Machine Learning/Python/Ensemble Learning/Kc_House_data_Analysis_and_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "prospective-promise",
   "metadata": {},
   "source": [
    "   # kc house data Analysis and Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "built-pension",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as  np\n",
    "import seaborn as sb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import linear_model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "viral-provision",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('kc_house_data.csv',delimiter=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "defensive-listing",
   "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>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n",
    "#notice the varible bedrooms and grade are ordinal variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "negative-boundary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21613 non-null  int64  \n",
      " 1   date           21613 non-null  object \n",
      " 2   price          21613 non-null  float64\n",
      " 3   bedrooms       21613 non-null  int64  \n",
      " 4   bathrooms      21613 non-null  float64\n",
      " 5   sqft_living    21613 non-null  int64  \n",
      " 6   sqft_lot       21613 non-null  int64  \n",
      " 7   floors         21613 non-null  float64\n",
      " 8   waterfront     21613 non-null  int64  \n",
      " 9   view           21613 non-null  int64  \n",
      " 10  condition      21613 non-null  int64  \n",
      " 11  grade          21613 non-null  int64  \n",
      " 12  sqft_above     21613 non-null  int64  \n",
      " 13  sqft_basement  21613 non-null  int64  \n",
      " 14  yr_built       21613 non-null  int64  \n",
      " 15  yr_renovated   21613 non-null  int64  \n",
      " 16  zipcode        21613 non-null  int64  \n",
      " 17  lat            21613 non-null  float64\n",
      " 18  long           21613 non-null  float64\n",
      " 19  sqft_living15  21613 non-null  int64  \n",
      " 20  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(5), int64(15), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaningful-rings",
   "metadata": {},
   "source": [
    " lets drop the 'id' column. Not important for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "substantial-disorder",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('id',inplace=True,axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chronic-threat",
   "metadata": {},
   "source": [
    "there should be interger in columns 'bathrooms' and 'floors' not float numbers. Let's turn them on integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "racial-pocket",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bathrooms'] = np.round(data['bathrooms'])\n",
    "data['floors'] = np.round(data['floors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "sufficient-spray",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-video",
   "metadata": {},
   "source": [
    "## Engineer Datetime Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinguished-active",
   "metadata": {},
   "source": [
    "Out Date is of type object and our unsupervised Machine Learning algorithms only work with numeric variables so we've to convert date object into numbers \n",
    "[NOTE]Decision trees, Regression trees, and ensemble-based on these methods can handle categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "composite-township",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = pd.to_datetime(data['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "refined-reggae",
   "metadata": {},
   "source": [
    "By converting the strings into datetimes, this exposes all the pandas dt properties."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "artificial-newport",
   "metadata": {},
   "source": [
    "![title](date.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "objective-laptop",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Syed Muhammmad Hamza\\anaconda3\\envs\\PY37\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "data['year'] = data['date'].dt.year\n",
    "data['month'] = data['date'].dt.month\n",
    "data['week'] = data['date'].dt.week\n",
    "data['day'] = data['date'].dt.day\n",
    "#data['hour'] = data['date'].dt.hour\n",
    "#data['minute'] = data['date'].dt.minute\n",
    "data['dayofweek'] = data['date'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "individual-wilderness",
   "metadata": {},
   "source": [
    "I would start by graphing the time variable vs other variables and looking for trends.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "younger-jesus",
   "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": [
    "plt.plot(data['date'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('time')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-semester",
   "metadata": {},
   "source": [
    "there's no seasonality, Periodic pattern, or trend in daily data that means we don't need any new feature to catch any seasonality, Periodic pattern, or trend but let's resample our data on different time scales for further analysis and drop some feaure that we generated from date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "interesting-kuwait",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['date'],axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "welsh-presentation",
   "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": [
    "plt.plot(data['day'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('DAILY')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "comprehensive-receiver",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['day'],axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "about-eight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x8g3DyAxm4A3DMHKKGXgjnNbWePmGYWQGM/BGODffDM3Nw/Oam718w6gWFslVEWbgjXC6u+H224eH6d1+u0V1GNVjaD+e7dtB9ch+PGbky2IG3ihPdzds2waDg95fM+5GNbH9eCom0Th4EdkGvAoMAIdU1d7uZBhGPGw/noqpxkKnTlV9qQrnMQwjj5x0kueW8cs3QjEXjWEY2cb246kYUdXkKhf5FfAKoMA/q+oSnzI9QA9AW1tbx/Lly0Pr7O/vp6WlJQFps0k96VtPuoLpG4dJK1ZwytKljO3r48CkSWydP5++uXMdS+iOarZtZ2fn2kD3t6omloATC38nARuAt4eV7+jo0HKsWrWqbJk8UU/61pOuqqZvnqmmrsAaDbCpibpoVPWFwt8+4DvAuUmezzAMwzhCYgZeRCaKyFFD/wPzgI1Jnc8wDMMYTpJRNG3Ad0Rk6Dz/oqo/SPB8hmEYRhGJGXhV3QpMT6p+wzAMIxwLkzTqF9vfxMg59kYnoz4Z2t9kaAn80P4mYFsxGLnBRvBGfWL7mxh1gBl4oz6x/U2MOsAMvFGfBO1jUkv7m9gcglEGM/BGfXLhhfHys4btkW5EwAy8UZ88+GC8/KxhcwhGBMzAG/VJrfvga11+oyqYgTfqk1r3wde6/EZVMANv1Ce1vsd4rctvVAUz8EZ90t0NS5YMf5n4kiW1s8ip1uU3qoKtZDXql+7u2jaItS6/kTg2gjeqh8VtG0ZViTyCF5EpwKmqukJExgNNqvpqcqIZucL2fjGMqhNpBC8iHwHuA/65kDUZ+G5CMhl5xOK2DaPqRHXRfAJ4G7AbQFWfxXvPqmFEw+K2DaPqRDXwB1T14NCBiDQBmoxIRi6xuG3DqDpRDfwjIvJZYLyI/BHwr8ADUb4oIo0i8gsR+X6lQho5wOK2jSjYRLxTohr464