M1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2/2 [==============================] - 2s 317ms/step - loss: 1096957.8750 - mae: 943.5599 - val_loss: 1617116.0000 - val_mae: 1004.4245\n",
      "Epoch 2/10\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 1096788.8750 - mae: 943.4708 - val_loss: 1616940.6250 - val_mae: 1004.3469\n",
      "Epoch 3/10\n",
      "2/2 [==============================] - 0s 49ms/step - loss: 1096635.2500 - mae: 943.3914 - val_loss: 1616789.3750 - val_mae: 1004.2785\n",
      "Epoch 4/10\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 1096507.0000 - mae: 943.3237 - val_loss: 1616661.5000 - val_mae: 1004.2170\n",
      "Epoch 5/10\n",
      "2/2 [==============================] - 0s 53ms/step - loss: 1096378.1250 - mae: 943.2581 - val_loss: 1616538.5000 - val_mae: 1004.1578\n",
      "Epoch 6/10\n",
      "2/2 [==============================] - 0s 60ms/step - loss: 1096255.5000 - mae: 943.1944 - val_loss: 1616420.5000 - val_mae: 1004.1016\n",
      "Epoch 7/10\n",
      "2/2 [==============================] - 0s 50ms/step - loss: 1096151.2500 - mae: 943.1404 - val_loss: 1616310.5000 - val_mae: 1004.0482\n",
      "Epoch 8/10\n",
      "2/2 [==============================] - 0s 51ms/step - loss: 1096046.8750 - mae: 943.0869 - val_loss: 1616209.6250 - val_mae: 1003.9983\n",
      "Epoch 9/10\n",
      "2/2 [==============================] - 0s 54ms/step - loss: 1095939.2500 - mae: 943.0325 - val_loss: 1616112.0000 - val_mae: 1003.9512\n",
      "Epoch 10/10\n",
      "2/2 [==============================] - 0s 56ms/step - loss: 1095837.7500 - mae: 942.9828 - val_loss: 1616016.1250 - val_mae: 1003.9055\n",
      "1/1 [==============================] - 0s 170ms/step\n",
      "MSE: 1688457.04\n",
      "R2: -2.25\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# Load the scraped data\n",
    "df = pd.read_csv(\"delhiclean.csv\")\n",
    "\n",
    "# Define the features and target\n",
    "X = df[[\"distance\", \"ratings\", \"wifi\"]]\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Normalize the features\n",
    "X = (X - X.mean()) / X.std()\n",
    "\n",
    "# Reshape the data into sequences of length 10\n",
    "# Each sequence contains 10 consecutive days of data\n",
    "X_seq = np.array([X[i:i+10] for i in range(len(X)-10)])\n",
    "y_seq = np.array([y[i+10] for i in range(len(y)-10)])\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_seq, y_seq, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create and fit the model\n",
    "model = keras.Sequential([\n",
    "  layers.SimpleRNN(32, activation=\"relu\", input_shape=(10, 3)),\n",
    "  layers.Dense(1)\n",
    "])\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model performance\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n",
    "\n",
    "# Predict the price for the next day\n",
    "# Assume we have a new hotel with distance = 5 km, ratings = 3, and wifi = 1\n",
    "#new_hotel = np.array([[5, 3, 1]])\n",
    "#new_hotel = (new_hotel - X.mean()) / X.std()\n",
    "#new_hotel = new_hotel.reshape(1, 3, 1)\n",
    "##new_hotel = new_hotel.reshape(1, 1, 3)\n",
    "#new_price = model.predict(new_hotel)\n",
    "#print(f\"Predicted price for the new hotel: {new_price[0][0]:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2/2 [==============================] - 2s 318ms/step - loss: 1167155.2500 - mae: 951.5715 - val_loss: 1843675.2500 - val_mae: 1111.6627\n",
      "Epoch 2/10\n",
      "2/2 [==============================] - 0s 45ms/step - loss: 1167026.6250 - mae: 951.5088 - val_loss: 1843567.8750 - val_mae: 1111.6152\n",
      "Epoch 3/10\n",
      "2/2 [==============================] - 0s 58ms/step - loss: 1166905.7500 - mae: 951.4482 - val_loss: 1843458.1250 - val_mae: 1111.5657\n",
      "Epoch 4/10\n",
      "2/2 [==============================] - 0s 67ms/step - loss: 1166776.5000 - mae: 951.3823 - val_loss: 1843340.0000 - val_mae: 1111.5138\n",
      "Epoch 5/10\n",
      "2/2 [==============================] - 0s 52ms/step - loss: 1166640.8750 - mae: 951.3138 - val_loss: 1843210.7500 - val_mae: 1111.4583\n",
      "Epoch 6/10\n",
      "2/2 [==============================] - 0s 44ms/step - loss: 1166503.0000 - mae: 951.2456 - val_loss: 1843069.3750 - val_mae: 1111.3982\n",
      "Epoch 7/10\n",
      "2/2 [==============================] - 0s 75ms/step - loss: 1166334.1250 - mae: 951.1622 - val_loss: 1842890.5000 - val_mae: 1111.3242\n",
      "Epoch 8/10\n",
      "2/2 [==============================] - 0s 66ms/step - loss: 1166148.2500 - mae: 951.0720 - val_loss: 1842679.5000 - val_mae: 1111.2375\n",
      "Epoch 9/10\n",
      "2/2 [==============================] - 0s 75ms/step - loss: 1165929.1250 - mae: 950.9632 - val_loss: 1842415.7500 - val_mae: 1111.1285\n",
      "Epoch 10/10\n",
      "2/2 [==============================] - 0s 74ms/step - loss: 1165650.1250 - mae: 950.8242 - val_loss: 1842099.3750 - val_mae: 1110.9943\n",
      "MSE: 1269131.40\n",
      "R2: -2.70\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the scraped data\n",
    "df = pd.read_csv(\"newdelhi.csv\")\n",
    "\n",
    "# Define the features and target\n",
    "X = df[[\"distance\", \"ratings\", \"amneties\"]]\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Normalize the features\n",
    "X = (X - X.mean()) / X.std()\n",
    "\n",
    "# Reshape the data into sequences of length 10\n",
    "# Each sequence contains 10 consecutive days of data\n",
    "X_seq = np.array([X[i:i+10] for i in range(len(X)-10)])\n",
    "y_seq = np.array([y[i+10] for i in range(len(y)-10)])\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_seq, y_seq, test_size=0.2, random_state=2)\n",
    "\n",
    "# Create and fit the model\n",
    "model = keras.Sequential([\n",
