AirBNB Regression Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d067380",
   "metadata": {},
   "source": [
    "# Regression Analysis\n",
    "\n",
    "Contents\n",
    "\n",
    "1) Import libraries and data\n",
    "\n",
    "2) Data Cleaning\n",
    "\n",
    "3) Data prep for Regression analysis - scatterplot using matlib\n",
    "   a) price vs total_reviews\n",
    "   b) minimum_nights vs price\n",
    "   c) host_listings_count vs price\n",
    "\n",
    "4) State hypothesis\n",
    "\n",
    "5) Reshape the variables into NumPy arrays\n",
    "\n",
    "6) Split data into training set and test set\n",
    "\n",
    "7) Run linear regression on data set\n",
    "\n",
    "8) Plot that shows the regression line on the test set\n",
    "\n",
    "9) Interpretations and results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e21bf87",
   "metadata": {},
   "source": [
    "# 1) Importing libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "338960d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b3327c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This option ensures that the graphs you create are displayed within the notebook without the need to \"call\" them specifically.\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "739c704a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn project folder path into a string\n",
    "\n",
    "path = r'/Users/giorjeanmutuc/AirBNB NYC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efedf3fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "\n",
    "df_clean = pd.read_csv(os.path.join(path, '01 Data', 'Prepared Data', 'AB_NYC_clean.csv'), index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2807fe",
   "metadata": {},
   "source": [
    "# 2) Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed0e1116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>2787</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>2845</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>4632</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>4869</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>7192</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  host_id neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0  2539     2787            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1  2595     2845           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2  3647     4632           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  3831     4869            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4  5022     7192           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
       "0     Private room    149               1                  9  2018-10-19   \n",
       "1  Entire home/apt    225               1                 45  2019-05-21   \n",
       "2     Private room    150               3                  0         NaN   \n",
       "3  Entire home/apt     89               1                270  2019-07-05   \n",
       "4  Entire home/apt     80              10                  9  2018-11-19   \n",
       "\n",
       "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
       "0               0.21                               6               365  \n",
       "1               0.38                               2               355  \n",
       "2               0.00                               1               365  \n",
       "3               4.64                               1               194  \n",
       "4               0.10                               1                 0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83c1cd88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns \n",
    "\n",
    "df_clean.rename(columns = {'minimum_nights' : 'min_nights', 'number_of_reviews' : 'total_reviews','reviews_per_month' : 'reviews_per_mon','calculated_host_listings_count' : 'host_listings_count'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb34c83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>min_nights</th>\n",
       "      <th>total_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_mon</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539</td>\n",
       "      <td>2787</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>40.64749</td>\n",
       "      <td>-73.97237</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>6</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2595</td>\n",
       "      <td>2845</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>40.75362</td>\n",
       "      <td>-73.98377</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>2019-05-21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>2</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3647</td>\n",
       "      <td>4632</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>40.80902</td>\n",
       "      <td>-73.94190</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3831</td>\n",
       "      <td>4869</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>40.68514</td>\n",
       "      <td>-73.95976</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>4.64</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5022</td>\n",
       "      <td>7192</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>40.79851</td>\n",
       "      <td>-73.94399</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  host_id neighbourhood_group neighbourhood  latitude  longitude  \\\n",
       "0  2539     2787            Brooklyn    Kensington  40.64749  -73.97237   \n",
       "1  2595     2845           Manhattan       Midtown  40.75362  -73.98377   \n",
       "2  3647     4632           Manhattan        Harlem  40.80902  -73.94190   \n",
       "3  3831     4869            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
