Ames Machine Learning Project - Bryce Ferraro - Model - Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "450e3b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages and implement notebook settings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "import scipy\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "kf = KFold(shuffle = True, random_state = 0)\n",
    "\n",
    "\n",
    "pd.set_option('display.max_rows', 1000); pd.set_option('display.max_columns', 1000); pd.set_option('display.width', 1000)\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e568505b",
   "metadata": {},
   "source": [
    "# Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15d0a5c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "current_directory = os.getcwd()\n",
    "\n",
    "dataframes = ['features.csv', 'features_dum_drop.csv', 'features_dum_nodrop.csv', 'features_encoded.csv',\n",
    "              'response.csv',\n",
    "              'X_train_drop.csv', 'X_test_drop.csv', 'y_train_drop.csv', 'y_test_drop.csv',\n",
    "              'X_train_nodrop.csv', 'X_test_nodrop.csv', 'y_train_nodrop.csv', 'y_test_nodrop.csv',\n",
    "              'X_train_encoded.csv', 'X_test_encoded.csv', 'y_train_encoded.csv', 'y_test_encoded.csv',\n",
    "              'X_train_drop_scaled.csv', 'X_test_drop_scaled.csv',\n",
    "              'X_train_nodrop_scaled.csv', 'X_test_nodrop_scaled.csv']\n",
    "\n",
    "loaded_dataframes = {}\n",
    "\n",
    "for file_name in dataframes:\n",
    "    \n",
    "    df_name = file_name.split('.csv')[0]\n",
    "    file_path = os.path.join(current_directory, file_name)\n",
    "    loaded_dataframes[df_name] = pd.read_csv(file_path, sep='\\t')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20bd3491",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = loaded_dataframes['features']\n",
    "features_dum_drop = loaded_dataframes['features_dum_drop']\n",
    "features_dum_nodrop = loaded_dataframes['features_dum_nodrop']\n",
    "features_encoded = loaded_dataframes['features_encoded']\n",
    "\n",
    "response = loaded_dataframes['response']\n",
    "\n",
    "\n",
    "X_train_drop = loaded_dataframes['X_train_drop']\n",
    "X_test_drop= loaded_dataframes['X_test_drop']\n",
    "y_train_drop = loaded_dataframes['y_train_drop']['SalePrice']\n",
    "y_test_drop = loaded_dataframes['y_test_drop']['SalePrice']\n",
    "\n",
    "X_train_encoded = loaded_dataframes['X_train_encoded']\n",
    "X_test_encoded = loaded_dataframes['X_test_encoded']\n",
    "y_train_encoded = loaded_dataframes['y_train_encoded']['SalePrice']\n",
    "y_test_encoded = loaded_dataframes['y_test_encoded']['SalePrice']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcee6233",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1755e48c",
   "metadata": {},
   "source": [
    "## Random Forest - Dummification - Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72c248b6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train score: 0.9851330341300014\n",
      "Best test score: 0.9055876420950552\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "rf.fit(X = X_train_drop,y = np.log(y_train_drop))\n",
    "\n",
    "print('Best train score: ' + str(rf.score(X = X_train_drop,y = np.log(y_train_drop))))\n",
    "print('Best test score: ' + str(rf.score(X = X_test_drop,y = np.log(y_test_drop))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56a10b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Model</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Drop</td>\n",
       "      <td>0.985133</td>\n",
       "      <td>0.905588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model  Train Score  Test Score\n",
       "0  Random Forest_Drop     0.985133    0.905588"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_drop_train_test_score = pd.DataFrame({'Model': ['Random Forest_Drop'],\n",
    "              'Train Score': [rf.score(X = X_train_drop,y = np.log(y_train_drop))],\n",
    "              'Test Score': [rf.score(X = X_test_drop,y = np.log(y_test_drop))]\n",
    "             })\n",
    "rf_drop_train_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6ed4b63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Feature', ylabel='Importance'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_drop_feature_importances = pd.Series(rf.feature_importances_, index=X_train_drop.columns).sort_values(ascending=False)\n",
    "\n",
    "\n",
    "sns.barplot(data = pd.DataFrame(data = pd.DataFrame(data = rf_drop_feature_importances,  columns = [ 'Importance']).reset_index().rename(columns = {'index': 'Feature'})), y = 'Importance', x = 'Feature')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a59cc1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_intersection(lst1, lst2):\n",
    "    lst3 = [value for value in lst1 if value in lst2]\n",
