kaggle_bulldozer_price_prediction.ipynb

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   "source": [
    "# 🚂Predicting the Sale Price of Bulldozers using Machine Learning\n",
    "\n",
    "## The Goal or Problem Description\n",
    "* **\"Predict the auction sale price for a piece of heavy equipment to create a \"blue book\" for bulldozers\".**\n",
    "\n",
    "The goal is to try and obtain **a better RMSLE** compared to the best score obtained at in the **Kaggle Competition**.\n",
    "\n",
    "## Dataset\n",
    "For Dataset Description see **Kaggle's** [Blue Book for Bulldozers](https://www.kaggle.com/c/bluebook-for-bulldozers/data). The data for this competition is split into three parts:\n",
    "\n",
    "* **Train.csv** is the training set, which contains data through the end of 2011.\n",
    "* **Valid.csv** is the validation set, which contains data from January 1, 2012 - April 30, 2012 You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard.\n",
    "* **Test.csv** is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition\n",
    "\n",
    "## Evaluation\n",
    "\"The evaluation metric for this competition is the **RMSLE (root mean squared log error)** between the actual and predicted auction prices.\" See [Evaluation](https://www.kaggle.com/c/bluebook-for-bulldozers/overview) on Kaggle.\n",
    "\n",
    "## Features\n",
    "The key fields in **train.csv** are:\n",
    "\n",
    "* **SalesID**: the uniue identifier of the sale\n",
    "* **MachineID**: the unique identifier of a machine.  A machine can be sold multiple times\n",
    "* **saleprice**: what the machine sold for at auction (only provided in train.csv)\n",
    "* **saledate**: the date of the sale.\n",
    "\n",
    "See also provide a [Data Dictionary](view-source:https://www.kaggle.com/c/bluebook-for-bulldozers/data?select=Data+Dictionary.xlsx) with all columns' descriptions in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9080dbe2-c5e3-4a63-9ecd-dd5b16c5f614",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVR, LinearSVR \n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "from regression_evaluation_metrics import rmsle, regression_scores_from_model\n",
    "from regression_evaluation_metrics import plot_feature_importance\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60ff8f8-94f0-417a-a869-c972403f850d",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "**See Notebook `end_to_end_bulldozer_price_prediction.ipynb` for data exploration**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962a3dc1-4fa2-4154-ad0c-f26e9a2e4fa3",
   "metadata": {},
   "source": [
    "### Converting Categorical columns into Numeric using `pd.Categorical`\n",
    "`pd.Categorical(values).codes` converts missing values or `NaN` into `-1`. Adding 1 to the result will convert missing values to `0`.\n",
    "We are adding columns to **keep information about missing values**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d749721a-9d99-4265-bc71-19ab18e1fced",
   "metadata": {},
   "source": [
    "### Helper function to preprocess datasets"
   ]
  },
  {
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   "execution_count": 66,
   "id": "2be69a95-0798-48a2-9964-3bb10ed7bbad",
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   "source": [
    "def preprocess_data(data_df):\n",
    "    df = data_df.copy()\n",
    " \n",
    "    # Handling numerical columns\n",
    "    for col in list(df.select_dtypes(exclude = ['object'])):\n",
    "        df[col + '_is_missing'] = df[col].isnull().astype(int)\n",
    "        df[col] = df[col].fillna(df[col].median())\n",
    "        \n",
    "    # Handling categorical columns\n",
    "    for col in list(df.select_dtypes(exclude = ['number', 'datetime64'])):\n",
    "        df[col + '_is_missing'] = df[col].isnull().astype(int)\n",
    "        df[col] = pd.Categorical(df[col]).codes + 1\n",
    "\n",
    "    df['saleYear'] = df.saledate.dt.year\n",
    "    df['saleMonth'] = df.saledate.dt.month\n",
    "    df['saleDay'] = df.saledate.dt.day\n",
    "    df['saleDayOfWeek'] = df.saledate.dt.dayofweek\n",
    "    df['saleDayOfYear'] = df.saledate.dt.dayofyear\n",
    "    df.drop('saledate', axis = 1, inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d62fa6-9253-42c6-b2be-975fcc86faf1",
   "metadata": {},
   "source": [
    "## Load Training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36edf317-e649-4090-b8ab-a9b69eb0fd5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('../resources/data/bulldozers/Train.csv', parse_dates = ['saledate'], low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00a89f15-6b03-43e9-a733-233369c4a607",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
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       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
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       "      <th>UsageBand</th>\n",
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       "      <th>Stick_Length</th>\n",
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       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
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       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246      66000     999089     3157         121           3.0      2004   \n",
       "1  1139248      57000     117657       77         121           3.0      1996   \n",
       "2  1139249      10000     434808     7009         121           3.0      2001   \n",
       "3  1139251      38500    1026470      332         121           3.0      2001   \n",
       "4  1139253      11000    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate  ... Undercarriage_Pad_Width  \\\n",
