e+06   \n",
      "3  0.147001    0.005000  0.910973                -1.169549e+06   \n",
      "4  0.143997    0.006001  0.889294                -1.340036e+06   \n",
      "\n",
      "   test_neg_mean_absolute_error  test_neg_root_mean_squared_error  \n",
      "0                   -866.462665                      -1092.518649  \n",
      "1                   -875.120708                      -1145.668725  \n",
      "2                   -941.150442                      -1276.503835  \n",
      "3                   -780.047780                      -1081.456697  \n",
      "4                   -880.065823                      -1157.599204  \n"
     ]
    }
   ],
   "source": [
    "regressor = XGBRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "xgb_mae = evaluate_metric(XGBRegressor, X_train, X_val, y_train, y_val, error=metric_name)\n",
    "print(f\"MAE: {round(xgb_mae,2)}\")\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(\"Cross Validation\")\n",
    "xgb_scores = cross_validate(regressor, X, y, cv=folds_5, scoring=scoring_metrics)\n",
    "xgb_dict = read_test_result(xgb_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6766eb92",
   "metadata": {},
   "source": [
    "### RandomForestRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "c87349c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 750.46\n",
      "-------------------------------------------------------\n",
      "Cross Validation\n",
      "   fit_time  score_time   test_r2  test_neg_mean_squared_error  \\\n",
      "0  0.786609    0.021996  0.910565                -1.193312e+06   \n",
      "1  0.811790    0.019999  0.892918                -1.277615e+06   \n",
      "2  0.585818    0.018997  0.899848                -1.479802e+06   \n",
      "3  0.696386    0.020036  0.917007                -1.090281e+06   \n",
      "4  0.579917    0.016997  0.902667                -1.178163e+06   \n",
      "\n",
      "   test_neg_mean_absolute_error  test_neg_root_mean_squared_error  \n",
      "0                   -838.023507                      -1092.388298  \n",
      "1                   -890.145598                      -1130.316341  \n",
      "2                   -915.190964                      -1216.471030  \n",
      "3                   -772.210604                      -1044.165310  \n",
      "4                   -815.803912                      -1085.432195  \n"
     ]
    }
   ],
   "source": [
    "regressor = RandomForestRegressor()\n",
    "regressor.fit(X_train, y_train)\n",
    "forest_mae = evaluate_metric(RandomForestRegressor, X_train, X_val, y_train, y_val, error=metric_name, max_leaf_nodes=80)\n",
    "print(f\"MAE: {round(forest_mae,2)}\")\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(\"Cross Validation\")\n",
    "forest_scores = cross_validate(regressor, X, y, cv=folds_5, scoring=scoring_metrics)\n",
    "forest_dict = read_test_result(forest_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "cb7c7b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " num_leaf_nodes: 10 MAE : 829.49 with RandomForestRegressor \n",
      " num_leaf_nodes: 20 MAE : 767.23 with RandomForestRegressor \n",