EXgSeBjwIPAn8V8bvXApvji2bkCovbNsphG6g5J6qBHw/cpqqXqur7gNsKeaGIyGTgImBp5SIauaG7G7Ztg8FB768Zd6MYm4h3jqiWd6WLyOPAXFXtLxy3AD9U1d8v8737gK8ARwGLVPVdPmV6gB6Atra2juXLl4fK0t/fT0tLS1mZ80I96VtPuoLpW8r5c+YgPvZIRXhk5cokRXNONdu2s7Nzraqe4/uhqpZNwPooeSWfvwv4p8L/s4HvlztPR0eHlmPVqlVly+SJetK3nnRVNX1HMGWKquecGZ6mTHEnRG+vV5+I97e3113dRVSzbYE1GmBTo7po9ojI2UMHItIB7CvznbcBl4jINmA5MEdEeiOezzCyR71NAFZb36Qn4uvQxx/VwC8E/lVEHhORx4B7gKvDvqCqn1HVyaraDlwGrFTVD45GWMNIjXozDmnom/REfB36+CMZeFX9OXA6sAD4OPAWVV2bpGCGkSnqzTikpW+SE/F1uNgu1MCLyJzC3/cCFwNvBk4FLi7kRUJVV6vPBKthVEQarpJ6Mw551LcOF9uVG8GfX/h7sU8yg13vuDK0hXrOnzOnfD1puUrqzTjkUd86XGwXauBV9fMi0gD8h6peWZKuqpKMRhZxZWiL6pEo9aTlOqg343DhhfHyK6HaT2L1uNguKLymOAGPRik32uQ0TLJK4VBJk9lQOlchbXHrEfEvLzI6faLguE9ltm1VEwlZHKZvb6/qhAnD654wIb37tIbbFgdhkj8SkUUi8kYROX4oJfezM0rqLeIhDVz5aOPWk6broJ5W4ibtg8/SpHWO7UVUA38VXvTMI8CaopRNstR58oorQxu3nnpzlaRF0j+kWZrEzbG9iGrgpwK3ABuA9cA/AGckJNPoyVLnySuuDG3ceurRj5oGSf+QZmkSN8f2IqqBvxN4C/B/8Yz7Wwp52SRLnSevuDK0RfVo1HrqyVWSFkn/kC5eDGPGDM8bMyadJ7Ec24uoBv40VZ2vqqsKqQc4LUnBRkUeHuPjhA6mRVxDGxQ1UajnkZUrs2Gw621LgiCS/iEt3VgswsaHiZAHexFE0OxrcQLuAGYWHZ9HYSMxl8miaAokHWGQxrWJoFMmokqqGN2RCX2ryDB9q7GxWBxyGkUT1cBvBgaBbYU0CGzCewHIE1HqiJIqMvC1bMiDSLLzpxWeFkGnTBi8MDlr2AiEksYOi3HDXWvsPq81Az8lLEWpI0qKbeCzFkvriiRjvdMaOUXQKRMGL0jOob7lsK9lQt+0nlji9MNaus8LP0SDVfwhGrWBr1aKbeCz9pjniiT1SmuhUK2P4BsbnbdJpvVN4B6qeHBWK/d5Sj9EYQY+6iRrNgkLb6rlibIkJ33SihjIUtREGEHXfmDAv3yaoXRx+7hfeZchgnHkiROlUythjFmMpw+y/GkkZyP41tbaeaQLIqlHvbQed3t7VZubh5+3uTl7k6yq/v7epJfuVyJjnHbs7VUdM2Z4+TFjvHvFhV5JTqLXygg+padjcuuiCepUrjptBkjE6KUxYeXSRZPRKKC4jCpgIK7RC7onWlrc6JWkCy5NH3ySbeKI/Bp4Vf8GsA2psoerSdZq3OxBbZhk28bVK24f9ys7lFzolfQketI/6n71V/KUlDEffOpGvTg5i4NP65GuGqO8WsXVCC/ptq2FqJJKyocZeBfUyiS6Hy49ARmLoklsklVExonIz0Rkg4hsEpEvJHWuEVRjZZrfhFIWJ1ni8PGPQ1OTN+nV1OQdu2LxYmhuHp7X3By/TZKeFEyrDePq5ep6uqKWV4MGtfmuXf7lw/paxlZlJxlFcwCYo6rTgRnABSIyM8HzHSHpfTSCthfdvt2/fNZm+/34+Mfh1luPRIsMDHjHLo28SPhxFFxFAWWtDSvRK871bG0NzncRjZPWJnAuouXitm1Ym2Rti5Ggob3LBEwA1gHnhZVzulVBklQjVrraj3pBsjc2uqnf1SO8KxeKyzas0D88Kh98XBfNggX+5bu6quZndn7vJt0X4kbjZdAHL97nySAijcBa4E3ALap6nU+ZHqAHoK2trWP58uWhdfb399PS0pKAtNE5f84c7/VyJSgwOHYsjQcOHM4bGDuWZxYtom/u3Mj1T1qxgtNuvHFEPb++4AJe9/jjjO3r48CkSWydPz9WvWGc39mJ3/hPgUdWrRp9/UHXTMR7nCV6205asYJTli6NfB38yr/ly1920oZBbRWlzUv1fdNNN3HiAw8gg4NoQwMvXHwxWxYu9P1ulOtZzMzLLmPczp0j8gcbGmgYHByRv7+tjcd97sWgeva3tbF1/vzQdnF974bJ4ic7+PcFILANgch9bTTXZjR0dnauVdVzfD8MsvwuE3AssAqYFlau5kfwrvYrCaq/NFLB5ejA5Qje7xoETVi1th7+WmIhoXEn0KoUGlfVEXzYFgxBfc2PsO9UezO5SvazCZIxyUiiCNdmNJCFKBrg88CisDI1Y+CTfhSLczO6ih4JeoRfsCBe5w+6NhMnpmPgk14MN4qQ3FFF0bj6QYjrkgoqH6F/Om/fpCOPXMmTwDYXxaRi4IETgGML/48HHgPeFfadmjHwqsnG5QZ1lAoNSWQWLDjSGRsbjxh3F4YkgvyJtG2YAU7yaSvuCL6SHwoXP7wLFsRr3zhtm3T7Jr12wJU8Cd+7aRn4s4BfAE8AG4HPlftOVQx8LWw76tdRgjpn0rH9SbsCkl7JmuG4+apvnOdiAVclo9QkAwbS6gtB5y0MkgaLB0kJt20mXDRRUuIGPmkfnEtKb4q4Iy1XxB31jMIlEmmVctw2rMQAx+0Lfk8+ERj11tdp9Nm4TwJp9ds4sseVJWPXwAz8EEn7Y1WTX85ehSX0w3DpGy4j56j2GQprQxeujGqEDSYpp0vi9MNqPJm4kD0Oo3iKSeIeNQM/xCjcB5FI4KYrq2/SI78qjiwjuSziprhtWMWJu0QGK2kZziDS3BcqKSqJSDIDn+IIPqxhXNQ/ipuurL7VGGFXyRUQadIxborbhnENkqsomrhk0XD6uapq5YcoDkE6BbXJxInmotFqGPhKYqLjkMBNV1bfuFEi1XBTxcVvEi6unK7a0OUIPo5Lqug6ZGLyOC5hK2X99qFPe44rKn5tEqTruHH++Q0NibZVvg183NGlq4k4P7I0gm9t9X/Bhl/ZsOQywiDOtQ+bmEqyDV354CNMrI1qkjVNH7wfQf7nhobgF72kNUk8Wvdk0GAibqrlMMlKUqov3XbR2RKQp2xoWdA5gxYWVZJc3BRBBq+SFaWuXElB5eNGxVTomhh1mGQaBjKIuH0qjSfGuPenq3mgPC50qiRl8qXbroxG3HPGHc2VntOVcW9oSHbFZFhycW3iXuO4IW1B9QTpFLTwJ4s+9ThU0r5J37ulxLUXceeBgn60LEyyQgOf1sq0pEdKLn64XBn4uDeiy3PEvTYufNhxQ90qqadArkbwQX7puCnJH7Qwe9HVNTyvq6uy+aqgJ2+LosngCD6tJwQXP1wtLf51NDX55weVD7sR/WQPmlByZeDDyie55B7iz2nE8cGH7QUU1G+y5J4c0qHUVRX3Gqcxgi+dBB5KU6e6WdORMPk18EmPsF0+IcSZGHQRERK2ICjOnjNh9biYxI1r4Eex2dWo6gnrC0HnjWMEwgYTcX38LuZMXN1DQddZJDs++LB2r/DHzwy8T6pKFE0cImxxOwIXhtzFBFRcwxkke9AoLGjU4yp1dcXXK8iQuKgnboqwXUEicf9+RquSORNXI+kEjOeoiDtfVSFm4H1S5naTDDPALgx5mFHy27QoDmH7u7vwVbtKra2H/Z+D5Yx7mDxJ+87jpjADGWdDqrgpbsRG0vNYrp40gnARBGEGvgYNvIvOE9bwScbHhs3GR9UpruxBdVUysiz1wzc0qI4d61924sToIaFD7epio6cFC/wX4LhovzADGffVeWHnKJ1PCZpfGZInzg+dqxG8q2iloLqDYuzjyDJ1anCbhJ07JMQ28sDMgZ2qPwPf2+tm9Zyr0VxQirsqs9TYhk34xJUl6IaOO7IUiW84XW2dEMeABc0huPyRjtOngp6s4p437MUqKYTwFbdLpJXKruaZ/Ag7p18UTZHskZ7US+sYSglPlNefga/Ed+6Hixt96LxBPzijvamDbty4MgaNOCuZmHJ1zVwYHlexzJXIH7dPJdkHgwy/K1dJBIbdu3Gvgx9x64jrjgoaKMYdBAS99tLR01P9GXgXnUc1vp82bJQU51Ey6SeHuJ0qzsg4TTnjtGFY8otaqSSMNE7bxjUCrlIVF1INu3ddvAM4TC8/4hpUV09zQfI4mv8wA1+cXISQxd0nJW7HSvKGDvL1xh21VRIZ5DcaqsTH70LOoHj9oJW7cWUMaltX8e6uom6SjDsvwfkI3lUQhMu1En4pjyN44I3AKmAzsAm4ttx3EnfRVLJtZ1w/sB9xf6mDGt7PBx/Wyf0mOyMs0ohMUDx9kDvKxUKYsCigMhNfozpvUIq7lUORPKOaiKtEh5R3dXS+SDGor4UNwOLsMxR2Lf2eyOvJBw/8DnB24f+jgF8CU8O+4ywOPmh23dWWsmHEHcH7dbiwSIuoN3qYzzWO7GFl/aI4gtxRQTddJUY1qP44N7vLFbdxNycLihqK+8Prd96wwU0cN2ECRFqk2NVV0bVMZEvssGtZZhBTd1E0wPeAPwor43Qlq6t4V1dGL86se1DUycSJo/eFB4XLxb2WQT7poKeVpOcVgs7r0pAHGQ0XoXquolnSHNyEyRTlBy3uCDiIuO6roGswimuZlTh48T5PFhFpBx4Fpqnq7pLPeoAegLa2to7ly5eH1tXf309LSwsAMy+7jHE7d44os7+tjcd96jm/sxPxqVOBR1atGpE/acUKTrvxRhoPHDicNzB2LM8sWkTf3Lkjyv/BhRfStG/fiPxD48fzv/PmceIDDyCDg2hDAy9cfDEnPvAADYODvvIEyVmcPzB2LA0HDkQqO8TBo4+m8cCBEToNjB3LmN27fcs37d1Lw6FDh/MGm5qQQ4dinTcP+ZR8pnjXorHo2gxx8Oij+cn3vjciP6jPDjY0+PaF/W1tbJ0/n1OWLmVsXx8HJk1i6/z5vv1viEkrVowo/5YvfxnxuddVhM2f/axv/W+66aYRfXbLwoW+9QfJE+ceentXl+81GGxo4NGHHw7Ut5SgaxzYtiI8snJloPwjruXixWXtSLGdCqqnb+7cWNcyiM7OzrWqeo7vh0GW31UCWoC1wHvLlU18N8mwX3E/XE6OJhVqGHQNgp4C4kaDuEoul+KnkSp5EojbR4JShRteRerLQW6LoNF03En6OPdQ2DWIo2uQ2zKo78d9igmboC8QydPg6KmNtFw0QDPwEPDpKOUT300ybgeqxs2bx5Skr70aPxSuVrj64cpV5co15CoUMOieCxuEld5zcftUJb5zFz+WYXLG2YbC0YtAUjHweE9D3wJuivqdVH3wcULRgiI5anmU6mqZfleXu6iVJLcSCErjxrnZJTMoVC9p+YN2Cx26X5Lqs0X7Jw07b9iTw2jbMyhIIew7WQqfDLuWMUjLwP9BwS31BLC+kC4M+47TKJqkIyfiPNYm6RZxpZOrelytBE0rxd0WeMyY4OgdV3H/LlLQRKWrhVRhe6r75QftTZR0inMNglb5ulwAFXTeGIQZ+IZY3vwYqOp/qqqo6lmqOqOQHkzqfMO44QbYu3d43t69MH68u3P41b9lC3R1Dc/v6oJvfAMmTBieX3pcKYODMGbM8LzS46j1uGDXLv9rUyuoxssH+MM/HHl8771w8ODw/IMHw+tJkiVLYNkyaG+Hhgbv77JlsHixm/5TqusQTz3ln1806Vo1Wlv98597zj9/+3a48krvr+qR4xkzEhMRgAsvdFdXkOVPIzlz0YT9Ovot/nH5i1zqVxty5yQ5EvBzKQTFwacVxph0qmX3WLWSX98PWrcQNPJOK7l4Kgx6iom6uZ/rVAUffGIj+KoQNFIPo3SkOnTsYoQtAgMDw/MGBuCjH/Uvf/PN0Nw8PK/0OAqvvTbyeNw4/7rnzPGv47TT4p/Xj7Fj3dRTyXldjETzjF/fv/VW//7zzDP+dTQ2JiNbOZYsgSlTvHtsyhTvOC4PPuj/FPPqq/7lVUcj8RG6uvzvxVJbMUTQE0UF1LaBd3UhXn7ZTQcK6hB79kBPz/BHvZ4e77Pbbx9+3ttvr1yPYnbtgvnzj9yQjY3e8U9/6l8+6FE6LkGP6kmzf//I6+/qBq1HgozPwICbQUkabN8OV101/D686iq3fVZk5PGb3+zfN4NcRied5E6e0bpVXKbYLpqk9+5O+rEzaBLHRd1B77xMWidL6adS14KLl7YMpdZW/9XaSa8YTnLDtWq3x1CqZG8sXzOVVxfN/v1pSzA6hkbyfiP70aJa25OdeWbqVIDDq2OHjp2hGn4chfHjoalpeF5Tkzc5Wrpy99Ahd5P0Qfj15Ur0SoMgOffs8fccdHc7O3VT+SIZZs8eN/W8/LKbeuLS0DA6IyxSO508LVpaoL8/bSmGs2ULULRsfssWb84kSwMWv3vr0KHsXctap7vbqUEvpbZH8HEJmnQ7/nj//IaEL89oRz3VMu61Mnnp5xtOa9I3DL/wySwZd6M6lPrrEyC/Bn7BguETjAsWwFFHBZf3m11P+rGzFliwAD784eHX8sMfTlemIPxcE7t2pSOLYZRD1d/uOKQqu0lG5ZxzztE1a9aEllm9ejWzZ8/2DsJ+Af30Cis/YcJw98iECV55V26gWmXqVNi2beS1qRV/vrmx3