       "0                      68.0       Low 2006-11-16  ...                     NaN   \n",
       "1                    4640.0       Low 2004-03-26  ...                     NaN   \n",
       "2                    2838.0      High 2004-02-26  ...                     NaN   \n",
       "3                    3486.0      High 2011-05-19  ...                     NaN   \n",
       "4                     722.0    Medium 2009-07-23  ...                     NaN   \n",
       "\n",
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       "0          NaN   NaN             NaN          NaN              NaN        NaN   \n",
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       "2          NaN   NaN             NaN          NaN              NaN        NaN   \n",
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       "\n",
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       "3             NaN               NaN               NaN  \n",
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    "train_data.head()"
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   "cell_type": "markdown",
   "id": "4ec0e82f-f9da-44b1-863f-6731dd2d5185",
   "metadata": {},
   "source": [
    "### Preprocess Training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1655f7bf-18f7-4fe4-b997-528eae2552d8",
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    {
     "name": "stdout",
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     "text": [
      "CPU times: total: 1.81 s\n",
      "Wall time: 2.37 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_df = preprocess_data(train_data)"
   ]
  },
  {
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   "execution_count": 26,
   "id": "7d46eb1a-a59d-40cc-ae85-fe9b4fc3a2d8",
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      "(401125, 53) (401125, 110)\n"
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      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
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       "1  1139248      57000     117657       77         121           3.0      1996   \n",
       "2  1139249      10000     434808     7009         121           3.0      2001   \n",
       "3  1139251      38500    1026470      332         121           3.0      2001   \n",
       "4  1139253      11000    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  ...  \\\n",
       "0                      68.0          2          950  ...   \n",
       "1                    4640.0          2         1725  ...   \n",
       "2                    2838.0          1          331  ...   \n",
       "3                    3486.0          1         3674  ...   \n",
       "4                     722.0          3         4208  ...   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                            1                      1   \n",
       "1                            1                      1   \n",
       "2                            1                      1   \n",
       "3                            1                      1   \n",
       "4                            1                      1   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                           1                             0   \n",
       "1                           1                             0   \n",
       "2                           1                             1   \n",
       "3                           1                             1   \n",
       "4                           1                             1   \n",
       "\n",
       "   Steering_Controls_is_missing  saleYear  saleMonth  saleDay  saleDayOfWeek  \\\n",
       "0                             0      2006         11       16              3   \n",
       "1                             0      2004          3       26              4   \n",
       "2                             1      2004          2       26              3   \n",
       "3                             1      2011          5       19              3   \n",
       "4                             1      2009          7       23              3   \n",
       "\n",
       "   saleDayOfYear  \n",
       "0            320  \n",
       "1             86  \n",
       "2             57  \n",
       "3            139  \n",
       "4            204  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_data.shape, train_df.shape)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4d15dda9-0dd4-476d-b950-12babab13caf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 5), sharex = False)\n",
    "\n",
    "sns.histplot(train_data.SalePrice, kde = True, bins = 50, ax = ax[0])\n",
    "ax[0].title.set_text('Historgram (SalePrice) Before Transform')\n",
    "sns.histplot(train_df.SalePrice, kde = True, bins = 50, ax = ax[1])              \n",
    "ax[1].title.set_text('Historgram (SalePrice) After Transform');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05bf3632-9a65-4c47-ae4b-a31f9c5e467a",
   "metadata": {},
   "source": [
    "### Train - Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "68fd2f55-dcc4-4ba9-9120-5d3ebcd3c6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = train_df.drop('SalePrice', axis = 1), train_df.SalePrice\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b853d85-a71e-4714-9ba8-c19c251debad",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning for `RandomForestRegressor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8af18bea-1848-4cbb-8cae-b7592da181e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "CPU times: total: 16min 43s\n",
      "Wall time: 19min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': np.arange(10, 100, 10),\n",