    "    return lst3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61743531",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Condition2',\n",
       " 'GarageFinish',\n",
       " 'Functional',\n",
       " 'PoolQC',\n",
       " 'ExterQual',\n",
       " 'HeatingQC',\n",
       " 'PavedDrive',\n",
       " 'FireplaceQu',\n",
       " 'Fence',\n",
       " 'SaleCondition',\n",
       " 'LandSlope',\n",
       " 'Condition1',\n",
       " 'SaleType',\n",
       " 'Foundation',\n",
       " 'RoofMatl',\n",
       " 'RoofStyle',\n",
       " 'LotShape',\n",
       " 'MiscFeature',\n",
       " 'BsmtFinType1',\n",
       " 'Utilities',\n",
       " 'Neighborhood',\n",
       " 'LandContour',\n",
       " 'BsmtQual',\n",
       " 'CentralAir',\n",
       " 'GarageQual',\n",
       " 'Exterior2nd',\n",
       " 'Alley',\n",
       " 'BldgType',\n",
       " 'Street',\n",
       " 'Exterior1st',\n",
       " 'GarageType',\n",
       " 'KitchenQual',\n",
       " 'BsmtCond',\n",
       " 'Heating',\n",
       " 'HouseStyle',\n",
       " 'ExterCond',\n",
       " 'MasVnrType',\n",
       " 'MSZoning',\n",
       " 'LotConfig',\n",
       " 'GarageCond',\n",
       " 'Electrical',\n",
       " 'BsmtFinType2',\n",
       " 'BsmtExposure']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_signif_feat_list_rf_drop = []\n",
    "\n",
    "for feat in rf_drop_feature_importances.index:\n",
    "    if \"_\" in feat:\n",
    "        cat_signif_feat_list_rf_drop.append(feat.split('_')[0])\n",
    "\n",
    "\n",
    "cat_features_col = features.select_dtypes(include=['object']).columns\n",
    "\n",
    "cat_signif_feat_list_rf_drop = list(set(list_intersection(cat_signif_feat_list_rf_drop, cat_features_col)))\n",
    "cat_signif_feat_list_rf_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6606dd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual SalePrice')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = rf.predict(X_train_drop), y = np.log(y_train_drop))\n",
    "plt.title('Random Forest (Log Train Data): Actual vs. Predicted')\n",
    "plt.xlabel('Predicted SalePrice')\n",
    "plt.ylabel('Actual SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3859f52e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual SalePrice')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = rf.predict(X_test_drop), y = np.log(y_test_drop))\n",
    "plt.title('Random Forest (Log Test Data): Actual vs. Predicted')\n",
    "plt.xlabel('Predicted SalePrice')\n",
    "plt.ylabel('Actual SalePrice')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c367431e",
   "metadata": {},
   "source": [
    "## Random Forest - Dummification - Cross-Validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3cb90b97",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "rf_drop_cv = cross_val_score(estimator = rf, X = features_dum_drop, y = np.log(response), cv = kf, scoring = 'r2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e9cfc8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation score: 0.8959176264969526\n",
      "Std dev cross-validation score: 0.011052736880113474\n"
     ]
    }
   ],
   "source": [
    "print('Mean cross-validation score: ' + str(rf_drop_cv.mean()))\n",
    "print('Std dev cross-validation score: ' + str(rf_drop_cv.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc9eca0",
   "metadata": {},
   "source": [
    "## Random Forest - Dummification - Hyperparameter Tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "693e4597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 15, 20, 25, 30, 35, 40],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3], &#x27;min_samples_leaf&#x27;: [1],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: [40, 50, 60]},\n",
       "             return_train_score=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 15, 20, 25, 30, 35, 40],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3], &#x27;min_samples_leaf&#x27;: [1],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: [40, 50, 60]},\n",
       "             return_train_score=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={'max_depth': [10, 15, 20, 25, 30, 35, 40],\n",
       "                         'max_features': [1, 2, 3], 'min_samples_leaf': [1],\n",
       "                         'min_samples_split': [2, 3, 4],\n",
       "                         'n_estimators': [40, 50, 60]},\n",
       "             return_train_score=True)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "\n",
    "params = {'n_estimators': [40, 50, 60],\n",
    "         'max_depth': [10, 15, 20, 25, 30, 35, 40],\n",
    "         'max_features': [1,2,3],\n",
    "         'min_samples_leaf': [1],\n",
    "         'min_samples_split': [2,3,4]}\n",
    "\n",
    "gsCV = GridSearchCV(rf, params, cv = kf, return_train_score= True)\n",
    "\n",
