Lab10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "total_rows = 998403\n",
    "rows_desired = 10000\n",
    "rows_to_skip = total_rows - rows_desired\n",
    "\n",
    "skips = sorted(random.sample(range(total_rows)[1:], rows_to_skip))\n",
    "dta = pd.read_csv('./training.csv', skiprows = skips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>airconditioningtypeid</th>\n",
       "      <th>architecturalstyletypeid</th>\n",
       "      <th>basementsqft</th>\n",
       "      <th>bathroomcnt</th>\n",
       "      <th>bedroomcnt</th>\n",
       "      <th>buildingclasstypeid</th>\n",
       "      <th>buildingqualitytypeid</th>\n",
       "      <th>calculatedbathnbr</th>\n",
       "      <th>decktypeid</th>\n",
       "      <th>finishedfloor1squarefeet</th>\n",
       "      <th>...</th>\n",
       "      <th>fireplaceflag</th>\n",
       "      <th>structuretaxvaluedollarcnt</th>\n",
       "      <th>taxvaluedollarcnt</th>\n",
       "      <th>assessmentyear</th>\n",
       "      <th>landtaxvaluedollarcnt</th>\n",
       "      <th>taxamount</th>\n",
       "      <th>taxdelinquencyflag</th>\n",
       "      <th>taxdelinquencyyear</th>\n",
       "      <th>censustractandblock</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52011.0</td>\n",
       "      <td>1196269.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1144258.0</td>\n",
       "      <td>14592.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11751547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>271309.0</td>\n",
       "      <td>387288.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>115979.0</td>\n",
       "      <td>4920.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11780147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229699.0</td>\n",
       "      <td>536878.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>307179.0</td>\n",
       "      <td>5621.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14737547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>406100.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>406100.0</td>\n",
       "      <td>4847.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167656147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>993.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>221047.0</td>\n",
       "      <td>442094.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>221047.0</td>\n",
       "      <td>4652.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.111001e+13</td>\n",
       "      <td>17093678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   airconditioningtypeid  architecturalstyletypeid  basementsqft  bathroomcnt  \\\n",
       "0                    NaN                       NaN           NaN          0.0   \n",
       "1                    NaN                       NaN           NaN          1.0   \n",
       "2                    NaN                       NaN           NaN          3.0   \n",
       "3                    NaN                       NaN           NaN          0.0   \n",
       "4                    NaN                       NaN           NaN          2.5   \n",
       "\n",
       "   bedroomcnt  buildingclasstypeid  buildingqualitytypeid  calculatedbathnbr  \\\n",
       "0         0.0                  4.0                    NaN                NaN   \n",
       "1         0.0                  NaN                    NaN                1.0   \n",
       "2         3.0                  NaN                    NaN                3.0   \n",
       "3         0.0                  NaN                    NaN                NaN   \n",
       "4         4.0                  NaN                    NaN                2.5   \n",
       "\n",
       "   decktypeid  finishedfloor1squarefeet  ...  fireplaceflag  \\\n",
       "0         NaN                       NaN  ...            NaN   \n",
       "1         NaN                       NaN  ...            NaN   \n",
       "2         NaN                       NaN  ...            NaN   \n",
       "3         NaN                       NaN  ...            NaN   \n",
       "4         NaN                     993.0  ...            NaN   \n",
       "\n",
       "   structuretaxvaluedollarcnt  taxvaluedollarcnt  assessmentyear  \\\n",
       "0                     52011.0          1196269.0          2016.0   \n",
       "1                    271309.0           387288.0          2016.0   \n",
       "2                    229699.0           536878.0          2016.0   \n",
       "3                         NaN           406100.0          2016.0   \n",
       "4                    221047.0           442094.0          2016.0   \n",
       "\n",
       "   landtaxvaluedollarcnt  taxamount  taxdelinquencyflag  taxdelinquencyyear  \\\n",
       "0              1144258.0   14592.40                 NaN                 NaN   \n",