    "    'max_depth': [None, 3, 5, 10],\n",
    "    'min_samples_split': np.arange(2, 20, 2),\n",
    "    'min_samples_leaf': np.arange(1, 20, 2),\n",
    "    'max_features': [0.5, 1, 'sqrt', 'log2'],\n",
    "    'max_samples': [10000]\n",
    "}\n",
    "\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(),\n",
    "                              param_distributions = param_grid,\n",
    "                              cv = 5,\n",
    "                              n_iter = 100,\n",
    "                              verbose = True\n",
    "                             )\n",
    "\n",
    "rs_model.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9a69010-2b28-4dc3-870d-13bf989de8ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 70,\n",
       " 'min_samples_split': 8,\n",
       " 'min_samples_leaf': 3,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 0.5,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4d4817-474a-483b-addb-7721644bd1de",
   "metadata": {},
   "source": [
    "### Evaluate the randomized model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c106b460-5ac5-4f00-a312-bff41bb5b202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5899.674014751508,\n",
       " 'Validation MAE': 6038.724948871902,\n",
       " 'Training RMSLE': 0.2696716009214912,\n",
       " 'Validation RMSLE': 0.27372412471257695,\n",
       " 'Training R^2': 0.8441841365688003,\n",
       " 'Validation R^2': 0.8389509739601098}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = regression_scores_from_model(rs_model, X_train, y_train, X_val, y_val)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef4d99e-4f70-41a1-b63a-2019059ceb20",
   "metadata": {},
   "source": [
    "### Load and preprocess the Validation dataset\n",
    "The **validation set** includes all data in 2012. According to [Dataset Description](https://www.kaggle.com/c/bluebook-for-bulldozers/data?select=Data+Dictionary.xlsx):\n",
    "\n",
    "* **Valid.csv** is the validation set, which contains data from **January 1, 2012 - April 30, 2012** You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "201adf03-0a09-4e52-bab8-05bf0c364e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_data = pd.read_csv('../resources/data/bulldozers/Valid.csv', parse_dates = ['saledate'], low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2ccb755c-4d1a-4537-b370-8193cef93cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11573, 52)\n"
     ]
    },
    {
     "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>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1222837</td>\n",
       "      <td>902859</td>\n",
       "      <td>1376</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>375L</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1222839</td>\n",
       "      <td>1048320</td>\n",
       "      <td>36526</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>4412.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>TX300LC2</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>12' 4\"</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1222841</td>\n",
       "      <td>999308</td>\n",
       "      <td>4587</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>10127.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>270LC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>12' 4\"</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1222843</td>\n",
       "      <td>1062425</td>\n",
       "      <td>1954</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>4682.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>892DLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1222845</td>\n",
       "      <td>1032841</td>\n",
       "      <td>4701</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2002</td>\n",
       "      <td>8150.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>544H</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1222837     902859     1376         121             3      1000   \n",
       "1  1222839    1048320    36526         121             3      2006   \n",
       "2  1222841     999308     4587         121             3      2000   \n",
       "3  1222843    1062425     1954         121             3      1000   \n",
       "4  1222845    1032841     4701         121             3      2002   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                       0.0       NaN 2012-01-05        375L  ...   \n",
       "1                    4412.0    Medium 2012-01-05    TX300LC2  ...   \n",
       "2                   10127.0    Medium 2012-01-05       270LC  ...   \n",
       "3                    4682.0       Low 2012-01-05      892DLC  ...   \n",
       "4                    8150.0    Medium 2012-01-04        544H  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length                Thumb  \\\n",
       "0     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "1     None or Unspecified               12' 4\"  None or Unspecified   \n",
       "2     None or Unspecified               12' 4\"  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  None or Unspecified   \n",
       "4                     NaN                  NaN                  NaN   \n",
       "\n",
       "       Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
       "0  None or Unspecified       Double              NaN        NaN   \n",
       "1                  Yes       Double              NaN        NaN   \n",
       "2  None or Unspecified       Double              NaN        NaN   \n",
       "3  None or Unspecified       Double              NaN        NaN   \n",
       "4                  NaN          NaN              NaN        NaN   \n",
       "\n",