    "gsCV.fit(X_train_drop, np.log(y_train_drop))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6d5e046",
   "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>model_number</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>score_type</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.060934</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.707287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.061120</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.709720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.062895</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.707485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.050446</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.699186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.050987</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.704406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_number  score_difference   score_type     score\n",
       "0             1          0.060934  train_score  0.707287\n",
       "1             2          0.061120  train_score  0.709720\n",
       "2             3          0.062895  train_score  0.707485\n",
       "3             4          0.050446  train_score  0.699186\n",
       "4             5          0.050987  train_score  0.704406"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsCV_rf_train_vs_test_df = pd.DataFrame(data = {'train_score': gsCV.cv_results_['mean_train_score'],\\\n",
    "                                               'test_score': gsCV.cv_results_['mean_test_score']}).reset_index().rename(columns = {'index': 'model_number'})\n",
    "gsCV_rf_train_vs_test_df['model_number'] = gsCV_rf_train_vs_test_df['model_number'] + 1\n",
    "gsCV_rf_train_vs_test_df['score_difference'] = gsCV_rf_train_vs_test_df['train_score'] - gsCV_rf_train_vs_test_df['test_score']\n",
    "\n",
    "gsCV_rf_train_vs_test_df = gsCV_rf_train_vs_test_df.melt(id_vars = ['model_number','score_difference'], value_vars = ['train_score', 'test_score'], var_name = 'score_type', value_name = 'score')\n",
    "\n",
    "gsCV_rf_train_vs_test_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94bb05d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='model_number', ylabel='score'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = gsCV_rf_train_vs_test_df, y = 'score', x = 'model_number', hue = 'score_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e39af1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.859800555490023\n",
      "Best parameters: {'max_depth': 40, 'max_features': 3, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 60}\n"
     ]
    }
   ],
   "source": [
    "print('Best score: ' + str(gsCV.best_score_))\n",
    "print('Best parameters: ' + str(gsCV.best_params_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52fc424f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train score: 0.980299105653034\n",
      "Best test score: 0.8686556062203734\n"
     ]
    }
   ],
   "source": [
    "rf.set_params(max_depth = 40, max_features = 3, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 60)\n",
    "rf.fit(X = X_train_drop,y = np.log(y_train_drop))\n",
    "print('Best train score: ' + str(rf.score(X = X_train_drop,y = np.log(y_train_drop))))\n",
    "print('Best test score: ' + str(rf.score(X = X_test_drop,y = np.log(y_test_drop))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f56274a3",
   "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>Model</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Drop_Tuned</td>\n",
       "      <td>0.980299</td>\n",
       "      <td>0.868656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Score  Test Score\n",
       "0  Random Forest_Drop_Tuned     0.980299    0.868656"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_drop_tuned_train_test_score = pd.DataFrame({'Model': ['Random Forest_Drop_Tuned'],\n",
    "              'Train Score': [rf.score(X = X_train_drop,y = np.log(y_train_drop))],\n",
    "              'Test Score': [rf.score(X = X_test_drop,y = np.log(y_test_drop))]\n",
    "             })\n",
    "rf_drop_tuned_train_test_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3684c2",
   "metadata": {},
   "source": [
    "## Random Forest - Dummification - Post Tuning - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c7ef2ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0, max_depth = 40, max_features = 3, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 60)\n",
    "\n",
    "rf_drop_cv_tuned = cross_val_score(estimator = rf, X = features_dum_drop, y = np.log(response), cv = kf, scoring = 'r2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "495b91bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation score: 0.8601999971474535\n",
      "Std dev cross-validation score: 0.005282099112726272\n"
     ]
    }
   ],
   "source": [
    "print('Mean cross-validation score: ' + str(rf_drop_cv_tuned.mean()))\n",