NPcPDy0srnZu85pRVDVKq2tsG/fyHsrph9eRAJ3k8zvCN6PsBheP194XOPe2uo/MTVxYrx6ssRTT9X2ZK0Zd/f4hfbWSqhkljhwwP/euuEGZ6eobQNftN9ypPyg2F5XzJjhv9Bp5sxkz2sYaVPvT7qVEDRh7XChU21H0QSNzoLyGxuTNfIrV/r7gWO8rMAwMk9Pz5GRp8vQXsMjKOijAmp7BB80atizx3/yIukRvLkDjHqgll12dUZtj+DD+NCHjkTBbN/uHduEm2EYWcfhupz8GvigTcUMwzCyjLlock4VFkAYhpFRHC56MwOfRcyNZBjZII1V3A4jkhIz8CJym4j0icjGpM5hGIaRKH5v66ohkhzB3wFckGD9hmEYyVIa4lyNkGeHLtok38n6KJDSNo2GYRg1ikMXbepRNCLSA/QAtLW1sXr16tDy/f39h8u8XYQGn4uhFG3FmqN8P7Imo+Vb/mjy/ciajNXIf6SMHYxKopuNiUg78H1VnRalvNPNxgzDMGqVGHbZNhszDMOoQ8zAG4Zh5JQkwyTvBv4LOE1EdohIRt8SYRiGkU8Sm2RV1Q8kVbdhGIZRHnPRGIZh5BQz8IZhGDnFDLxhGEZOMQNvGIaRU8zAG4Zh5BQz8IZhGDnFDLxhGEZOMQNvGIaRU8zAG4Zh5BQz8IZhGDnFDLxhGEZOMQNvGIaRU8zAG4Zh5BQz8IZhGDnFDLxhGEZOMQNvGIaRU8zAG4Zh5JREDbyIXCAiz4jIFhG5PslzGYZhGMNJ8p2sjcAtwDuBqcAHRGSq05OceKLT6uoakbQlMOIwZox//rHHVlUMIwEaG51VleQI/lxgi6puVdWDwHLg3U7P8PzzI438iSdCb69/+a4uN/kLFoz8rKsr+LxxiWpsRTxZSjtEYyO0tPh/p7XVX/a77vKvp7XVv56g+h12zliMG+efHzQICMpfsAAaSm6LhgaYGjA2CTKocQ3txInQ3Dw8r7nZ61N++bfdNrztGxu941deGSnr1KnBfTlIzq4u/34St32DrluQPH4EnTOoDZsCXjU9caJ/fm+vv65hfb/0HhUJ1nXq1JEyNTV57eXHnXf651eAqKqzyoZVLPI+4AJVnV84/jPgPFW9uqRcD9AD0NbW1rF8+fLQevv7+2kJMi5FTFqxglOWLmVsXx8HJk1i6/z59M2dy5tuuokTH3gAGRxEGxp44eKL2bJwYezycc579MaNvOF736O4Syjw/Lvfze5p0yKXf/nss5n4/PMjZPQ7J8DpX/saDYcOHa5jsKmJp6+7jr65cyPLDnDajTfSeODA4XIDY8fyzKJFHL1x44hrs3vaNE7/8pdpKOpXgyK88ta3cvy6db46HbdhAw0DA0fKNzbyyvTpweWfeMJXLz95tixcyJl//uccv27d4fIvn302T37967HbtuOKK2jZvv3wcf+UKay9447AevzK/88HP+h7fZ7+7GdjtUtQ2TCCrkNQfpAspy9ePGxkOAg8fcMNgdc/6j330syZnHj//b7XJk7b7nznOzn9q18d0aeevt7zEke9lpNWrAi8h4LqCbqWQW3oom07OzvXquo5vh+qaiIJuBRYWnT8Z8A/hH2no6NDy7Fq1aqyZTLJggWqjY2q4P1dsCBS+cGo5f3o7VWdMkVVxPvb21uJ5PHrCSofdA0K5Qdjlh+1XmkRpG+tUOH1j3TvptVnHddTTTsFrNEgOxz0wWgTMAt4qOj4M8Bnwr6TawNfIfWkbz3pqmr65pmsGPgkffA/B04VkZNFZAxwGXB/guczDMMwigiYjRg9qnpIRK4GHgIagdtUdVNS5zMMwzCGk5iBB1DVB4EHkzyHYRiG4Y+tZDUMw8gpZuANwzBySmJx8JUgIi8C28sUex3wUhXEyQr1pG896Qqmb56ppq5TVPUEvw8yZeCjICJrNCioP4fUk771pCuYvnkmK7qai8YwDCOnmIE3DMPIKbVo4JekLUCVqSd960lXMH3zTCZ0rTkfvGEYhhGNWhzBG4ZhGBEwA28YhpFTasbA5/31fyJym4j0icjGorzjReRHIvJs4e9xacroEhF5o4isEpHNIrJJRK4t5OdOZxEZJyI/E5ENBV2/UMjPna7FiEijiPxCRL5fOM6tviKyTUSeFJH1IrKmkJe6vjVh4Kvy+r/0uQO4oCTveuBhVT0VeLhwnBcOAX+uqm8BZgKfKLRpHnU+AMxR1enADOACEZlJPnUt5lpgc9Fx3vXtVNUZRfHvqetbEwaearz+L2VU9VHg5ZLsdwND7++6E/jjasqUJKr6a1VdV/j/VTxD8AZyqHNh2+7+wmFzISk51HUIEZkMXAQsLcrOrb4BpK5vrRj4NwD/U3S8o5CXd9pU9dfgGURgUsryJIKItANvBX5KTnUuuCvWA33Aj1Q1t7oWuAn4S7w3+g2RZ30V+KGIrC28hhQyoG+i2wU7xO8t1BbfmQNEpAX4NrBQVXdL1BeO1xiqOgDMEJFjge+IyLSURUoMEXkX0Keqa0VkdsriVIu3qeoLIjIJ+JGIPJ22QFA7I/gdwBuLjicDL6QkSzXZKSK/A1D425eyPE4RkWY8475MVf+tkJ1rnVX1N8BqvPmWvOr6NuASEdmG506dIyK95FdfVPWFwt8+4Dt4buXU9a0VA1+vr/+7H7i88P/lwPdSlMUp4g3VvwlsVtW/K/oodzqLyAmFkTsiMh6YCzxNDnUFUNXPqOpkVW3Hu1dXquoHyam+IjJRRI4a+h+YB2wkA/rWzEpWEbkQz6839Pq/xelK5BYRuRuYjbfN6E7g88B3gXuBk4DngEtVtXQitiYRkT8AHgOe5Iif9rN4fvhc6SwiZ+FNsjXiDaruVdW/EZFWcqZrKQUXzSJVfVde9RWRU/BG7eC5vf9FVRdnQd+aMfCGYRhGPGrFRWMYhmHExAy8YRhGTjEDbxiGkVPMwBuGYeQUM/CGYRg5xQy8UZeISGth57/1IvK/IvJ84f9+EfmntOUzDBdYmKRR94jIXwP9qnpj2rIYhktsBG8YRYjI7KL9y/9aRO4UkR8W9vt+r4j8bWHf7x8UtlpARDpE5JHCRlMPDS1PN4y0MQNvGOH8Lt62t+8GeoFVqnomsA+4qGDk/wF4n6p2ALcBuVplbdQutbKbpGGkxX+o6msi8iTeVgM/KOQ/CbQDpwHT8HYQpFDm1ynIaRgjMANvGOEcAFDVQRF5TY9MWg3i3T8CbFLVWWkJaBhBmIvGMEbHM8AJIjILvC2QReSMlGUyDMAMvGGMisIrJN8HfE1ENgDrgd9PVSjDKGBhkoZhGDnFRvCGYRg5xQy8YRhGTjEDbxiGkVPMwBuGYeQUM/CGYRg5xQy8YRhGTjEDbxiGkVP+P+Cvoov9a+4wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['week'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('WEEKLY')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dated-shade",
   "metadata": {},
   "source": [
    "there's no seasonality, Periodic pattern, or trend in weekly data lets drop this feaure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "acute-matrix",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['week'],axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "median-accounting",
   "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": [
    "plt.plot(data['year'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('YEARLY')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adult-hierarchy",
   "metadata": {},
   "source": [
    "usually you will keep year feature even tho there is not much infomation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "olive-concord",
   "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": [
    "plt.plot(data['dayofweek'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('dayofweek')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "spoken-activity",
   "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": [
    "plt.plot(data['month'],data['price'],'ro',label='house prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('price')\n",
    "plt.title('MONTHLY')\n",
    "plt.grid(True)\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "critical-training",
   "metadata": {},
   "source": [
    "By doing TimeSeries analysis we came to conclusion there is no periodic daily,weekly.monthly or annual trend in our data. So you would not want to encode any more time variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "blind-fiction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='price'>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eB3xglH3uAO4Y/xmbmVkRJmt6s5mZzRAONGZmVigHGjMzK5QDjZmZFcqBxszMCuVAY2ZmhXKgMTOzQjnQmJlZoRxozMysUA40ZmZWKAcaMzMrlAONmZkVyoHGzMwK5UBjZmaFcqAxM7NCOdCYmVmhHGjMzKxQDjRmZlYoBxozMyuUA42ZmRXKgcbMzApVaKCRtF3Sk5K2Sto8bNsnJIWkRen9uyRtSfW3SHpnru6Dkn6YjrNV0pJU3i7pG5K2SXpY0orcPmskPZt+1hTZTjMzG125CZ9xSUTsyhdIWga8C3g+V7wL+KWI2CnpzcBG4PTc9qsjYkiwAq4B9kTEmZJWA7cAV0laCNwIdAEBbJG0ISL2TGjLzMzsiCbr1tmtwCfJggAAEfFYROxMb58GZklqP8JxrgDuTK/vBi6VJOAyYFNEdKfgsgm4fCIbYGZm41N0oAng/nQrbC2ApPcCL0bE42Ps937gsYjoz5V9Od02uyEFE8h6PC8AREQF2At05suTHQztHZHOZa2kzZI2v/LKK8fYRDMzG8u4b51Jei2wMiK+LWk2UI6I/UfY7aJ0K2wJsEnSM8D1wKoxPudNZLfA8nWujogXJZ0E3AN8EPgqoBEOEWOUDy2IuA24DaCrq+uw7WZmdvzG1aOR9D/Ibk19MRUtBf7+SPvVb4VFxMvAeuDtwBnA45K2p+M8Kuk16XOWpnofiogf5Y7zYvq9H1gHnJ827QCWpX3LwHygO1+eO9+dmJlZ04331tlHgYuAfQAR8SywZKwdJHWkHgiSOsh6KI9ExJKIWBERK8gCwrkR8RNJJwP/CFwXEd/LHaecm5nWCrwHeCpt3gDUZ5RdCTwQEUE2kWCVpAWSFqTP3jjOtpqZ2QQa762z/ogYqA+NpN7DkW41nQKsT/uUgXURcd8Y9T8GnAncIOmGVLYK6AE2piDTAnwb+FLafjtwl6RtZD2Z1QAR0S3pZuCRVO+miOgeZ1vNzGwCjTfQfFfS7wGzJb0L+A3g/461Q0Q8B7zlCHVW5F5/Gvj0KFXPG2X/PuADo2y7A7hjrM83M5vuIoJ9fRV2Hehn94F+dvcMsOvAQO51P51z27h45eLCzmG8geZ3yZ5ZeRL4deBbwF8VdVJmZnZk1VrQ3TPA7p5+dh/Ifg8PIrsPDFCpDb0BVRIsmNPGorntLF84h1Pnzyr0PMcbaGYDd0TElwAktaSyg0WdmJnZTNZfqabgkQWOXQcOBZRdKZC8enCAYTGE1haxaG47CzvaeONr5rFobhsLO9pZNDcLLJ0dbZw8p42W0qHJubt7+inSeAPNd4BfAA6k97OB+4H/UsRJmZlNVxHBwYFqo7cxWi9kf3/lsH072lpYOLedRR1tvLZzDp1z21nU0U7n3DYWzW2js6Odk2aVOfSo4dQw3kAzKyLqQYaIOCBpTkHnZGZ2QqpFsLd38PAAcmCAXbnbW32DtcP2PXl2KwvntrHkpFn89KnzUhDJeiEL57bR2dHGnLZmZA2beOM96x5J50bEowCSzgN6izstM7OppVKt0X1woHE7a