       "  Travel_Controls Differential_Type Steering_Controls  \n",
       "0             NaN               NaN               NaN  \n",
       "1             NaN               NaN               NaN  \n",
       "2             NaN               NaN               NaN  \n",
       "3             NaN               NaN               NaN  \n",
       "4             NaN          Standard      Conventional  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(valid_data.shape)\n",
    "valid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "82cb0f7b-1de1-4580-868a-ba815d368330",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_df = preprocess_data(valid_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "95ad4f73-9710-437d-9e80-001f4440fe0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>fiBaseModel</th>\n",
       "      <th>...</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1222837</td>\n",
       "      <td>902859</td>\n",
       "      <td>1376</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>294</td>\n",
       "      <td>122</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1222839</td>\n",
       "      <td>1048320</td>\n",
       "      <td>36526</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>4412.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1612</td>\n",
       "      <td>728</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1222841</td>\n",
       "      <td>999308</td>\n",
       "      <td>4587</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2000</td>\n",
       "      <td>10127.0</td>\n",
       "      <td>3</td>\n",
       "      <td>161</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1222843</td>\n",
       "      <td>1062425</td>\n",
       "      <td>1954</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>4682.0</td>\n",
       "      <td>2</td>\n",
       "      <td>687</td>\n",
       "      <td>248</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1222845</td>\n",
       "      <td>1032841</td>\n",
       "      <td>4701</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2002</td>\n",
       "      <td>8150.0</td>\n",
       "      <td>3</td>\n",
       "      <td>398</td>\n",
       "      <td>156</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1222837     902859     1376         121             3      1000   \n",
       "1  1222839    1048320    36526         121             3      2006   \n",
       "2  1222841     999308     4587         121             3      2000   \n",
       "3  1222843    1062425     1954         121             3      1000   \n",
       "4  1222845    1032841     4701         121             3      2002   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                       0.0          0          294          122  ...   \n",
       "1                    4412.0          3         1612          728  ...   \n",
       "2                   10127.0          3          161           75  ...   \n",
       "3                    4682.0          2          687          248  ...   \n",
       "4                    8150.0          3          398          156  ...   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                            1                      1   \n",
       "1                            1                      1   \n",
       "2                            1                      1   \n",
       "3                            1                      1   \n",
       "4                            1                      1   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                           1                             1   \n",
       "1                           1                             1   \n",
       "2                           1                             1   \n",
       "3                           1                             1   \n",
       "4                           1                             0   \n",
       "\n",
       "   Steering_Controls_is_missing  saleYear  saleMonth  saleDay  saleDayOfWeek  \\\n",
       "0                             1      2012          1        5              3   \n",
       "1                             1      2012          1        5              3   \n",
       "2                             1      2012          1        5              3   \n",
       "3                             1      2012          1        5              3   \n",
       "4                             0      2012          1        4              2   \n",
       "\n",
       "   saleDayOfYear  \n",
       "0              5  \n",
       "1              5  \n",
       "2              5  \n",
       "3              5  \n",
       "4              4  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b51ee409-4e24-494a-9352-f402df3a2d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((11573, 108), (401125, 110))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df.shape, train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1f3803a2-2f1a-4117-9d39-4f50f3f9a85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SalePrice', 'SalePrice_is_missing'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(list(train_df)) - set(list(valid_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c2d3334f-adb6-49c7-89ad-eec942a777f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = list(valid_df).index('MachineID_is_missing')\n",
    "valid_df.insert(idx, 'SalePrice_is_missing', [0] * len(valid_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2909c66b-957b-4595-a061-053ac161aa74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 109)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ffe97c3-a96d-42b7-b235-d7f2ee86c20f",
   "metadata": {},