      " num_leaf_nodes: 30 MAE : 748.17 with RandomForestRegressor \n",
      " num_leaf_nodes: 40 MAE : 740.08 with RandomForestRegressor \n",
      " num_leaf_nodes: 50 MAE : 746.28 with RandomForestRegressor \n",
      " num_leaf_nodes: 60 MAE : 747.52 with RandomForestRegressor \n",
      " num_leaf_nodes: 70 MAE : 737.48 with RandomForestRegressor \n",
      " num_leaf_nodes: 80 MAE : 742.18 with RandomForestRegressor \n",
      " num_leaf_nodes: 90 MAE : 748.49 with RandomForestRegressor \n",
      " num_leaf_nodes: 100 MAE : 748.7 with RandomForestRegressor \n",
      " num_leaf_nodes: 110 MAE : 753.92 with RandomForestRegressor \n",
      " num_leaf_nodes: 120 MAE : 752.15 with RandomForestRegressor \n",
      " num_leaf_nodes: 130 MAE : 748.7 with RandomForestRegressor \n",
      " num_leaf_nodes: 140 MAE : 765.53 with RandomForestRegressor \n",
      " num_leaf_nodes: 150 MAE : 759.18 with RandomForestRegressor \n",
      " num_leaf_nodes: 160 MAE : 765.84 with RandomForestRegressor \n",
      " num_leaf_nodes: 170 MAE : 769.74 with RandomForestRegressor \n",
      " num_leaf_nodes: 180 MAE : 767.53 with RandomForestRegressor \n",
      " num_leaf_nodes: 190 MAE : 769.42 with RandomForestRegressor \n",
      "-------------------------------------------------------\n",
      "Min MAE: 737.48\n"
     ]
    }
   ],
   "source": [
    "regressor = RandomForestRegressor\n",
    "metric_list = []\n",
    "for nodes in num_leaf_nodes:\n",
    "    metric = evaluate_metric(regressor, X_train, X_val, y_train, y_val, error=metric_name, max_leaf_nodes=nodes)\n",
    "    print(f\" num_leaf_nodes: {nodes} {metric_name.upper()} : {round(metric,2)} with {regressor.__name__} \")\n",
    "    metric_list.append(metric)\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(f\"Min MAE: {round(min(metric_list),2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b06ba028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 894.83\n",
      "-------------------------------------------------------\n",
      "Cross Validation\n",
      "   fit_time  score_time   test_r2  test_neg_mean_squared_error  \\\n",
      "0  0.004034    0.002964  0.850489                -1.994892e+06   \n",
      "1  0.003000    0.001999  0.848354                -1.809308e+06   \n",
      "2  0.006049    0.004950  0.871719                -1.895420e+06   \n",
      "3  0.002964    0.004000  0.864726                -1.777105e+06   \n",
      "4  0.004992    0.003036  0.854911                -1.756231e+06   \n",
      "\n",
      "   test_neg_mean_absolute_error  test_neg_root_mean_squared_error  \n",
      "0                  -1001.069325                      -1412.406463  \n",
      "1                  -1023.908111                      -1345.105351  \n",
      "2                  -1051.343429                      -1376.742510  \n",
      "3                   -952.228672                      -1333.080864  \n",
      "4                   -990.789919                      -1325.228741  \n"
     ]
    }
   ],
   "source": [
    "regressor = ElasticNet()\n",