6TB9e6ekcdD6reuVnTO4bzXLmj0PjrT7ayFHW20tkzfZPrjDTS/BfytpPpDj6cCVxVyRmZmTdY3WKU7BY+RxkJ2H+jn1YODhz3T0VYu0Zl6HWedNo/OFFA601jIorntzJ/dOmQ8ZCYaV6CJiEckvRF4A1l6l2ciYrDQMzMzO04RQU9/tREwhvdCutPtrQMjjYe0tzTGP85Y1MGijhRAUm9k0dw25rZPvfGQqWjMQCPpnRHxgKRfGbZppSQi4u8KPDczs1HVIth7cDDrhfQM0D1Kb6S/MnQ8RMD8Oa0s6mjnlHmzOOu0+SmItA0ZXJ/V2jI5DZuGjtSjeTvwAPBLI2wLwIHGzCbcYLWWng+pT+2tD6ingNIzQHfPANVh4yEtJdHZkQ2cv27xXH52RZrS2wgibSyY5uMhU9GYgSYibpRUAu6NiL9p0jmZ2TTWO1A9/AHDfEDpGeDVg4ffmW8vlxpB482nzWs8E9I5t73xev6cVkq+lTXlHHGMJiJqkj4GONCY2agigv19lUbQyD8Tkp/q2zNQPWzfk9rLjV7HTy2ee6gXkhtc72hr8XjICWq8s842SfoE8A2yJJdAlryykLMysymlWgtePTj8KfWhvZDdBwYYqB4+HrJgTjb+cdrJszj79PksTFN664PrCzs8HjLdjTfQfJhsTOY3hpW/bmJPx8yabbBaO9QLGTaltz643t1zeKqTckksTFN4z1wylwvOONT7qAeRBXNaKXs8ZMYbb6A5iyzI/DxZwPl/wF8WdVJmNjEODlQavY2hAeRQL2Rv7+HjIbNaS43bVmcvPbnxTEg+b9a82R4PsfEZb6C5k2zRsz9P7381lf3XIk7KzMZWT/0+1ljI7p4BDo40HjKr3Bg8X7nkpPSU+tBB9TkeD7EJNN5A84aIyK8t80+SHi/ihMxmuvp4yK5RbmPVA8pg9fBUJyfPyZIrLl0wh7csO3nYzKw2Fna00V72eIg113gDzWOSLoyI7wNIugD43hH2MbNhBiq1EdObZE+pZ2V7Rkj9Xi6pMRPr9aecxKK5nblZWVn5gmGp382mivEGmguAD0l6Pr1fDvxA0pNARMTZhZyd2Qnk4ECl0QvpzmXrzQeUfX2HpzqZ3drSGEQ/p94LGTa1d94UTP1uNl7jDTSXF3oWZlNYLYJ9vYOHTe3dlbL11sdHegcPHw+ZP7s1DaS38cbXnNS4jVUfXO+ce+Kmfjcbr/Em1fxx0SdiNhkaS+HmB9VHGBMZOfV71utYvnAO5y5fMGQspLMjez6kreypvWaFXkpJ2g7sB6pAJSK6cts+AXwOWBwRu1LZdcA1qf7HI2JjKj8P+ArZyp7fAn4zIkJSO/BV4DxgN3BVRGxP+6wBPpU+7tMRcWeRbbWpp7EU7rA1Q/KztF7tPXw8pK2llG5dtfHTr5nXuI1VXztkpKVwzWx0zeizX1IPJHWSlgHvAp7PlZ0FrAbeBJwGfFvS6yOiCnwBWAt8nyzQXA7cSxaU9kTEmZJWA7cAV0laCNwIdJE997NF0oaI2FNsU60ZIoKegerhKxemZIv18tGWwq3fulrR2ZE9pd4YC5m6S+Gancgm6+bwrcAngW/myq4Avh4R/cB/SNoGnJ96RfMi4iEASV8F3kcWaK4A/iDtfzfweWXfEJcBm+opciRtIgtOf11ss+x4He9SuJ25pXAXDRsL6exoZ3abp/aaNVvRgSaA+yUF8MWIuE3Se4EXI+LxYVeNp5P1WOp2pLLB9Hp4eX2fFwAioiJpL9CZLx9hnwZJa8l6SixfvvxY22jjlF8K9/CxkNGXwm1JqU46O9pYsaijsRRufQncmbAUrtmJrOhAc1FE7JS0hCwx5zPA9cCqEeqOdK8ixig/1n0OFUTcBtwG0NXVddh2G7++weqIT6bnn1gfbSncel6ss06b11h0Kp8vy0vhmp3YCg00EbEz/X5Z0nqyhdTOAOq9maXAo5LOJ+t1LMvtvhTYmcqXjlBObp8dksrAfKA7lb9j2D4PTmDTZoz8UriHj4UcCihjL4XbzhmLhy6Fuyg9aNjR7lQnZtNdYYFGUgdQioj96fUq4KaIWJKrsx3oiohdkjYA6yT9GdlkgJXAv0ZEVdJ+SRcCDwMfAv4iHWIDsAZ4CLgSeCDNRtsIfFbSglRvFXBdUW09UVVr9fGQoYPou3rSA4epfGCEpXBPntNKZ0c7r5k/izefPv+wqb1eCtfM6ors0ZwCrE9Xq2VgXUTcN1rliHha0t8A/wZUgI+mGWcAH+HQ9OZ70w/A7cBdaeJAN9msNSKiW9LNwCOp3k0zbe2c+lK4wx8q3JV7ZmTMpXDntvO6xXM5/4xhDxh6KVwzO0qK8NAEZGM0mzdvPqZ99x4cZOsLe1jY0T7BZzWy/FK4u3JjId1Dng8ZeyncIWMhuV7IfKd+N5txdvf0c/HKxZSOYSxU0pb8M5Ijce6LKeTQUrj1IDIwYgLGIy2Fe+biuUPGQurlXgrXzCaDA02T5JfCzc/EGhpExl4K9/STZ3P26fNzQcRL4ZrZ1OdAMwEGKjVe3t/HS3v7Rp3aO9pSuPUHCVfmlsLNryHipXDN7ETnQHOcfrK3jwv/6DuHlc9ubWnkxjp7wcm5p9QPDa57KVwzmwkcaI7TorltfOTtP8VAtcryhR2NddWd+t3MLONvw+NUbilx7dt/qqmzzszMTiS++W9mZoVyoDEzs0I50JiZWaEcaMzMrFAONGZmVigHGjMzK5QDjZmZFcqBxszMCuVAY2ZmhXKgMTOzQjnQmJlZoRxozMysUA40ZmZWKAcaMzMrVKGBRtJ2SU9K2ippcyq7WdITqex+Sael8qtTWf2nJumctO1BST/MbVuSytslfUPSNkkPS1qR++w1kp5NP2uKbKeZmY2uGT2aSyLinIjoSu8/FxFnR8Q5wD8Avw8QEV9L9c4BPghsj4itueNcXd8eES+nsmuAPRFxJnArcAuApIXAjcAFwPnAjZIWFNpKMzMbUdNvnUXEvtzbDiBGqParwF+P43BXAHem13cDl0oScBmwKSK6I2IPsAm4/NjP2szMjlXRgSaA+yVtkbS2XijpM5JeAK4m9WiGuYrDA82X022zG1IwATgdeAEgIirAXqAzX57sSGVDSForabOkza+88sqxtdDMzMZUdKC5KCLOBd4NfFTSxQARcX1ELAO+Bnwsv4OkC4CDEfFUrvjqiPgZ4G3p54P16iN8ZoxRPrQg4raI6IqIrsWLFx9l08zMbDwKDTQRsTP9fhlYTzZekrcOeP+wstUM681ExIvp9/60T/04O4BlAJLKwHygO1+eLAV2Hl9rzMzsWBQWaCR1SDqp/hpYBTwlaWWu2nuBZ3L7lIAPAF/PlZUlLUqvW4H3APXezgagPqPsSuCBiAhgI7BK0oI0CWBVKjMzsyYrF3jsU4D1aTilDKyLiPsk3SPpDUAN+DFwbW6fi4EdEfFcrqwd2JiCTAvwbeBLadvtwF2StpH1ZFYDRES3pJuBR1K9myKiu4hGmpnZ2AoLNClYvGWE8uG3yvLbHgQuHFbWA5w3Sv0+sh7QSNvuAO4Y/xmbmVkRnBnAzMwK5UBjZmaFcqAxM7NCOdCYmVmhHGjMzKxQDjRmZlYoBxozMyuUA42ZmRXKgcbMzArlQGNmZoVyoDEzs0I50JiZWaEcaMzMrFAONGZmVigHGjMzK5QDjZmZFcqBxszMCuVAY2ZmhXKgMTOzQjnQmJlZoQoNNJK2S3pS0lZJm1PZzZKeSGX3Szotla+Q1JvKt0r6y9xxzkvH2SbpzyUplbdL+kYqf1jSitw+ayQ9m37WFNlOMzMbXTN6NJdExDkR0ZXefy4izo6Ic4B/AH4/V/dHqe45EXFtrvwLwFpgZfq5PJVfA+yJiDOBW4FbACQtBG4ELgDOB26UtKCY5pmZ2ViafussIvbl3nYAMVZ9SacC8yLioYgI4KvA+9LmK4A70+u7gUtTb+cyYFNEdEfEHmATh4KTmZk1UdGBJoD7JW2RtLZeKOkzkl4ArmZoj+YMSY9J+q6kt6Wy04EduTo7Ull92wsAEVEB9gKd+fIR9jEzsyYqOtBcFBHnAu8GPirpYoCIuD4ilgFfAz6W6r4ELI+ItwK/DayTNA/QCMet94JG2zbWPg2S1kraLGnzK6+8cjTtMjOzcSo00ETEzvT7ZWA92XhJ3jrg/alOf0TsTq+3AD8CXk/WG1ma22cpsDO93gEsA5BUBuYD3fnyEfbJn99tEdEVEV2LFy8+9oaamdmoCgs0kjoknVR/DawCnpK0MlftvcAzqc5iSS3p9evIBv2fi4iXgP2SLkzjLx8Cvpn23wDUZ5RdCTyQxnE2AqskLUiTAFalMjMza7Jygcc+BVifZiKXgXURcZ+keyS9AagBPwbqs8suBm6SVAGqwLUR0Z22fQT4CjAbuDf9ANwO3CVpG1lPZjVARHRLuhl4JNW7KXcsMzNrosICTUQ8B7xlhPL3j1L/HuCeUbZtBt48Qnkf8IFR9rkDuOMoTtnMzArgzABmZlYoBxozMyuUA42ZmRXKgcbMzArlQGNmZoVyoDEzs0IV+RyNmZlNkoigFtnvACKgFkEEBOl3KiuaA42ZWYGGf9HDoS/8Q+8PBYb89iwkHCIBMXKyx+HhoiQoqURLCVokWkolyi2iJFEuiZaSKJVESTCrtYVSaaQUkRPDgcbMZoyjucqPVL8REHLfw8O/4JXbEgSSiBREBLSUhJR9uZdL2Zd9qQVaSi0pEJQoiUPbS0rBIduvJIb8lsiOkX7D0PdK9aYKBxoza7rjucqPYdfuE32Vn325Q7lUyq76BeWWUuPLXdS/1IVKDH0vckEg1S2wp3CicKAxm+GadZWfV7/KL5UOfcnXb+Mc61V+/veJcJU/kzjQmE0RY33RH/VVfv2Yw97Xy5TbdixX+S0SLS06pqv8kr/wZxwHGrMRTORVfv1L/Viv8luUfpeyL/p6PV/l24nCgcamtCNd5Ue6N1/EVX5LKd26Ocar/PyVvq/ybSZzoLFxa3yBF3EvX0Ac+1V+/Qr9WK7yJRC+yjcrigPNCehor/Lz9YMYcuV+pKv8vPpVfkv6Qm4plWgtC+GrfDMbnQPNBKkBg9Va7qp/4mfsSNmxjuUqvx4Esi90X+WbWfM40EyAcouY21amUq1l9/LL6Ypfx3aVP+S9r/LN7ATnQDMBOtrL/OwZCyf7NMzMpiRnbzYzs0I50JiZWaEKDTSStkt6UtJWSZtT2c2Snkhl90s6LZW/S9KWVH+LpHfmjvOgpB+mfbZKWpLK2yV9Q9I2SQ9LWpHbZ42kZ9PPmiLbaWZmo2vGGM0lEbEr9/5zEXEDgKSPA78PXAvsAn4pInZKejOwETg9t9/VEbF52LGvAfZExJmSVgO3AFdJWgjcCHSRTeLaImlDROwpooFmZja6pt86i4h9ubcdpMc1IuKxiNiZyp8GZklqP8LhrgDuTK/vBi5VNiXrMmBTRHSn4LIJuHyi2mBmZuNXdKAJ4P50K2xtvVDSZyS9AFxN1qMZ7v3AYxHRnyv7crptdoMOze89HXgBICIqwF6gM1+e7GBo76h+HmslbZa0+ZVXXjn2VpqZ2aiKDjQXRcS5wLuBj0q6GCAiro+IZcDXgI/ld5D0JrJbYL+eK746In4GeFv6+WC9+gifGWOUDy2IuC0iuiKia/HixUfXMjMzG5dCA039VlhEvAysB84fVmUdWe8FAElLU70PRcSPcsd5Mf3en/apH2cHsCztWwbmA9358mQpsBMzM2u6wiYDSOoAShGxP71eBdwkaWVEPJuqvRd4JtU/GfhH4LqI+F7uOGXg5IjYJakVeA/w7bR5A7AGeAi4EnggIkLSRuCzkhakequA68Y63y1btuyS9OPjaPIisgkNM8lMa/NMay+4zTPF8bT5tUeqUOSss1OA9Wk4pQysi4j7JN0j6Q1k6cF+TDbjDLJbaGcCN0i6IZWtAnqAjSnItJAFmS+l7bcDd0naRtaTWQ0QEd2SbgYeSfVuiojusU42Io7r3pmkzRHRdTzHONHMtDbPtPaC2zxTFN1mRRw2dGHHwH+c099May+4zTNF0W12ZgAzMyuUA83EuW2yT2ASzLQ2z7T2gts8UxTaZt86MzOzQrlHY2ZmhXKgMTOzQjnQAJKWSfonST+Q9LSk30zlCyVtShmgN9Wfy5HUmeofkPT5UY65QdJTY3zmdSnr9A8lXVZMy0bX7DaPlZ27WSbj3znVWZ6O8YmJb9WYnzsZf9dnS3oofd6TkmYV07pRP7/Zf9etku5Mbf2BpDGf1yvCRLZZo2TKH+Ezj+77K1vDfmb/AKcC56bXJwH/DpwF/DHwu6n8d4Fb0usO4OfJngH6/AjH+xWyDAZPjfJ5ZwGPA+3AGcCPgJZp3ua3Aqel128GXpzu/865evcAfwt8Yjq3l+x5uSeAt6T3nTPg7/rXgK+n13OA7cCKE7XNwINA1xE+76i/v9yjASLipYh4NL3eD/yALAlnPjv0ncD7Up2eiPgXoG/4sSTNBX4b+PQYH3kF2R9nf0T8B7CNw9PzFKrZbY5jy849oSbh3xlJ7wOeI2tzU01Ce1cBT0TE4+l4uyOiOjGtGZ9JaHMAHcoymMwGBoB9Y9SfcBPZ5nE66u8vB5phlC2e9lbgYeCUiHgJsn9MYMRu5DA3A38KHByjzriySzdLk9qcN1J27qZqRpuVpV76X8AfHu/5Hq8m/Ru/HghJGyU9KumTx3fWx6dJbb6bLHvJS8DzwJ/EEbKQFGkC2gwjZ8rPO+rvLweanHQFcw/wWzF03Zzx7n8