       "4  5022     7192           Manhattan   East Harlem  40.79851  -73.94399   \n",
       "\n",
       "         room_type  price  min_nights  total_reviews last_review  \\\n",
       "0     Private room    149           1              9  2018-10-19   \n",
       "1  Entire home/apt    225           1             45  2019-05-21   \n",
       "2     Private room    150           3              0         NaN   \n",
       "3  Entire home/apt     89           1            270  2019-07-05   \n",
       "4  Entire home/apt     80          10              9  2018-11-19   \n",
       "\n",
       "   reviews_per_mon  host_listings_count  availability_365  \n",
       "0             0.21                    6               365  \n",
       "1             0.38                    2               355  \n",
       "2             0.00                    1               365  \n",
       "3             4.64                    1               194  \n",
       "4             0.10                    1                 0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19370f1a",
   "metadata": {},
   "source": [
    "#### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "034fbe79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "host_id                    0\n",
       "neighbourhood_group        0\n",
       "neighbourhood              0\n",
       "latitude                   0\n",
       "longitude                  0\n",
       "room_type                  0\n",
       "price                      0\n",
       "min_nights                 0\n",
       "total_reviews              0\n",
       "last_review            10052\n",
       "reviews_per_mon            0\n",
       "host_listings_count        0\n",
       "availability_365           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "\n",
    "df_clean.isnull().sum()\n",
    "\n",
    "# Missing dates (NAN) under last_reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5429d3f5",
   "metadata": {},
   "source": [
    "#### Duplicates check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e08365b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check duplicates\n",
    "\n",
    "dups = df_clean.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "770223e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48895,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dups.shape # No duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c68d66ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Extreme values check "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2e2f6f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff750d5e9d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows extreme values for price\n",
    "\n",
    "sns.displot(df_clean['price'], bins=25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb69105e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff750dcfc10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows extreme values for price\n",
    "\n",
    "sns.displot(df_clean['total_reviews'], bins=25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e22afb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff7445e45e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXbElEQVR4nO3df5Bd5X3f8ffHkg3ENhiIYBRJrvCgSQNuioNC8I+mjuUGxfEY2kKsNA7KVK5Sl3TsuE0C9nTadIaOaTPBoSkk1LgIgg0KMUFxi20qbKetKVh2ML8JsklAgSDZOJg0Mbbg2z/us/HVerVapD377Grfr5k795zvPc/Z71msj88+99xzU1VIkubei3o3IEmLlQEsSZ0YwJLUiQEsSZ0YwJLUydLeDcy19evX1yc+8YnebUhaXDJVcdGdAX/1q1/t3YIkAQMHcJI/SXJPkruS7Gi145LcmuTh9nzs2PYXJdmZ5KEkZ43VT2/72ZnksiRp9SOS3NDqdyRZPeTxSNJsmosz4B+rqtOqam1bvxDYXlVrgO1tnSSnABuAU4H1wOVJlrQxVwCbgTXtsb7VNwFfr6qTgUuBS+bgeCRpVvSYgjgb2NKWtwDnjNWvr6pnq+oRYCdwRpLlwNFVdXuNPrZ3zaQxE/u6EVg3cXYsSfPd0AFcwKeSfCHJ5lY7saqeAGjPJ7T6CuCxsbG7Wm1FW55c32dMVe0FngaOH+A4JGnWDX0VxOur6vEkJwC3Jnlwmm2nOnOtaerTjdl3x6Pw3wzwyle+cvqOJWmODHoGXFWPt+fdwE3AGcCTbVqB9ry7bb4LWDU2fCXweKuvnKK+z5gkS4FjgKem6OPKqlpbVWuXLVs2OwcnSYdosABO8tIkL59YBn4cuBfYBmxsm20Ebm7L24AN7cqGkxi92XZnm6Z4JsmZbX73/EljJvZ1LnBbeXs3SQvEkFMQJwI3tffElgIfqapPJPk8sDXJJuBR4DyAqrovyVbgfmAvcEFVPdf29S7gauAo4Jb2ALgKuDbJTkZnvhsGPB5JmlVZbCeMa9eurR07dvRuQ9Li4ifhJGk+MYAlqRMDWJI6MYAlqRMDeAZWrHolSQ74WLHKD3lImrlFdz/gg/H4rsd4+29/7oDb3fDzr5uDbiQdLjwDlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6sQAlqRODGBJ6mTwAE6yJMkfJfl4Wz8uya1JHm7Px45te1GSnUkeSnLWWP30JPe01y5LklY/IskNrX5HktVDH48kzZa5OAN+N/DA2PqFwPaqWgNsb+skOQXYAJwKrAcuT7KkjbkC2AysaY/1rb4J+HpVnQxcClwy7KFI0uwZNICTrAR+EvjQWPlsYEtb3gKcM1a/vqqerapHgJ3AGUmWA0dX1e1VVcA1k8ZM7OtGYN3E2bEkzXdDnwF/EPhl4Pmx2olV9QRAez6h1VcAj41tt6vVVrTlyfV9xlTVXuBp4PjJTSTZnGRHkh179uw5xEOSpNkxWAAneSuwu6q+MNMhU9Rqmvp0Y/YtVF1ZVWurau2yZctm2I4kDWvpgPt+PfC2JG8BjgSOTvI7wJNJllfVE216YXfbfhewamz8SuDxVl85RX18zK4kS4FjgKeGOiBJmk2DnQFX1UVVtbKqVjN6c+22qnoHsA3Y2DbbCNzclrcBG9qVDScxerPtzjZN8UySM9v87vmTxkzs69z2M77rDFiS5qMhz4D35wPA1iSbgEeB8wCq6r4kW4H7gb3ABVX1XBvzLuBq4CjglvYAuAq4NslORme+G+bqICTpUM1JAFfVZ4DPtOWvAev2s93FwMVT1HcAr56i/k1agEvSQuMn4SSpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpk8ECOMmRSe5M8qUk9yX51VY/LsmtSR5uz8eOjbkoyc4kDyU5a6x+epJ72muXJUmrH5Hkhla/I8nqoY5HkmbbkGfAzwJvqqq/C5wGrE9yJnAhsL2q1gDb2zpJTgE2AKcC64HLkyxp+7oC2AysaY/1rb4J+HpVnQxcClwy4PFI0qwaLIBr5C/b6ovbo4CzgS2tvgU4py2fDVxfVc9W1SPATuCMJMuBo6vq9qoq4JpJYyb2dSOwbuLsWJLmu0HngJMsSXIXsBu4taruAE6sqicA2vMJbfMVwGNjw3e12oq2PLm+z5iq2gs8DRw/RR+bk+xIsmPPnj2zdHSSdGgGDeCqeq6qTgNWMjqbffU0m0915lrT1KcbM7mPK6tqbVWtXbZs2QG6lqS5MSdXQVTVXwCfYTR3+2SbVqA9726b7QJWjQ1bCTze6iunqO8zJslS4BjgqSGOQZJm25BXQSxL8oq2fBTwZuBBYBuwsW22Ebi5LW8DNrQrG05i9GbbnW2a4pkkZ7b53fMnjZnY17nAbW2eWJLmvaUD7ns5sKVdyfAiYGtVfTzJ7cDWJJuAR4HzAKrqviRbgfuBvcAFVfVc29e7gKuBo4Bb2gPgKuDaJDsZnfluGPB4JGlWDRbAVXU38Jop6l8D1u1nzMXAxVPUdwDfNX9cVd+kBbgkLTR+Ek6SOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOjGAJakTA1iSOplRACd5/UxqkqSZm+kZ8H+eYU2SNEPT3gsiyWuB1wHLkrx37KWjgSVTj5IkzcSBbsbzEuBlbbuXj9W/wej2j5KkgzRtAFfVZ4HPJrm6qv50jnqSpEVhprejPCLJlcDq8TFV9aYhmpKkxWCmAfy7wG8BHwKeO8C2kqQZmGkA762qKwbtRJIWmZlehvYHSf5FkuVJjpt4DNqZJB3mZnoGPPHFl780VivgVbPbjiQtHjMK4Ko6aehGJGmxmVEAJzl/qnpVXTO77UjS4jHTKYgfHls+ktG3Gn8RMIAl6SDNdAriX46vJzkGuHaQjiRpkTjY21H+FbBmNhuRpMVmpnPAf8DoqgcY3YTnB4CtQzUlSYvBTOeAf21seS/wp1W1a4B+JGnRmNEURLspz4OM7oh2LPCtIZuSpMVgpt+I8VPAncB5wE8BdyTxdpSSdAhmOgXxfuCHq2o3QJJlwP8EbhyqMUk63M30KogXTYRv87UXMFaSNIWZngF/IskngY+29bcD/2OYliRpcTjQd8KdDJxYVb+U5B8BbwAC3A5cNwf9SdJh60DTCB8EngGoqo9V1Xur6hcZnf1+cNjWJOnwdqAAXl1Vd08uVtUORl9PJEk6SAcK4COnee2o2WxEkhabAwXw55P8s8nFJJuALwzTkiQtDge6CuI9wE1JfobvBO5a4CXAPxywL0k67E0bwFX1JPC6JD8GvLqV/3tV3TZ4Z5J0mJvp/YA/DXx64F4kaVHx02yS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdDBbASVYl+XSSB5Lcl+TdrX5ckluTPNyejx0bc1GSnUkeSnLWWP30JPe01y5LklY/IskNrX5HktVDHY8kzbYhz4D3Av+qqn4AOBO4IMkpwIXA9qpaA2xv67TXNgCnAuuBy5Msafu6AtgMrGmP9a2+Cfh6VZ0MXApcMuDxSNKsGiyAq+qJqvpiW34GeABYAZwNbGmbbQHOactnA9dX1bNV9QiwEzgjyXLg6Kq6vaoKuGbSmIl93Qismzg7lqT5bk7mgNvUwGuAOxh9zf0TMApp4IS22QrgsbFhu1ptRVueXN9nTFXtBZ4Gjp/i529OsiPJjj179szSUUnSoRk8gJO8DPg94D1V9Y3pNp2iVtPUpxuzb6HqyqpaW1Vrly1bdqCWJWlODBrASV7MKHyvq6qPtfKTbVqB9ry71XcBq8aGrwQeb/WVU9T3GZNkKXAM8NTsH4kkzb4hr4IIcBXwQFX9+thL24CNbXkjcPNYfUO7suEkRm+23dmmKZ5Jcmbb5/mTxkzs61zgtjZPLEnz3oy+E+4gvR74WeCeJHe12vuADwBb21fbPwqcB1BV9yXZCtzP6AqKC6rquTbuXcDVwFHALe0Bo4C/NslORme+GwY8HkmaVYMFcFX9b6aeowVYt58xFwMXT1HfwXe+lXm8/k1agEvSQuMn4SSpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpEwNYkjoxgCWpk8ECOMmHk+xOcu9Y7bgktyZ5uD0fO/baRUl2JnkoyVlj9dOT3NNeuyxJWv2IJDe0+h1JVg91LJI0hCHPgK8G1k+qXQhsr6o1wPa2TpJTgA3AqW3M5UmWtDFXAJuBNe0xsc9NwNer6mTgUuCSwY5EkgYwWABX1R8CT00qnw1sactbgHPG6tdX1bNV9QiwEzgjyXLg6Kq6vaoKuGbSmIl93Qismzg7lqSFYK7ngE+sqicA2vMJrb4CeGxsu12ttqItT67vM6aq9gJPA8cP1rkkzbL58ibcVGeuNU19ujHfvfNkc5IdSXbs2bPnIFuUpNk11wH8ZJtWoD3vbvVdwKqx7VYCj7f6yinq+4xJshQ4hu+e8gCgqq6sqrVVtXbZsmWzdCiSdGjmOoC3ARvb8kbg5rH6hnZlw0mM3my7s01TPJPkzDa/e/6kMRP7Ohe4rc0TS9KCsHSoHSf5KPBG4HuT7AL+LfABYGuSTcCjwHkAVXVfkq3A/cBe4IKqeq7t6l2Mrqg4CrilPQCuAq5NspPRme+GoY5FkoYwWABX1U/v56V1+9n+YuDiKeo7gFdPUf8mLcAlaSGaL2/CSdKiYwBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicGsCR1YgBLUicLPoCTrE/yUJKdSS7s3Y8kzdSCDuAkS4D/AvwEcArw00lO6dbQi5aSZEaPFate2a1NSfPD0t4NHKIzgJ1V9RWAJNcDZwP3d+nm+b28/bc/N6NNb/j51w3cjKT5LlXVu4eDluRcYH1VvbOt/yzwI1X1C5O22wxsbqvfDzz0An/U9wJfPcR2h7YQegT7nE0LoUdYGH0O3eNXq2r95OJCPwPOFLXv+n+UqroSuPKgf0iyo6rWHuz4ubAQegT7nE0LoUdYGH326nFBzwEDu4BVY+srgcc79SJJL8hCD+DPA2uSnJTkJcAGYFvnniRpRhb0FERV7U3yC8AngSXAh6vqvgF+1EFPX8yhhdAj2OdsWgg9wsLos0uPC/pNOElayBb6FIQkLVgGsCR1YgBPYz59zDnJqiSfTvJAkvuSvLvVj0tya5KH2/OxY2Muar0/lOSsOex1SZI/SvLxedzjK5LcmOTB9jt97XzrM8kvtv/W9yb5aJIj50OPST6cZHeSe8dqL7ivJKcnuae9dlmSqS4rne0+/1P7b353kpuSvKJrn1XlY4oHozf1vgy8CngJ8CXglI79LAd+qC2/HPhjRh+//o/Aha1+IXBJWz6l9XwEcFI7liVz1Ot7gY8AH2/r87HHLcA72/JLgFfMpz6BFcAjwFFtfSvwc/OhR+BHgR8C7h2rveC+gDuB1zK6nv8W4CfmoM8fB5a25Ut69+kZ8P79zcecq+pbwMTHnLuoqieq6ott+RngAUb/SM9mFCa053Pa8tnA9VX1bFU9AuxkdEyDSrIS+EngQ2Pl+dbj0Yz+cV4FUFXfqqq/mG99MrpK6agkS4HvYXSNe/ceq+oPgacmlV9QX0mWA0dX1e01SrlrxsYM1mdVfaqq9rbV/8voswPd+jSA928F8NjY+q5W6y7JauA1wB3AiVX1BIxCGjihbdar/w8Cvww8P1abbz2+CtgD/Lc2VfKhJC+dT31W1Z8BvwY8CjwBPF1Vn5pPPU7yQvta0ZYn1+fSP2V0Rgud+jSA929GH3Oea0leBvwe8J6q+sZ0m05RG7T/JG8FdlfVF2Y6ZIraXPyOlzL60/SKqnoN8P8Y/dm8Pz1+l8cyOis7Cfg+4KVJ3jHdkClq3f/3yv776tpvkvcDe4HrJkr76WfQPg3g/Zt3H3NO8mJG4XtdVX2slZ9sfybRnne3eo/+Xw+8LcmfMJqyeVOS35lnPU783F1VdUdbv5FRIM+nPt8MPFJVe6rq28DHgNfNsx7HvdC+dvGdP//H64NLshF4K/AzbVqhW58G8P7Nq485t3derwIeqKpfH3tpG7CxLW8Ebh6rb0hyRJKTgDWM3kwYTFVdVFUrq2o1o9/XbVX1jvnUY+vzz4HHknx/K61jdAvT+dTno8CZSb6n/bdfx2jefz71OO4F9dWmKZ5JcmY7vvPHxgwmyXrgV4C3VdVfTep/7vsc4l3Sw+UBvIXR1QZfBt7fuZc3MPrT527grvZ4C3A8sB14uD0fNzbm/a33h5jld5hn0O8b+c5VEPOuR+A0YEf7ff4+cOx86xP4VeBB4F7gWkbv0HfvEfgoo3npbzM6Q9x0MH0Ba9uxfRn4Tdoncwfucyejud6Jf0O/1bNPP4osSZ04BSFJnRjAktSJASxJnRjAktSJASxJnRjAktSJAazDXpK3ZZZvJ9ruH3HKAba5Osm5U9RXJ/kns9mPFiYDWIe9qtpWVR+Y5X2+s6ruP8jhqwEDWAawFrZ2NvlgOyO9N8l1Sd6c5P+0m4OfkeTnkvxm2/7qdlPtzyX5ylRnqGP7fmOSz+Q7N26/buJm3K2+ti1vSvLHrfZfJ35W86NT/KwPAH8vyV0Z3XT91CR3tvW7k6wZ6NelecYA1uHgZOA3gB8E/jajs8s3AP8aeN8U2y9vr7+VURhO5zXAexjdsPtVjG449DeSfB/wb4AzgX/Qfv6BftaFwP+qqtOq6lLgnwO/UVWnMfrY6y60KBjAOhw8UlX3VNXzwH3A9hp9xv4eRn/uT/b7VfV8m0I48QD7vrOqdrV93zXF/s4APltVT9XormW/exA/63bgfUl+BfhbVfXXB+hJhwkDWIeDZ8eWnx9bf57RvX+n2/5A3+81vu1zU+zvhYyfctuq+gjwNuCvgU8medMB9qnDhAEsHZo7gb+f5Nj21UH/eAZjnmH0vX4AJHkV8JWquozRbRF/cJBONe9MdXYgaYaq6s+S/AdGXw/1OKP7Cj99gGF3A3uTfAm4GjgSeEeSbwN/Dvz74TrWfOLtKKVDlORlVfWX7Qz4JuDDVXVT7740/zkFIR26f5fkLkY37X6E0Q3epQPyDFiLXpK/w+gbJ8Y9W1U/0qMfLR4GsCR14hSEJHViAEtSJwawJHViAEtSJ/8fU2X91Z+A5MEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows extreme values for price\n",
    "\n",
    "sns.displot(df_clean['min_nights'], bins=25) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b067f632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff750d46c40>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows extreme values for price\n",
    "\n",
    "sns.displot(df_clean['availability_365'], bins=25) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24edc47",
   "metadata": {},
   "source": [
    "# 3) Data prep for Regression analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "714f2908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3a) Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df_clean.plot(x = 'price', y='total_reviews',style='o')\n",
    "plt.title('Price vs Total Reviews')  \n",
    "plt.xlabel('Price')  \n",
    "plt.ylabel('Total Reviews')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "325ec9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3b) Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df_clean.plot(x = 'min_nights', y='price',style='o')\n",
    "plt.title('Minimum Nights vs Price')  \n",
    "plt.xlabel('Minimum Nights')  \n",
    "plt.ylabel('Price')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "748c9197",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3c) Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df_clean.plot(x = 'host_listings_count', y='price',style='o')\n",