    "    layers.SimpleRNN(32, activation=\"relu\", input_shape=(10, 3)),\n",
    "    layers.Dense(1)\n",
    "])\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Get the user-defined price range\n",
    "lower_price_limit = float(input(\"Enter lower price limit: \"))\n",
    "upper_price_limit = float(input(\"Enter upper price limit: \"))\n",
    "\n",
    "# Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n",
    "\n",
    "# Predict the price for the next day\n",
    "# Assume we have a new hotel with distance = 5 km, ratings = 3, and wifi = 1\n",
    "#new_hotel = np.array([[5, 3, 1]])\n",
    "#new_hotel = (new_hotel - X.mean()) / X.std()\n",
    "#new_hotel = new_hotel.reshape(1, 10, 3)\n",
    "#new_price = model.predict(new_hotel)\n",
    "\n",
    "# Check if the predicted price is within the user-defined price range\n",
    "#if lower_price_limit <= new_price[0][0] <= upper_price_limit:\n",
    "#    print(f\"Predicted price for the new hotel: {new_price[0][0]:.2f}\")\n",
    "#else:\n",
    " #   print(\n",
    "  #      f\"Sorry, the predicted price of {new_price[0][0]:.2f} is not within your price range.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_15760\\3389744464.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\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>price</th>\n",
       "      <th>distance</th>\n",
       "      <th>ratings</th>\n",
       "      <th>wifi</th>\n",
       "      <th>AC</th>\n",
       "      <th>TV</th>\n",
       "      <th>Queen Sized Bed</th>\n",
       "      <th>Geyser</th>\n",
       "      <th>King Sized Bed</th>\n",
       "      <th>Power backup</th>\n",
       "      <th>Mini Fridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>576</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>649</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2173</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>999</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>892</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1499</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>946</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.576471</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>722</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.576471</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>674</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>949</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  distance   ratings  wifi  AC  TV  Queen Sized Bed  Geyser  \\\n",
       "0     576       6.5  3.100000     1   1   1                0       0   \n",
       "1     649       3.7  3.200000     1   1   1                0       0   \n",
       "2    2173       3.4  5.000000     1   1   1                0       0   \n",
       "3     999       3.6  3.800000     1   1   1                0       1   \n",
       "4     892       3.6  2.700000     1   1   1                0       1   \n",
       "..    ...       ...       ...   ...  ..  ..              ...     ...   \n",
       "95   1499       5.0  4.000000     1   1   1                0       1   \n",
       "96    946       4.5  3.576471     1   1   1                0       1   \n",
       "97    722       2.0  3.576471     1   1   1                0       1   \n",
       "98    674       7.0  2.700000     1   1   1                0       0   \n",
       "99    949       4.5  2.700000     1   1   1                0       0   \n",
       "\n",
       "    King Sized Bed  Power backup  Mini Fridge  \n",
       "0                0             0            0  \n",
       "1                0             1            0  \n",
       "2                0             0            1  \n",
       "3                0             1            0  \n",
       "4                0             1            0  \n",
       "..             ...           ...          ...  \n",
       "95               0             1            0  \n",
       "96               0             1            0  \n",
       "97               0             1            0  \n",
       "98               0             0            0  \n",
       "99               1             0            1  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#added more features\n",
    "import requests \n",
    "from bs4 import BeautifulSoup as Soup\n",
    "import pandas as pd\n",
    "def delhi():\n",
    "    df=pd.DataFrame()\n",
    "    headers = {'User-Agent': 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36'}\n",
    "    for page in range (1,6):\n",
    "        req = requests.get(\"https://www.oyorooms.com/hotels-in-delhi/{} \", headers=headers).text\n",
    "        soup = Soup(req, 'html.parser')\n",
    "        apts = soup.find_all(\"div\", {\"class\": \"oyo-row oyo-row--no-spacing listingHotelDescription\"})\n",
    "        l = []\n",
    "        obj = {}\n",
    "        apts1 = soup.find_all(itemprop='url')\n",
    "        for a in range(0, len(apts)):\n",
    "            try:\n",
    "                obj[\"pricing\"] = apts[a].find(\"span\", {\"class\": \"listingPrice__finalPrice\"}).text\n",
    "            except: obj[\"pricing\"] = None\n",
    "            try:\n",
    "                obj[\"Distance\"] = apts[a].find(\"span\", {\"class\": \"listingHotelDescription__distanceText\"}).text\n",
    "            except:\n",
    "                obj[\"Distance\"] = None\n",
    "            try:\n",
    "                link= apts1[a].get('content')\n",
    "                req1 = requests.get(link, headers=headers).text\n",
    "                soup2 = Soup(req1, 'html.parser')\n",
    "                obj[\"amneties\"] = soup2.find(\"div\", {\"class\": \"c-2cojj\"}).text\n",
    "            except:\n",
    "                obj[\"amneties\"] = None\n",
    "            try:\n",
    "                obj[\"ratings\"] = apts[a].find(\"div\", {\"class\": \"hotelRating\"}).text\n",
