real-estate-part3.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Price with Neighborhood\n",
    "\n",
    "Goals\n",
    "\n",
    "1> Build a model to predict apartment price based on neighbourhood\n",
    "2> Encode categorical data to train linear model\n",
    "3> Prevent Overfitting using regularization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from glob import glob\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from category_encoders import OneHotEncoder\n",
    "from IPython.display import VimeoVideo\n",
    "from sklearn.linear_model import LinearRegression, Ridge  # noqa F401\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.utils.validation import check_is_fitted\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrangle(filepath):\n",
    "    # Read CSV file\n",
    "    df = pd.read_csv(filepath)\n",
    "\n",
    "    # Subset data: Apartments in \"Capital Federal\", less than 400,000\n",
    "    mask_ba = df[\"place_with_parent_names\"].str.contains(\"Capital Federal\")\n",
    "    mask_apt = df[\"property_type\"] == \"apartment\"\n",
    "    mask_price = df[\"price_aprox_usd\"] < 400_000\n",
    "    df = df[mask_ba & mask_apt & mask_price]\n",
    "\n",
    "    # Subset data: Remove outliers for \"surface_covered_in_m2\"\n",
    "    low, high = df[\"surface_covered_in_m2\"].quantile([0.1, 0.9])\n",
    "    mask_area = df[\"surface_covered_in_m2\"].between(low, high)\n",
    "    df = df[mask_area]\n",
    "\n",
    "    # Split \"lat-lon\" column\n",
    "    df[[\"lat\", \"lon\"]] = df[\"lat-lon\"].str.split(\",\", expand=True).astype(float)\n",
    "    df.drop(columns=\"lat-lon\", inplace=True)\n",
    "\n",
    "    # Extracting neibhourhood from locations\n",
    "\n",
    "    df[\"neighborhood\"] =df[\"place_with_parent_names\"].str.split(\"|\", expand =True)[3]\n",
    "    df.drop(columns=\"place_with_parent_names\", inplace=True)\n",
    "    \n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use glob to import multiple files wothout repeating importing task \n",
    "\n",
    "files = glob(\"C:\\\\Users\\\\ujjwa\\\\Desktop\\\\Hackathons and Challenges\\\\Buenos-aries-real-estate\\\\buenos-airies-real-estate-*.csv\")\n",
    "files \n",
    "\n",
    "\n",
    "# Update wrangle with for loop  \n",
    "\n",
    "frames = [] # for storing all the data frame in a single frame\n",
    "\n",
    "for file in files:\n",
    "    df = wrangle(file)\n",
    "    frames.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>operation</th>\n",
       "      <th>property_type</th>\n",
       "      <th>price</th>\n",
       "      <th>currency</th>\n",
       "      <th>price_aprox_local_currency</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>price_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>properati_url</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1955949.6</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1697.368421</td>\n",
       "      <td>1842.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://chacarita.properati.com.ar/10qlv_venta_...</td>\n",
       "      <td>-34.584651</td>\n",
       "      <td>-58.454693</td>\n",
       "      <td>Chacarita</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>87000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1319128.8</td>\n",
       "      <td>87000.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1812.500000</td>\n",
       "      <td>2071.428571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://villa-luro.properati.com.ar/12m82_venta...</td>\n",
       "      <td>-34.638979</td>\n",
       "      <td>-58.500115</td>\n",
       "      <td>Villa Luro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1789163.2</td>\n",
       "      <td>118000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2185.185185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://caballito.properati.com.ar/11wqh_venta_...</td>\n",
       "      <td>-34.615847</td>\n",
       "      <td>-58.459957</td>\n",
       "      <td>Caballito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>864256.8</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1357.142857</td>\n",
       "      <td>1357.142857</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>364</td>\n",
       "      <td>http://constitucion.properati.com.ar/k2f0_vent...</td>\n",
       "      <td>-34.625222</td>\n",
       "      <td>-58.382382</td>\n",