OcGZErD9S1RHKJmWeeRPbXK8/Unbupmpim/8QuDUiDhz9WU6cJra3THZL5ur0+5clXXq0nzcRmtjm84EqcBrZbaT/Kel1R/t5E+F425yMlil/yEeNUDbm95cDTaIsl9o9wNci4u9S8X9KOjVtPxV4+QiH+TngPEnbgX8BXi/pwRHqTYns0k1u86jZuZupyW2+APjjVO+3gN+T9LER6hVmEv6uvxsRuyLiIPAt4Nzjb8XRaXKbfw24LyIGI8tS/z2ylX2baoLaTIyeKT/vqL+/HGiA1D28HfhBRPxZblM9OzTp9zfHOk5EfCEiTouIFWRXdP8eEe8YoeoGYLWkdklnACuBfz2+VhydZrdZo2TnbqZmtzki3hYRK1K9/w18NiJGnNlUhEn4u94InC1pThqzeDvwb8fXiqMzCW1+HninMh3AhaSM9M0yUW2WVJa0KL2uZ8ofabbd0X9/jTVTYKb8kP0hBdmMma3p5xfJZs18B3g2/V6Y22c7WcboA2QR/qxhx1xBbqYK2ZIIN+XeX082W+OHwLune5uBT5Hdy96a+1kynds8rN4f0PxZZ5Pxd/3fyCY+PAX88Qz4u55LNqPwabKg+jsnapvJZqNtScd5Gvg/pNlkI/w7H9X3l1PQmJlZoXzrzMzMCuVAY2ZmhXKgMTOzQjnQmJlZoRxozMysUA40ZmZWKAcas2lEUstkn4PZcA40ZpNE0s31tUPS+89I+rik35H0iKQnJP1hbvvfK1vL52lJa3PlByTdJOlhstQpZlOKA43Z5LmdlCJEUglYDfwnWUqP84FzyPJtXZzqfzgiziPLpfVxSZ2pvIPsyfULIkv/bjallCf7BMxmqojYLmm3pLcCpwCPAT9Ltq7LY6naXLLA889kweWXU/myVL6bLHvwPc08d7Oj4UBjNrn+CvjvwGuAO4BLgT+KiC/mK0l6B/ALwM9FxMGUSbi+THJfNHmBMbOj4VtnZpNrPXA5WU9mY/r5cFpbBEmnp3Xb5wN7UpB5I1mWYLMTgns0ZpMoIgYk/RPwauqV3C/pp4GH0uKGB8gyIt8HXCvpCbKMud+frHM2O1rO3mw2idIkgEeBD0TEs5N9PmZF8K0zs0ki6SxgG/AdBxmbztyjMTOzQrlHY2ZmhXKgMTOzQjnQmJlZoRxozMysUA40ZmZWqP8PSoMmsFj4ZhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.lineplot(x='year',y='price',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cubic-hammer",
   "metadata": {},
   "source": [
    "A linear analysis showing us the increase of the price along the years that's kind of what you would expect from correct data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "innocent-indonesian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='grade', ylabel='price'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.lineplot(x='grade',y='price',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-selection",
   "metadata": {},
   "source": [
    "the higher the grade the higher the price and our this graph confirms it aswell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "central-fleece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bedrooms', ylabel='price'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.lineplot(x='bedrooms',y='price',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "statutory-launch",
   "metadata": {},
   "source": [
    "this plot is an interesting one cause I was expecting linear growth in prices with an increase in the number of bedrooms and this graph shows a sharp decline in price after 8 bedrooms which indicates bedrooms alone are not a good indicator of price and later we will see there is a 0.5 correlation between bedrooms and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "abroad-eclipse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bathrooms', ylabel='price'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.lineplot(x='bathrooms',y='price',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "violent-shame",
   "metadata": {},
   "source": [
    "this plot indicates number of bathrooms is a  better indicator of price as compare to bedrooms tho there is still high varince from bathrooms value 4 to 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "antique-candle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bathrooms', ylabel='price'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(x='bathrooms',y='price',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "welcome-assignment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='grade', ylabel='price'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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paZsAAd8l2n2WSS5ntUiSlpyqOdf3tbTJw7dvbGl7QCcQ8F2s3WeZzNSzWpCXnJfvbTcCvku0+yyTVPXaPSfeaDSkoRdaP+IefEGNQ40xH4rBPZUOssbeFyRJPuO05vsO7pGYoplQDsv3thsBj5aq1+t6eNtmzV3SfN/DxXVA2jawuem+hwan2bEWmdxpi8U8/oVVgrvnHE5bLDEabx0CHi03d4l09vVuaZvP3h5jbq/Vanp27uEkc/C1ntqo7bPxtEWmTGYuAj5zs+I0wsEXq03R7N1ffD5jYaU21TO9buWOKZPuN+sDfs2aNXrmmdFrhRw4cEBHjx6t3M5JJ52k+fPnj9p+zjnnqL+//6Xv2x249XpdW7duVoWr13WkfLmNp5tPmewd577OjUZDh4bGH3FP1aFBjTknPrkpk+LneWHPiuY790yu7Zx1xT9xTMmsD/ihoSH9ZN8+zT/hJxFHJE0ioyKO6uiBw8dtO3C4aH+ker2ux7ZuVs+Zzdt0Gbh7nmoeuAPPj7290WhUfhmnNj8W+JIo2+40pkxexpQJTjTrA75Wq2nx0ee09ucqvG2fpHXf268FtePncScTuGdWuHbnmG4J3FqtpsGhZyvte3hv8XnOGdXbxviYMsGJZn3A565Wq2nv3mqB+2JxVl+lkbw1duBObcrkwuY7M2XyEkbjqIqAl7Rj6IjWfW9/0/127yvmTJYuqnanwx1DR3TifeRrtZpeqBi4z5dXy1cZybckcPcVgVs7t3ng1s4du+3ZOGUCdKuuC/j+/n5t2LBh1Pb9+/e/dAOFKmxr4cLR0y5XX331cSE0mQA8VM5xLlheYcQpadUY7U+m3nNlvcXnNa+3+DwCt1MmOycuMS+O9ui6gG+3dgcggZtetwQuc+LoNE9mVDzpxu1rJP25pJMl3RgR/3Wi/fv6+mLjxu5Y5GmskDgWEBdeOHpEnWLxL+pNrd54AT9jF1MDJmB7U0SMeaVfshG87ZMl/aWkqyXtlHS/7Tsi4tFUNVNr94iMelNDUAOFZCN42z8n6WMR8cvl9zdIUkR8crzndNMIHgBmgolG8NVOB5maZZJ2jPh+Z7kNANAGKQN+rNWmRr1dsL3a9kbbGwcGBhJ2BwBml5QBv1PS+SO+Xy5p1KWWEbE+Ivoioq+nh9WdAKBVUgb8/ZJW2f4p2/MkvU3SHQnrAQBGSHYWTUQctv0+Sd9QcZrkTRHxSKp6AIDjJb3QKSK+JulrKWsAAMaWcooGANBBBDwAZCrpUgWTZXtA0hNTeOrZkqot0Th97axFPepRb/bUm2qtCyJizFMQuyrgp8r2xvGu5JrJtahHPerNnnopajFFAwCZIuABIFO5BPz6TGtRj3rUmz31Wl4rizl4AMBouYzgAQAnIOABIFMzNuBt32R7t+0tbaq3wPb3bT9k+xHbH29T3ZNt/8D2nW2o9WPbD9t+0HbyO6/YPtP2l2xvs721vElMqloXla/r2MeQ7Q8krPfvy9+TLbZvsZ30dlm231/WeiTF6xrr7832YtsbbD9efj4rcb23lq/vqO2Wnk44Tr115e/mZtu32T4zcb3/VNZ60PZdtmvTrTNjA17SZyVd08Z6ByRdFRGXSLpU0jW2L29D3fdL2tqGOse8ISIubdO5v38u6esR8UpJlyjh64yIx8rXdamk10jaL+m2FLVsL5P07yT1RcTFKhbbe1uKWmW9iyW9R9JrVfwcr7W9qsVlPqvRf29/KOmbEbFK0jfL71PW2yLp1yXd3cI6E9XbIOniiHiVpH+RdEPieusi4lXl7+idkv54ukVmbMBHxN2S9rSxXkTEi+W3c8uPpEeobS+X9CuSbkxZpxNsny7pSkmflqSIOBgRz7ep/BslbY+IqVw1XdUcSafYniNpoca4F0IL/YykeyNif0QclvRPkn6tlQXG+Xu7XtLN5dc3S/pXKetFxNaIeKxVNSrUu6v8eUrSvSruaZGy3tCIbxepBfkyYwO+E8rpkgcl7Za0ISLuS1zyzyR9SNLRxHWOCUl32d5ke3XiWr2SBiR9ppyCutH2osQ1j3mbpFtSNR4RuyT9iaQnJT0laW9E3JWqnoqR7ZW2l9heKOktOv5mO6mcExFPSVL5eWkbanbK70n6x9RFbP8X2zskvUOzeQTfCRFxpHz7tFzSa8u3xknYvlbS7ojYlKrGGK6IiMskvVnSe21fmbDWHEmXSeqPiFdL2qfWvsUfU3nzmesk3ZqwxlkqRrc/JakmaZHtd6aqFxFbJf03FVMKX5f0kKTDEz4Jldn+iIqf59+nrhURH4mI88ta75tuewT8FJRTCd9R2mMAV0i6zvaPJX1e0lW2/y5hPUVEo/y8W8X89GsTltspaeeId0FfUhH4qb1Z0gMR8UzCGm+S9KOIGIiIQ5K+LOnnE9ZTRHw6Ii6LiCtVvPV/PGW90jO2z5Ok8vPuNtRsK9vvknStpHdEey8a+pyk35huIwR8RbZ7jh1Ft32Kij/ibanqRcQNEbE8IlaqmFL4VkQkGwXaXmT7tGNfS/olFW/9k4iIpyXtsH1RuemNkh5NVW+Etyvh9EzpSUmX215o2ypeW9ID5baXlp9XqDgQmfo1SsUtON9Vfv0uSbe3oWbb2L5G0n+QdF1E7G9DvZEHxq9TK/IlImbkh4pf4KckHVIxGnx34nqvkvQDSZtVBN8ft/G1vl7SnYlr9Kp4a/+QpEckfaQNr+tSSRvLn+lXJJ2VuN5CSYOSzmjDa/t4+Qe6RdL/ljQ/cb17VPyDfEjSGxO0P+rvTdISFWfPPF5+Xpy43q+VXx+Q9IykbySu90NJOyQ9WH78deJ6/1D+vmyW9FVJy6Zbh6UKACBTTNEAQKYIeADIFAEPAJki4AEgUwQ8AGSKgAemoVyB8+xO9wMYCwEPnKBcIAyY8fhFxqxj+z+qWMxph6RnJW1ScTn6/1OxRMQdtv9F0h9Jmqfi4qh3RMQztpeouEilR9L3JXlEu+9UsUzwPEn3Sfr9iDjSrtcFnIgRPGaV8kYRvyHp1Sou6R+57v2ZEfGLEfEpSd+VdHkUC6F9XsWqnpL0UUnfLbffIWlF2e7PSPotFQu2XSrpiIp/IkDHMILHbPMLkm6PiJ9Iku2vjnjsCyO+Xi7pC+UiWvMk/ajcfqWKfwyKiP9j+7ly+xtV3Ejk/mL5GZ2iDBffwsxCwGO28QSP7Rvx9f+Q9KcRcYft10v62IjHxlrfw5JujohW3vUHmBamaDDbfFfSr5b32D1VxR2zxnKGpF3l1+8asf1ulVMvtt8s6dh9SL8p6V+PWNVxse0LWt15YDIIeMwqEXG/irnzh1Ss075R0t4xdv2YpFtt36PiQOwxH1dx96QHVCyp/GTZ7qMqDsreZXuziptvnJfoZQCVsJokZh3bp0bEi+Xt7e6WtDoiHuh0v4BWYw4es9F62z8raYGKeXPCHVliBA8AmWIOHgAyRcADQKYIeADIFAEPAJki4AEgU/8fqSqjvRklnD8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxplot(x='grade',y='price',data=data,showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fallen-carrier",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.corr()\n",