    "regressor.fit(X_train, y_train)\n",
    "elastic_net_mae = evaluate_metric(ElasticNet, X_train, X_val, y_train, y_val, error=metric_name)\n",
    "print(f\"MAE: {round(elastic_net_mae,2)}\")\n",
    "print(\"-------------------------------------------------------\")\n",
    "print(\"Cross Validation\")\n",
    "elastic_net_scores = cross_validate(regressor, X, y, cv=folds_5, scoring=scoring_metrics)\n",
    "elastic_net_dict = read_test_result(elastic_net_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "caf51737",
   "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>Average 5-fold test_r2</th>\n",
       "      <th>Average 5-fold test_neg_mean_squared_error</th>\n",
       "      <th>Average 5-fold test_neg_mean_absolute_error</th>\n",
       "      <th>Average 5-fold test_neg_root_mean_squared_error</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.858043</td>\n",
       "      <td>-1.846538e+06</td>\n",
       "      <td>-1004.226659</td>\n",
       "      <td>-1358.496220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elastic Net</th>\n",
       "      <td>0.858040</td>\n",
       "      <td>-1.846591e+06</td>\n",
       "      <td>-1003.867891</td>\n",
       "      <td>-1358.512786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.898104</td>\n",
       "      <td>-1.329040e+06</td>\n",
       "      <td>-868.569483</td>\n",
       "      <td>-1150.749422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.904601</td>\n",
       "      <td>-1.243835e+06</td>\n",
       "      <td>-846.274917</td>\n",
       "      <td>-1113.754635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Average 5-fold test_r2  \\\n",
       "Model name                                  \n",
       "Linear Regression                0.858043   \n",
       "Elastic Net                      0.858040   \n",
       "XGBoost                          0.898104   \n",
       "Random Forest                    0.904601   \n",
       "\n",
       "                   Average 5-fold test_neg_mean_squared_error  \\\n",
       "Model name                                                      \n",
       "Linear Regression                               -1.846538e+06   \n",
       "Elastic Net                                     -1.846591e+06   \n",
       "XGBoost                                         -1.329040e+06   \n",
       "Random Forest                                   -1.243835e+06   \n",
       "\n",
       "                   Average 5-fold test_neg_mean_absolute_error  \\\n",
       "Model name                                                       \n",
       "Linear Regression                                 -1004.226659   \n",
       "Elastic Net                                       -1003.867891   \n",
       "XGBoost                                            -868.569483   \n",
       "Random Forest                                      -846.274917   \n",
       "\n",
       "                   Average 5-fold test_neg_root_mean_squared_error  \n",
       "Model name                                                          \n",
       "Linear Regression                                     -1358.496220  \n",