       "      <td>Constitución</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1364616.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1578.947368</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>450</td>\n",
       "      <td>http://once.properati.com.ar/suwa_venta_depart...</td>\n",
       "      <td>-34.610610</td>\n",
       "      <td>-58.412511</td>\n",
       "      <td>Once</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   operation property_type     price currency  price_aprox_local_currency  \\\n",
       "4       sell     apartment  129000.0      USD                   1955949.6   \n",
       "9       sell     apartment   87000.0      USD                   1319128.8   \n",
       "29      sell     apartment  118000.0      USD                   1789163.2   \n",
       "40      sell     apartment   57000.0      USD                    864256.8   \n",
       "41      sell     apartment   90000.0      USD                   1364616.0   \n",
       "\n",
       "    price_aprox_usd  surface_total_in_m2  surface_covered_in_m2  \\\n",
       "4          129000.0                 76.0                   70.0   \n",
       "9           87000.0                 48.0                   42.0   \n",
       "29         118000.0                  NaN                   54.0   \n",
       "40          57000.0                 42.0                   42.0   \n",
       "41          90000.0                 57.0                   50.0   \n",
       "\n",
       "    price_usd_per_m2  price_per_m2  floor  rooms expenses  \\\n",
       "4        1697.368421   1842.857143    NaN    NaN      NaN   \n",
       "9        1812.500000   2071.428571    NaN    NaN      NaN   \n",
       "29               NaN   2185.185185    NaN    2.0      NaN   \n",
       "40       1357.142857   1357.142857    5.0    2.0      364   \n",
       "41       1578.947368   1800.000000    NaN    3.0      450   \n",
       "\n",
       "                                        properati_url        lat        lon  \\\n",
       "4   http://chacarita.properati.com.ar/10qlv_venta_... -34.584651 -58.454693   \n",
       "9   http://villa-luro.properati.com.ar/12m82_venta... -34.638979 -58.500115   \n",
       "29  http://caballito.properati.com.ar/11wqh_venta_... -34.615847 -58.459957   \n",
       "40  http://constitucion.properati.com.ar/k2f0_vent... -34.625222 -58.382382   \n",
       "41  http://once.properati.com.ar/suwa_venta_depart... -34.610610 -58.412511   \n",
       "\n",
       "    neighborhood  \n",
       "4      Chacarita  \n",
       "9     Villa Luro  \n",
       "29     Caballito  \n",
       "40  Constitución  \n",
       "41          Once  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(frames)\n",
    "type(frames[0])\n",
    "frames[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6582, 17)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate Data frame (different frame into single frame)\n",
    "\n",
    "df =pd.concat(frames, ignore_index=True)\n",
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration Begins Now...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Chacarita\n",
       "1         Villa Luro\n",
       "2          Caballito\n",
       "3       Constitución\n",
       "4               Once\n",
       "            ...     \n",
       "6577         Liniers\n",
       "6578         Palermo\n",
       "6579       Mataderos\n",
       "6580       Mataderos\n",
       "6581       Monserrat\n",
       "Name: 3, Length: 6582, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract neibhourhoods from location \n",
    "\n",
    "df[\"place_with_parent_names\"].str.split(\"|\", expand =True)[3]\n",
    "\n",
    "# As we extract neighbour now we update our wrangle function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check your work\n",
    "assert df.shape == (6582, 17), f\"`df` is the wrong size: {df.shape}.\"\n",
    "assert (\n",
    "    \"place_with_parent_names\" not in df\n",
    "), 'Remember to remove the `\"place_with_parent_names\"` column.'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"price_aprox_usd\"\n",
    "features = [\"neighborhood\"]\n",
    "y_train = df[target]\n",
    "X_train = df[features]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean apt price: 132383.83701458524\n",
      "Baseline MAE: 44860.10834274133\n"
     ]
    }
   ],
   "source": [
    "# Baseline\n",
    "\n",
    "y_mean = y_train.mean()\n",
    "y_pred_baseline = [y_mean] * len(y_train)\n",
    "print(\"Mean apt price:\", y_mean)\n",
    "\n",