    "            except:\n",
    "                obj[\"ratings\"] = None\n",
    "            \n",
    "            l.append(obj)\n",
    "            obj = {}\n",
    "        for i in l:\n",
    "            price = i[\"pricing\"]\n",
    "            distance = i[\"Distance\"]\n",
    "            amneties= i[\"amneties\"]\n",
    "            ratings= i[\"ratings\"]\n",
    "            df = df.append({\"price\": price,\"distance\": distance,\"amneties\":amneties,\"ratings\":ratings}, ignore_index=True)\n",
    "#dataCleaning\n",
    "    df[\"price\"] = df[\"price\"].str.replace(r\"₹\", \"\")\n",
    "    df[\"ratings\"] = df[\"ratings\"].str.replace(r\"₹\", \"\")\n",
    "    df['price'] = pd.to_numeric(df['price'])\n",
    "    df['distance']=df['distance'].str.replace(r\"km\",\"\")\n",
    "    df['distance'] = pd.to_numeric(df['distance'])\n",
    "    df[['ratings', 'last_name','y','x']] = df['ratings'].str.split(' ', expand=True)\n",
    "    df = df.drop(columns=['last_name','y','x'],axis=1)\n",
    "    df['ratings'] = pd.to_numeric(df['ratings'],errors='coerce')\n",
    "    import re\n",
    "    df['wifi'] = df['amneties'].str.extract(r'(Wifi)', expand=False,flags=re.IGNORECASE)\n",
    "    df['AC'] = df['amneties'].str.extract(r'(AC)', expand=False)\n",
    "    df['amneties']=df['amneties'].str.replace(r\"CCTV\",\"\")\n",
    "    df['TV'] = df['amneties'].str.extract(r'(TV)', expand=False)\n",
    "    df['Queen Sized Bed'] = df['amneties'].str.extract(r'(Queen Sized Bed)', expand=False)\n",
    "    df['Geyser'] = df['amneties'].str.extract(r'(Geyser)', expand=False)\n",
    "    df['King Sized Bed'] = df['amneties'].str.extract(r'(King Sized Bed)', expand=False)\n",
    "    df['Power backup'] = df['amneties'].str.extract(r'(Power backup)', expand=False)\n",
    "    df['Mini Fridge'] = df['amneties'].str.extract(r'(Mini Fridge)', expand=False)\n",
    "    df= df.drop(columns=['amneties'],axis= 1)\n",
    "    df['AC'] = df['AC'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['TV'] = df['TV'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['wifi'] = df['wifi'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['Queen Sized Bed'] = df['Queen Sized Bed'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['King Sized Bed'] = df['King Sized Bed'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['Geyser'] = df['Geyser'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['Power backup'] = df['Power backup'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['Mini Fridge'] = df['Mini Fridge'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['Mini Fridge'] = df['Mini Fridge'].fillna('').replace({'': 0, '[^0]': 1}, regex=True)\n",
    "    df['ratings'] = df['ratings'].fillna(df['ratings'].mean())\n",
    "    return df\n",
    "df= delhi()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 21429.21\n",
      "R2: 0.94\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Length of values (1) does not match length of index (3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 30\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[39m# Predict the price for a new hotel\u001b[39;00m\n\u001b[0;32m     29\u001b[0m new_hotel \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([[\u001b[39m5\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m1\u001b[39m]])\n\u001b[1;32m---> 30\u001b[0m new_hotel_norm \u001b[39m=\u001b[39m (new_hotel \u001b[39m-\u001b[39;49m X\u001b[39m.\u001b[39;49mmean()) \u001b[39m/\u001b[39m X\u001b[39m.\u001b[39mstd()\n\u001b[0;32m     31\u001b[0m new_hotel_flat \u001b[39m=\u001b[39m new_hotel_norm\u001b[39m.\u001b[39mreshape(\u001b[39m1\u001b[39m, \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m     32\u001b[0m new_price \u001b[39m=\u001b[39m xgb_model\u001b[39m.\u001b[39mpredict(new_hotel_flat)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:2113\u001b[0m, in \u001b[0;36mNDFrame.__array_ufunc__\u001b[1;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m   2109\u001b[0m \u001b[39m@final\u001b[39m\n\u001b[0;32m   2110\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array_ufunc__\u001b[39m(\n\u001b[0;32m   2111\u001b[0m     \u001b[39mself\u001b[39m, ufunc: np\u001b[39m.\u001b[39mufunc, method: \u001b[39mstr\u001b[39m, \u001b[39m*\u001b[39minputs: Any, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any\n\u001b[0;32m   2112\u001b[0m ):\n\u001b[1;32m-> 2113\u001b[0m     \u001b[39mreturn\u001b[39;00m arraylike\u001b[39m.\u001b[39marray_ufunc(\u001b[39mself\u001b[39m, ufunc, method, \u001b[39m*\u001b[39minputs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\arraylike.py:265\u001b[0m, in \u001b[0;36marray_ufunc\u001b[1;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[0;32m    262\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n\u001b[0;32m    264\u001b[0m \u001b[39m# for binary ops, use our custom dunder methods\u001b[39;00m\n\u001b[1;32m--> 265\u001b[0m result \u001b[39m=\u001b[39m maybe_dispatch_ufunc_to_dunder_op(\u001b[39mself\u001b[39m, ufunc, method, \u001b[39m*\u001b[39minputs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    266\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNotImplemented\u001b[39m:\n\u001b[0;32m    267\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\_libs\\ops_dispatch.pyx:113\u001b[0m, in \u001b[0;36mpandas._libs.ops_dispatch.maybe_dispatch_ufunc_to_dunder_op\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\ops\\common.py:72\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[39mreturn\u001b[39;00m \u001b[39mNotImplemented\u001b[39m\n\u001b[0;32m     70\u001b[0m other \u001b[39m=\u001b[39m item_from_zerodim(other)\n\u001b[1;32m---> 72\u001b[0m \u001b[39mreturn\u001b[39;00m method(\u001b[39mself\u001b[39;49m, other)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\arraylike.py:114\u001b[0m, in \u001b[0;36mOpsMixin.