       "Elastic Net                                           -1358.512786  \n",
       "XGBoost                                               -1150.749422  \n",
       "Random Forest                                         -1113.754635  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = pd.DataFrame([lr_dict, elastic_net_dict ,xgb_dict, forest_dict])\n",
    "metric_df['Model name'] = ['Linear Regression','Elastic Net','XGBoost', 'Random Forest']\n",
    "metric_df.set_index('Model name', inplace=True)\n",
    "# metric_df.to_csv('Minmax_scaling.csv')\n",
    "metric_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "e1077eab",
   "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>MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>891.865190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elastic Net</th>\n",
       "      <td>894.830317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>847.404753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>750.457075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          MAE\n",
       "Model name                   \n",
       "Linear Regression  891.865190\n",
       "Elastic Net        894.830317\n",
       "XGBoost            847.404753\n",
       "Random Forest      750.457075"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric_df = pd.DataFrame()\n",
    "metric_df[\"MAE\"] = [lr_mae, elastic_net_mae, xgb_mae, forest_mae]\n",
    "metric_df['Model name'] = ['Linear Regression', 'Elastic Net', 'XGBoost', 'Random Forest' ]\n",
    "metric_df.set_index('Model name', inplace=True)\n",
    "# metric_df.to_csv('Minmax_scaling.csv')\n",
    "metric_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c6fce7",
   "metadata": {},
   "source": [
    "### Comments\n",
    "\n",
    "1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e49620d",
   "metadata": {},
   "source": [
    "### Mlflow Example Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "681a984e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example usage\n",
    "if __name__ == \"__main__\":\n",
    "    df = load_data(filename=\"data.csv\")\n",
    "    regressor = RandomForestRegressor\n",
    "    metric_name = \"mae\"\n",
    "    X_train, X_val, y_train, y_val = build_pipeline(df)\n",
    "    nodes = 50\n",
    "    num_features = len(select_best_features(df))\n",
    "    \n",
    "    with mlflow.start_run():\n",
    "        mlflow.log_param(\"num_features\", num_features)\n",
    "        mlflow.log_param(\"regressor\", \"RandomForestRegressor\")\n",
    "        mae = evaluate_metric(regressor, X_train, X_val, y_train, y_val, error=\"mae\", n_estimators=100, max_leaf_nodes=nodes)\n",
    "        mlflow.log_param(\"Mean Absolute Error\", mae)\n",
    "        print(\"MAE:\", mae)\n",
    "\n",
    "#run mlflow server\n",
    "!mlflow ui\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "486f57ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_1', 'feature_2', 'feature_4', 'feature_9']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_best_features(dataf, num_features=4):\n",