    "print('Std dev cross-validation score: ' + str(rf_drop_cv_tuned.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8308fccc",
   "metadata": {},
   "source": [
    "## Random Forest - Encoded - Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f1cffa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train score: 0.985300238642769\n",
      "Best test score: 0.9063965790558169\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "rf.fit(X = X_train_encoded,y = np.log(y_train_encoded))\n",
    "\n",
    "print('Best train score: ' + str(rf.score(X = X_train_encoded,y = np.log(y_train_encoded))))\n",
    "print('Best test score: ' + str(rf.score(X = X_test_encoded,y = np.log(y_test_encoded))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6541a22b",
   "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>Model</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Encoded</td>\n",
       "      <td>0.9853</td>\n",
       "      <td>0.906397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  Train Score  Test Score\n",
       "0  Random Forest_Encoded       0.9853    0.906397"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_encoded_train_test_score = pd.DataFrame({'Model': ['Random Forest_Encoded'],\n",
    "              'Train Score': [rf.score(X = X_train_encoded,y = np.log(y_train_encoded))],\n",
    "              'Test Score': [rf.score(X = X_test_encoded,y = np.log(y_test_encoded))]\n",
    "             })\n",
    "rf_encoded_train_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a268e447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Feature', ylabel='Importance'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_encoded_feature_importances = pd.Series(rf.feature_importances_, index=X_train_encoded.columns).sort_values(ascending=False)\n",
    "rf_encoded_feature_importances = pd.DataFrame(data = rf_encoded_feature_importances,  columns = [ 'Importance']).reset_index().rename(columns = {'index': 'Feature'})\n",
    "\n",
    "sns.barplot(data = rf_encoded_feature_importances, y = 'Importance', x = 'Feature')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a286309",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'OverallQual'),\n",
       " Text(1, 0, 'HighQualFinSF'),\n",
       " Text(2, 0, 'TotalSF'),\n",
       " Text(3, 0, 'GrLivArea'),\n",
       " Text(4, 0, 'LotArea'),\n",
       " Text(5, 0, 'GarageCars'),\n",
       " Text(6, 0, 'YearsBetweenBuildvsSold'),\n",
       " Text(7, 0, 'YearRemodAdd'),\n",
       " Text(8, 0, 'GarageArea'),\n",
       " Text(9, 0, 'CentralAir'),\n",
       " Text(10, 0, 'OverallCond'),\n",
       " Text(11, 0, 'YearsBetweenRemodvsSold'),\n",
       " Text(12, 0, 'YearBuilt'),\n",
       " Text(13, 0, 'BsmtUnfSF'),\n",
       " Text(14, 0, 'GarageYrBlt'),\n",
       " Text(15, 0, 'BsmtFinSF1'),\n",
       " Text(16, 0, 'BsmtFinSF'),\n",
       " Text(17, 0, '1stFlrSF'),\n",
       " Text(18, 0, 'Neighborhood'),\n",
       " Text(19, 0, 'TotalPorch_DeckSF'),\n",
       " Text(20, 0, 'LotFrontage'),\n",
       " Text(21, 0, 'BsmtQual'),\n",
       " Text(22, 0, 'TotalBsmtSF'),\n",
       " Text(23, 0, 'Fireplaces'),\n",
       " Text(24, 0, '2ndFlrSF'),\n",
       " Text(25, 0, 'BsmtFinType1'),\n",
       " Text(26, 0, 'OpenPorchSF'),\n",
       " Text(27, 0, 'GarageType'),\n",
       " Text(28, 0, 'MoSold'),\n",
       " Text(29, 0, 'MSZoning'),\n",
       " Text(30, 0, 'KitchenQual'),\n",
       " Text(31, 0, 'SaleCondition'),\n",
       " Text(32, 0, 'MasVnrArea'),\n",
       " Text(33, 0, 'TotalFullBath'),\n",
       " Text(34, 0, 'GarageCond'),\n",
       " Text(35, 0, 'WoodDeckSF'),\n",
       " Text(36, 0, 'MSSubClass'),\n",
       " Text(37, 0, 'PavedDrive'),\n",
       " Text(38, 0, 'EnclosedPorch'),\n",
       " Text(39, 0, 'TotRmsAbvGrd'),\n",
       " Text(40, 0, 'Exterior1st'),\n",
       " Text(41, 0, 'ExterQual'),\n",
       " Text(42, 0, 'YearsBetweenBuildvsRemod'),\n",
       " Text(43, 0, 'Exterior2nd'),\n",
       " Text(44, 0, 'YrSold'),\n",
       " Text(45, 0, 'HouseStyle'),\n",
       " Text(46, 0, 'BedroomAbvGr'),\n",
       " Text(47, 0, 'HeatingQC')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot = sns.barplot(data = rf_encoded_feature_importances.loc[rf_encoded_feature_importances['Importance'] > 0.001, :], y = 'Importance', x = 'Feature')\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=90)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6572bd09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'CentralAir'),\n",
       " Text(1, 0, 'Neighborhood'),\n",
       " Text(2, 0, 'BsmtQual'),\n",
       " Text(3, 0, 'BsmtFinType1'),\n",
       " Text(4, 0, 'GarageType'),\n",
       " Text(5, 0, 'MSZoning'),\n",
       " Text(6, 0, 'KitchenQual'),\n",
       " Text(7, 0, 'SaleCondition'),\n",
       " Text(8, 0, 'GarageCond'),\n",