    "print(\"Baseline MAE:\", mean_absolute_error(y_train,y_pred_baseline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6582, 57)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform using One Hot Encoder\n",
    "\n",
    "X_train.head() # it is of categorical type which is not possible as regression model works on mathematical process\n",
    "\n",
    "\n",
    "# Instantiate one hot encoder \n",
    "ohe = OneHotEncoder(use_cat_names=True)\n",
    "\n",
    "# Fit model\n",
    "ohe.fit(X_train)\n",
    "\n",
    "# Transform\n",
    "XT_train = ohe.transform(X_train)\n",
    "\n",
    "XT_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)),\n",
       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('onehotencoder',\n",
       "                 OneHotEncoder(cols=['neighborhood'], use_cat_names=True)),\n",
       "                ('linearregression', LinearRegression())])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Pipeline\n",
    "\n",
    "model = make_pipeline(\n",
    "    OneHotEncoder(use_cat_names=True),\n",
    "    LinearRegression()\n",
    ")\n",
    "\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MAE: 39346.8\n"
     ]
    }
   ],
   "source": [
    "# Evaluate\n",
    "\n",
    "y_pred_training = model.predict(X_train)\n",
    "mae_training = mean_absolute_error(y_train,y_pred_training)\n",
    "print(\"Training MAE:\", round(mae_training, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       191784.0\n",
       "1       191784.0\n",
       "2        97784.0\n",
       "3       164448.0\n",
       "4       105864.0\n",
       "          ...   \n",
       "1310    128728.0\n",
       "1311    164912.0\n",
       "1312    160856.0\n",
       "1313    125448.0\n",
       "1314    157240.0\n",
       "Length: 1315, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test for testset\n",
    "\n",
    "X_test = wrangle(\"C:\\\\Users\\\\ujjwa\\\\Desktop\\\\Hackathons and Challenges\\\\Buenos-aries-real-estate\\\\housing-in-buenos-aires_data_buenos-aires-test-features.csv\")[\"neighborhood\"]\n",
    "y_pred_test = pd.Series(model.predict(X_test))\n",
    "y_pred_test\n",
    "\n",
    "\n",
    "# Overfitting ....... (not a valid model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>operation</th>\n",
       "      <th>property_type</th>\n",
       "      <th>price</th>\n",
       "      <th>currency</th>\n",
       "      <th>price_aprox_local_currency</th>\n",
       "      <th>price_aprox_usd</th>\n",
       "      <th>surface_total_in_m2</th>\n",
       "      <th>surface_covered_in_m2</th>\n",
       "      <th>price_usd_per_m2</th>\n",
       "      <th>price_per_m2</th>\n",
       "      <th>floor</th>\n",
       "      <th>rooms</th>\n",
       "      <th>expenses</th>\n",
       "      <th>properati_url</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>3259916.00</td>\n",
       "      <td>215000.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>5375.000000</td>\n",
       "      <td>6142.857143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>http://recoleta.properati.com.ar/12j4v_venta_d...</td>\n",
       "      <td>-34.588993</td>\n",
       "      <td>-58.400133</td>\n",
       "      <td>Recoleta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>341550.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>5178717.72</td>\n",
       "      <td>341550.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3795.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://recoleta.properati.com.ar/100t0_venta_d...</td>\n",
       "      <td>-34.588044</td>\n",
       "      <td>-58.398066</td>\n",
       "      <td>Recoleta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>1386000.0</td>\n",
       "      <td>ARS</td>\n",
       "      <td>1382153.13</td>\n",
       "      <td>91156.62</td>\n",
       "      <td>39.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2337.349231</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://monserrat.properati.com.ar/t05l_venta_d...</td>\n",
       "      <td>-34.623320</td>\n",
       "      <td>-58.397461</td>\n",
       "      <td>Monserrat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1592052.00</td>\n",
       "      <td>105000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3181.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://belgrano.properati.com.ar/zsd5_venta_de...</td>\n",
       "      <td>-34.553897</td>\n",
       "      <td>-58.451939</td>\n",