    "\n",
    "    dataf = clean_dataset(dataf)\n",
    "    X = dataf.drop(\"y\", axis=1)\n",
    "    y = dataf.y\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25, random_state=42, shuffle=True)\n",
    "    select = SelectKBest(k=num_features)\n",
    "    selected_features = select.fit(X_train, y_train)\n",
    "    ix_selected = selected_features.get_support(indices=True)\n",
    "    col_selected = [X.columns[i] for i in ix_selected]\n",
    "\n",
    "    return col_selected\n",
    "\n",
    "df = load_data(\"data.csv\")\n",
    "best_features = select_best_features(df)\n",
    "best_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7744ddd4-6f63-4819-88c1-78ec0400d130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Feature 6    0.944290\n",
       "Feature 5    0.674791\n",
       "Feature 7    0.589136\n",
       "Feature 4    0.581476\n",
       "Feature 8    0.469359\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQJMEXAAAAAEkSfAEAAACQpGZZFwAAQGErHzY96xK2iyVjTsm6BABgG3PHFwAAAABJEnwBAAAAkCTBFwAAAABJEnwBAAAAkCTBFwAAAABJEnwBAAAAkCTBFwAAAABJalTwdeedd0Z5eXm0aNEievbsGXPnzt3ksRMnToxjjjkmdt9999h9992joqJis8cDAAAAwLaQd/A1bdq0qKysjJEjR8b8+fOjW7du0adPn/jwww83evzs2bPj7LPPjueffz7mzJkTnTt3jhNOOCHee++9rS4eAAAAADYl7+Br7NixMWjQoBg4cGB07do1JkyYEC1btozJkydv9PiHH344LrnkkjjssMPiwAMPjEmTJkVtbW3MmjVrq4sHAAAAgE3JK/hav359zJs3LyoqKv7yBxQXR0VFRcyZM6dBf8aaNWviyy+/jD322GOTx6xbty5WrVpVbwMAAACAfOQVfH388cdRU1MTZWVl9faXlZVFVVVVg/6Mq666Kjp27FgvPPtbo0ePjjZt2tRtnTt3zqdMAAAAANi+3+o4ZsyYmDp1ajzxxBPRokWLTR43fPjwWLlyZd22fPny7VglAAAAAClols/Bbdu2jZKSkqiurq63v7q6Otq3b7/Zc//1X/81xowZE88991wceuihmz22tLQ0SktL8ykNAAAAAOrJ646v5s2bR/fu3es9mP7PD6rv1avXJs+75ZZb4oYbboiZM2dGjx49Gl8tAAAAADRQXnd8RURUVlbGgAEDokePHnHkkUfGuHHjYvXq1TFw4MCIiOjfv3906tQpRo8eHRERP/vZz2LEiBExZcqUKC8vr3sW2C677BK77LLLNvxVAAAAAOAv8g6++vXrFx999FGMGDEiqqqq4rDDDouZM2fWPfB+2bJlUVz8lxvJ7rrrrli/fn18//vfr/fnjBw5Mq677rqtqx4AAAAANiHv4CsiYsiQITFkyJCNvjZ79ux6Py9ZsqQxbwEAAAAAW2W7fqsjAAAAAGwvgi8AAAAAkiT4AgAAACBJgi8AAAAAkiT4AgAAACBJgi8AAAAAkiT4AgAAACBJgi8AAAAAkiT4AgAAACBJgi8AAAAAkiT4AgAAACBJgi8AAAAAkiT4AgAAACBJgi8AAAAAktQs6wIAAIDtp3zY9KxL2C6WjDkl6xIAKADu+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSYIvAAAAAJIk+AIAAAAgSc2yLgAAAIDGKx82PesStoslY07JugSgCXLHFwAAAABJEnwBAAAAkCTBFwAAAABJEnwBAAAAkCTBFwAAAABJEnwBAAAAkCTBFwAAAABJapZ1AQAAAMDXyodNz7qE7WLJmFOyLoEdhDu+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEhSo4KvO++8M8rLy6NFixbRs2fPmDt37maP/8UvfhEHHnhgtGjRIg455JCYMWNGo4oFAAAAgIZqlu8J06ZNi8rKypgwYUL07Nkzxo0bF3369IlFixZFu3btNjj+5ZdfjrPPPjtGjx4d//RP/xRTpkyJ008/PebPnx8HH3zwNvklAAAAAApN+bDpWZewXSwZc0rWJWxS3sHX2LFjY9CgQTFw4MCIiJgwYUJMnz49Jk+eHMOGDdvg+DvuuCNOPPHEuOKKKyIi4oYbbohnn302xo8fHxMmTNjoe6xbty7WrVtX9/PKlSsjImLVqlX5lrtVatet2a7vl5Xt/f9rVvQzLfqZHj1Ni36mRT/Top/p0dO06Gda9PObfb9cLrflg3N5WLduXa6kpCT3xBNP1Nvfv3//3KmnnrrRczp37py7/fbb6+0bMWJE7tBDD93k+4wcOTIXETabzWaz2Ww2m81ms9lsNttGt+XLl28xy8rrjq+PP/44ampqoqysrN7+srKyWLhw4UbPqaqq2ujxVVVVm3yf4cOHR2VlZd3PtbW1sWLFivjWt74VRUVF+ZTcpKxatSo6d+4cy5cvj9atW2ddDltJP9Oin+nR07ToZ1r0My36mR49TYt+pmVH6Wcul4vPPvssOnbsuMVj8/6o4/ZQWloapaWl9fbttttu2RSTgdatWyd9ge5o9DMt+pkePU2LfqZFP9Oin+nR07ToZ1p