   "source": [
    "### Training a model using best hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d6702d2b-6a8f-4744-8518-6f417719c28f",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_reg_model_1 = RandomForestRegressor(n_estimators = 70,\n",
    "                                     min_samples_leaf = 3,\n",
    "                                     min_samples_split = 8,\n",
    "                                     max_features = 0.5,\n",
    "                                     n_jobs = -1\n",
    "                                    )\n",
    "\n",
    "rf_reg_model_1.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecd0ac6-6f93-4053-a0a1-28c330d50806",
   "metadata": {},
   "source": [
    "## Scoring the model\n",
    "### Root Mean Squared Log Error (RMSLE)\n",
    "**RMSLE** is the evaluation metric for this project as stated in the Dataset Description on Kaggle.\n",
    "* **RMSLE** is non-negative floating point value (the best value is 0.0),  \n",
    "* or an array of floating point values, one for each individual target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "46edeb59-05c2-40cd-bccd-6b3bb7d2066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 2755.663988841703,\n",
       " 'Validation MAE': 4420.6705018208995,\n",
       " 'Training RMSLE': 0.13858082178019596,\n",
       " 'Validation RMSLE': 0.21162737517863744,\n",
       " 'Training R^2': 0.9619830137389823,\n",
       " 'Validation R^2': 0.907592410274837}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = regression_scores_from_model(rf_reg_model_1, X_train, y_train, X_val, y_val)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e80221-d5b2-44f0-8267-ed57fc396dc0",
   "metadata": {},
   "source": [
    "### Predictions on the Validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eace2d39-8aa1-45b4-84b0-d7fbb43dbea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_valid = rf_reg_model_1.predict(valid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "622d2e41-a04c-4c8d-a86c-328113fae495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31554.05462593, 72435.53404928, 35397.50828537, ...,\n",
       "       14237.19499746, 15212.77705429, 19388.70017879])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_valid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894bc55b-73e9-4ab7-9f3b-5c65742ac782",
   "metadata": {},
   "source": [
    "### Visualizing the residues\n",
    "The residues of regression model should be **normally distributed**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "626f4083-ebd3-42ef-a91c-bd7d9e1e181f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds_val = rf_reg_model_1.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "dd8bafba-4db4-4882-98a6-43f148a5aeff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residues = y_val - y_preds_val \n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 5))\n",
    "sns.histplot(residues, kde = True, ax = ax[0])\n",
    "ax[0].title.set_text('Historgram Residues')\n",
    "ax[0].set_xticklabels(ax[0].get_xticks(), rotation = 90)\n",
    "sns.scatterplot(x = y_val, y = y_preds_val,  ax = ax[1])\n",
    "ax[1].set_xticklabels(ax[1].get_xticks(), rotation = 90)\n",
    "ax[1].title.set_text('Scatter plot (Truth Labels Vs Preds)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fd3d14-8976-4651-9c53-b0e913463a32",
   "metadata": {},
   "source": [
    "### Testing the model (`rf_reg_model_1`) using the test dataset provided\n",
    "* **Test.csv** is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "205932cf-11be-49c5-8e83-ef500919032a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12457, 52)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>580G</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>936</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                    3688.0       Low 2012-05-03        580G  ...   \n",
       "1                   28555.0      High 2012-05-10         936  ...   \n",
       "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
       "3                    8940.0      High 2012-05-10       330CL  ...   \n",
       "4                    2286.0       Low 2012-05-10        650K  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
       "0                     NaN                  NaN     NaN                  NaN   \n",
       "1                     NaN                  NaN     NaN                  NaN   \n",
       "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
       "4                     NaN                  NaN     NaN                  NaN   \n",
       "\n",
       "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "0          NaN                  NaN        NaN                  NaN   \n",
       "1          NaN                  NaN        NaN                  NaN   \n",
       "2       Double                  NaN        NaN                  NaN   \n",
       "3       Triple                  NaN        NaN                  NaN   \n",
       "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
       "\n",
       "  Differential_Type Steering_Controls  \n",
       "0               NaN               NaN  \n",
       "1          Standard      Conventional  \n",
       "2               NaN               NaN  \n",
       "3               NaN               NaN  \n",
       "4               NaN               NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('../resources/data/bulldozers/Test.csv', parse_dates = ['saledate'], low_memory = False)\n",
    "print(df_test.shape)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3bc40a-d00b-456b-98d9-0df7f24ba174",
   "metadata": {},
   "source": [