       "1               115979.0    4920.64                 NaN                 NaN   \n",
       "2               307179.0    5621.28                 NaN                 NaN   \n",
       "3               406100.0    4847.65                 NaN                 NaN   \n",
       "4               221047.0    4652.66                 NaN                 NaN   \n",
       "\n",
       "   censustractandblock         Id  \n",
       "0                  NaN   11751547  \n",
       "1                  NaN   11780147  \n",
       "2                  NaN   14737547  \n",
       "3                  NaN  167656147  \n",
       "4         6.111001e+13   17093678  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['bathroomcnt', 'bedroomcnt', 'fips', 'latitude', 'longitude',\n",
      "       'propertylandusetypeid', 'rawcensustractandblock', 'regionidcounty',\n",
      "       'roomcnt', 'Id'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#clean data - drop rows w/o values in taxamount column, also drop NA values\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "\n",
    "dta_noNA = dta\n",
    "dta_noNA = dta_noNA.dropna(axis=0, subset=[\"taxamount\"])\n",
    "\n",
    "dta_noNA = dta_noNA.dropna(axis=1, how=\"any\")\n",
    "\n",
    "x = dta_noNA.drop([\"parcelid\", \"structuretaxvaluedollarcnt\", \"taxvaluedollarcnt\", \"assessmentyear\", \"landtaxvaluedollarcnt\",\n",
    "                  \"taxamount\", \"taxdelinquencyflag\", \"hashottuborspa\", \"propertycountylandusecode\", \n",
    "                   \"propertyzoningdesc\"], axis = 1, errors=\"ignore\")\n",
    "\n",
    "print(dta_noNA.drop([\"parcelid\", \"structuretaxvaluedollarcnt\", \"taxvaluedollarcnt\", \"assessmentyear\", \"landtaxvaluedollarcnt\",\n",
    "                  \"taxamount\", \"taxdelinquencyflag\", \"hashottuborspa\", \"propertycountylandusecode\", \n",
    "                     \"propertyzoningdesc\"], axis = 1, errors=\"ignore\").columns)\n",
    "\n",
    "y = dta_noNA[\"taxamount\"]\n",
    "\n",
    "scalingModel = preprocessing.StandardScaler().fit(x)\n",
    "x_scaled = scalingModel.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regression:\n",
      "-2657.0170885469224\n",
      "Linear Regression:\n",
      "-3068.3442950298045\n",
      "OMP:\n",
      "-3064.7392207849757\n",
      "LASSO:\n",
      "-3065.8151747593806\n",
      "RIDGE:\n",
      "-3066.2106976700347\n",
      "SVR:\n",
      "-3190.590595893886\n",
      "Huber:\n",
      "-2853.101903627652\n",
      "Single Tree:\n",
      "-3308.356084762866\n",
      "Adaboost Regressor:\n",
      "-3201.346801558401\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import OrthogonalMatchingPursuit\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.linear_model import HuberRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import RadiusNeighborsRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "print(\"Random Forest Regression:\")\n",
    "RFR = RandomForestRegressor(n_estimators=10)\n",
    "print(min(cross_val_score(RFR, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"Linear Regression:\")\n",
    "LR = LinearRegression()\n",
    "print(min(cross_val_score(LR, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"OMP:\")\n",
    "OMP = OrthogonalMatchingPursuit()\n",
    "print(min(cross_val_score(OMP, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"LASSO:\")\n",
    "LASSO = linear_model.Lasso(max_iter=1000000)\n",
    "print(min(cross_val_score(LASSO, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"RIDGE:\")\n",
    "ridgeReg = Ridge(max_iter=10000)\n",
    "print(min(cross_val_score(ridgeReg, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"SVR:\")\n",
    "SVR = LinearSVR(tol=1e-5, max_iter=1000000)\n",
    "print(min(cross_val_score(SVR, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"Huber:\")\n",
    "HUBER = HuberRegressor()\n",
    "print(min(cross_val_score(HUBER, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"Single Tree:\")\n",
    "TREE = DecisionTreeRegressor(random_state=1693)\n",
    "print(min(cross_val_score(TREE, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))\n",
    "\n",
    "print(\"Adaboost Regressor:\")\n",
    "AR = AdaBoostRegressor(base_estimator=None, n_estimators=5, learning_rate=1.0, loss='linear', random_state=None)\n",
    "print(min(cross_val_score(AR, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Model Accuracy:\n",
      "-2690.7315391874586\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "ensemble = VotingRegressor(estimators=[('RFR', RFR),\n",
    "                                      ('LR', LR),\n",
    "                                      ('OMP', OMP),\n",
    "                                       ('LASSO', LASSO),\n",
    "                                       ('RIDGE', ridgeReg),\n",