__rsub__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[39m@unpack_zerodim_and_defer\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m__rsub__\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__rsub__\u001b[39m(\u001b[39mself\u001b[39m, other):\n\u001b[1;32m--> 114\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_arith_method(other, roperator\u001b[39m.\u001b[39;49mrsub)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\series.py:6259\u001b[0m, in \u001b[0;36mSeries._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   6257\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_arith_method\u001b[39m(\u001b[39mself\u001b[39m, other, op):\n\u001b[0;32m   6258\u001b[0m     \u001b[39mself\u001b[39m, other \u001b[39m=\u001b[39m ops\u001b[39m.\u001b[39malign_method_SERIES(\u001b[39mself\u001b[39m, other)\n\u001b[1;32m-> 6259\u001b[0m     \u001b[39mreturn\u001b[39;00m base\u001b[39m.\u001b[39;49mIndexOpsMixin\u001b[39m.\u001b[39;49m_arith_method(\u001b[39mself\u001b[39;49m, other, op)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\base.py:1327\u001b[0m, in \u001b[0;36mIndexOpsMixin._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   1324\u001b[0m \u001b[39mwith\u001b[39;00m np\u001b[39m.\u001b[39merrstate(\u001b[39mall\u001b[39m\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m   1325\u001b[0m     result \u001b[39m=\u001b[39m ops\u001b[39m.\u001b[39marithmetic_op(lvalues, rvalues, op)\n\u001b[1;32m-> 1327\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_construct_result(result, name\u001b[39m=\u001b[39;49mres_name)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\series.py:3223\u001b[0m, in \u001b[0;36mSeries._construct_result\u001b[1;34m(self, result, name)\u001b[0m\n\u001b[0;32m   3219\u001b[0m     \u001b[39mreturn\u001b[39;00m (res1, res2)\n\u001b[0;32m   3221\u001b[0m \u001b[39m# We do not pass dtype to ensure that the Series constructor\u001b[39;00m\n\u001b[0;32m   3222\u001b[0m \u001b[39m#  does inference in the case where `result` has object-dtype.\u001b[39;00m\n\u001b[1;32m-> 3223\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_constructor(result, index\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex)\n\u001b[0;32m   3224\u001b[0m out \u001b[39m=\u001b[39m out\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m)\n\u001b[0;32m   3226\u001b[0m \u001b[39m# Set the result's name after __finalize__ is called because __finalize__\u001b[39;00m\n\u001b[0;32m   3227\u001b[0m \u001b[39m#  would set it back to self.name\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\series.py:461\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    459\u001b[0m     index \u001b[39m=\u001b[39m default_index(\u001b[39mlen\u001b[39m(data))\n\u001b[0;32m    460\u001b[0m \u001b[39melif\u001b[39;00m is_list_like(data):\n\u001b[1;32m--> 461\u001b[0m     com\u001b[39m.\u001b[39;49mrequire_length_match(data, index)\n\u001b[0;32m    463\u001b[0m \u001b[39m# create/copy the manager\u001b[39;00m\n\u001b[0;32m    464\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, (SingleBlockManager, SingleArrayManager)):\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\common.py:571\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    568\u001b[0m \u001b[39mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m    569\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(data) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(index):\n\u001b[1;32m--> 571\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    572\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mLength of values \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    573\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(data)\u001b[39m}\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    574\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mdoes not match length of index \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    575\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(index)\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    576\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length of values (1) does not match length of index (3)"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Flatten the input sequences\n",
    "X_train_flat = X_train.reshape(X_train.shape[0], -1)\n",
    "X_test_flat = X_test.reshape(X_test.shape[0], -1)\n",
    "\n",
    "# Train the XGBoost model\n",
    "xgb_model = xgb.XGBRegressor(\n",
    "    objective='reg:squarederror', n_estimators=100, max_depth=3)\n",
    "xgb_model.fit(X_train_flat, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = xgb_model.predict(X_test_flat)\n",
    "\n",
    "# Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n",
    "\n",
    "# Get the user-defined price range\n",
    "lower_price_limit = float(input(\"Enter lower price limit: \"))\n",
    "upper_price_limit = float(input(\"Enter upper price limit: \"))\n",
    "\n",
    "# Filter the predictions within the user-defined price range\n",
    "y_pred_filtered = np.clip(y_pred, lower_price_limit, upper_price_limit)\n",
    "\n",
    "# Predict the price for a new hotel\n",
    "new_hotel = np.array([[5, 3, 1]])\n",
    "new_hotel_norm = (new_hotel - X.mean()) / X.std()\n",
    "new_hotel_flat = new_hotel_norm.reshape(1, -1)\n",
    "new_price = xgb_model.predict(new_hotel_flat)\n",
    "new_price_filtered = np.clip(new_price, lower_price_limit, upper_price_limit)\n",
    "print(f\"Predicted price for the new hotel: {new_price_filtered[0]:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_8052\\542564569.py:15: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  X = (X - X.mean()) / X.std()\n",