2hH62adOmQcfl9a2Obdu2jZKSkqiurq63v7q6Otq3b7/Rc9q3b5/X8QAAAACwLeQVfDVv3jy6d+8es2bNqttXW1sbs2bNil69em30nF69etU7PiLi2Wef3eTxAAAAALAt5P1Rx8rKyhgwYED06NEjjjzyyBg3blysXr267lse+/fvH506dYrRo0dHRMRll10Wxx57bNx2221xyimnxNSpU+PVV1+Nu+++e9v+JgkoLS2NkSNHbvAxT5om/UyLfqZHT9Oin2nRz7ToZ3r0NC36mRb93FBRLteQ736sb/z48XHrrbdGVVVVHHbYYfHzn/88evbsGRER3/ve96K8vDzuu+++uuN/8YtfxDXXXBNLliyJ/fffP2655ZY4+eSTt9kvAQAAAAB/q1HBFwAAAAAUurye8QUAAAAATYXgCwAAAIAkCb4AAADYwPvvv591CQBbTfAFwA7JIy4BYPO+853vxJQpU7IuA2CrCL5gG1u9enXce++9cfXVV8f48ePjk08+ybok8vAv//Iv8eKLL2ZdBttBaWlpvPnmm1mXATu8N998M+69995YuHBhREQsXLgwBg8eHBdeeGH8+te/zrg62LHddNNN8aMf/Sh+8IMfxIoVK7IuB/gbX3zxRbz00kvxxhtvbPDa2rVr44EHHsigqsLjWx0zMH/+/Nh9991jn332iYiIBx98MCZMmBDLli2LvffeO4YMGRJnnXVWxlXSUF27do2XXnop9thjj1i+fHl897vfjT/96U9xwAEHxDvvvBPNmjWLV155pa7fFLbi4uIoKiqK/fbbLy666KIYMGBAtG/fPuuy2AqVlZUb3X/HHXfEeeedF9/61rciImLs2LHbsyy2wvjx42Pu3Llx8sknx1lnnRUPPvhgjB49Ompra+PMM8+M66+/Ppo1a5Z1mTTAzJkz47TTTotddtkl1qxZE0888UT0798/unXrFrW1tfHCCy/EM888E8cdd1zWpdJAH3zwQdx1113x0ksvxQcffBDFxcWx7777xumnnx4XXHBBlJSUZF0ieXr33XfjoosuijfeeCMmTpwYffv2zbokviHLly+PkSNHxuTJk7MuhQZ466234oQTTohly5ZFUVFRHH300TF16tTo0KFDRERUV1dHx44do6amJuNKsyf4ykC3bt3itttui4qKipg0aVL8+Mc/jkGDBsVBBx0UixYtikmTJsUdd9wRF154Ydal0gDFxcVRVVUV7dq1i/POOy/efffdmDFjRrRp0yY+//zzOOOMM2LPPfd0m3gTUVxcHM8++2w89dRT8fDDD8fKlSvjpJNOikGDBsXJJ58cxcVulG1qiouLo1u3brHbbrvV2//CCy9Ejx49olWrVlFUVOTOkibixhtvjFtuuSVOOOGE+O///u/4yU9+ErfeemsMHTo0iouL4/bbb4/BgwfHqFGjsi6VBjjqqKPiuOOOixtvvDGmTp0al1xySQwePDhuuummiIgYPnx4zJs3L5555pmMK6UhXn311aioqIguXbrEzjvvHHPmzIlzzjkn1q9fH08//XR07do1Zs6cGbvuumvWpdII48ePj6FDh8ZBBx20wT8uzJ8/P6Oq2JYWLFgQRxxxhKCkiTjjjDPiyy+/jPvuuy8+/fTT+MlPfhJvvPFGzJ49O7797W8Lvv6K4CsDLVu2jDfffDP23nvvOOKII2Lw4MExaNCgutenTJkSN910U/zhD3/IsEoa6q+Dr/322y8mTJgQxx9/fN3rL7/8cpx11lmxbNmyDKukof66n19++WU88cQTMXny5HjuueeirKwsLrjgghg4cGB06dIl61JpoDFjxsTdd98dkyZNqnfXyE477RQLFiyIrl27Zlgd+erSpUvccsstceaZZ8aCBQuie/fucf/998e5554bERFPPPFEXHnllfH2229nXCkN0aZNm5g3b1506dIlamtro7S0NObOnRuHH354RES8/vrrUVFREVVVVRlXSkMcffTRcfzxx8fIkSMjIuKhhx6K8ePHxyuvvBJ/+tOf4rjjjovvfve7cccdd2RcKflaunRpDBw4MF5//fX40Y9+tEHw9eeeU9iefPLJzb6+ePHiuPzyywUlTURZWVk899xzccghh0TE18+vveSSS2LGjBnx/PPPR6tWrQRf/z/BVwbatm0bTz/9dHTv3j3KysrimWeeiW7dutW9/s4778QhhxwSa9asybBKGqq4uDiqq6tjzz33jE6dOsXTTz8dBx98cN3rS5cujQMPPDC++OKLDKukof46+Ppry5Yti8mTJ8d9990Xy5cvt4A0Mf/zP/8T5513XvTt2zdGjx4dO+20k+CriWrZsmUsXLgwvv3tb0dERPPmzeN3v/tdfOc734mIr//O7dq1a6xevTrLMmmgNm3axPz582O//faLiIhdd901FixYEPvuu29EWEObmpYtW8brr79e17/a2tpo0aJFLF++PMrKyuLZZ5+NCy64IN57772MKyUfEydOjMsvvzwqKiriP/7jP2LPPffMuiQa6c+P9NhcBFBUVOS/c5uI1q1bx29/+9s46KCD6u0fMmRI/PKXv4wpU6bE9773Pf0MD7fPxEknnRR33XVXREQce+yx8dhjj9V7/dFHH3U3SRPzj//4j3HEEUfEqlWrYtGiRfVeW7p0ad0zhGi6vv3tb8d1110X7777bsycOTPrcsjT3//938e8efPio48+ih49esTrr78eRUVFWZdFI7Rv377uAa5vv/121NTU1Hug6x/+8IcNgmsKV3l5eb278+bMmVMXakZ8/Y8Of35WCYWvXbt28cEHH9T9XF1dHV999VW0bt06IiL2339/D0hvYk488cS46qqrYvz48fH4448LvZq4Dh06xOOPPx61tbUb3XxktWk58MAD49VXX91g//jx4+O0006LU089NYOqCpMnv2bgZz/7WfTu3TuOPfbY6NGjR9x2220xe/bsumd8vfLKK/HEE09kXSYN9Le3du+yyy71fn7qqafimGOO2Z4lsRX23nvvzT54t6ioqN5HWWk6dtlll7j//vtj6tSpUVFR4V+/mqhzzz03+vfvH6eddlrMmjUrrrzyyvjpT38an3zySRQVFcVNN90U3//+97MukwYaPHhwvVn86