    "#### Process Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17680ca4-6122-4e26-b553-f5ecdf3b16b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = preprocess_data(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "34e61f60-662a-419b-b118-cc973f9b10c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12457, 108)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>fiBaseModel</th>\n",
       "      <th>...</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>2</td>\n",
       "      <td>499</td>\n",
       "      <td>180</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>1</td>\n",
       "      <td>831</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1177</td>\n",
       "      <td>404</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>1</td>\n",
       "      <td>287</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                            1                      1   \n",
       "1                            1                      1   \n",
       "2                            1                      1   \n",
       "3                            1                      1   \n",
       "4                            0                      0   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                           1                             1   \n",
       "1                           1                             0   \n",
       "2                           1                             1   \n",
       "3                           1                             1   \n",
       "4                           0                             1   \n",
       "\n",
       "   Steering_Controls_is_missing  saleYear  saleMonth  saleDay  saleDayOfWeek  \\\n",
       "0                             1      2012          5        3              3   \n",
       "1                             0      2012          5       10              3   \n",
       "2                             1      2012          5       10              3   \n",
       "3                             1      2012          5       10              3   \n",
       "4                             1      2012          5       10              3   \n",
       "\n",
       "   saleDayOfYear  \n",
       "0            124  \n",
       "1            131  \n",
       "2            131  \n",
       "3            131  \n",
       "4            131  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_df.shape)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "842dba58-dec5-42ba-992b-e0b64242c2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = test_df.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68eee66b-3d89-497f-bd8c-38d970874013",
   "metadata": {},
   "source": [
    "### Evaluating the model using preprocessed test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1e4a9458-630d-4ecf-a0a1-eee86770bde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SalePrice_is_missing'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(list(X_train)) - set(list(test_df))      # find the missing column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a7ce0b-4989-432a-974e-d3b067443a1b",
   "metadata": {},
   "source": [
    "**The column `MachineID_is_missing` has to be pushed one index position ahead and `SalePrice_is_missing` inserted in its position to match the order in the training data.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3cdb622f-476d-430a-ac67-1819ac14aecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1907a002-b99f-44be-8904-76fd1e0a019c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "616ae2dc-c7de-4c1d-b3f4-ea64c036ce2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12457, 109)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = list(test_df).index('MachineID_is_missing')\n",
    "test_df.insert(idx, 'SalePrice_is_missing', [0]*len(test_df))\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "27e826a0-953c-47e2-878d-bd81568c3fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 188 ms\n",
      "Wall time: 120 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_preds_test = rf_reg_model_1.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cb498b0a-b861-4b96-8a7a-79964a67143b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17600.42662694, 16529.48954815, 51707.11484477, ...,\n",
       "       12451.55124241, 17281.03529824, 26699.45740529])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4b768ba9-30c3-4aa0-90d3-780cb3c21e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12457"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_preds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9c2e5c-ea7b-4d41-a0c8-ac630306fba2",
   "metadata": {},
   "source": [
    "### Formatting the results as indicated by Kaggle\n",
    "https://www.kaggle.com/c/bluebook-for-bulldozers/overview \n",
    "\n",
    "* Have a header: \"SalesID,SalePrice\"\n",
    "* Contain two columns\n",
    "    * **SalesID**: SalesID for the validation set in sorted order\n",
    "    * **SalePrice**: Your predicted price of the sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "252822c5-301b-48d3-8bbd-6ec73cb0445f",
   "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>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>17600.426627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>16529.489548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>51707.114845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>68350.422954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>52660.506920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID     SalePrice\n",
       "0  1227829  17600.426627\n",
       "1  1227844  16529.489548\n",
       "2  1227847  51707.114845\n",
       "3  1227848  68350.422954\n",
