library_kha.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>checking_hour</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>room</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>436</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>Monday</td>\n",
       "      <td>436</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>436</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>Monday</td>\n",
       "      <td>436</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>Monday</td>\n",
       "      <td>436</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18412</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>520D</td>\n",
       "      <td>Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18413</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>520D</td>\n",
       "      <td>Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18414</th>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>520D</td>\n",
       "      <td>Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18415</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>520D</td>\n",
       "      <td>Available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18416</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>438</td>\n",
       "      <td>Available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18417 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       checking_hour  hour  minute day_of_week  room       status\n",
       "0                  6    12       0      Monday   436  Unavailable\n",
       "1                  6    12      15      Monday   436  Unavailable\n",
       "2                  6    15       0      Monday   436  Unavailable\n",
       "3                  6    11      30      Monday   436  Unavailable\n",
       "4                  6    12      30      Monday   436  Unavailable\n",
       "...              ...   ...     ...         ...   ...          ...\n",
       "18412             12    21       0      Sunday  520D    Available\n",
       "18413             12    21      15      Sunday  520D    Available\n",
       "18414             12    21      30      Sunday  520D    Available\n",
       "18415             12    19      15      Sunday  520D    Available\n",
       "18416             12    22       0      Sunday   438    Available\n",
       "\n",
       "[18417 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     8673\n",
       "12    6048\n",
       "16    3696\n",
       "Name: checking_hour, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.checking_hour.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "436     877\n",
       "442     877\n",
       "441     877\n",
       "514A    877\n",
       "520C    877\n",
       "520D    877\n",
       "514C    877\n",
       "514D    877\n",
       "520B    877\n",
       "520A    877\n",
       "356     877\n",
       "358     877\n",
       "257     877\n",
       "258     877\n",
       "305     877\n",
       "306     877\n",
       "438     877\n",
       "440     877\n",
       "357     877\n",
       "437     877\n",
       "514B    877\n",
       "Name: room, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.room.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18417 entries, 0 to 18416\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   checking_hour  18417 non-null  int64 \n",
      " 1   hour           18417 non-null  int64 \n",
      " 2   minute         18417 non-null  int64 \n",
      " 3   day_of_week    18417 non-null  object\n",
      " 4   room           18417 non-null  object\n",
      " 5   status         18417 non-null  object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 863.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['status'] = df['status'].apply(lambda x: 0 if x=='Unavailable' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop('status', axis=1)\n",
    "y = df['status'].copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encode = OneHotEncoder(drop = 'first')\n",
    "encode.fit(X_train)\n",
    "X_train = encode.transform(X_train)\n",
    "X_test = encode.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['checking_hour_12', 'checking_hour_16', 'hour_7', 'hour_8',\n",
       "       'hour_9', 'hour_10', 'hour_11', 'hour_12', 'hour_13', 'hour_14',\n",
       "       'hour_15', 'hour_16', 'hour_17', 'hour_18', 'hour_19', 'hour_20',\n",
       "       'hour_21', 'hour_22', 'hour_23', 'minute_15', 'minute_30',\n",
       "       'minute_45', 'day_of_week_Monday', 'day_of_week_Saturday',\n",
       "       'day_of_week_Sunday', 'day_of_week_Thursday',\n",
       "       'day_of_week_Tuesday', 'day_of_week_Wednesday', 'room_258',\n",
       "       'room_305', 'room_306', 'room_356', 'room_357', 'room_358',\n",
       "       'room_436', 'room_437', 'room_438', 'room_440', 'room_441',\n",
       "       'room_442', 'room_514A', 'room_514B', 'room_514C', 'room_514D',\n",