    "'''compute the standard\n",
    "correlation coefficient (also called Pearson’s r) between every pair of\n",
    "attributes using the corr() method:'''\n",
    "corr_matrix=data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "finished-discipline",
   "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>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>...</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.308350</td>\n",
       "      <td>0.518912</td>\n",
       "      <td>0.702035</td>\n",
       "      <td>0.089661</td>\n",
       "      <td>0.244906</td>\n",
       "      <td>0.266369</td>\n",
       "      <td>0.397293</td>\n",
       "      <td>0.036362</td>\n",
       "      <td>0.667434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054012</td>\n",
       "      <td>0.126434</td>\n",
       "      <td>-0.053203</td>\n",
       "      <td>0.307003</td>\n",
       "      <td>0.021626</td>\n",
       "      <td>0.585379</td>\n",
       "      <td>0.082447</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>-0.010081</td>\n",
       "      <td>-0.001695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.308350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489609</td>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.031703</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>0.079532</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154178</td>\n",
       "      <td>0.018841</td>\n",
       "      <td>-0.152668</td>\n",
       "      <td>-0.008931</td>\n",
       "      <td>0.129473</td>\n",
       "      <td>0.391638</td>\n",
       "      <td>0.029244</td>\n",
       "      <td>-0.009838</td>\n",
       "      <td>-0.001533</td>\n",
       "      <td>0.009843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.518912</td>\n",
       "      <td>0.489609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701784</td>\n",
       "      <td>0.089988</td>\n",
       "      <td>0.326466</td>\n",
       "      <td>0.065428</td>\n",
       "      <td>0.202647</td>\n",
       "      <td>-0.066685</td>\n",
       "      <td>0.591269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377433</td>\n",
       "      <td>0.069322</td>\n",
       "      <td>-0.144088</td>\n",
       "      <td>0.049235</td>\n",
       "      <td>0.149157</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.086952</td>\n",
       "      <td>-0.024469</td>\n",
       "      <td>0.009359</td>\n",
       "      <td>0.005006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>0.702035</td>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.701784</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172826</td>\n",
       "      <td>0.321987</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>-0.058753</td>\n",
       "      <td>0.762704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.318049</td>\n",
       "      <td>0.055363</td>\n",
       "      <td>-0.199430</td>\n",
       "      <td>0.052529</td>\n",
       "      <td>0.240223</td>\n",
       "      <td>0.756420</td>\n",
       "      <td>0.183286</td>\n",
       "      <td>-0.029038</td>\n",
       "      <td>0.011810</td>\n",
       "      <td>0.005152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>0.089661</td>\n",
       "      <td>0.031703</td>\n",
       "      <td>0.089988</td>\n",
       "      <td>0.172826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001947</td>\n",
       "      <td>0.021604</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>-0.008958</td>\n",
       "      <td>0.113621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.007644</td>\n",
       "      <td>-0.129574</td>\n",
       "      <td>-0.085683</td>\n",
       "      <td>0.229521</td>\n",
       "      <td>0.144608</td>\n",
       "      <td>0.718557</td>\n",
       "      <td>0.005468</td>\n",
       "      <td>-0.002369</td>\n",
       "      <td>-0.002758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.244906</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.326466</td>\n",
       "      <td>0.321987</td>\n",
       "      <td>-0.001947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025839</td>\n",
       "      <td>0.027971</td>\n",
       "      <td>-0.223259</td>\n",
       "      <td>0.407675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381396</td>\n",
       "      <td>0.015424</td>\n",
       "      <td>-0.022506</td>\n",
       "      <td>0.064603</td>\n",
       "      <td>0.088845</td>\n",
       "      <td>0.242730</td>\n",
       "      <td>-0.010895</td>\n",
       "      <td>-0.022473</td>\n",
       "      <td>0.013963</td>\n",
       "      <td>0.004277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>0.266369</td>\n",
       "      <td>-0.006582</td>\n",
       "      <td>0.065428</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.021604</td>\n",
       "      <td>0.025839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401857</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>0.082775</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026161</td>\n",
       "      <td>0.092885</td>\n",
       "      <td>0.030285</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>-0.041910</td>\n",
       "      <td>0.086463</td>\n",
       "      <td>0.030703</td>\n",
       "      <td>-0.004165</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>-0.004668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>view</th>\n",
       "      <td>0.397293</td>\n",
       "      <td>0.079532</td>\n",
       "      <td>0.202647</td>\n",
       "      <td>0.284611</td>\n",
       "      <td>0.074710</td>\n",
       "      <td>0.027971</td>\n",
       "      <td>0.401857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045990</td>\n",
       "      <td>0.251321</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053440</td>\n",
       "      <td>0.103917</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>0.006157</td>\n",
       "      <td>-0.078400</td>\n",
       "      <td>0.280439</td>\n",
       "      <td>0.072575</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>-0.005638</td>\n",
       "      <td>0.006186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>0.036362</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>-0.066685</td>\n",