       " Text(9, 0, 'PavedDrive'),\n",
       " Text(10, 0, 'Exterior1st'),\n",
       " Text(11, 0, 'ExterQual'),\n",
       " Text(12, 0, 'Exterior2nd'),\n",
       " Text(13, 0, 'HouseStyle'),\n",
       " Text(14, 0, 'HeatingQC'),\n",
       " Text(15, 0, 'Functional'),\n",
       " Text(16, 0, 'Condition1'),\n",
       " Text(17, 0, 'LotShape'),\n",
       " Text(18, 0, 'FireplaceQu'),\n",
       " Text(19, 0, 'Foundation'),\n",
       " Text(20, 0, 'BsmtExposure'),\n",
       " Text(21, 0, 'GarageFinish'),\n",
       " Text(22, 0, 'ExterCond'),\n",
       " Text(23, 0, 'LandContour'),\n",
       " Text(24, 0, 'Electrical'),\n",
       " Text(25, 0, 'BsmtCond'),\n",
       " Text(26, 0, 'GarageQual'),\n",
       " Text(27, 0, 'LotConfig'),\n",
       " Text(28, 0, 'RoofStyle'),\n",
       " Text(29, 0, 'SaleType'),\n",
       " Text(30, 0, 'Fence'),\n",
       " Text(31, 0, 'LandSlope'),\n",
       " Text(32, 0, 'BldgType'),\n",
       " Text(33, 0, 'MasVnrType'),\n",
       " Text(34, 0, 'Alley'),\n",
       " Text(35, 0, 'BsmtFinType2'),\n",
       " Text(36, 0, 'Heating')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cat_features_col = features.select_dtypes(include=['object']).columns\n",
    "\n",
    "\n",
    "plot = sns.barplot(data = rf_encoded_feature_importances.loc[(rf_encoded_feature_importances['Importance'] > 0.0001) & (rf_encoded_feature_importances['Feature'].isin(list_intersection(list(rf_encoded_feature_importances['Feature']),cat_features_col))), :], y = 'Importance', x = 'Feature')\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "284eef76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual SalePrice')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = rf.predict(X_train_encoded), y = np.log(y_train_encoded))\n",
    "plt.title('Random Forest (Log Train Data): Actual vs. Predicted')\n",
    "plt.xlabel('Predicted SalePrice')\n",
    "plt.ylabel('Actual SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c050e44",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Actual SalePrice')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = rf.predict(X_test_encoded), y = np.log(y_test_encoded))\n",
    "plt.title('Random Forest (Log Test Data): Actual vs. Predicted')\n",
    "plt.xlabel('Predicted SalePrice')\n",
    "plt.ylabel('Actual SalePrice')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f83fc33a",
   "metadata": {},
   "source": [
    "## Random Forest - Encoded - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "faf36da6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "rf_encoded_cv = cross_val_score(estimator = rf, X = features_encoded, y = np.log(response), cv = kf, scoring = 'r2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f439b37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation score: 0.8961400238701227\n",
      "Std dev cross-validation score: 0.010958527189272652\n"
     ]
    }
   ],
   "source": [
    "print('Mean cross-validation score: ' + str(rf_encoded_cv.mean()))\n",
    "print('Std dev cross-validation score: ' + str(rf_encoded_cv.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984f3f72",
   "metadata": {},
   "source": [
    "## Random Forest - Encoded - Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "44ffe92f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 15, 20, 25, 30, 35, 40],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3], &#x27;min_samples_leaf&#x27;: [1],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: [40, 50, 60]},\n",
       "             return_train_score=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [10, 15, 20, 25, 30, 35, 40],\n",
       "                         &#x27;max_features&#x27;: [1, 2, 3], &#x27;min_samples_leaf&#x27;: [1],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: [40, 50, 60]},\n",
       "             return_train_score=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=RandomForestRegressor(random_state=0),\n",
       "             param_grid={'max_depth': [10, 15, 20, 25, 30, 35, 40],\n",
       "                         'max_features': [1, 2, 3], 'min_samples_leaf': [1],\n",
       "                         'min_samples_split': [2, 3, 4],\n",
       "                         'n_estimators': [40, 50, 60]},\n",
       "             return_train_score=True)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0)\n",
    "\n",
    "\n",
    "params = {'n_estimators': [40, 50, 60],\n",
    "         'max_depth': [10, 15, 20, 25, 30, 35, 40],\n",
    "         'max_features': [1,2,3],\n",
    "         'min_samples_leaf': [1],\n",
    "         'min_samples_split': [2,3,4]}\n",
    "\n",
    "gsCV = GridSearchCV(rf, params, cv = kf, return_train_score= True)\n",
    "\n",
    "gsCV.fit(X_train_encoded, np.log(y_train_encoded))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fd253c30",