       "       'room_520A', 'room_520B', 'room_520C', 'room_520D'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2585  176]\n",
      " [ 286 1558]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.94      0.92      2761\n",
      "           1       0.90      0.84      0.87      1844\n",
      "\n",
      "    accuracy                           0.90      4605\n",
      "   macro avg       0.90      0.89      0.89      4605\n",
      "weighted avg       0.90      0.90      0.90      4605\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "rdc = RandomForestClassifier()\n",
    "rdc.fit(X_train, y_train)\n",
    "predictions = rdc.predict(X_test)\n",
    "print(confusion_matrix(y_test, predictions))\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = pd.DataFrame({\n",
    "    'features': encode.get_feature_names_out(),\n",
    "    'values': rdc.feature_importances_ }).sort_values(by='values', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance_15 = feature_importance.head(15).sort_values(by='values', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='features'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance_15.plot(kind='barh', x='features', y='values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Dirty Models and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop('status', axis=1)\n",
    "y = df['status']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = X_train.drop(['day_of_week', 'room'], axis=1)\n",
    "cat = X_train[['day_of_week', 'room']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models Dictionary \n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "import xgboost as xgb\n",
    "\n",
    "model_dict = {\n",
    "    'Logreg': LogisticRegression(),\n",
    "    'sgd': SGDClassifier(),\n",
    "    'naive': GaussianNB(),\n",
    "    'knn': KNeighborsClassifier(),\n",
    "    'tree': DecisionTreeClassifier(),\n",
    "    'rfc': RandomForestClassifier(),\n",
    "    'adaboost': AdaBoostClassifier(),\n",
    "    'svm': SVC(),\n",
    "    'gbc': GradientBoostingClassifier(),\n",
    "    'quadra': QuadraticDiscriminantAnalysis(),\n",
    "    'xgb': xgb.XGBClassifier(objective=\"binary:logistic\")\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_score(model_dict, X, y, scoring=None, save_model = False):\n",
    "    \"\"\"Train model in a model dictionary, calculate the score and return a datafame.\n",
    "    Parameters\n",
    "    --------\n",
    "    model_dict: dict\n",
    "        A dictionary with the key containing the name of the model and the values \n",
    "        containining the model\n",
    "    X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
    "        The training input samples. Internally, its dtype will be converted to `dtype=np.float32`. \n",
    "        If a sparse matrix is provided, it will be converted into a sparse `csc_matrix`.\n",
    "    y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
    "        The target values (class labels in classification, real numbers in regression).\n",
    "    scoring: default = `neg_mean_squared_error`\n",
    "        The scoring method\n",
    "    save_model: default = False\n",
    "        If True, the model is saved to a pickle file\n",
    "    Return \n",
    "    --------\n",
    "    score_df: `pd.DataFrame`\n",
    "        This dataframe containing columns of model, mean score, and standard deviation    \n",
    "    \"\"\"\n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    score_df = pd.DataFrame(columns=['Model', 'Score', 'Std'])\n",
    "    for name, model in model_dict.items():\n",
    "        # Train the model\n",
    "        model.fit(X, y)\n",
    "        # Calculate the score\n",
    "        if scoring == None:\n",
    "            scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error') \n",
    "            scores = np.sqrt(-scores)\n",
    "        else:\n",
    "            scores = cross_val_score(model, X, y, scoring=scoring)       \n",
    "        \n",
    "        # Add the score to the score_df\n",
    "        adding_df = pd.DataFrame({'Model': [name], 'Score': [scores.mean()], 'Std':[scores.std()]})\n",
    "        score_df = pd.concat([score_df, adding_df])  \n",
    "        # Print information      \n",
    "        print('{}:'.format(name))\n",
    "        print(\"Scores:\", scores)\n",
    "        print(\"Mean:\", scores.mean())\n",
    "        print(\"Standard deviation:\", scores.std())\n",
    "        print('---------------------')\n",
    "        # Save the results\n",
    "        if save_model == True:\n",
    "            import pickle\n",
    "            with open(f'{name}_baseline.pkl','wb') as f:\n",
    "                pickle.dump(model,f)\n",
    "        \n",
    "    # Sort the dataset and reset the index\n",
    "    score_df = score_df.sort_values(by='Score').reset_index(drop=True)\n",
    "    # Return the result\n",