       "      <td>-0.058753</td>\n",
       "      <td>-0.008958</td>\n",
       "      <td>-0.223259</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>0.045990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.361417</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>-0.106500</td>\n",
       "      <td>-0.092824</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>-0.045589</td>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.007011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.667434</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>0.591269</td>\n",
       "      <td>0.762704</td>\n",
       "      <td>0.113621</td>\n",
       "      <td>0.407675</td>\n",
       "      <td>0.082775</td>\n",
       "      <td>0.251321</td>\n",
       "      <td>-0.144674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.446963</td>\n",
       "      <td>0.014414</td>\n",
       "      <td>-0.184862</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>0.198372</td>\n",
       "      <td>0.713202</td>\n",
       "      <td>0.119248</td>\n",
       "      <td>-0.030387</td>\n",
       "      <td>0.008376</td>\n",
       "      <td>0.002092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>0.605567</td>\n",
       "      <td>0.477600</td>\n",
       "      <td>0.599360</td>\n",
       "      <td>0.876597</td>\n",
       "      <td>0.183512</td>\n",
       "      <td>0.488411</td>\n",
       "      <td>0.072075</td>\n",
       "      <td>0.167649</td>\n",
       "      <td>-0.158214</td>\n",
       "      <td>0.755923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423898</td>\n",
       "      <td>0.023285</td>\n",
       "      <td>-0.261190</td>\n",
       "      <td>-0.000816</td>\n",
       "      <td>0.343803</td>\n",
       "      <td>0.731870</td>\n",
       "      <td>0.194050</td>\n",
       "      <td>-0.023823</td>\n",
       "      <td>0.009872</td>\n",
       "      <td>0.006079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_basement</th>\n",
       "      <td>0.323816</td>\n",
       "      <td>0.303093</td>\n",
       "      <td>0.334909</td>\n",
       "      <td>0.435043</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>-0.245659</td>\n",
       "      <td>0.080588</td>\n",
       "      <td>0.276947</td>\n",
       "      <td>0.174105</td>\n",
       "      <td>0.168392</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.133124</td>\n",
       "      <td>0.071323</td>\n",
       "      <td>0.074845</td>\n",
       "      <td>0.110538</td>\n",
       "      <td>-0.144765</td>\n",
       "      <td>0.200355</td>\n",
       "      <td>0.017276</td>\n",
       "      <td>-0.015687</td>\n",
       "      <td>0.006035</td>\n",
       "      <td>-0.000683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.054012</td>\n",
       "      <td>0.154178</td>\n",
       "      <td>0.377433</td>\n",
       "      <td>0.318049</td>\n",
       "      <td>0.053080</td>\n",
       "      <td>0.381396</td>\n",
       "      <td>-0.026161</td>\n",
       "      <td>-0.053440</td>\n",
       "      <td>-0.361417</td>\n",
       "      <td>0.446963</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.224874</td>\n",
       "      <td>-0.346869</td>\n",
       "      <td>-0.148122</td>\n",
       "      <td>0.409356</td>\n",
       "      <td>0.326229</td>\n",
       "      <td>0.070958</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>-0.006226</td>\n",
       "      <td>0.007311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>0.126434</td>\n",
       "      <td>0.018841</td>\n",
       "      <td>0.069322</td>\n",
       "      <td>0.055363</td>\n",
       "      <td>0.007644</td>\n",
       "      <td>0.015424</td>\n",
       "      <td>0.092885</td>\n",
       "      <td>0.103917</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>0.014414</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.224874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.064357</td>\n",
       "      <td>0.029398</td>\n",
       "      <td>-0.068372</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>0.007854</td>\n",
       "      <td>-0.023707</td>\n",
       "      <td>0.012827</td>\n",
       "      <td>-0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-0.053203</td>\n",
       "      <td>-0.152668</td>\n",
       "      <td>-0.144088</td>\n",
       "      <td>-0.199430</td>\n",
       "      <td>-0.129574</td>\n",
       "      <td>-0.022506</td>\n",
       "      <td>0.030285</td>\n",
       "      <td>0.084827</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>-0.184862</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.346869</td>\n",
       "      <td>0.064357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267048</td>\n",
       "      <td>-0.564072</td>\n",
       "      <td>-0.279033</td>\n",
       "      <td>-0.147221</td>\n",
       "      <td>0.001189</td>\n",
       "      <td>-0.000171</td>\n",
       "      <td>0.003339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>0.307003</td>\n",
       "      <td>-0.008931</td>\n",
       "      <td>0.049235</td>\n",
       "      <td>0.052529</td>\n",
       "      <td>-0.085683</td>\n",
       "      <td>0.064603</td>\n",
       "      <td>-0.014274</td>\n",
       "      <td>0.006157</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>0.114084</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.148122</td>\n",
       "      <td>0.029398</td>\n",
       "      <td>0.267048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.135512</td>\n",
       "      <td>0.048858</td>\n",
       "      <td>-0.086419</td>\n",
       "      <td>-0.029212</td>\n",
       "      <td>0.014961</td>\n",
       "      <td>-0.016213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>0.021626</td>\n",
       "      <td>0.129473</td>\n",
       "      <td>0.149157</td>\n",
       "      <td>0.240223</td>\n",
       "      <td>0.229521</td>\n",
       "      <td>0.088845</td>\n",
       "      <td>-0.041910</td>\n",
       "      <td>-0.078400</td>\n",
       "      <td>-0.106500</td>\n",
       "      <td>0.198372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.409356</td>\n",
       "      <td>-0.068372</td>\n",
       "      <td>-0.564072</td>\n",
       "      <td>-0.135512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.334605</td>\n",
       "      <td>0.254451</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>-0.008134</td>\n",
       "      <td>0.003930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>0.585379</td>\n",
       "      <td>0.391638</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.756420</td>\n",
       "      <td>0.144608</td>\n",
       "      <td>0.242730</td>\n",
       "      <td>0.086463</td>\n",