       "      <td>Belgrano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>89681.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1359779.19</td>\n",
       "      <td>89681.00</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1949.586957</td>\n",
       "      <td>2299.512821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>http://villa-del-parque.properati.com.ar/12q2f...</td>\n",
       "      <td>-34.628813</td>\n",
       "      <td>-58.472230</td>\n",
       "      <td>Villa del Parque</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8553</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1698188.80</td>\n",
       "      <td>112000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2947.368421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://caballito.properati.com.ar/12d47_venta_...</td>\n",
       "      <td>-34.626138</td>\n",
       "      <td>-58.440472</td>\n",
       "      <td>Caballito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8563</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2198548.00</td>\n",
       "      <td>145000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3625.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://palermo.properati.com.ar/10st9_venta_de...</td>\n",
       "      <td>-34.590708</td>\n",
       "      <td>-58.415568</td>\n",
       "      <td>Palermo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8572</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2729232.00</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2769.230769</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>http://nunez.properati.com.ar/zoia_venta_depar...</td>\n",
       "      <td>-34.537851</td>\n",
       "      <td>-58.470731</td>\n",
       "      <td>Nuñez</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8582</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>119500.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>1811906.80</td>\n",
       "      <td>119500.00</td>\n",
       "      <td>44.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2715.909091</td>\n",
       "      <td>2987.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://villa-crespo.properati.com.ar/10auv_ven...</td>\n",
       "      <td>-34.596715</td>\n",
       "      <td>-58.444045</td>\n",
       "      <td>Villa Crespo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8585</th>\n",
       "      <td>sell</td>\n",
       "      <td>apartment</td>\n",
       "      <td>4045624.0</td>\n",
       "      <td>ARS</td>\n",
       "      <td>4034395.31</td>\n",
       "      <td>266078.94</td>\n",
       "      <td>87.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3058.378621</td>\n",
       "      <td>52540.571429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://colegiales.properati.com.ar/10nb4_venta...</td>\n",
       "      <td>-34.574537</td>\n",
       "      <td>-58.455439</td>\n",
       "      <td>Colegiales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1315 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     operation property_type      price currency  price_aprox_local_currency  \\\n",
       "2         sell     apartment   215000.0      USD                  3259916.00   \n",
       "9         sell     apartment   341550.0      USD                  5178717.72   \n",
       "12        sell     apartment  1386000.0      ARS                  1382153.13   \n",
       "13        sell     apartment   105000.0      USD                  1592052.00   \n",
       "17        sell     apartment    89681.0      USD                  1359779.19   \n",
       "...        ...           ...        ...      ...                         ...   \n",
       "8553      sell     apartment   112000.0      USD                  1698188.80   \n",
       "8563      sell     apartment   145000.0      USD                  2198548.00   \n",
       "8572      sell     apartment   180000.0      USD                  2729232.00   \n",
       "8582      sell     apartment   119500.0      USD                  1811906.80   \n",
       "8585      sell     apartment  4045624.0      ARS                  4034395.31   \n",
       "\n",
       "      price_aprox_usd  surface_total_in_m2  surface_covered_in_m2  \\\n",
       "2           215000.00                 40.0                   35.0   \n",
       "9           341550.00                  NaN                   90.0   \n",
       "12           91156.62                 39.0                   33.0   \n",