    "    return score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With Polynomial Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler, OneHotEncoder\n",
    "num_pipeline = Pipeline([\n",
    "    ('poly', PolynomialFeatures()),\n",
    "    ('scale', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('encode', OneHotEncoder())\n",
    "])\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs = list(num)\n",
    "cat_attribs = ['day_of_week', 'room']\n",
    "full_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_attribs),\n",
    "    ('cat', cat_pipeline, cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prepared = full_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logreg:\n",
      "Scores: [0.85378212 0.84147666 0.84902245 0.8410572  0.83852281]\n",
      "Mean: 0.8447722477352142\n",
      "Standard deviation: 0.005708939638116235\n",
      "---------------------\n",
      "sgd:\n",
      "Scores: [0.84328628 0.83460007 0.82766112 0.83671253 0.82476466]\n",
      "Mean: 0.8334049321972911\n",
      "Standard deviation: 0.006597807513904963\n",
      "---------------------\n",
      "naive:\n",
      "Scores: [0.7567861  0.76112921 0.76466329 0.76828385 0.75887038]\n",
      "Mean: 0.761946566595985\n",
      "Standard deviation: 0.00410616933827429\n",
      "---------------------\n",
      "knn:\n",
      "Scores: [0.83279045 0.83821933 0.83454019 0.83562636 0.82983345]\n",
      "Mean: 0.8342019543973942\n",
      "Standard deviation: 0.0028056643415382073\n",
      "---------------------\n",
      "tree:\n",
      "Scores: [0.91060442 0.91349982 0.90658943 0.9160029  0.90984794]\n",
      "Mean: 0.9113088990416708\n",
      "Standard deviation: 0.0032167641742125276\n",
      "---------------------\n",
      "rfc:\n",
      "Scores: [0.90626131 0.90662324 0.90007241 0.89790007 0.90007241]\n",
      "Mean: 0.9021858881574378\n",
      "Standard deviation: 0.0035665362054239655\n",
      "---------------------\n",
      "adaboost:\n",
      "Scores: [0.85559175 0.84762939 0.84286749 0.84069515 0.84395366]\n",
      "Mean: 0.8461474857975058\n",
      "Standard deviation: 0.00522856355005575\n",
      "---------------------\n",
      "svm:\n",
      "Scores: [0.87187839 0.86970684 0.86278059 0.86060825 0.86459088]\n",
      "Mean: 0.865912991655797\n",
      "Standard deviation: 0.004235078276671654\n",
      "---------------------\n",
      "gbc:\n",
      "Scores: [0.87043069 0.86319218 0.86133237 0.85517741 0.85372918]\n",
      "Mean: 0.8607723661930711\n",
      "Standard deviation: 0.006006036358488611\n",
      "---------------------\n",
      "quadra:\n",
      "Scores: [0.82410423 0.82084691 0.82404055 0.8352643  0.83091962]\n",
      "Mean: 0.8270351230166499\n",
      "Standard deviation: 0.005266124310962498\n",
      "---------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb:\n",
      "Scores: [0.91494752 0.91965255 0.91527878 0.9275887  0.91636495]\n",
      "Mean: 0.9187665025291538\n",
      "Standard deviation: 0.004714500286679822\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "score_df_poly = train_and_score(model_dict, X_train_prepared, y_train, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Score</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>naive</td>\n",
       "      <td>0.761947</td>\n",
       "      <td>0.004106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>quadra</td>\n",
       "      <td>0.827035</td>\n",
       "      <td>0.005266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sgd</td>\n",
       "      <td>0.833405</td>\n",
       "      <td>0.006598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>knn</td>\n",
       "      <td>0.834202</td>\n",
       "      <td>0.002806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logreg</td>\n",
       "      <td>0.844772</td>\n",
       "      <td>0.005709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>adaboost</td>\n",
       "      <td>0.846147</td>\n",
       "      <td>0.005229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gbc</td>\n",
       "      <td>0.860772</td>\n",
       "      <td>0.006006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.865913</td>\n",
       "      <td>0.004235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rfc</td>\n",
       "      <td>0.902186</td>\n",
       "      <td>0.003567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tree</td>\n",
       "      <td>0.911309</td>\n",
       "      <td>0.003217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.918767</td>\n",
       "      <td>0.004715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model     Score       Std\n",
       "0      naive  0.761947  0.004106\n",
       "1     quadra  0.827035  0.005266\n",
       "2        sgd  0.833405  0.006598\n",
       "3        knn  0.834202  0.002806\n",
       "4     Logreg  0.844772  0.005709\n",
       "5   adaboost  0.846147  0.005229\n",
       "6        gbc  0.860772  0.006006\n",