       "4  1227863  52660.506920"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame()\n",
    "df_results['SalesID'] = test_df.SalesID\n",
    "df_results['SalePrice'] = y_preds_test\n",
    "df_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f547e0b8-e914-4ad5-a996-5b94d34fec19",
   "metadata": {},
   "source": [
    "### Save results as `.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "93ad1041-5335-4b56-acd6-73f0b8ae870b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.to_csv('./model_preds/bulldozers_price_predictions_2.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf04492-8df9-4fa6-aed8-ba4f766b7be1",
   "metadata": {},
   "source": [
    "### Persisting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1b578351-9dc9-41b8-8ff7-648106ba9737",
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(rf_reg_model_1, './model_preds/rf_reg_bulldozers_price_prediction_model_best.joblib');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c95b52-bedb-4262-a238-d6a6145fc464",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e48144c3-3988-472b-b4d6-768cdc22a671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_feature_importance(rf_reg_model_1, X_train, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e9e936-387f-4c40-87bf-65d1ba7c3354",
   "metadata": {},
   "source": [
    "### Feature Selection using `PCA`\n",
    "The model would certain not live with a whopping **109 features**! To reduce the effect known as the ***curse of dimensionality*** I used **PCA** to generate **25 features** from the **109**. The result is a **drastic reduction in training time to under 3 minutes** (on my laptop) with a comparable RMSLE. There is always a trade-off in efficiency or whatever evaluation metrics because **PCA always involves some loss in information**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3b8c3adb-1fd2-4108-b245-de425f24da8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((320900, 109), (80225, 109))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a3cc70eb-254b-4204-b17b-7cbe4ece5599",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 25, svd_solver = 'full')\n",
    "pca.fit(X_train)\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_val_pca = pca.transform(X_val)\n",
    "X_valid_pca = pca.transform(valid_df)\n",
    "X_test_pca = pca.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b06bc20d-c5ef-41bd-b1a2-84917dedc306",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 14min 56s\n",
      "Wall time: 2min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_reg_pca = RandomForestRegressor(n_estimators = 70,\n",
    "                                     min_samples_leaf = 3,\n",
    "                                     min_samples_split = 8,\n",
    "                                     max_features = 0.5,\n",
    "                                     n_jobs = -1\n",
    "                                    )\n",
    "\n",
    "rf_reg_pca.fit(X_train_pca, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ef7a3921-cc6c-490c-9fe3-91f130cf650b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 3164.2608642181863,\n",
       " 'Validation MAE': 5883.631843060221,\n",
       " 'Training RMSLE': 0.15860726091696453,\n",
       " 'Validation RMSLE': 0.2720977227896572,\n",
       " 'Training R^2': 0.9503672292424992,\n",
       " 'Validation R^2': 0.8393739077684095}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_pca = regression_scores_from_model(rf_reg_pca, X_train_pca, y_train, X_val_pca, y_val)\n",
    "scores_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e528c234-3462-40c5-ad54-d99cddd38a87",
   "metadata": {},
   "source": [
    "### Prediction using the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f70d6e5a-9f0f-44d3-b0d1-db98168c06bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14826.8001443 , 16738.45924215, 33304.33126397, ...,\n",
       "       43211.96758599, 48763.4841829 , 47947.73645048])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds_test_pca = rf_reg_pca.predict(X_test_pca)\n",
    "y_preds_test_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8fcb886-42fe-48a3-922c-f20c2def2052",
   "metadata": {},
   "source": [
    "### Presenting the results as required by Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3a7fafe9-09d4-4e6f-ab83-4790ae680eb4",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>14826.800144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>16738.459242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>33304.331264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>31548.617563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>30622.385898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID     SalePrice\n",
       "0  1227829  14826.800144\n",
       "1  1227844  16738.459242\n",
       "2  1227847  33304.331264\n",
       "3  1227848  31548.617563\n",
       "4  1227863  30622.385898"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame()\n",
    "df_results['SalesID'] = test_df.SalesID\n",
    "df_results['SalePrice'] = y_preds_test_pca\n",
    "df_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "a18b87fd-aeba-451d-9701-dad8a5183c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.to_csv('./model_preds/bulldozers_price_predictions_pca.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e069d755-149e-44a6-90c8-f504a509282c",
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(rf_reg_pca, './model_preds/rf_reg_bulldozers_price_prediction_model_pca.joblib');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11519eea-4dd3-4ae2-beac-af6edbcda292",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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