       "13          105000.00                  NaN                   33.0   \n",
       "17           89681.00                 46.0                   39.0   \n",
       "...               ...                  ...                    ...   \n",
       "8553        112000.00                  NaN                   38.0   \n",
       "8563        145000.00                  NaN                   40.0   \n",
       "8572        180000.00                 65.0                   60.0   \n",
       "8582        119500.00                 44.0                   40.0   \n",
       "8585        266078.94                 87.0                   77.0   \n",
       "\n",
       "      price_usd_per_m2  price_per_m2  floor  rooms  expenses  \\\n",
       "2          5375.000000   6142.857143    NaN    1.0    3500.0   \n",
       "9                  NaN   3795.000000    8.0    2.0       NaN   \n",
       "12         2337.349231  42000.000000    NaN    NaN       NaN   \n",
       "13                 NaN   3181.818182    1.0    1.0       NaN   \n",
       "17         1949.586957   2299.512821    NaN    1.0    1500.0   \n",
       "...                ...           ...    ...    ...       ...   \n",
       "8553               NaN   2947.368421    NaN    2.0       NaN   \n",
       "8563               NaN   3625.000000    1.0    2.0       NaN   \n",
       "8572       2769.230769   3000.000000    NaN    3.0    3500.0   \n",
       "8582       2715.909091   2987.500000    NaN    2.0       NaN   \n",
       "8585       3058.378621  52540.571429    NaN    3.0       NaN   \n",
       "\n",
       "                                          properati_url        lat        lon  \\\n",
       "2     http://recoleta.properati.com.ar/12j4v_venta_d... -34.588993 -58.400133   \n",
       "9     http://recoleta.properati.com.ar/100t0_venta_d... -34.588044 -58.398066   \n",
       "12    http://monserrat.properati.com.ar/t05l_venta_d... -34.623320 -58.397461   \n",
       "13    http://belgrano.properati.com.ar/zsd5_venta_de... -34.553897 -58.451939   \n",
       "17    http://villa-del-parque.properati.com.ar/12q2f... -34.628813 -58.472230   \n",
       "...                                                 ...        ...        ...   \n",
       "8553  http://caballito.properati.com.ar/12d47_venta_... -34.626138 -58.440472   \n",
       "8563  http://palermo.properati.com.ar/10st9_venta_de... -34.590708 -58.415568   \n",
       "8572  http://nunez.properati.com.ar/zoia_venta_depar... -34.537851 -58.470731   \n",
       "8582  http://villa-crespo.properati.com.ar/10auv_ven... -34.596715 -58.444045   \n",
       "8585  http://colegiales.properati.com.ar/10nb4_venta... -34.574537 -58.455439   \n",
       "\n",
       "          neighborhood  \n",
       "2             Recoleta  \n",
       "9             Recoleta  \n",
       "12           Monserrat  \n",
       "13            Belgrano  \n",
       "17    Villa del Parque  \n",
       "...                ...  \n",
       "8553         Caballito  \n",
       "8563           Palermo  \n",
       "8572             Nuñez  \n",
       "8582      Villa Crespo  \n",
       "8585        Colegiales  \n",
       "\n",
       "[1315 rows x 17 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients len: 57\n",
      "[-4.66574255e+16 -4.66574255e+16 -4.66574255e+16 -4.66574255e+16\n",
      " -4.66574255e+16]\n"
     ]
    }
   ],
   "source": [
    "# Communicate Result\n",
    "\n",
    "\n",
    "# Extract intercept and cofficients\n",
    "\n",
    "intercept = model.named_steps[\"linearregression\"].intercept_\n",
    "coefficients = model.named_steps[\"linearregression\"].coef_\n",
    "print(\"coefficients len:\", len(coefficients))\n",
    "print(coefficients[:5])  # First five coefficients\n",
    "\n",
    "\n",
    "# Note that these are a little funky and tellling that it are kind of weird...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features len: 57\n",
      "['neighborhood_Chacarita' 'neighborhood_Villa Luro'\n",
      " 'neighborhood_Caballito' 'neighborhood_Constitución' 'neighborhood_Once']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ujjwa\\AppData\\Roaming\\Python\\Python310\\site-packages\\category_encoders\\utils.py:362: FutureWarning: `get_feature_names` is deprecated in all of sklearn. Use `get_feature_names_out` instead.\n",
      "  warnings.warn(\"`get_feature_names` is deprecated in all of sklearn. Use `get_feature_names_out` instead.\",\n"
     ]
    }
   ],
   "source": [
    "# Extract Feature Names of One Hot Encoder\n",