    "                                       ('SVR', SVR),\n",
    "                                       ('HUBER', HUBER),\n",
    "                                       ('TREE', TREE),\n",
    "                                      ('AR', AR),])\n",
    "\n",
    "print(\"Ensemble Model Accuracy:\")\n",
    "print(min(cross_val_score(ensemble, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HistGradient:\n",
      "-2640.4066315685927\n"
     ]
    }
   ],
   "source": [
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "\n",
    "print(\"HistGradient:\")\n",
    "hg = HistGradientBoostingRegressor(loss='least_squares', learning_rate=0.1, max_iter=150, max_leaf_nodes=35, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=255, scoring=None, validation_fraction=0.1, n_iter_no_change=None, tol=1e-07, verbose=0, random_state=None)\n",
    "print(min(cross_val_score(hg, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient:\n",
      "-2791.5434218498344\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_hastie_10_2\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "print(\"Gradient:\")\n",
    "gbc = GradientBoostingRegressor(n_estimators=150, learning_rate=1.0, max_depth=2, random_state=0).fit(x_scaled, y)\n",
    "print(min(cross_val_score(gbc, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Model Accuracy:\n",
      "-2581.621069115841\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "new = VotingRegressor(estimators=[('RFR', RFR),\n",
    "                                      ('hg', hg),\n",
    "                                      ('gbc', gbc)])\n",
    "\n",
    "print(\"Ensemble Model Accuracy:\")\n",
    "print(min(cross_val_score(new, x_scaled, y, cv=5, scoring='neg_mean_absolute_error')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3051: DtypeWarning: Columns (46) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "test_dta = pd.read_csv('./test.csv')\n",
    "test_dta.columns\n",
    "\n",
    "x_test = test_dta[['bathroomcnt', 'bedroomcnt', 'fips', 'latitude', 'longitude', 'propertylandusetypeid', \n",
    "                  'rawcensustractandblock', 'regionidcounty', 'roomcnt', 'Id']]\n",
    "\n",
    "x_test_scaled = scalingModel.transform(x_test)\n",
    "\n",
    "#replace missing data w the mean of that column\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy=\"mean\")\n",
    "x_test_scaled = imputer.fit_transform(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17686.95492858,  9669.08811485,  6327.85851794, ...,\n",
       "        5600.98864594, 23866.4045299 , 10741.65552478])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.fit(x_scaled, y)\n",
    "new.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11404347</td>\n",
       "      <td>17686.954929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11781947</td>\n",
       "      <td>9669.088115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12144747</td>\n",
       "      <td>6327.858518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13117147</td>\n",
       "      <td>3166.767041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13117747</td>\n",
       "      <td>1362.017019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99102</th>\n",
       "      <td>12380956</td>\n",
       "      <td>4051.691416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99103</th>\n",
       "      <td>17192487</td>\n",
       "      <td>4077.514720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99104</th>\n",
       "      <td>13027001</td>\n",
       "      <td>5600.988646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99105</th>\n",
       "      <td>14358678</td>\n",
       "      <td>23866.404530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99106</th>\n",
       "      <td>14635775</td>\n",
       "      <td>10741.655525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99107 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Id     Predicted\n",
       "0      11404347  17686.954929\n",
       "1      11781947   9669.088115\n",
       "2      12144747   6327.858518\n",
       "3      13117147   3166.767041\n",
       "4      13117747   1362.017019\n",
       "...         ...           ...\n",
       "99102  12380956   4051.691416\n",
       "99103  17192487   4077.514720\n",
       "99104  13027001   5600.988646\n",
       "99105  14358678  23866.404530\n",
       "99106  14635775  10741.655525\n",
       "\n",
       "[99107 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_predictions = test_dta[[\"Id\"]]\n",
    "output_predictions[\"Predicted\"] = new.predict(x_test_scaled)\n",
    "output_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_predictions.to_csv(\"video_estimates.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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