      "C:\\Users\\jaida\\AppData\\Local\\Temp\\ipykernel_8052\\542564569.py:15: FutureWarning: The default value of numeric_only in DataFrame.std is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  X = (X - X.mean()) / X.std()\n"
     ]
    },
    {
     "ename": "XGBoostError",
     "evalue": "[10:39:09] C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\data\\data.cc:461: Check failed: valid: Label contains NaN, infinity or a value too large.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mXGBoostError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 32\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[39m# Train the XGBoost model\u001b[39;00m\n\u001b[0;32m     31\u001b[0m model \u001b[39m=\u001b[39m xgb\u001b[39m.\u001b[39mXGBRegressor(objective\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mreg:squarederror\u001b[39m\u001b[39m\"\u001b[39m, n_estimators\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m)\n\u001b[1;32m---> 32\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[0;32m     34\u001b[0m \u001b[39m# Get the user-defined price range\u001b[39;00m\n\u001b[0;32m     35\u001b[0m lower_price_limit \u001b[39m=\u001b[39m \u001b[39mfloat\u001b[39m(\u001b[39minput\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mEnter lower price limit: \u001b[39m\u001b[39m\"\u001b[39m))\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:620\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    618\u001b[0m \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args):\n\u001b[0;32m    619\u001b[0m     kwargs[k] \u001b[39m=\u001b[39m arg\n\u001b[1;32m--> 620\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py:988\u001b[0m, in \u001b[0;36mXGBModel.fit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m    986\u001b[0m \u001b[39mwith\u001b[39;00m config_context(verbosity\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbosity):\n\u001b[0;32m    987\u001b[0m     evals_result: TrainingCallback\u001b[39m.\u001b[39mEvalsLog \u001b[39m=\u001b[39m {}\n\u001b[1;32m--> 988\u001b[0m     train_dmatrix, evals \u001b[39m=\u001b[39m _wrap_evaluation_matrices(\n\u001b[0;32m    989\u001b[0m         missing\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmissing,\n\u001b[0;32m    990\u001b[0m         X\u001b[39m=\u001b[39;49mX,\n\u001b[0;32m    991\u001b[0m         y\u001b[39m=\u001b[39;49my,\n\u001b[0;32m    992\u001b[0m         group\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m    993\u001b[0m         qid\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m    994\u001b[0m         sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[0;32m    995\u001b[0m         base_margin\u001b[39m=\u001b[39;49mbase_margin,\n\u001b[0;32m    996\u001b[0m         feature_weights\u001b[39m=\u001b[39;49mfeature_weights,\n\u001b[0;32m    997\u001b[0m         eval_set\u001b[39m=\u001b[39;49meval_set,\n\u001b[0;32m    998\u001b[0m         sample_weight_eval_set\u001b[39m=\u001b[39;49msample_weight_eval_set,\n\u001b[0;32m    999\u001b[0m         base_margin_eval_set\u001b[39m=\u001b[39;49mbase_margin_eval_set,\n\u001b[0;32m   1000\u001b[0m         eval_group\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m   1001\u001b[0m         eval_qid\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m   1002\u001b[0m         create_dmatrix\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_dmatrix,\n\u001b[0;32m   1003\u001b[0m         enable_categorical\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menable_categorical,\n\u001b[0;32m   1004\u001b[0m         feature_types\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfeature_types,\n\u001b[0;32m   1005\u001b[0m     )\n\u001b[0;32m   1006\u001b[0m     params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_xgb_params()\n\u001b[0;32m   1008\u001b[0m     \u001b[39mif\u001b[39;00m callable(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobjective):\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py:448\u001b[0m, in \u001b[0;36m_wrap_evaluation_matrices\u001b[1;34m(missing, X, y, group, qid, sample_weight, base_margin, feature_weights, eval_set, sample_weight_eval_set, base_margin_eval_set, eval_group, eval_qid, create_dmatrix, enable_categorical, feature_types)\u001b[0m\n\u001b[0;32m    428\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_wrap_evaluation_matrices\u001b[39m(\n\u001b[0;32m    429\u001b[0m     missing: \u001b[39mfloat\u001b[39m,\n\u001b[0;32m    430\u001b[0m     X: Any,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    444\u001b[0m     feature_types: Optional[FeatureTypes],\n\u001b[0;32m    445\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[Any, List[Tuple[Any, \u001b[39mstr\u001b[39m]]]:\n\u001b[0;32m    446\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Convert array_like evaluation matrices into DMatrix.  Perform validation on the\u001b[39;00m\n\u001b[0;32m    447\u001b[0m \u001b[39m    way.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 448\u001b[0m     train_dmatrix \u001b[39m=\u001b[39m create_dmatrix(\n\u001b[0;32m    449\u001b[0m         data\u001b[39m=\u001b[39;49mX,\n\u001b[0;32m    450\u001b[0m         label\u001b[39m=\u001b[39;49my,\n\u001b[0;32m    451\u001b[0m         group\u001b[39m=\u001b[39;49mgroup,\n\u001b[0;32m    452\u001b[0m         qid\u001b[39m=\u001b[39;49mqid,\n\u001b[0;32m    453\u001b[0m         weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[0;32m    454\u001b[0m         base_margin\u001b[39m=\u001b[39;49mbase_margin,\n\u001b[0;32m    455\u001b[0m         feature_weights\u001b[39m=\u001b[39;49mfeature_weights,\n\u001b[0;32m    456\u001b[0m         missing\u001b[39m=\u001b[39;49mmissing,\n\u001b[0;32m    457\u001b[0m         enable_categorical\u001b[39m=\u001b[39;49menable_categorical,\n\u001b[0;32m    458\u001b[0m         feature_types\u001b[39m=\u001b[39;49mfeature_types,\n\u001b[0;32m    