    "plt.title('`Host Listings vs Price')  \n",
    "plt.xlabel('Host Listings Count')  \n",
    "plt.ylabel('Price')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1bc0236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA1F0lEQVR4nO3de5xU9X3/8debZYFFxfWCVhfMktSCFxQUlQTbmkuDuUqNUYg2pDWxjzxsEhNLg42PRG2stDZpLq3+4iUVoyKo6QZjDCYa28SKsmZBJIZEAwKLUaKgKIsuy+f3xzmzzM7OmcvOmTlnZj/Px2MfO/vdMzPf850z53O+l/P9ysxwzjnnhmpE0hlwzjlX3zyQOOecq4gHEueccxXxQOKcc64iHkicc85VxAOJc865inggccOapPslzQ8ff0LSL0p83hWSbivw/3WSzsjdVtJRkl6T1FR57pORXWbOgQcSV2ckPSxpu6TRcbyemb3PzBbH8Vo5r3ucmT2cJ32Tme1vZn3Qvz+fjPv9yyHpFklvhgHuZUk/kTQlavtqlZmrXx5IXN2Q1A78KWDAh5PNTcP5VzPbH5gAvAjckruBAn7OcIP4QeHqyceBlQQnuUxz1GhJOyQdn9lI0nhJPZIOk3SQpB9K2hbWZH4oaULWtpE1AknflLRZ0quSnpD0pzmbjJG0VNJOSb+UdGLWczdKek+e12yXZJJGSrqaIDD+R1gb+A9J/ynpaznPuVfSJXle6/9J+rectB9I+kL4+IuSusP8rZf07vzFuo+Z7QLuAI7PKp+rJT0C7ALemltmkj4l6enwfX4l6aQw/UhJ94Rlv0HSZ4u9v6tPHkhcPfk4cHv4M1vS4Wb2BvB9YF7WducC/2NmLxIc4/8FvAU4CugB/qPE91sFTAMOJji53iVpTNb/zwLuyvp/h6TmUnfGzL4E/Bz4u7C56++AxcC8zJW/pEOBdwNL8rzEHcB5khRuexDwXuBOSZOBvwNOMbMDgNnAxmJ5krQ/cD7QlZX8V8BFwAHAcznbfxS4guCzGUdQU3wpzP+9wBqgLdyHSyTNLpYHV388kLi6IOl0gmCwzMyeAJ4FPhb++w4GBpKPhWmY2Utmdo+Z7TKzncDVwJ+X8p5mdlv4/D1m9jVgNDA5a5MnzOxuM+sFvg6MAWYOfS/BzB4HXiE48QLMBR42sxfybP5zgma+TE3pHOBRM9sK9IX5PVZSs5ltNLNnC7z130vaATwD7A98Iut/t5jZurAcenOe90mCZrFVFnjGzJ4DTgHGm9lVZvammf0OuDHcH9dgPJC4ejEfeMDM/hD+fUeYBvAQ0CLpNElvIahF/DeApLGSviPpOUmvAv8LtJYyakrSpWGTzSvhSfZA4NCsTTZnHpjZXmALcGQlOxlaDFwQPr4A+F6+jSyYcfVO9gXRjxHU1jCzZ4BLCGoLL0q6U1KhvP2bmbWa2R+Z2Ydzgs7myGfBRIKgnustwJFhs+OOsPz+ETi8wGu5OjUy6Qw4V4ykFoLmqiZJvw+TRxMEhBPNbI2kZQQn1BeAH4a1D4BLCWoRp5nZ7yVNI2i2UZH3/FPgiwQ1g3VmtlfS9pznTczafgRBR/XWMncv3/TbtwFPhX0uxwAdBZ6/BHhA0iLgNOAv+1/Y7A7gDknjgO8A/0LQTFWuQlOEbwbeFpG+wcyOHsL7uTrjNRJXD+YQNNUcS1DbmEZwgv05Qds8hP0FBO37d2Q99wCCfpEdkg4GvlLiex4A7AG2ASMlfZmgDyDbyZLOljSS4Or/DYLBAOV4AXhrdoKZbSHon/kecI+Z9UQ92cy6wjzeBKwwsx0AkiZLepeCYdK7Ccqgr8y8leImgmaxk8NRXX8c1gofB14NO/xbJDVJOl7SKVXIg0uYBxJXD+YD/xXeg/H7zA9Bp/n5kkaa2WPA6wRNS/dnPfcbQAvwB4KT/I9LfM8V4ev8hqCDeTeDm3h+QBC8thNc6Z+dpw+hmG8C54Qjyr6Vlb4YmEpEs1aOJcB7GBhARwOLCPb798BhBE1LsTKzuwj6ne4AdhLUng4O75P5EEHQ3xDm4yaC5kHXYOQLWzmXPpL+jKCJqz3sf3EutbxG4lzKhEOIPwfc5EHE1QMPJM6liKRjgB3AEQTNcs6lnjdtOeecq4jXSJxzzlVk2N1Hcuihh1p7e3vS2XDOubryxBNP/MHMxuf737ALJO3t7XR2diadDeecqyuSnov6nzdtOeecq4gHEueccxXxQOKcc64iw66PxLlient72bJlC7t37046KzUxZswYJkyYQHNzyUupODeABxLncmzZsoUDDjiA9vZ2wjWjGpaZ8dJLL7FlyxYmTZqUdHZcnapaIJH0XeCDwItmllm282BgKdBOsFrbuWa2PfzfZcCFBDOUftbMVoTpJxMsrdoC/Aj4nJlZOKvprcDJwEvAeWa2sRr7cv6Nj/LIsy/3/z3rbQdz+6feXo23qiuXd6xlyWOb6TOjSWLeaRP56pypSWerYrt37x4WQQRAEocccgjbtm1LOiuuijq6url2xXq27ujhyNYWFsyezJzpbbG9fjX7SG4BzsxJWwg8GK5R8GD4N5KOJVg57bjwOddlLTx0PcEyn0eHP5nXvBDYbmZ/DPw7wVoLscsNIgCPPPsy59/4aDXerm5c3rGW21Zuoi+cGaHPjNtWbuLyjrUJ5ywewyGIZAynfR2OOrq6uez7a+ne0YMB3Tt6uOz7a+no6o7tPaoWSMzsf4GXc5LPIpgem/D3nKz0O83sDTPbQLDc56mSjgDGmdmj4Wpwt+Y8J/NadwPvVhW+EblBpFj6cLHksfyL5kWlO+eSce2K9fT0DlyKpqe3j2tXrI/tPWo9autwM3seIPx9WJjexsC1HraEaW3h49z0Ac8xsz0E61wfku9NJV0kqVNSp1fh49EXMUdbVLqrji9/+cv89Kc/TTobLsW27si/LlpU+lCkpbM9X03CCqQXes7gRLMbgBsAZsyY4We6GDRJeYNG0zBsJql2+3OUvr4+rrrqqqq/j6tvR7a20J0naBzZ2hLbe9S6RvJC2FxF+PvFMH0LWetfs2/t6y3h49z0Ac8Jlzo9kMFNaRWb9baDy0ofLuadNrGs9EZVrfbnjRs3MmXKFObPn88JJ5zAOeecw65du2hvb+eqq67i9NNP56677uITn/gEd999NwCrVq3iHe94ByeeeCKnnnoqO3fupK+vjwULFnDKKadwwgkn8J3vfCeGvXb1ZMHsybQ0Nw1Ia2luYsHsybG9R60DyXKCZVMJf/8gK32upNGSJhF0qj8eNn/tlDQz7P/4eM5zMq91DvCQVWFO/Ns/9fZBQcNHbcFX50zlgplH9ddAmiQumHlUQ4zaKkc125/Xr1/PRRddxJNPPsm4ceO47rrrgOC+j1/84hfMnTu3f9s333yT8847j29+85usWbOGn/70p7S0tHDzzTdz4IEHsmrVKlatWsWNN97Ihg0bKs6bqx9zprdxzdlTaWttQUBbawvXnD011lpzNYf/LgHOAA6VtAX4CsEa0sskXQhsAj4KYGbrJC0DfgXsAS4O13wG+DT7hv/ez771uG8GvifpGYKayL5vVcyGe9CI8tU5U4dd4MhVzfbniRMnMmvWLAAuuOACvvWtYEn38847b9C269ev54gjjuCUU04BYNy4cQA88MADPPnkk/21lldeeYXf/va3fs/IMDNneltVm1urFkjMbF7Ev94dsf3VwNV50juB4/Ok7yYMRM4lpZrtz7mDEDN/77fffoO2NbO8w3jNjG9/+9vMnj274vw4F8Xn2nKuAtVsf960aROPPhrcr7RkyRJOP/30yG2nTJnC1q1bWbVqFQA7d+5kz549zJ49m+uvv57e3l4AfvOb3/D6669XnDfnsnkgca4C1Wx/PuaYY1i8eDEnnHACL7/8Mp/+9Kcjtx01ahRLly7lM5/5DCeeeCJ/8Rd/we7du/nkJz/Jsccey0knncTxxx/P3/7t37Jnz56K8+ZctmG3ZvuMGTPMF7ZyhTz99NMcc8wxieZh48aNfPCDH+Spp56qyfulYZ9dukl6wsxm5Puf10icc85VxAOJcynU3t5es9qIc5XyQOJcHsOpyXc47aurDg8kzuUYM2YML7300rA4wWbWIxkzZkzSWXF1LC1zbTmXGhMmTGDLli3DZo2OzAqJzg2VBxLncjQ3N/ud386VwZu2nHPOVcQDiXPOuYp4IHHOOVcRDyTOOecq4oHEOedcRTyQOOecq4gHEueccxXxQOKcc64iHkicc85VxAOJc865inggcc45VxEPJM455yrikzY652qio6uba1esZ+uOHo5sbWHB7MmxrG3vkueBxDlXdR1d3Vz2/bX09PYB0L2jh8u+vxbAg0kD8KYt51zVXbtifX8Qyejp7ePaFesTypGLkwcS51zVbd3RU1a6qy8eSJxzVXdka0tZ6a6+eCBxzlXdgtmTaWluGpDW0tzEgtmTE8qRi5N3tjvnqi7Toe6jthqTBxLnXE3Mmd7mgaNBedOWc865inggcc45V5FEAomkz0taJ+kpSUskjZF0sKSfSPpt+PugrO0vk/SMpPWSZmelnyxpbfi/b0lSEvvjnHNp1tHVzaxFDzFp4X3MWvQQHV3dsb5+zQOJpDbgs8AMMzseaALmAguBB83saODB8G8kHRv+/zjgTOA6SZnhH9cDFwFHhz9n1nBXnHMu9TKzCnTv6MHYN6tAnMEkqaatkUCLpJHAWGArcBawOPz/YmBO+Pgs4E4ze8PMNgDPAKdKOgIYZ2aPmpkBt2Y9xznnHLWZVaDmgcTMuoF/AzYBzwOvmNkDwOFm9ny4zfPAYeFT2oDNWS+xJUxrCx/npg8i6SJJnZI6t23bFufuOOdcqtViVoEkmrYOIqhlTAKOBPaTdEGhp+RJswLpgxPNbjCzGWY2Y/z48eVm2Tnn6lYtZhVIomnrPcAGM9tmZr3A94F3AC+EzVWEv18Mt98CTMx6/gSCprAt4ePcdOecc6FazCqQRCDZBMyUNDYcZfVu4GlgOTA/3GY+8IPw8XJgrqTRkiYRdKo/HjZ/7ZQ0M3ydj2c9x7myVXtki3NJmDO9jWvOnkpbawsC2lpbuObsqbHeHFrzO9vN7DFJdwO/BPYAXcANwP7AMkkXEgSbj4bbr5O0DPhVuP3FZpbpOfo0cAvQAtwf/jhXNl8vwzWyas8qoGDA0/AxY8YM6+zsTDobLmVmLXqI7jydj22tLTyy8F0J5Mi5dJH0hJnNyPc/v7PdOXy9DOcq4ZM2OkcwgiVfjaSa62X4GuauFmpxnHmNxDlqv15GLe42dq5Wx5kHEueozciWbL6GuauFWh1n3rTlXKiW62V4n4yrhVodZ14jcS4Bvoa5q4VaHWceSJxLgK9h7mqhVseZN205lwBfw9zVQq2OM78h0TnnXFF+Q6Jzzrmq8UDinHOuIh5InHPOVcQDiXPOuYp4IHHOOVcRH/7rnHMNrBaTNnogcc65BlWrBdu8acs55xpUrSZt9EDinHMNyidtdM45VxGftNE551xFfNLGFPElUSvnZehc7WW+Y1csX8eOnl4AxjTHX3/wQFJErUY9NDIvQ+eS9caevf2Pt+/qjf37501bRaR5SdSOrm5mLXqISQvvY9aih1K73neay9C5RleL75/XSIpI65Ko9XSVn9YydG44qMX3z2skRaR1SdR6uspPaxk6NxzU4vvngaSItC6JWk9X+WktQ+eGg1p8/7xpq4i0Lol6ZGsL3XmCRhqv8tNahs4NB7X4/vlSu3Uqt48EgquMa86e6ido51zsCi216zWSOuVX+c65tPBAUsfmTG/zwOGcS1wigURSK3ATcDxgwN8A64GlQDuwETjXzLaH218GXAj0AZ81sxVh+snALUAL8CPgc1aFtjq/K9s5Vy+SOF8lNWrrm8CPzWwKcCLwNLAQeNDMjgYeDP9G0rHAXOA44EzgOkmZIQjXAxcBR4c/Z8ad0UxfRPeOHox992uk9eY/59zwldT5quaBRNI44M+AmwHM7E0z2wGcBSwON1sMzAkfnwXcaWZvmNkG4BngVElHAOPM7NGwFnJr1nNiU0/3azjnhrekzldJ1EjeCmwD/ktSl6SbJO0HHG5mzwOEvw8Lt28DNmc9f0uY1hY+zk0fRNJFkjoldW7btq2szNbT/RrOueEtqfNVEoFkJHAScL2ZTQdeJ2zGiqA8aVYgfXCi2Q1mNsPMZowfP76szPpd2c65epHU+SqJQLIF2GJmj4V/300QWF4Im6sIf7+Ytf3ErOdPALaG6RPypMfK78p2ztWLpM5XNQ8kZvZ7YLOkzJ69G/gVsByYH6bNB34QPl4OzJU0WtIkgk71x8Pmr52SZkoS8PGs58RmzvQ2rjl7Km2tLQhoa23xm/6cc6mU1PkqkTvbJU0jGP47Cvgd8NcEQW0ZcBSwCfiomb0cbv8lgiHCe4BLzOz+MH0G+4b/3g98ptjw30a5s90552qp0J3tPkWKc865ogoFEp/91znnXEU8kDjnnKuIBxLnnHMV8UDinHOuIh5InHPOVaSkQCLpTyQ9KOmp8O8TJF1e3aw555yrB6XWSG4ELgN6AczsSYIZeYeFjq5uZi16iEkL72PWood85l/nnMtS6nokY83s8eAG8n57qpCf1Mld0jYzLTPgd7c75xyl10j+IOlthJMiSjoHeL5quUoRn0beOecKK7VGcjFwAzBFUjewAbigarlKEZ9G3jmXhHpambWkQGJmvwPeE64bMsLMdlY3W+lxZGsL3XmChk8j75yrlnprUi8pkEj6Z+Bfw5UMkXQQcKmZNfzIrQWzJ/OFpavZm5U2Ikx3jauergZd44lqUr/y3nVlHYeZ47h7Rw9NEn1mtFXheC61j+R9mSACYGbbgffHlosU63zu5QFBBGBvmO4aU1LrXjuXEdV0vn1Xb8nHYfZxDNAXTtBbjeO51EDSJGl05g9JLcDoAts3jCWPbS4r3dU/H2Dhklao6bzU4zDfcZwR9/FcaiC5DXhQ0oWS/gb4CbA4tlykWF/ENPtR6a7++QALl7RCTeelHofFtovzeC4pkJjZvwJXA8cAxwH/FKY1vCblWxo+Ot3Vv6TWvXYuY870NlpbmvP+r9TjsNh2cR7PJc+1ZWb3m9nfm9mlZrYithyk3LzTJpaV7upfUuteO5ftig8fV9FxmO84HsrrlKLgqC1JvzCz0yXtJLwZMfMvwMxsXGw5SamvzpkKBH0ifWY0Scw7bWJ/ums8mdEsPmrLJanS4zD7+dUeteVL7TrnnCuqoqV2JY3IzPrrnHPO5SoaSMxsL7BG0lE1yI9zzrk6U+pcW0cA6yQ9DryeSTSzD1clV84552JzecfaqvbzlhpIroztHZ1zztXM5R1ruW3lpv6/+8z6/44rmBRs2pI0RtIlwEeBKcAjZvY/mZ9YcuCcc65qajE7R7E+ksXADGAt8D7ga7G9s3POuaqrxewcxZq2jjWzqQCSbgYej+2dnXPOVV3m/pF86XEpViPpzTwws2GxtK5zzjWSWszOUaxGcqKkV8PHAlrCv4fNne2uPL6Oh3PVMdTvVi1m5ygYSMws/0QtzuVRb6u6OVcvKvludXR187Nfb2NvlaZHgTImbXSuGF/HozwdXd3MWvQQkxbex6xFD/nCWS7SUL9btVqkzQOJi42v41E6X4XRlWOo361aXdyVekNi7CQ1AZ1At5l9UNLBwFKgHdgInBsu6Yuky4ALgT7gs5lp7CWdDNwCtAA/Aj5nVZiF0tv988stl9axzWzf1TtoO1/HY3BZ7XpzT+QX3I8tl+vI1pb+JXNz0wup1cVdkjWSzwFPZ/29EHjQzI4GHgz/RtKxwFyCBbXOBK4LgxDA9cBFwNHhz5lxZ9KvHPPLVy6v7d5Dc9PAIYW+jkf+ssoXcMFrby6/oa6RU6tF2hIJJJImAB8AbspKPot9y/cuBuZkpd9pZm+Y2QbgGeBUSUcA48zs0bAWcmvWc2Lj7f755SuX3r3GfqNG0tbagoC21hauOXvqsL/CLrR2di6vvbl85kxv45qzp5b93arVIm1JNW19A/gH4ICstMPN7HkAM3te0mFhehuwMmu7LWFab/g4N30QSRcR1Fw46qjyJjH2dv/8ovb/lZ5eVn/lvTXOTbqVc6y8c8r4KubE1bM509vKviir1SJtNQ8kkj4IvGhmT0g6o5Sn5EmzAumDE81uAG6AYGGr0nIaGGrbZKPzcildVFnl87Nfb6tybtxwM5QAVK4kmrZmAR+WtBG4E3iXpNuAF8LmKsLfL4bbbwGyb8GcAGwN0yfkSY/VgtmTaR4xMGY1j9Cwb/f3dc1LV2jt7FylBhznylHtoeY1DyRmdpmZTTCzdoJO9IfM7AJgOTA/3Gw+8IPw8XJgrqTRkiYRdKo/HjaD7ZQ0U5KAj2c9J165dZ/4pqipW0Ntsx2O8pVV1DRHcc5/5BzUZsBQYsN/81gELJN0IbCJYOp6zGydpGXAr4A9wMVmlum5/DT7hv/eH/7E6toV6+ntG9ga1ttnPkyT2lSZ02gow8Fzy6p94X15t4tzRlbnoPCAobi+v4kGEjN7GHg4fPwS8O6I7a4Grs6T3gkcX70ceme7GyiuaWDaIvpN2ryPyVUg30VOLc5hfmd7EbUah+2GrpZTjcQ1HDzJPiafmqUxRTVhHdjSnHf7OM9hHkiK8E7ldKv1DaNxXd0l1cfkN9g2rqiLHImqn8PS1EeSSrUah+2Gphbtv9niHPacRB9TrcvL1U7UxcyOXb38+3nTqnoO80BSguHaqVwPat2HtWD25AF9JFBfNVTv82tchS5yqn0O86YtV9dq3YdV78Oevc+vcSXZDO81ElfXkqgh1HMNtd5rVC5aks3wHkhcXfM+rPJ4eTW2fBc5tVgGQ1VYviPVZsyYYZ2dnUlnwznnqi73vicIJuY4f+ZRZa/ZLukJM5uR73/eR+Kccw0q3yg9A25fuSnWId8eSJxzrkFFjcYziHVNJQ8kzjnXoAqNxotzyLd3tpfg8o61LHlsM31mNEnMO21i2e2LGY2+/nuj71+9i/p8hvK5DdfPupz9TrqMFsyezOeXrs67UFOcQ749kBRxecdablu5qf/vPrP+v8sNJnFN+JdWjb5/9S7q8+l87mXueaK7rM9tuH7W5ex3GspozvQ2rvnRr3hh55uD/jd2VHwNUt60VcSSxzaXlV5Io6//3uj7F7daT54Y9fkseWxz2Z/bcP2sy9nvtJRRviAC8NsXX4/tPbxGUkTU+hBDWTei0aenaPT9i1MSV6tRn0PUsVzoc6v1Z510E1FGOfs9nL4PXiMpImrFuqGsZNfo01M0+v7FKYmr1ajPIepYLvS51fKzTtOMxeXs93D6PnggKWLeaRPLSi+k0aekf+eU8WWlD2dJXK1GHX/zTptY9nFZy2M5LU1EUN5+p+X7PuttB5eVPhQeSIqY8Zb8hR2VXki9T/hXzM9+va2s9OEsiavVqOPvq3Omln1c1vJYTlMTUTn7nZbv++2fevugoDHrbQdz+6feHtt7+BQpRUy78gF29PQOSm9taWb1V94bZ9ZS0w48VJMW3pd3mKGADYs+UOvspFq+qStampsa6sIiLrMWPRS5LPEjC9+VQI6Gp0JTpHhnexH5gkih9KFKw1DBSsW56FOj88kTS+czFpevo6ubK5av6z9PHTS2ma986LiqHV/etJUSaWoHHqoFsyeT222rMD1tig29rWRobqnP7XzuZX7/ym4M+P0ru+l87uWKXq9RpaWJqF50dHWz4K41Ay52t+/q5ZKlq5l25QNVOX68RlLEqCbxZt/gBptRTeWP2iokTe3AQ3VX56ZBTVsWpqfpS1+s9ldJ7bDU55Z6o2sj1FTjUM9rwNTatSvW07s3f5fFjp7eqhw/XiMpIuoDiUofqkYYKvjIs/mvqKPSk1Ks9ldJ7bDU55Z6o2sj1FRdbRW7+Ozp7ePKe9fF+p4eSIqIGosQ9xiFtAwVHA6K1f4qqR2W+txSb3RthJqqq61SLj637+r1aeRrKc4bEgvxduDaaR3bnDc98wWspHZY6nNLPa4aoabqamvB7Mk0jyh+fvJp5GsozhsSi5kzvY1HFr6LDYs+wCML31V3QaQWNz5VqqOrm9d27xmU3tyk/tpfJbXDUp9b6nHlNdXhqaOrm+lXPUD7wvtoX3hfWZ3kc6a3ce1HT6S1Jf8FU0actVoPJEV8dc5ULph5VP+VYpPEBUNYpnI4qMWNT5WK6ojcb9TI/sBdSe2w1OeWelxVq6Y63EeCpVlHVzcL7l7D9l37Rl3t6OllwV1rygomq7/y3kGjKLPFWav1GxLdsOI3TfrNkGkXdQMmlH8TZtRrCfj386aV9Xn7mu0p4FeA6eB9Dj4SLO2GMutylHdOGZ/33q7zZx7lw3/rTZpmLx3uvM/BR4Kl3VBmXc6no6ube57oHlADzwSRuJvma35DoqSJwK3AHwF7gRvM7JuSDgaWAu3ARuBcM9sePucy4EKgD/isma0I008GbgFagB8Bn7MqtNW1L7xvUNrGMppBCl0Bpr0pod7n/8qVhqlJki7TqKlsRkh0dHWn8vNNusxqacHsySy4ew29OTdCN49QWRc8VyxfN+i8Y8BtKzexvKubJ688M47sAsnUSPYAl5rZMcBM4GJJxwILgQfN7GjgwfBvwv/NBY4DzgSuk5S5pLweuAg4OvyJr2RC+YJIofR86vUKsFFrUkmOjktDmearlUFwD0saP980lFktzZnexrXnnMhBWcPUW1uaufajJ5Z8rHZ0dRecD/DVN/o44Ss/rjivGTWvkZjZ88Dz4eOdkp4G2oCzgDPCzRYDDwNfDNPvNLM3gA2SngFOlbQRGGdmjwJIuhWYA9xfq30pVb1OZljPNam0SkOZZt7n0mVrBt0AmcbPNw1lFrdiNaxKp4Qppb/r1Tf6im5TqkT7SCS1A9OBx4DDwyCTCTaHhZu1AdnzRmwJ09rCx7np+d7nIkmdkjq3bav92hj12i5frzWpNEtLmc6Z3sbeISyxm4S0lFlcqlnDygzqiRr1VS2JBRJJ+wP3AJeY2auFNs2TZgXSByea3WBmM8xsxvjxtV+tr17vWh/KCKdqjU5rlFFvaRo1lqa8FFIv+SxVVA3rkqWrKzq2swNUrSUSSCQ1EwSR283s+2HyC5KOCP9/BPBimL4FyL7ddwKwNUyfkCc9lerxrvVya1LVutJqpDbyqP6J7a+/UfP9qZeachJLOFfzwqVQTaqSYztfgCpk3OjBx+FQ1TyQSBJwM/C0mX0961/Lgfnh4/nAD7LS50oaLWkSQaf642Hz105JM8PX/HjWc2LT0py/iKLSG0m5NalCbdmVfDEb6b6HOdPb+MjJg8tvV+/esu5cjisv9VBTrvUSztW+cClWkxrqsV1OTWSkiHXUVhLrkcwC/gpYK2l1mPaPwCJgmaQLgU3ARwHMbJ2kZcCvCEZ8XWxmmbPKp9k3/Pd+qtDR3hcxXXxUOjTWUMVyOv2irrQyX8ShrqnRaG3kUSfA3r1