    "\n",
    "feature_names = model.named_steps[\"onehotencoder\"].get_feature_names()\n",
    "print(\"features len:\", len(feature_names))\n",
    "print(feature_names[:5])  # First five feature names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood_Chacarita      -4.665743e+16\n",
       "neighborhood_Villa Luro     -4.665743e+16\n",
       "neighborhood_Caballito      -4.665743e+16\n",
       "neighborhood_Constitución   -4.665743e+16\n",
       "neighborhood_Once           -4.665743e+16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating  Feature Important Series\n",
    "\n",
    "feat_imp = pd.Series(coefficients, index=feature_names)\n",
    "feat_imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price = 4.665742554148654e+16\n",
      "+ (-4.665742554136984e+16 * neighborhood_Chacarita)\n",
      "+ (-4.665742554136899e+16 * neighborhood_Villa Luro)\n",
      "+ (-4.665742554135781e+16 * neighborhood_Caballito)\n",
      "+ (-4.665742554141131e+16 * neighborhood_Constitución)\n",
      "+ (-4.665742554137032e+16 * neighborhood_Once)\n",
      "+ (-4.665742554136422e+16 * neighborhood_Almagro)\n",
      "+ (-4.6657425541321624e+16 * neighborhood_Palermo)\n",
      "+ (-4.665742554137766e+16 * neighborhood_Flores)\n",
      "+ (-4.665742554132209e+16 * neighborhood_Belgrano)\n",
      "+ (-4.665742554138204e+16 * neighborhood_Liniers)\n",
      "+ (-4.665742554136109e+16 * neighborhood_Villa Crespo)\n",
      "+ (-4.665742554137896e+16 * neighborhood_San Cristobal)\n",
      "+ (-4.6657425541375464e+16 * neighborhood_Congreso)\n",
      "+ (-4.665742554135325e+16 * neighborhood_Saavedra)\n",
      "+ (-4.665742554137993e+16 * neighborhood_Balvanera)\n",
      "+ (-4.665742554139978e+16 * neighborhood_Parque Avellaneda)\n",
      "+ (-4.665742554129475e+16 * neighborhood_Recoleta)\n",
      "+ (-4.6657425541362104e+16 * neighborhood_San Telmo)\n",
      "+ (-4.665742554132568e+16 * neighborhood_Nuñez)\n",
      "+ (-4.665742554131206e+16 * neighborhood_Barrio Norte)\n",
      "+ (-4.665742554137469e+16 * neighborhood_Parque Centenario)\n",
      "+ (-4.665742554136386e+16 * neighborhood_Abasto)\n",
      "+ (-4.6657425541375976e+16 * neighborhood_Centro / Microcentro)\n",
      "+ (-4.665742554138795e+16 * neighborhood_)\n",
      "+ (-4.6657425541375016e+16 * neighborhood_Paternal)\n",
      "+ (-4.66574255413896e+16 * neighborhood_Mataderos)\n",
      "+ (-4.665742554141802e+16 * neighborhood_Villa Lugano)\n",
      "+ (-4.665742554135538e+16 * neighborhood_Coghlan)\n",
      "+ (-4.6657425541293256e+16 * neighborhood_Las Cañitas)\n",
      "+ (-4.665742554135545e+16 * neighborhood_Villa Urquiza)\n",
      "+ (-4.665742554138875e+16 * neighborhood_Monserrat)\n",
      "+ (-4.6657425541376184e+16 * neighborhood_Villa Pueyrredón)\n",
      "+ (-4.6657425541384216e+16 * neighborhood_Parque Patricios)\n",
      "+ (-4.6657425541378904e+16 * neighborhood_San Nicolás)\n",
      "+ (-4.665742554138067e+16 * neighborhood_Villa del Parque)\n",
      "+ (-4.665742554137486e+16 * neighborhood_Boedo)\n",
      "+ (-4.6657425541375944e+16 * neighborhood_Parque Chacabuco)\n",
      "+ (-4.6657425541372776e+16 * neighborhood_Barracas)\n",
      "+ (-4.66574255414029e+16 * neighborhood_Parque Chas)\n",
      "+ (-4.66574255413293e+16 * neighborhood_Colegiales)\n",
      "+ (-4.66574255413599e+16 * neighborhood_Villa General Mitre)\n",
      "+ (-4.6657425541380104e+16 * neighborhood_Villa Ortuzar)\n",
      "+ (-4.665742554136414e+16 * neighborhood_Villa Devoto)\n",
      "+ (-4.665742554138229e+16 * neighborhood_Floresta)\n",
      "+ (-4.665742554134032e+16 * neighborhood_Retiro)\n",
      "+ (-4.665742554137392e+16 * neighborhood_Versalles)\n",
      "+ (-4.66574255413967e+16 * neighborhood_Boca)\n",
      "+ (-4.665742554123713e+16 * neighborhood_Puerto Madero)\n",
      "+ (-4.665742554136888e+16 * neighborhood_Agronomía)\n",
      "+ (-4.665742554137161e+16 * neighborhood_Monte Castro)\n",
      "+ (-4.665742554137629e+16 * neighborhood_Tribunales)\n",
      "+ (-4.665742554138865e+16 * neighborhood_Villa Santa Rita)\n",
      "+ (-4.665742554140206e+16 * neighborhood_Velez Sarsfield)\n",
      "+ (-4.665742554144211e+16 * neighborhood_Villa Soldati)\n",
      "+ (-4.665742554137794e+16 * neighborhood_Villa Real)\n",
      "+ (-4.665742554142073e+16 * neighborhood_Pompeya)\n",
      "+ (-4.665742554141211e+16 * neighborhood_Catalinas)\n"
     ]
    }
   ],
   "source": [
    "#   Equation of Model\n",