459\u001b[0m         ref\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m    460\u001b[0m     )\n\u001b[0;32m    462\u001b[0m     n_validation \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m \u001b[39mif\u001b[39;00m eval_set \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mlen\u001b[39m(eval_set)\n\u001b[0;32m    464\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mvalidate_or_none\u001b[39m(meta: Optional[Sequence], name: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Sequence:\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\sklearn.py:908\u001b[0m, in \u001b[0;36mXGBModel._create_dmatrix\u001b[1;34m(self, ref, **kwargs)\u001b[0m\n\u001b[0;32m    906\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:  \u001b[39m# `QuantileDMatrix` supports lesser types than DMatrix\u001b[39;00m\n\u001b[0;32m    907\u001b[0m         \u001b[39mpass\u001b[39;00m\n\u001b[1;32m--> 908\u001b[0m \u001b[39mreturn\u001b[39;00m DMatrix(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs, nthread\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:620\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    618\u001b[0m \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args):\n\u001b[0;32m    619\u001b[0m     kwargs[k] \u001b[39m=\u001b[39m arg\n\u001b[1;32m--> 620\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:754\u001b[0m, in \u001b[0;36mDMatrix.__init__\u001b[1;34m(self, data, label, weight, base_margin, missing, silent, feature_names, feature_types, nthread, group, qid, label_lower_bound, label_upper_bound, feature_weights, enable_categorical)\u001b[0m\n\u001b[0;32m    751\u001b[0m \u001b[39massert\u001b[39;00m handle \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    752\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle \u001b[39m=\u001b[39m handle\n\u001b[1;32m--> 754\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_info(\n\u001b[0;32m    755\u001b[0m     label\u001b[39m=\u001b[39;49mlabel,\n\u001b[0;32m    756\u001b[0m     weight\u001b[39m=\u001b[39;49mweight,\n\u001b[0;32m    757\u001b[0m     base_margin\u001b[39m=\u001b[39;49mbase_margin,\n\u001b[0;32m    758\u001b[0m     group\u001b[39m=\u001b[39;49mgroup,\n\u001b[0;32m    759\u001b[0m     qid\u001b[39m=\u001b[39;49mqid,\n\u001b[0;32m    760\u001b[0m     label_lower_bound\u001b[39m=\u001b[39;49mlabel_lower_bound,\n\u001b[0;32m    761\u001b[0m     label_upper_bound\u001b[39m=\u001b[39;49mlabel_upper_bound,\n\u001b[0;32m    762\u001b[0m     feature_weights\u001b[39m=\u001b[39;49mfeature_weights,\n\u001b[0;32m    763\u001b[0m )\n\u001b[0;32m    765\u001b[0m \u001b[39mif\u001b[39;00m feature_names \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    766\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfeature_names \u001b[39m=\u001b[39m feature_names\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:620\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    618\u001b[0m \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args):\n\u001b[0;32m    619\u001b[0m     kwargs[k] \u001b[39m=\u001b[39m arg\n\u001b[1;32m--> 620\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:819\u001b[0m, in \u001b[0;36mDMatrix.set_info\u001b[1;34m(self, label, weight, base_margin, group, qid, label_lower_bound, label_upper_bound, feature_names, feature_types, feature_weights)\u001b[0m\n\u001b[0;32m    816\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdata\u001b[39;00m \u001b[39mimport\u001b[39;00m dispatch_meta_backend\n\u001b[0;32m    818\u001b[0m \u001b[39mif\u001b[39;00m label \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 819\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_label(label)\n\u001b[0;32m    820\u001b[0m \u001b[39mif\u001b[39;00m weight \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    821\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_weight(weight)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:950\u001b[0m, in \u001b[0;36mDMatrix.set_label\u001b[1;34m(self, label)\u001b[0m\n\u001b[0;32m    942\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Set label of dmatrix\u001b[39;00m\n\u001b[0;32m    943\u001b[0m \n\u001b[0;32m    944\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    947\u001b[0m \u001b[39m    The label information to be set into DMatrix\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    949\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mdata\u001b[39;00m \u001b[39mimport\u001b[39;00m dispatch_meta_backend\n\u001b[1;32m--> 950\u001b[0m dispatch_meta_backend(\u001b[39mself\u001b[39;49m, label, \u001b[39m'\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mfloat\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\data.py:1127\u001b[0m, in \u001b[0;36mdispatch_meta_backend\u001b[1;34m(matrix, data, name, dtype)\u001b[0m\n\u001b[0;32m   1125\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m   1126\u001b[0m \u001b[39mif\u001b[39;00m _is_numpy_array(data):\n\u001b[1;32m-> 1127\u001b[0m     _meta_from_numpy(data, name, dtype, handle)\n\u001b[0;32m   1128\u001b[0m     \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m   1129\u001b[0m \u001b[39mif\u001b[39;00m _is_pandas_df(data):\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\data.py:1050\u001b[0m, in \u001b[0;36m_meta_from_numpy\u001b[1;34m(data, field, dtype, handle)\u001b[0m\n\u001b[0;32m   1048\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mMasked array is not supported.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   1049\u001b[0m interface_str \u001b[39m=\u001b[39m _array_interface(data)\n\u001b[1;32m-> 1050\u001b[0m _check_call(_LIB\u001b[39m.\u001b[39;49mXGDMatrixSetInfoFromInterface(handle, c_str(field), interface_str))\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:279\u001b[0m, in \u001b[0;36m_check_call\u001b[1;34m(ret)\u001b[0m\n\u001b[0;32m    268\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Check the return value of C API call\u001b[39;00m\n\u001b[0;32m    269\u001b[0m \n\u001b[0;32m    270\u001b[0m \u001b[39mThis function will raise exception when error occurs.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    276\u001b[0m \u001b[39m    return value from API calls\u001b[39;00m\n\u001b[0;32m    277\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    278\u001b[0m \u001b[39mif\u001b[39;00m ret \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 279\u001b[0m     \u001b[39mraise\u001b[39;00m XGBoostError(py_str(_LIB\u001b[39m.\u001b[39mXGBGetLastError()))\n",