zumIyJ+9atfebB9E3L66afHxRdfHLfeemuUlpbGDTfcEMcee2zsvPPOERGxaNGi6NSpU8ZVko+ampr43//939hrr72yLoVtoHv37jFv3rw47bTTNvr6lu4Go7CcccYZ8cgjj8T555+/wWvjx4+P2tramDBhQgaVFR4fdczIp59+GmPGjImnnnoqFi9eHLW1tdGhQ4fo3bt3DB06NHr06JF1iQDJ+uMf/xjz5s2LioqKaNWqVdblkIfa2toYM2ZMzJkzJ4466qgYNmxYTJs2La688spYs2ZN9O3bN8aPH6+vkIHPP/88Lrroonj88cejpqYmevXqFQ899FDdN1s/88wzsXLlyvjBD36QcaWwY3rxxRdj9erVceKJJ2709dWrV8err74axx577HauDL5Zgi8AAGCbWbt2bXz11Vcb3AUPAFkQfAEAAACQJA+3BwAAACBJgi8AAAAAkiT4AgAAACBJgi/Yhh588MHo3bt3dOzYMZYuXRoREePGjYtf/vKXGVdGY+hnevQUCpf5BIDGsYZunuArYy7QdNx1111RWVkZJ598cnz66adRU1MTERG77bZbjBs3LtviyJt+pkdP02MNTYf5TI/5hMJmRtNhDd0ywVeGXKBp+bd/+7eYOHFiXH311VFSUlK3v0ePHvH73/8+w8poDP1Mj56mxRqaFvOZFvMJhc2MpsUaumWCrwy5QNPy7rvvxuGHH77B/tLS0li9enUGFbE19DM9epoWa2hazGdazCcUNjOaFmvolgm+MuQCTcs+++wTr7322gb7Z86cGQcddND2L4itop/p0dO0WEPTYj7TYj6hsJnRtFhDt6xZ1gXsyP58ge6999719rtAm6bKysq49NJLY+3atZHL5WLu3LnxyCOPxOjRo2PSpElZl0ee9DM9epoWa2hazGdazCcUNjOaFmtoA+TIzMSJE3OdOnXKTZ06NdeqVavcI488krvxxhvr/jdNz0MPPZTr0qVLrqioKFdUVJTr1KlTbtKkSVmXRSPpZ3r0NB3W0PSYz3SYTyhsZjQ91tDNK8rlcrmsw7cd2cMPPxzXXXddvPPOOxER0bFjxxg1alRcdNFFGVdGPr766quYMmVK9OnTJ8rKymLNmjXx+eefR7t27bIujUbQz/ToaZqsoWkwn2kyn1DYzGgarKENI/jKiAs0PS1btow333xzg1uGaZr0Mz16mg5raHrMZzrMJxQ2M5oea+iWebh9Rpo1axYXX3xxrF27NiK+vlj9ZdO0HXnkkfG73/0u6zLYRvQzPXqaDmtoesxnOswnFDYzmh5r6JZ5uH2G/nyBSmbTcMkll8Tll18ef/zjH6N79+7RqlWreq8feuihGVVGY+hnevQ0LdbQtJjPtJhPKGxmNC3W0C3zUccMPfroozF8+PAYOnSoCzQBxcUb3kBZVFQUuVwuioqKoqamJoOqaCz9TI+epsUamhbzmRbzCYXNjKbFGrplgq8MuUDTsnTp0s2+7l9Umhb9TI+epsUamhbzmRbzCYXNjKbFGrplgq8MuUABoHGsoVC4zCcUNjPKjkbwBdvIAw88sNnX+/fvv50qYVvQz/ToKRQu8wkAjWMN3TLBV4ZcoGnZfffd6/385Zdfxpo1a6J58+bRsmXLWLFiRUaV0Rj6mR49TYs1NC3mMy3mEwqbGU2LNXTLBF8ZcoGm7+23347BgwfHFVdcEX369Mm6HLaSfqZHT5sua2j6zGfTZT6hsJnR9FlD6xN8FRgXaHpeffXVOO+882LhwoVZl8I2oJ/p0dN0WEPTYz7TYT6hsJnR9FhD/2LDr3MgU/vvv3+MGTMmLrvssqxLYRtp1qxZvP/++1mXwTain+nR03RYQ9NjPtNhPqGwmdH0WEP/olnWBbAhF2jT9OSTT9b7OZfLxQcffBDjx4+P3r17Z1QVjaWf6dHTHYM1tGkynzsG8wmFzYw2TdbQLfNRxwxt7gLt3Llz/OpXv8qoMhqjuLj+DZRFRUWx5557xnHHHRe33XZbdOjQIaPKaAz9TI+epsUamhbzmRbzCYXNjKbFGrplgq8MuUABoHGsoVC4zCcUNjPKjsYzvjJUW1tbb6upqYmqqqqYMmWKv2yaoOuvvz7WrFmzwf4vvvgirr/++gwqYmvoZ3r0NC3W0LSYz7SYTyhsZjQt1tAtE3xlyAWallGjRsXnn3++wf41a9bEqFGjMqiIraGf6dHTtFhD02I+02I+obCZ0bRYQ7dM8JUhF2hacrlcFBUVbbB/wYIFsccee2RQEVtDP9Ojp2mxhqbFfKbFfEJhM6NpsYZumW91zJALNA277757FBUVRVFRURxwwAH1elpTUxOff/55XHzxxRlWSD70Mz16miZraBrMZ5rMJxQ2M5oGa2jDCb4y4AJNy7hx4yKXy8WFF14Yo0aNijZt2tS91rx58ygvL49evXplWCH50M/06GlarKFpMZ9pMZ9Q2MxoWqyhDedbHTNw//33112g48aNc4Em4oUXXoijjjoqdtppp6xLYRvQz/ToaRqsoWkyn2kwn1DYzGiarKFbJvjKkAs0XWvXro3169fX29e6deuMqmFr6Wd69LTps4amy3w2feYTCpsZTZc1dOMEXwXCBdr0rVmzJq688sp49NFH45NPPtng9ZqamgyqorH0Mz16mi5raNNnPtNlPqGwmdGmzxq6Zb7VMUNr1qyJIUOGRLt27aJVq1ax++6719toWq644or49a9/HXfddVeUlpbGpEmTYtSoUdGxY8d44IEHsi6PPOlnevQ0LdbQtJjPtJhPKGxmNC3W0AbIkZlLLrkkd9BBB+Uee+yx3M4775ybPHly7oYbbsjttddeuYceeijr8shT586dc88//3wul8vldt1119zbb7+dy+VyuQceeCB30kknZVgZjaGf6dHTtFhD02I+02I+obCZ0bRYQ7dM8JUhF2haWrVqlVu6dGkul8vlOnXqlPvtb3+by+VyucWLF+datWqVZWk0gn6mR0/TYg1Ni/lMi/mEwmZG02IN3TIfdczQihUrYt99942Irz9HvWLFioiIOProo+M3v/lNlqXRCPvuu2+8++67ERFx4IEHxqOPPhoREU899VTstttuGVZGY+hnevQ0LdbQtJjPtJhPKGxmNC3W0C0TfGXIBZqWgQMHxoIFCyIiYtiwYXHnnXdGixYtYujQoXHFFVdkXB350s/06GlarKFpMZ9pMZ9Q2MxoWqyhW+ZbHTN0++23R0lJSfz4xz+O5557Lvr27Ru5XC6+/PLLGDt2bFx22WVZl8hWWLp0acybNy+6dOkShx56aNblsJX0Mz162rRZQ9NmPps28wmFzYymzRq6IcFXAXGBpmPt2rXRokWLrMtgG9HP9Ohpeqyh6TCf6TGfUNjMaDqsoRvno44FYu3atbH33nvHmWee6S+bJqqmpiZuuOGG6NSpU+yyyy6xePHiiIi49tpr45577sm4OvKln+nR03RZQ5s+85ku8wmFzYw2fdbQLRN8ZcgFmpabbrop7rvvvrjllluiefPmdfsPPvjgmDRpUoaV0Rj6mR49TYs1NC3mMy3mEwqbGU2LNXTLBF8ZcoGm5YEHHoi77747zj333CgpKanb361bt1i4cGGGldEY+pkePU2LNTQt5jMt5hMKmxlNizV0ywRfGXKBpuW9996LLl26bLC/trY2vvzyywwqYmvoZ3r0NC3W0LSYz7SYTyhsZjQt1tAtE3xlyAWalq5du8aLL764wf7HHnssDj/88AwqYmvoZ3r0NC3W0LSYz7SYTyhsZjQt1tAta5Z1ATuyP1+ge++9d739LtCmacSIETFgwIB47733ora2Nh5//PFYtGhRPPDAA/Ff//VfWZdHnvQzPXqaFmtoWsxnWswnFDYzmhZraAPkyMx//ud/5tq0aZMbM2ZMrmXLlrlbb70198Mf/jDXvHnz3DPPPJN1eTTCb37zm1xFRUVuzz33zO2888653r17555++umsy6KR9DM9epoOa2h6zGc6zCcUNjOaHmvo5hXlcrlc1uHbjuzFF1+M66+/PhYsWBCff/55HHHEETFixIg44YQTsi6NBlq8eHHss88+UVRUlHUpbAP6mR49TZc1tOkzn+kyn1DYzGjTZw1tOMFXBlygaSkpKYkPPvgg2rVrFxER/fr1i5///OdRVlaWcWU0hn6mR0/TYg1Ni/lMi/mEwmZG02INbTgPt8/A/vvvHx999FHdz/369Yvq6uoMK2Jr/G12PGPGjFi9enVG1bC19DM9epoWa2hazGdazCcUNjOaFmtowwm+MuACBYDGsYZC4TKfUNjMKDsqwRdspaKiog1uF3b7cNOln+nRUyhc5hMAGsca2nDNsi5gR+QCTUsul4sLLrggSktLIyJi7dq1cfHFF0erVq3qHff4449nUR550s/06GlarKFpMZ9pMZ9Q2MxoWqyhDSf4yoALNC0DBgyo9/N5552XUSVsC/qZHj1NizU0LeYzLeYTCpsZTYs1tOF8q2MGBg4c2KDj7r333m+4EgBoWqyhULjMJxQ2M8qOSvAFAAAAQJI83B4AAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEiS4AsAAACAJAm+AAAAAEjS/wcODIxb50/jdgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_frequent_entry = df.mode()\n",
    "\n",
    "# Checks for each entry if it contains the most frequent entry\n",
    "df_freq = df.eq(most_frequent_entry.values, axis=1)\n",
    "\n",
    "# Computes the mean of the 'is_most_frequent' occurrence\n",
    "df_freq = df_freq.mean().sort_values(ascending=False)\n",
    "\n",
    "# Show the 5 top features with the highest ratio of singular value content\n",
    "display(df_freq.head())\n",
    "\n",
    "# Visualize the 'df_freq' table\n",
    "df_freq.plot.bar(figsize=(15, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1a4b4d98-5495-49f5-8955-154213f61439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1436, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling continuous features\n",
    "\n",
    "cols_continuous = df.select_dtypes(include=\"number\").nunique() > 12\n",
    "df_continuous = df[cols_continuous[cols_continuous].index]\n",
    "df_continuous.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42b381d6-2bfd-4c40-a79b-0f3e0d130ef9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_continuous, height=1.5, plot_kws={\"s\": 2, \"alpha\": 0.2});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f29074d-d9e8-4251-9417-aae87f1bd451",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = load_data(filename=\"data.csv\")\n",
    "df = clean_dataset(df)\n",
    "\n",
    "start = 92\n",
    "end = 96\n",
    "#df = df.drop(\"y\", axis=1)\n",
    "ll = df[start:end].values.tolist()\n",
    "\n",
    "df[start:end]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}