    "\n",
    "print(f\"price = {intercept.round(2)}\")\n",
    "for f, c in feat_imp.items():\n",
    "    print(f\"+ ({round(c, 2)} * {f})\")\n",
    "\n",
    "    # Note that it is not Valid "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CURSE OF DIMENSIONALITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)),\n",
       "                (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,\n",
       "                 OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)),\n",
       "                (&#x27;ridge&#x27;, Ridge())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(cols=[&#x27;neighborhood&#x27;], use_cat_names=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('onehotencoder',\n",
       "                 OneHotEncoder(cols=['neighborhood'], use_cat_names=True)),\n",
       "                ('ridge', Ridge())])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear model get panic over multiple dimensions and sometimes get shot out to different pridicte points which are unrealistic to prevent this case , we use ridge we prevent overfitting as by putting penalty on model to be cofficient neare to 0...\n",
    "\n",
    "# Reconstruction pipeline\n",
    "\n",
    "model2 = make_pipeline(\n",
    "    OneHotEncoder(use_cat_names=True),\n",
    "    Ridge()\n",
    ")\n",
    "\n",
    "model2.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MAE: 39350.22\n"
     ]
    }
   ],
   "source": [
    "y_pred_training = model2.predict(X_train)\n",
    "mae_training = mean_absolute_error(y_train,y_pred_training)\n",
    "print(\"Training MAE:\", round(mae_training, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       191265.428077\n",
       "1       191265.428077\n",
       "2        98232.051308\n",
       "3       165478.855330\n",
       "4       105929.146473\n",
       "            ...      \n",
       "1310    127777.538197\n",
       "1311    164459.057906\n",
       "1312    161355.968734\n",
       "1313    124801.695821\n",
       "1314    156960.975224\n",
       "Length: 1315, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test for testset\n",
    "\n",
    "X_test = wrangle(\"C:\\\\Users\\\\ujjwa\\\\Desktop\\\\Hackathons and Challenges\\\\Buenos-aries-real-estate\\\\housing-in-buenos-aires_data_buenos-aires-test-features.csv\")[\"neighborhood\"]\n",
    "y_pred_test = pd.Series(model2.predict(X_test))\n",
    "y_pred_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients len: 57\n",
      "[-2.89895934e+03 -6.29555347e+00  9.25289088e+03 -4.17487330e+04\n",
      " -3.23037446e+03]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Extract intercept and cofficients\n",
    "\n",
    "intercept = model2.named_steps[\"ridge\"].intercept_\n",
    "coefficients = model2.named_steps[\"ridge\"].coef_\n",
    "print(\"coefficients len:\", len(coefficients))\n",
    "print(coefficients[:5])  # First five coefficients\n",
    "\n",
    "\n",
    "# Note that this time is a little acceptable and be a kind of valid coefficients\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighborhood_Chacarita       -2898.959335\n",
       "neighborhood_Villa Luro         -6.295553\n",
       "neighborhood_Caballito        9252.890876\n",
       "neighborhood_Constitución   -41748.733031\n",
       "neighborhood_Once            -3230.374461\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp = pd.Series(coefficients, index=feature_names)\n",
    "feat_imp.head() # now it seeems a valid model prediction "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance for Apartment Prices')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance Bar chart\n",
    "\n",
    "# Sort coeff\n",
    "feat_imp.sort_values(key= abs) # largest coefficient in tails\n",
    "\n",
    "# To get most influential one \n",
    "feat_imp.sort_values(key= abs).tail(15)\n",
    "\n",
    "#plot the most influential using bar \n",
    "feat_imp.sort_values(key= abs).tail(15).plot(kind = \"barh\")\n",
    "plt.xlabel(\"Importance [USD]\")\n",
    "plt.ylabel(\"Features\")\n",
    "plt.title(\"Feature Importance for Apartment Prices\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this bar chart, we can see that the poshest neighborhoods in Buenos Aires like Puerto Madero and Recoleta increase the predicted price of an apartment, while more working-class neighborhoods like Villa Soldati and Villa Lugano decrease the predicted price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}