      "\u001b[1;31mXGBoostError\u001b[0m: [10:39:09] C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\\xgboost\\xgboost-ci-windows\\src\\data\\data.cc:461: Check failed: valid: Label contains NaN, infinity or a value too large."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score, mean_absolute_error\n",
    "\n",
    "# Load the scraped data\n",
    "df = pd.read_csv(\"newdelhi.csv\")\n",
    "\n",
    "# Define the features and target\n",
    "X = df[[\"distance\", \"ratings\", \"amneties\"]]\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Normalize the features\n",
    "X = (X - X.mean()) / X.std()\n",
    "\n",
    "# Reshape the data into sequences of length 10\n",
    "# Each sequence contains 10 consecutive days of data\n",
    "X_seq = np.array([X[i:i+10] for i in range(len(X)-10)])\n",
    "y_seq = np.array([y[i+10] for i in range(len(y)-10)])\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train_seq, X_test_seq, y_train, y_test = train_test_split(\n",
    "    X_seq, y_seq, test_size=0.2, random_state=2)\n",
    "\n",
    "# Flatten the input sequences\n",
    "X_train = X_train_seq.reshape(X_train_seq.shape[0], -1)\n",
    "X_test = X_test_seq.reshape(X_test_seq.shape[0], -1)\n",
    "\n",
    "# Train the XGBoost model\n",
    "model = xgb.XGBRegressor(objective=\"reg:squarederror\", n_estimators=100)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Get the user-defined price range\n",
    "lower_price_limit = float(input(\"Enter lower price limit: \"))\n",
    "upper_price_limit = float(input(\"Enter upper price limit: \"))\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Clip the predictions to the price range\n",
    "y_pred_clipped = np.clip(y_pred, lower_price_limit, upper_price_limit)\n",
    "\n",
    "# Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred_clipped)\n",
    "mae = mean_absolute_error(y_test, y_pred_clipped)\n",
    "evs = explained_variance_score(y_test, y_pred_clipped)\n",
    "r2 = r2_score(y_test, y_pred_clipped)\n",
    "\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"MAE: {mae:.2f}\")\n",
    "print(f\"EVS: {evs:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 300848.35\n",
      "MAE: 296.71\n",
      "EVS: 0.16\n",
      "R2: 0.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "Length of values (30) does not match length of index (3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 69\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[39m# Plot the feature importance\u001b[39;00m\n\u001b[0;32m     68\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m10\u001b[39m, \u001b[39m6\u001b[39m))\n\u001b[1;32m---> 69\u001b[0m feat_imp \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mSeries(model\u001b[39m.\u001b[39;49mfeature_importances_, index\u001b[39m=\u001b[39;49mX\u001b[39m.\u001b[39;49mcolumns)\n\u001b[0;32m     70\u001b[0m feat_imp\u001b[39m.\u001b[39msort_values(ascending\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\u001b[39m.\u001b[39mplot(kind\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbar\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     71\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mFeature Importance Score\u001b[39m\u001b[39m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\series.py:461\u001b[0m, in \u001b[0;36mSeries.__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    459\u001b[0m     index \u001b[39m=\u001b[39m default_index(\u001b[39mlen\u001b[39m(data))\n\u001b[0;32m    460\u001b[0m \u001b[39melif\u001b[39;00m is_list_like(data):\n\u001b[1;32m--> 461\u001b[0m     com\u001b[39m.\u001b[39;49mrequire_length_match(data, index)\n\u001b[0;32m    463\u001b[0m \u001b[39m# create/copy the manager\u001b[39;00m\n\u001b[0;32m    464\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, (SingleBlockManager, SingleArrayManager)):\n",
      "File \u001b[1;32mc:\\Users\\jaida\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\common.py:571\u001b[0m, in \u001b[0;36mrequire_length_match\u001b[1;34m(data, index)\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    568\u001b[0m \u001b[39mCheck the length of data matches the length of the index.\u001b[39;00m\n\u001b[0;32m    569\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    570\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(data) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(index):\n\u001b[1;32m--> 571\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    572\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mLength of values \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    573\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(data)\u001b[39m}\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    574\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mdoes not match length of index \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    575\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(index)\u001b[39m}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    576\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length of values (30) does not match length of index (3)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score, mean_absolute_error\n",