       "7        svm  0.865913  0.004235\n",
       "8        rfc  0.902186  0.003567\n",
       "9       tree  0.911309  0.003217\n",
       "10       xgb  0.918767  0.004715"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Model'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_df_poly.plot(kind='barh', x='Model', y='Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without polynomial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    # ('poly', PolynomialFeatures()),\n",
    "    ('scale', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('encode', OneHotEncoder())\n",
    "])\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs = list(num)\n",
    "cat_attribs = ['day_of_week', 'room']\n",
    "full_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_attribs),\n",
    "    ('cat', cat_pipeline, cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prepared = full_pipeline.fit_transform(X_train).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logreg:\n",
      "Scores: [0.71335505 0.70865002 0.70745836 0.70999276 0.70745836]\n",
      "Mean: 0.7093829105672008\n",
      "Standard deviation: 0.0021952080418471743\n",
      "---------------------\n",
      "sgd:\n",
      "Scores: [0.71950778 0.71045965 0.70637219 0.71578566 0.70456191]\n",
      "Mean: 0.7113374389987899\n",
      "Standard deviation: 0.00561913049433872\n",
      "---------------------\n",
      "naive:\n",
      "Scores: [0.72891784 0.74665219 0.73678494 0.74692252 0.73062998]\n",
      "Mean: 0.7379814938426812\n",
      "Standard deviation: 0.007651786808984747\n",
      "---------------------\n",
      "knn:\n",
      "Scores: [0.86608759 0.86681144 0.85590152 0.86133237 0.86350471]\n",
      "Mean: 0.8627275236044316\n",
      "Standard deviation: 0.0039271657991489795\n",
      "---------------------\n",
      "tree:\n",
      "Scores: [0.91965255 0.92073833 0.91708907 0.92831282 0.9192614 ]\n",
      "Mean: 0.9210108333903346\n",
      "Standard deviation: 0.00383872143289222\n",
      "---------------------\n",
      "rfc:\n",
      "Scores: [0.92435758 0.92833876 0.92795076 0.92867487 0.93193338]\n",
      "Mean: 0.9282510719518788\n",
      "Standard deviation: 0.002408159047838222\n",
      "---------------------\n",
      "adaboost:\n",
      "Scores: [0.8577633  0.84183858 0.84540188 0.83635047 0.84214337]\n",
      "Mean: 0.8446995219491663\n",
      "Standard deviation: 0.007149561619792098\n",
      "---------------------\n",
      "svm:\n",
      "Scores: [0.88961274 0.89395585 0.8884866  0.88559015 0.88088342]\n",
      "Mean: 0.8877057517317255\n",
      "Standard deviation: 0.004343500952839376\n",
      "---------------------\n",
      "gbc:\n",
      "Scores: [0.87260224 0.86463988 0.85988414 0.85445329 0.85626358]\n",
      "Mean: 0.8615686283759507\n",
      "Standard deviation: 0.0065235315768861915\n",
      "---------------------\n",
      "quadra:\n",
      "Scores: [0.72566051 0.69779225 0.75162925 0.72194062 0.71832006]\n",
      "Mean: 0.7230685407118951\n",
      "Standard deviation: 0.017234521144967214\n",
      "---------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb:\n",
      "Scores: [0.91929063 0.92761491 0.91708907 0.92686459 0.91310644]\n",
      "Mean: 0.9207931277670196\n",
      "Standard deviation: 0.005629601740681039\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "score_df = train_and_score(model_dict, X_train_prepared, y_train, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Score</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logreg</td>\n",
       "      <td>0.709383</td>\n",
       "      <td>0.002195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sgd</td>\n",
       "      <td>0.711337</td>\n",
       "      <td>0.005619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>quadra</td>\n",
       "      <td>0.723069</td>\n",
       "      <td>0.017235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>naive</td>\n",
       "      <td>0.737981</td>\n",
       "      <td>0.007652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adaboost</td>\n",
       "      <td>0.844700</td>\n",
       "      <td>0.007150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gbc</td>\n",
       "      <td>0.861569</td>\n",
       "      <td>0.006524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>knn</td>\n",
       "      <td>0.862728</td>\n",
       "      <td>0.003927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.887706</td>\n",
       "      <td>0.004344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.920793</td>\n",
       "      <td>0.005630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tree</td>\n",
       "      <td>0.921011</td>\n",
       "      <td>0.003839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rfc</td>\n",
       "      <td>0.928251</td>\n",
       "      <td>0.002408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model     Score       Std\n",