W8w7keljno9aff7U79/Ot+JhrKPvWJOWdVVoMbvPfY8GaOHHdT5LEqK1fkL9/A+DdEc+5Grg6T3oncHx8uRss36JWhdKH80JEUaPTmqSKvpj1OuotSpx3Lg8Htf78qx24su9liqpFDGXfopYmiLrkXfLY5tgCSeO3z9RYIzXDlCuqzT3qAC/1i1kvbfmliuvO5eGi1p9/LTr3M32m3zhvWmz71lZm/qK+l0PhS+3GrNKrmbiaxWrdvJZ5v57evv4qdlv4vlFXXqV+MdNwN3qcFsyezKV3rRnUPNo8Qrxzyvj+4ZuZcmxtaUaCHbt6Y9/3JJthS3nvQsdVtfKZr+mpWoEr6tiGYMLFcj6XqDvia8EDScwqqYbH1SxW6+a13PfrM+v/4mXer9IvZj205ZdjBMF8P9lOnXQQ9zzRPaAcgQF3KMf5WSbZDJvvvT+/dDWXLF3dHyiAosdVNdT6wiX32K7oc0loMndv2opZ1PDOXW/uKTrqI65msVo3r0W9X2Zd6HoZHVQrUWuirPzd9pKGb8b1WSbZDJvvvTMlkjlxXnnv4LmiapW/JIfrD/VziTquasFrJDHLHHBXLF834Epy+67eolcVcXXy1XqUS9TrZtaFzlxxDdfAkSuqvMpps47js0xyNFyx9+jp7YsMqo0+IKHQ51KoOTDJcvEaSRXMmd7GfqMHx+jMVXrU/RRxdfLV+k7gQq87HAYZlCuqvIqvsl38NeLIRy06/Ct5j9axzWXfk1ToPqa0zZoQVTYHtjQXvL8lyYEaHkiqpNBVevaBkH0TWlyjU2o9yqXQ68Z1lVTsy562k0Gu7Py9/sYeRuSJGiNGiOZ8/8gR12eZ5Gi4qCbgbK0tzYO2aW4Sr+3eU9bNgoVuMEzjrAkLZk+muWngcdDcJCQKNnmVUqbV4k1bVRLV6Z6rd6/x+aWr+fzS1RzZ2sJHTm7jZ7/elrfqmtZRLnOmtw1qysuI4yqpWOdjmu7dyfcZwcBO46jpvfv2GuPGNjN21MiajNpKcjRc7r0UuTfNiaCcWluaGdM8on/fX39jz6DyK3ZPUqE+vLGjRpZ8j1NNR7jltnIaA9Zwz9a9o4dJC+/rP3/8cM3zBaeQrwZfs72IQuuOfKPAmsf51sUuRdTa2aWss53kWtzVfO+o2Uwz61cX+3+tRJXB6JEjSv5iD6e147NlTtL5gkr2cTRp4X15Byblllv2SX8oZ7h8r1er71Yls/c2NwmMkjvdy1mgz9dsr5JCVeB8I5VKETU6o5SRHEmOwqnmyKxincJpmUIlqvzLuTocIdVFO362OPKWGSXV1toy6MTf09vH55eupj0iiMDAmm9uc1UhTcrflJhbk67GjL1R5VbJcdvbZ4mM3PKmrQoUq1LnjlSaftUDkdXTbNlV1Uz1uZSRHFFXMbU6oVZrZFaxe3PSMoVKHOWcGbmV3TwHpKbpLlfczYpRZVjo1Jjbr1POLLiZe1OK3eNUyoy9EL3Puc1i75wyfsA9Q9mvUWqzeJp4IKlQKSePyzvWsuSxzWUN78x0/H1hWXCTVpTMSI5CX5wjW1si23c7urq58t51/QGutaWZKz58XN4vRL4vQ3Z/TvshLTzy7MsDnnP0Yfvxky+ckTdfpbY5F7vTuJZ3Ikfp6OpmRMSkeRDctV7ulWJ2bTKOSQQraeOPem7cExwO7SQ6sFzLCejZsy8UKpdi+cre59yyaj+khf979uUB98nctnJT3te4Yvk6rvjwcSy4a01i94QMhfeRFFFsbfaDws7RqIPw8o61eQ+azHNf271nyAdMS3MTY5pHFKzltDQ38ZGT2wZc/WSnL121Oe+UCgeNbeYrHzquYP9LqfIFk3LbnAsFsQNbmunt28vrbwavVSgYlqqck25HV3fRL372cXJgS3NZfSaQ/4q8nP6UStr4o577kZPbIo9tGNz+XupgkaGcREcIvn5u0GdZTh/DfqOauPovSyuDLyxdzd4C2wj49/OmDfl7kp2nzLFcbXH1kXggKaJQIMnXsZX75YzqHITgwDuwpZk39/Sxq7fQITpYk8TXzj2Rzy9dHfn6mautqBFVUdNO59uXSpfvzD1gK+kgLxbUyu0ELdbsUOg1O7q6C34GGdkn/XLKMtO3VulggkrKO+q5+aYnz/7fka0tZZdpR1f3kOeLGts8goP2G523w74UbXlq2dnB7q2X3Ueh+Fbos0or72xPgXwdWz29fVy6bN+9IYUOZiMY4lhuEAH619seEdFZmH2CiBxuWuQiIrtpJe5+lko6yIu1gZczwCDffQS3r9xU0qCFzHNLOWFl30RX6okm0zwXx/0elZT3UPotMmWZ+X1biWV65b3rhjzp4K7evf1lO5RXyOQz6p6SQkEk83k0+l33UbyPpAr6zPonoKuWsaOaIq+ERfAlmH7VAwWbUIrVSGDfSSTuDsBKOsgrOfnlKjTnU7HXLLVTt2mEeKWnt6SBFrDvaj636Se31nTtivVcsnR1SfcLRZV369jmvDWy+558vj+/5dxxX67uHT1Mu/IBXunpLavJr1ay+z4KfV8yNatCg14amddIqqTaDYavv9kX+R6Z9O27eikUJ+adNpGmIndSZ07sC2ZPruiEkjvU8Z1Txg/5KruUYFPqbMvlfOkNhjRMs2+vFbyazZU56V+6bA3tYXl1PrdvEMOuN/ew9PHN/XnPHum14O41XN6xdtCw0qj1zbfv6uXSu9YMqj1kB71qH8s7enr7a+dplPmc5502Me//L5h5VH/wTvLu8iR5H0kRxTrbkyBRMECUYr9RTbSOHVX0RJrdcX15x1puX7lpSCeW5iYNaLJobhLnnTIxsj26kFI6/psEY5r3dVpm2syjpigvR6Ztv16uPpubxP6jR5ZcI6qEwoJutLNKZvBJ53Mv94/AbJKYd9rEQasMdnR186X/XluzDvNKeGf7EDVCIKlUuXe/Zp+EM52RcZxADxrbTNeX39v/d7kjpYZ6Ii/WERtcURo9RfquKh115+pP8wjYf0xz/5Qt+TrngboZvuuBZIg8kFRmBIAKdzyWI3MgF6plXDDzKL46Z2reQFONWsE3zptW0kgsCIad1sH5wrm84gok3tnuyrIXYm+36Ojq5tJlayI7Mm9buYkN217jl5teGXQncCXj9aNcumxNybvoQcQ5DyQuYdOveoDXdu8pOnos9455GHy3d1zKmYHAOeeBxCWsFh3Azrnq8uG/zjnnKuKBxDnnXEU8kDjnnKuIBxLnnHMV8UDinHOuIh5InHPOVcQDiXPOuYp4IHHOOVeRug8kks6UtF7SM5IWJp0f55wbbuo6kEhqAv4TeB9wLDBP0rHJ5so554aXug4kwKnAM2b2OzN7E7gTOCvhPDnn3LBS74GkDdic9feWMG0ASRdJ6pTUuW3btpplzjnnhoN6DyT5Vn8dNHWrmd1gZjPMbMb48fmXHHXOOTc09R5ItgDZCylPALYmlBfnnKsb3zhvWmyvVe/TyK8CjpY0CegG5gIfi/MNNi76QNFVEkeOEHvCFY5aW5r54IlH9C9Hm72CXvb659mr/bWObcYMXunpZUzzCN7Ys5e9Bk0SM996EOu27mRHTzDdeub12spY43woclcjjFpSNHfFwlLWtK4kH1Hvm1umUUv1Xt6xlttWbhqQNuttB3P7p95edj6GUvbZr5P7WVdaVpUo9/M+sKUZif4lZ3PLo5zPLbP9lfeu619WIPu7kvua3Tt6aJLoM+OgrO9OVL6L5asa36Fi7xO1v5kyyrfqZ77vfFQ5Z7+2BBYeY31mVTl31P1Su5LeD3wDaAK+a2ZXF9q+3KV2nXPONfhSu2b2I+BHSefDOeeGq3rvI3HOOZcwDyTOOecq4oHEOedcRTyQOOecq0jdj9oql6RtwHNDfPqhwB9izE61eD7jUw95BM9nnOohj1D7fL7FzPLe0T3sAkklJHVGDX9LE89nfOohj+D5jFM95BHSlU9v2nLOOVcRDyTOOecq4oGkPDcknYESeT7jUw95BM9nnOohj5CifHofiXPOuYp4jcQ551xFPJA455yriAeSEkk6U9J6Sc9IWph0fjIkbZS0VtJqSZ1h2sGSfiLpt+HvgxLI13clvSjpqay0yHxJuiws2/WSZieczyskdYdlujqcYTqxfEqaKOlnkp6WtE7S58L0VJVngXymrTzHSHpc0pown1eG6akpzwJ5TFVZ9jMz/ynyQzBF/bPAW4FRwBrg2KTzFeZtI3BoTtq/AgvDxwuBf0kgX38GnAQ8VSxfwLFhmY4GJoVl3ZRgPq8A/j7PtonkEzgCOCl8fADwmzAvqSrPAvlMW3kK2D983Aw8BsxMU3kWyGOqyjLz4zWS0pwKPGNmvzOzN4E7gbMSzlMhZwGLw8eLgTm1zoCZ/S/wck5yVL7OAu40szfMbAPwDEGZJ5XPKInk08yeN7Nfho93Ak8DbaSsPAvkM0pS+TQzey38szn8MVJUngXyGCWx7xB401ap2oDNWX9vofAXpJYMeEDSE5IuCtMON7PnIfhyA4cllruBovKVxvL9O0lPhk1fmSaOxPMpqR2YTnCFmtryzMknpKw8JTVJWg28CPzEzFJXnhF5hJSVJXggKZXypKVl3PQsMzsJeB9wsaQ/SzpDQ5C28r0eeBswDXge+FqYnmg+Je0P3ANcYmavFto0T1qS+UxdeZpZn5lNAyYAp0o6vsDmieQzIo+pK0vwQFKqLcDErL8nAFsTyssAZrY1/P0i8N8E1dkXJB0BEP5+MbkcDhCVr1SVr5m9EH6J9wI3sq+JILF8SmomODnfbmbfD5NTV5758pnG8swwsx3Aw8CZpLA8c/OY1rL0QFKaVcDRkiZJGgXMBZYnnCck7SfpgMxj4L3AUwR5mx9uNh/4QTI5HCQqX8uBuZJGS5oEHA08nkD+gP6TSMZfEpQpJJRPSQJuBp42s69n/StV5RmVzxSW53hJreHjFuA9wK9JUXlG5TFtZdmvVr369f4DvJ9gFMqzwJeSzk+Yp7cSjNRYA6zL5As4BHgQ+G34++AE8raEoOrdS3C1dGGhfAFfCst2PfC+hPP5PWAt8CTBF/SIJPMJnE7QTPEksDr8eX/ayrNAPtNWnicAXWF+ngK+HKanpjwL5DFVZZn58SlSnHPOVcSbtpxzzlXEA4lzzrmKeCBxzjlXEQ8kzjnnKuKBxDnnXEU8kLhUk/SXkkzSlApe4yZJx4aPN0o6tMj2r0WkXyXpPeHjhyXNGGJ+2pU123DcJP0ocw+Cc7UwMukMOFfEPOAXBDeBXjGUFzCzT8aRETP7chyvU21m9v7iWyVPUpOZ9SWdD1c5r5G41ArnbJpFcJPg3DDtfZKWZW1zhqR7w8fXS+rMXr8hTM9be5DUEU52uS5rwsvM/74m6ZeSHpQ0Pky7RdI5eV7nvZIeDbe/K8x37jYnh2tLPApcnJXeLunn4XN/KekdYfr3JJ2Vtd3tkj4s6TgF61SsDifuOzrPe22UdGj42k9LujHcxwfCu6Rzt/+QpMckdUn6qaTD82zzc0nTsv5+RNIJ4ewK35W0Knz+WUX26wwFa5bcQXBjnWsEtbz70X/8p5wf4ALg5vDx/xGsGzIS2ATsF6ZfD1wQPj44/N1EMDfRCeHfDwMzwscbCddvydq+heDu4UPCvw04P3z8ZeA/wse3AOdkvyZwKPC/Wfn5IuFdyDn78iTw5+HjawnXPwHGAmPCx0cDneHjPwc6wscHAhvCff92Vt5GAS153mtjmK92YA8wLUxflimrnO0Pgv6bkz8JfC3PNvOBb4SP/yQrn/+cVf6tBLM/7Fdgv84AXgcmJX18+U98P14jcWk2j2DtF8Lf88xsD/Bj4EOSRgIfYN+cSOdK+iXB1BLHESz2U8hnJa0BVhJMeJe5ut8LLA0f30Yw9UeUmeH7PKJgyu/5wFuyN5B0INBqZv8TJn0v69/NwI2S1gJ3ZfIcbvvHkg4Ly+GecN8fBf5R0heBt5hZT5F93GBmq8PHTxAEl1wTgBVhHhYQlF2uu4APKpiU8W8IgioE87stDPf9YWAMcFTUfoUet2DNDNcgvI/EpZKkQ4B3AcdLMoJahkn6B4KT/MUEC1KtMrOd4UR1fw+cYmbbJd1CcFKLev0zCCbCe7uZ7ZL0cIHtC80jJIK1IuYV2SbqNT4PvACcSNDUvDvrf98Dzido1vsbADO7Q9JjBAF0haRPmtlDBd77jazHfQS1r1zfBr5uZsvDcrkid4OwjH5CsIDSuQS1scy+fcTM1mdvL+mKAvv1eoH8ujrkNRKXVucAt5rZW8ys3cwmEjTvnE5w5XsS8Cn21RzGEZygXgnb+N9X5PUPBLaHJ8gpBDWLjBHh+wN8jKCzP8pKYJakPwaQNFbSn2RvYME04K9IytRszs/Jx/MWTAv+VwQBM+MW4JLwNdaFr/9W4Hdm9i2CSftOKLKfpTgQ6A4fzy+w3U3AtwiCd2ZVyRXAZyQpzN/0rNeM2i/XYDyQuLSaR7C+SrZ7gI9ZMNLnhwTB4ocAZraGoElrHfBd4JEir/9jYKSkJ4F/IggIGa8Dx0l6gqBWdFXUi5jZNuATwJLwtVYC+YYq/zXwn2Fne3Zz1HXAfEkrCfoe+q/WzewFguVq/ytr+/OAp8KmpCnArUX2sxRXAHdJ+jnwh6iNzOwJ4NWc/PwTQTPWk+GQ5n8K0yP3yzUen/3XuZSSNJZgZNNJZvZKCvJzJEFtcEpY03AO8BqJc6mk4MbHXwPfTkkQ+TjB+utf8iDicnmNxDnnXEW8RuKcc64iHkicc85VxAOJc865inggcc45VxEPJM455yry/wErGfqTPhwKJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df_clean.plot(x = 'availability_365', y='price',style='o')\n",
    "plt.title('Availability vs Price')  \n",
    "plt.xlabel('Availabile days in a year')  \n",
    "plt.ylabel('Price')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44fbfb2",
   "metadata": {},
   "source": [
    "#### Based on the scattered plot charts, it would be good to test the relationship of total_reviews and price."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6471d09",
   "metadata": {},
   "source": [
    "# 4) State hypothesis\n",
    "\n",
    "### The lower the price the more reviews a property gets. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb4f605e",
   "metadata": {},
   "source": [
    "# 5) Reshape variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ae23803",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the variables into NumPy arrays and put them into separate objects.\n",
    "\n",
    "X = df_clean['price'].values.reshape(-1,1)\n",
    "y = df_clean['total_reviews'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7d333cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[149],\n",
       "       [225],\n",
       "       [150],\n",
       "       ...,\n",
       "       [115],\n",
       "       [ 55],\n",
       "       [ 90]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "746931a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9],\n",
       "       [45],\n",
       "       [ 0],\n",
       "       ...,\n",
       "       [ 0],\n",
       "       [ 0],\n",
       "       [ 0]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b113efb",
   "metadata": {},
   "source": [
    "# 6) Split data into training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c517a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30198c0",
   "metadata": {},
   "source": [
    "# 7) Run linear regression on data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b68b925d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a regression object.\n",
    "\n",
    "regression = LinearRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12a7fb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the regression object onto the training set.\n",
    "\n",
    "regression.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "807cce43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the values of y using X.\n",
    "\n",
    "y_predicted = regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8c3dd7b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows the regression line from the model on the test set.\n",
    "\n",
    "plot_test = plt\n",
    "plot_test.scatter(X_test, y_test, color='gray', s = 15)\n",
    "plot_test.plot(X_test, y_predicted, color='red', linewidth =3)\n",
    "plot_test.title('Price vs Total Reviews (Test set)')\n",
    "plot_test.xlabel('Price')\n",
    "plot_test.ylabel('Total Reviews')\n",
    "plot_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "97438bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create objects that contain the model summary statistics.\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_predicted) \n",
    "r2 = r2_score(y_test, y_predicted) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bd5dc88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[-0.00851049]]\n",
      "Mean squared error:  1860.2148440330943\n",
      "R2 score:  0.0022161963441751364\n"
     ]
    }
   ],
   "source": [
    "# Print the model summary statistics. This is where you evaluate the performance of the model.\n",
    "\n",
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ae00f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.95473351],\n",
       "       [19.34628732],\n",
       "       [22.31644703],\n",
       "       ...,\n",
       "       [23.17600615],\n",
       "       [23.59301998],\n",
       "       [23.25260053]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4a1a7892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.954734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>19.346287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>22.316447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>24.648320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>23.805782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>24.452579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>156</td>\n",
       "      <td>24.018544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>23.380258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>24.146202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>118</td>\n",
       "      <td>24.273859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>23.303663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>23.805782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>106</td>\n",