   "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>model_number</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>score_type</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.082456</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.886290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.082779</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.887299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.083199</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.887891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.078613</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.883400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.078890</td>\n",
       "      <td>train_score</td>\n",
       "      <td>0.883637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model_number  score_difference   score_type     score\n",
       "0             1          0.082456  train_score  0.886290\n",
       "1             2          0.082779  train_score  0.887299\n",
       "2             3          0.083199  train_score  0.887891\n",
       "3             4          0.078613  train_score  0.883400\n",
       "4             5          0.078890  train_score  0.883637"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsCV_rf_train_vs_test_df = pd.DataFrame(data = {'train_score': gsCV.cv_results_['mean_train_score'],\\\n",
    "                                               'test_score': gsCV.cv_results_['mean_test_score']}).reset_index().rename(columns = {'index': 'model_number'})\n",
    "gsCV_rf_train_vs_test_df['model_number'] = gsCV_rf_train_vs_test_df['model_number'] + 1\n",
    "gsCV_rf_train_vs_test_df['score_difference'] = gsCV_rf_train_vs_test_df['train_score'] - gsCV_rf_train_vs_test_df['test_score']\n",
    "\n",
    "gsCV_rf_train_vs_test_df = gsCV_rf_train_vs_test_df.melt(id_vars = ['model_number','score_difference'], value_vars = ['train_score', 'test_score'], var_name = 'score_type', value_name = 'score')\n",
    "\n",
    "gsCV_rf_train_vs_test_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cf897a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='model_number', ylabel='score'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = gsCV_rf_train_vs_test_df, y = 'score', x = 'model_number', hue = 'score_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8790c421",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8871860539095872\n",
      "Best parameters: {'max_depth': 35, 'max_features': 3, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 60}\n"
     ]
    }
   ],
   "source": [
    "print('Best score: ' + str(gsCV.best_score_))\n",
    "print('Best parameters: ' + str(gsCV.best_params_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c2877cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train score: 0.9837859355766423\n",
      "Best test score: 0.8933118038344607\n"
     ]
    }
   ],
   "source": [
    "rf.set_params(max_depth = 35, max_features = 3, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 60)\n",
    "rf.fit(X = X_train_encoded,y = np.log(y_train_encoded))\n",
    "print('Best train score: ' + str(rf.score(X = X_train_encoded,y = np.log(y_train_encoded))))\n",
    "print('Best test score: ' + str(rf.score(X = X_test_encoded,y = np.log(y_test_encoded))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8453399e",
   "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>Model</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Encoded_Tuned</td>\n",
       "      <td>0.983786</td>\n",
       "      <td>0.893312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model  Train Score  Test Score\n",
       "0  Random Forest_Encoded_Tuned     0.983786    0.893312"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_encoded_tuned_train_test_score = pd.DataFrame({'Model': ['Random Forest_Encoded_Tuned'],\n",
    "              'Train Score': [rf.score(X = X_train_encoded,y = np.log(y_train_encoded))],\n",
    "              'Test Score': [rf.score(X = X_test_encoded,y = np.log(y_test_encoded))]\n",
    "             })\n",
    "rf_encoded_tuned_train_test_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58bc6f4",
   "metadata": {},
   "source": [
    "## Random Forest - Encoded - Post Tuning - Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c8b43996",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/Users/bryceferraro/anaconda3/lib/python3.11/site-packages/sklearn/base.py:1151: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "rf = RandomForestRegressor(random_state = 0, max_depth = 35, max_features = 3, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 60)\n",
    "\n",
    "rf_encoded_cv_tuned = cross_val_score(estimator = rf, X = features_encoded, y = np.log(response), cv = kf, scoring = 'r2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3babf4ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation score: 0.8861535295674872\n",