       "0     Logreg  0.709383  0.002195\n",
       "1        sgd  0.711337  0.005619\n",
       "2     quadra  0.723069  0.017235\n",
       "3      naive  0.737981  0.007652\n",
       "4   adaboost  0.844700  0.007150\n",
       "5        gbc  0.861569  0.006524\n",
       "6        knn  0.862728  0.003927\n",
       "7        svm  0.887706  0.004344\n",
       "8        xgb  0.920793  0.005630\n",
       "9       tree  0.921011  0.003839\n",
       "10       rfc  0.928251  0.002408"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Model'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_df.plot(kind='barh', x='Model', y='Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3 most promising models are rfc, xgb, and tree. Polynomial features do not work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With Floors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "class AddFloors(BaseEstimator, TransformerMixin):\n",
    "    # def __init__(self, add_floors = True):\n",
    "    #     self.add_floors = add_floors\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        X['room'] = X['room'].astype(str)\n",
    "        X['floor'] = X['room'].apply(lambda x: list(x)[0])\n",
    "        return X.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    # ('poly', PolynomialFeatures()),\n",
    "    ('scale', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('floors', AddFloors()),\n",
    "    ('encode', OneHotEncoder(drop='first'))\n",
    "])\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs = list(num)\n",
    "cat_attribs = ['day_of_week', 'room']\n",
    "full_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_attribs),\n",
    "    ('cat', cat_pipeline, cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prepared = full_pipeline.fit_transform(X_train).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logreg:\n",
      "Scores: [0.71299312 0.70285921 0.71542361 0.70926865 0.71433744]\n",
      "Mean: 0.7109764046101071\n",
      "Standard deviation: 0.004559529884373029\n",
      "---------------------\n",
      "sgd:\n",
      "Scores: [0.70466884 0.70647847 0.71469949 0.70347574 0.71542361]\n",
      "Mean: 0.7089492290149417\n",
      "Standard deviation: 0.005086621107735262\n",
      "---------------------\n",
      "naive:\n",
      "Scores: [0.71371697 0.71625045 0.72990587 0.71578566 0.71578566]\n",
      "Mean: 0.7182889234303614\n",
      "Standard deviation: 0.005874419998973859\n",
      "---------------------\n",
      "knn:\n",
      "Scores: [0.86391603 0.86210641 0.86097031 0.87219406 0.86314265]\n",
      "Mean: 0.8644658926546432\n",
      "Standard deviation: 0.0039890897921284665\n",
      "---------------------\n",
      "tree:\n",
      "Scores: [0.91205212 0.92073833 0.9134685  0.90224475 0.90731354]\n",
      "Mean: 0.9111634474695751\n",
      "Standard deviation: 0.0061991335417887275\n",
      "---------------------\n",
      "rfc:\n",
      "Scores: [0.92363373 0.92508143 0.9217958  0.9192614  0.91564084]\n",
      "Mean: 0.921082641914216\n",
      "Standard deviation: 0.0033447600070683273\n",
      "---------------------\n",
      "adaboost:\n",
      "Scores: [0.83460007 0.84111473 0.84974656 0.81969587 0.83743664]\n",
      "Mean: 0.8365187751772085\n",
      "Standard deviation: 0.009834948466772901\n",
      "---------------------\n",
      "svm:\n",
      "Scores: [0.87151647 0.87441187 0.88595221 0.87038378 0.87038378]\n",
      "Mean: 0.8745296214092135\n",
      "Standard deviation: 0.0058982109484208775\n",
      "---------------------\n",
      "gbc:\n",
      "Scores: [0.84762939 0.85088672 0.87110789 0.84866039 0.85083273]\n",
      "Mean: 0.8538234238880751\n",
      "Standard deviation: 0.008733204992424479\n",
      "---------------------\n",
      "quadra:\n",
      "Scores: [0.73470865 0.72529859 0.72990587 0.72737147 0.67161477]\n",
      "Mean: 0.7177798691355172\n",
      "Standard deviation: 0.023295362434553907\n",
      "---------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "c:\\Users\\Kha Do\\anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:887: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb:\n",
      "Scores: [0.91929063 0.92254796 0.91708907 0.90803765 0.91672701]\n",
      "Mean: 0.9167384620868029\n",
      "Standard deviation: 0.00481852401416152\n",
      "---------------------\n"
     ]
    }
   ],
   "source": [
    "score_df_floor = train_and_score(model_dict, X_train_prepared, y_train, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Score</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sgd</td>\n",
       "      <td>0.708949</td>\n",
       "      <td>0.005087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logreg</td>\n",
       "      <td>0.710976</td>\n",
       "      <td>0.004560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>quadra</td>\n",
       "      <td>0.717780</td>\n",