       "      <td>24.103649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>42</td>\n",
       "      <td>24.120670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>58</td>\n",
       "      <td>23.312174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>94</td>\n",
       "      <td>24.316411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>24.231306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>25</td>\n",
       "      <td>24.061097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>22</td>\n",
       "      <td>24.401516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>24.214285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>24.614278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>24.444069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>23.167496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>23.848335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>16.367617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>23.805782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37</td>\n",
       "      <td>23.252601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>22.316447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>22.954734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>23.737698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0        0  22.954734\n",
       "1        5  19.346287\n",
       "2        5  22.316447\n",
       "3       13  24.648320\n",
       "4        4  23.805782\n",
       "5        1  24.452579\n",
       "6      156  24.018544\n",
       "7       14  23.380258\n",
       "8        0  24.146202\n",
       "9      118  24.273859\n",
       "10       0  23.303663\n",
       "11       2  23.805782\n",
       "12     106  24.103649\n",
       "13      42  24.120670\n",
       "14      58  23.312174\n",
       "15      94  24.316411\n",
       "16       4  24.231306\n",
       "17      25  24.061097\n",
       "18      22  24.401516\n",
       "19      18  24.214285\n",
       "20       7  24.614278\n",
       "21      54  24.444069\n",
       "22       1  23.167496\n",
       "23       0  23.848335\n",
       "24       2  16.367617\n",
       "25       5  23.805782\n",
       "26      37  23.252601\n",
       "27       1  22.316447\n",
       "28       2  22.954734\n",
       "29       0  23.737698"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe comparing the actual and predicted values of y.\n",
    "\n",
    "data = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_predicted.flatten()})\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84f13b6",
   "metadata": {},
   "source": [
    "# 8) Plot that shows the regression line on the test set\n",
    "\n",
    "#### Compare how regression fits traning set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6cb647a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict.\n",
    "\n",
    "y_predicted_train = regression.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2a44558c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(y_train, y_predicted_train)\n",
    "r2 = r2_score(y_train, y_predicted_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc1a6dff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[-0.00851049]]\n",
      "Mean squared error:  2031.689790421126\n",
      "R2 score:  0.002120940611156974\n"
     ]
    }
   ],
   "source": [
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "beca0fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the training set results.\n",
    "\n",
    "plot_test = plt\n",
    "plot_test.scatter(X_train, y_train, color='green', s = 15)\n",
    "plot_test.plot(X_train, y_predicted_train, color='red', linewidth =3)\n",
    "plot_test.title('Price vs Total Reviews (Test set)')\n",
    "plot_test.xlabel('Price')\n",
    "plot_test.ylabel('Total Reviews')\n",
    "plot_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79be4ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpretations and insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f34bb2",
   "metadata": {},
   "source": [
    "# 10. Write your own interpretation of how well the line appears to fit the data.\n",
    "\n",
    "#### On the surface, it appears like the line is fit to the data. But a closer inspection of the summary statistics show that it's not a fit at all. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db17585",
   "metadata": {},
   "source": [
    "# 11. Check the model performance statistics—MSE and R2.\n",
    "\n",
    "#### MSE for both test and training models are very high (1860.21 and 2031.69 respectively) which indicates that the model is a bad fit.\n",
    "\n",
    "#### R2 score for both test and training models are closer to zero (.002 for both) which again indicates a poor fit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce02f30",
   "metadata": {},
   "source": [
    "# 12. Compare the predicted y values with the actual y values in a dataframe.\n",
    "\n",
    "#### The predicted y values and the actual y values are not even close to each other. The difference in the values are massive. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9325b13e",
   "metadata": {},
   "source": [
    "# 13. Reflections\n",
    "\n",
    "#### The model proved that price and total_reviews are possibly unrelated to each other or at least using just those variables. It's still possible that they are somehow related but there could be other variables or factors that affect to draw the relationship between the two. The fact that the predicted values show absolutely no commonality proves thhat the model is a bad fit. This also disproves the hypothesis that the lower the price, the higher the number of reviews. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "742085e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}