      "Std dev cross-validation score: 0.007670682197154449\n"
     ]
    }
   ],
   "source": [
    "print('Mean cross-validation score: ' + str(rf_encoded_cv_tuned.mean()))\n",
    "print('Std dev cross-validation score: ' + str(rf_encoded_cv_tuned.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115aebbf",
   "metadata": {},
   "source": [
    "# Model Results Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "98403b59",
   "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>Model Number</th>\n",
       "      <th>Random Forest_Drop</th>\n",
       "      <th>Random Forest_Drop_Tuned</th>\n",
       "      <th>Random Forest_Encoded</th>\n",
       "      <th>Random Forest_Encoded_Tuned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.904563</td>\n",
       "      <td>0.867702</td>\n",
       "      <td>0.904214</td>\n",
       "      <td>0.889740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.895470</td>\n",
       "      <td>0.862494</td>\n",
       "      <td>0.894247</td>\n",
       "      <td>0.882853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.910247</td>\n",
       "      <td>0.862364</td>\n",
       "      <td>0.910630</td>\n",
       "      <td>0.899480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.890853</td>\n",
       "      <td>0.853284</td>\n",
       "      <td>0.893156</td>\n",
       "      <td>0.879038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.878456</td>\n",
       "      <td>0.855155</td>\n",
       "      <td>0.878453</td>\n",
       "      <td>0.879657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model Number  Random Forest_Drop  Random Forest_Drop_Tuned  Random Forest_Encoded  Random Forest_Encoded_Tuned\n",
       "0           1.0            0.904563                  0.867702               0.904214                     0.889740\n",
       "1           2.0            0.895470                  0.862494               0.894247                     0.882853\n",
       "2           3.0            0.910247                  0.862364               0.910630                     0.899480\n",
       "3           4.0            0.890853                  0.853284               0.893156                     0.879038\n",
       "4           5.0            0.878456                  0.855155               0.878453                     0.879657"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_results = pd.DataFrame(data = {'Model Number': np.linspace(1,5,5),\n",
    "                                      'Random Forest_Drop': rf_drop_cv,\n",
    "                                      'Random Forest_Drop_Tuned': rf_drop_cv_tuned,\n",
    "                                      'Random Forest_Encoded': rf_encoded_cv,\n",
    "                                      'Random Forest_Encoded_Tuned': rf_encoded_cv_tuned\n",
    "                                      \n",
    "                                     }\n",
    "                             )\n",
    "\n",
    "rf_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9e897834",
   "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>Model</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Drop</td>\n",
       "      <td>0.985133</td>\n",
       "      <td>0.905588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Drop_Tuned</td>\n",
       "      <td>0.980299</td>\n",
       "      <td>0.868656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Encoded</td>\n",
       "      <td>0.985300</td>\n",
       "      <td>0.906397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest_Encoded_Tuned</td>\n",
       "      <td>0.983786</td>\n",
       "      <td>0.893312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model  Train Score  Test Score\n",
       "0           Random Forest_Drop     0.985133    0.905588\n",
       "0     Random Forest_Drop_Tuned     0.980299    0.868656\n",
       "0        Random Forest_Encoded     0.985300    0.906397\n",
       "0  Random Forest_Encoded_Tuned     0.983786    0.893312"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_train_test_score = pd.concat([rf_drop_train_test_score,\n",
    "           rf_drop_tuned_train_test_score,\n",
    "           rf_encoded_train_test_score,\n",
    "           rf_encoded_tuned_train_test_score\n",
    "          ])\n",
    "rf_train_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be2d08c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "current_directory = os.getcwd()\n",
    "\n",
    "dataframes = {'rf_results': rf_results,\n",
    "             'rf_train_test_score': rf_train_test_score\n",
    "             }\n",
    "\n",
    "\n",
    "for name, df in dataframes.items():\n",
    "\n",
    "    file_path = os.path.join(current_directory, f\"{name}.csv\")\n",
    "    \n",
    "    df.to_csv(file_path, sep='\\t', index=False)"
   ]
  }
 ],
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