       "      <td>0.023295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>naive</td>\n",
       "      <td>0.718289</td>\n",
       "      <td>0.005874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adaboost</td>\n",
       "      <td>0.836519</td>\n",
       "      <td>0.009835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gbc</td>\n",
       "      <td>0.853823</td>\n",
       "      <td>0.008733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>knn</td>\n",
       "      <td>0.864466</td>\n",
       "      <td>0.003989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.874530</td>\n",
       "      <td>0.005898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tree</td>\n",
       "      <td>0.911163</td>\n",
       "      <td>0.006199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>xgb</td>\n",
       "      <td>0.916738</td>\n",
       "      <td>0.004819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rfc</td>\n",
       "      <td>0.921083</td>\n",
       "      <td>0.003345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model     Score       Std\n",
       "0        sgd  0.708949  0.005087\n",
       "1     Logreg  0.710976  0.004560\n",
       "2     quadra  0.717780  0.023295\n",
       "3      naive  0.718289  0.005874\n",
       "4   adaboost  0.836519  0.009835\n",
       "5        gbc  0.853823  0.008733\n",
       "6        knn  0.864466  0.003989\n",
       "7        svm  0.874530  0.005898\n",
       "8       tree  0.911163  0.006199\n",
       "9        xgb  0.916738  0.004819\n",
       "10       rfc  0.921083  0.003345"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Model'>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_df_floor.plot(kind='barh', x='Model', y='Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original dataset performs the best. The three most promising models are Random Forest, Xgboost and Decision Tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune Random Forest Classifier using the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('scale', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('encode', OneHotEncoder())\n",
    "])\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "num_attribs = list(num)\n",
    "cat_attribs = ['day_of_week', 'room']\n",
    "full_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_attribs),\n",
    "    ('cat', cat_pipeline, cat_attribs)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prepared = full_pipeline.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{&#x27;bootstrap&#x27;: [True, False],\n",
       "                          &#x27;max_depth&#x27;: [10, 30, 50, 100],\n",
       "                          &#x27;max_features&#x27;: [&#x27;log2&#x27;, &#x27;sqrt&#x27;],\n",
       "                          &#x27;n_estimators&#x27;: [10, 50, 100]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{&#x27;bootstrap&#x27;: [True, False],\n",
       "                          &#x27;max_depth&#x27;: [10, 30, 50, 100],\n",
       "                          &#x27;max_features&#x27;: [&#x27;log2&#x27;, &#x27;sqrt&#x27;],\n",
       "                          &#x27;n_estimators&#x27;: [10, 50, 100]}],\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{'bootstrap': [True, False],\n",
       "                          'max_depth': [10, 30, 50, 100],\n",
       "                          'max_features': ['log2', 'sqrt'],\n",
       "                          'n_estimators': [10, 50, 100]}],\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will tune the three models\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Random Forest Classifier\n",
    "rfc = RandomForestClassifier()\n",
    "param_grid = [{\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [10, 30, 50, 100],\n",
    "    'max_features': ['log2', 'sqrt'],\n",
    "    'n_estimators': [10, 50, 100]\n",
    "}]\n",
    "grid_search = GridSearchCV(rfc, param_grid, cv = 5, scoring='accuracy', return_train_score=True)\n",
    "grid_search.fit(X_train_prepared, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 30,\n",
       " 'max_features': 'sqrt',\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prepared = full_pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2640   74]\n",
      " [ 196 1695]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95      2714\n",
      "           1       0.96      0.90      0.93      1891\n",
      "\n",
      "    accuracy                           0.94      4605\n",
      "   macro avg       0.94      0.93      0.94      4605\n",
      "weighted avg       0.94      0.94      0.94      4605\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "predictions = grid_search.predict(X_test_prepared)\n",
    "print(confusion_matrix(y_test, predictions))\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9413680781758957\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  }
 ],
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