    "\n",
    "# Load the scraped data\n",
    "df = pd.read_csv(\"delhiclean.csv\")\n",
    "\n",
    "# Define the features and target\n",
    "X = df[[\"distance\", \"ratings\", \"wifi\"]]\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Normalize the features\n",
    "X = (X - X.mean()) / X.std()\n",
    "\n",
    "# Reshape the data into sequences of length 10\n",
    "# Each sequence contains 10 consecutive days of data\n",
    "X_seq = np.array([X[i:i+10] for i in range(len(X)-10)])\n",
    "y_seq = np.array([y[i+10] for i in range(len(y)-10)])\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train_seq, X_test_seq, y_train, y_test = train_test_split(\n",
    "    X_seq, y_seq, test_size=0.2, random_state=2)\n",
    "\n",
    "# Flatten the input sequences\n",
    "X_train = X_train_seq.reshape(X_train_seq.shape[0], -1)\n",
    "X_test = X_test_seq.reshape(X_test_seq.shape[0], -1)\n",
    "\n",
    "# Train the XGBoost model\n",
    "model = xgb.XGBRegressor(objective=\"reg:squarederror\", n_estimators=100)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Get the user-defined price range\n",
    "lower_price_limit = float(input(\"Enter lower price limit: \"))\n",
    "upper_price_limit = float(input(\"Enter upper price limit: \"))\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Clip the predictions to the price range\n",
    "y_pred_clipped = np.clip(y_pred, lower_price_limit, upper_price_limit)\n",
    "\n",
    "# Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred_clipped)\n",
    "mae = mean_absolute_error(y_test, y_pred_clipped)\n",
    "evs = explained_variance_score(y_test, y_pred_clipped)\n",
    "r2 = r2_score(y_test, y_pred_clipped)\n",
    "\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"MAE: {mae:.2f}\")\n",
    "print(f\"EVS: {evs:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n",
    "\n",
    "# Plot the actual vs predicted prices\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred_clipped)\n",
    "plt.plot([lower_price_limit, upper_price_limit], [\n",
    "         lower_price_limit, upper_price_limit], color='red')\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Predicted Price')\n",
    "plt.title('Actual vs Predicted Prices')\n",
    "plt.show()\n",
    "\n",
    "# Plot the feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "feat_imp = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_imp.sort_values(ascending=False).plot(kind='bar')\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# Plot the residuals\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.residplot(y_test, y_pred_clipped, lowess=True)\n",
    "plt.xlabel('Actual Price')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "y contains NaN, infinity or a value too large",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 16\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[39m# Check for NaN or infinity values in y\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(y)\u001b[39m.\u001b[39many() \u001b[39mor\u001b[39;00m np\u001b[39m.\u001b[39misinf(y)\u001b[39m.\u001b[39many():\n\u001b[1;32m---> 16\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39my contains NaN, infinity or a value too large\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     18\u001b[0m \u001b[39m# Normalize the features\u001b[39;00m\n\u001b[0;32m     19\u001b[0m X \u001b[39m=\u001b[39m (X \u001b[39m-\u001b[39m X\u001b[39m.\u001b[39mmean()) \u001b[39m/\u001b[39m X\u001b[39m.\u001b[39mstd()\n",
      "\u001b[1;31mValueError\u001b[0m: y contains NaN, infinity or a value too large"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score, mean_absolute_error\n",
    "\n",
    "# Load the scraped data\n",
    "df = pd.read_csv(\"newdelhi.csv\")\n",
    "\n",
    "# Define the features and target\n",
    "X = df[[\"distance\", \"ratings\"]]\n",
    "y = df[\"price\"]\n",
    "\n",
    "# Check for NaN or infinity values in y\n",
    "if np.isnan(y).any() or np.isinf(y).any():\n",
    "    raise ValueError(\"y contains NaN, infinity or a value too large\")\n",
    "\n",
    "# Normalize the features\n",
    "X = (X - X.mean()) / X.std()\n",
    "\n",
    "# Reshape the data into sequences of length 10\n",
    "# Each sequence contains 10 consecutive days of data\n",
    "X_seq = np.array([X[i:i+10] for i in range(len(X)-10)])\n",
    "y_seq = np.array([y[i+10] for i in range(len(y)-10)])\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train_seq, X_test_seq, y_train, y_test = train_test_split(\n",
    "    X_seq, y_seq, test_size=0.2, random_state=2)\n",
    "\n",
    "# Flatten the input sequences\n",
    "X_train = X_train_seq.reshape(X_train_seq.shape[0], -1)\n",
    "X_test = X_test_seq.reshape(X_test_seq.shape[0], -1)\n",
    "\n",
    "# Train the XGBoost model\n",
    "model = xgb.XGBRegressor(objective=\"reg:squarederror\", n_estimators=100)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Get the user-defined price range\n",
    "lower_price_limit = float(input(\"Enter lower price limit: \"))\n",
    "upper_price_limit = float(input(\"Enter upper price limit: \"))\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Clip the predictions to the price range\n",
    "y_pred_clipped = np.clip(y_pred, lower_price_limit, upper_price_limit)\n",
    "\n",
    "# Evaluate the model performance\n",
    "mse = mean_squared_error(y_test, y_pred_clipped)\n",
    "mae = mean_absolute_error(y_test, y_pred_clipped)\n",
    "evs = explained_variance_score(y_test, y_pred_clipped)\n",
    "r2 = r2_score(y_test, y_pred_clipped)\n",
    "\n",
    "print(f\"MSE: {mse:.2f}\")\n",
    "print(f\"MAE: {mae:.2f}\")\n",
    "print(f\"EVS: {evs:.2f}\")\n",
    "print(f\"R2: {r2:.2f}\")\n"
   ]
  }
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