       "      <td>0.280439</td>\n",
       "      <td>-0.092824</td>\n",
       "      <td>0.713202</td>\n",
       "      <td>...</td>\n",
       "      <td>0.326229</td>\n",
       "      <td>-0.002673</td>\n",
       "      <td>-0.279033</td>\n",
       "      <td>0.048858</td>\n",
       "      <td>0.334605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183192</td>\n",
       "      <td>-0.021734</td>\n",
       "      <td>0.002449</td>\n",
       "      <td>0.004286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>0.082447</td>\n",
       "      <td>0.029244</td>\n",
       "      <td>0.086952</td>\n",
       "      <td>0.183286</td>\n",
       "      <td>0.718557</td>\n",
       "      <td>-0.010895</td>\n",
       "      <td>0.030703</td>\n",
       "      <td>0.072575</td>\n",
       "      <td>-0.003406</td>\n",
       "      <td>0.119248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070958</td>\n",
       "      <td>0.007854</td>\n",
       "      <td>-0.147221</td>\n",
       "      <td>-0.086419</td>\n",
       "      <td>0.254451</td>\n",
       "      <td>0.183192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000085</td>\n",
       "      <td>0.003546</td>\n",
       "      <td>0.004076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.003576</td>\n",
       "      <td>-0.009838</td>\n",
       "      <td>-0.024469</td>\n",
       "      <td>-0.029038</td>\n",
       "      <td>0.005468</td>\n",
       "      <td>-0.022473</td>\n",
       "      <td>-0.004165</td>\n",
       "      <td>0.001364</td>\n",
       "      <td>-0.045589</td>\n",
       "      <td>-0.030387</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>-0.023707</td>\n",
       "      <td>0.001189</td>\n",
       "      <td>-0.029212</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>-0.021734</td>\n",
       "      <td>-0.000085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.782389</td>\n",
       "      <td>0.028662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.010081</td>\n",
       "      <td>-0.001533</td>\n",
       "      <td>0.009359</td>\n",
       "      <td>0.011810</td>\n",
       "      <td>-0.002369</td>\n",
       "      <td>0.013963</td>\n",
       "      <td>0.008132</td>\n",
       "      <td>-0.005638</td>\n",
       "      <td>0.021978</td>\n",
       "      <td>0.008376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006226</td>\n",
       "      <td>0.012827</td>\n",
       "      <td>-0.000171</td>\n",
       "      <td>0.014961</td>\n",
       "      <td>-0.008134</td>\n",
       "      <td>0.002449</td>\n",
       "      <td>0.003546</td>\n",
       "      <td>-0.782389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofweek</th>\n",
       "      <td>-0.001695</td>\n",
       "      <td>0.009843</td>\n",
       "      <td>0.005006</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>-0.002758</td>\n",
       "      <td>0.004277</td>\n",
       "      <td>-0.004668</td>\n",
       "      <td>0.006186</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.002092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007311</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>-0.016213</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>0.004286</td>\n",
       "      <td>0.004076</td>\n",
       "      <td>0.028662</td>\n",
       "      <td>-0.047837</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  price  bedrooms  bathrooms  sqft_living  sqft_lot    floors  \\\n",
       "price          1.000000  0.308350   0.518912     0.702035  0.089661  0.244906   \n",
       "bedrooms       0.308350  1.000000   0.489609     0.576671  0.031703  0.177778   \n",
       "bathrooms      0.518912  0.489609   1.000000     0.701784  0.089988  0.326466   \n",
       "sqft_living    0.702035  0.576671   0.701784     1.000000  0.172826  0.321987   \n",
       "sqft_lot       0.089661  0.031703   0.089988     0.172826  1.000000 -0.001947   \n",
       "floors         0.244906  0.177778   0.326466     0.321987 -0.001947  1.000000   \n",
       "waterfront     0.266369 -0.006582   0.065428     0.103818  0.021604  0.025839   \n",
       "view           0.397293  0.079532   0.202647     0.284611  0.074710  0.027971   \n",
       "condition      0.036362  0.028472  -0.066685    -0.058753 -0.008958 -0.223259   \n",
       "grade          0.667434  0.356967   0.591269     0.762704  0.113621  0.407675   \n",
       "sqft_above     0.605567  0.477600   0.599360     0.876597  0.183512  0.488411   \n",
       "sqft_basement  0.323816  0.303093   0.334909     0.435043  0.015286 -0.245659   \n",
       "yr_built       0.054012  0.154178   0.377433     0.318049  0.053080  0.381396   \n",
       "yr_renovated   0.126434  0.018841   0.069322     0.055363  0.007644  0.015424   \n",
       "zipcode       -0.053203 -0.152668  -0.144088    -0.199430 -0.129574 -0.022506   \n",
       "lat            0.307003 -0.008931   0.049235     0.052529 -0.085683  0.064603   \n",
       "long           0.021626  0.129473   0.149157     0.240223  0.229521  0.088845   \n",
       "sqft_living15  0.585379  0.391638   0.497484     0.756420  0.144608  0.242730   \n",
       "sqft_lot15     0.082447  0.029244   0.086952     0.183286  0.718557 -0.010895   \n",
       "year           0.003576 -0.009838  -0.024469    -0.029038  0.005468 -0.022473   \n",
       "month         -0.010081 -0.001533   0.009359     0.011810 -0.002369  0.013963   \n",
       "dayofweek     -0.001695  0.009843   0.005006     0.005152 -0.002758  0.004277   \n",
       "\n",
       "               waterfront      view  condition     grade  ...  yr_built  \\\n",
       "price            0.266369  0.397293   0.036362  0.667434  ...  0.054012   \n",
       "bedrooms        -0.006582  0.079532   0.028472  0.356967  ...  0.154178   \n",
       "bathrooms        0.065428  0.202647  -0.066685  0.591269  ...  0.377433   \n",
       "sqft_living      0.103818  0.284611  -0.058753  0.762704  ...  0.318049   \n",
       "sqft_lot         0.021604  0.074710  -0.008958  0.113621  ...  0.053080   \n",
       "floors           0.025839  0.027971  -0.223259  0.407675  ...  0.381396   \n",
       "waterfront       1.000000  0.401857   0.016653  0.082775  ... -0.026161   \n",
       "view             0.401857  1.000000   0.045990  0.251321  ... -0.053440   \n",
       "condition        0.016653  0.045990   1.000000 -0.144674  ... -0.361417   \n",
       "grade            0.082775  0.251321  -0.144674  1.000000  ...  0.446963   \n",
       "sqft_above       0.072075  0.167649  -0.158214  0.755923  ...  0.423898   \n",
       "sqft_basement    0.080588  0.276947   0.174105  0.168392  ... -0.133124   \n",
       "yr_built        -0.026161 -0.053440  -0.361417  0.446963  ...  1.000000   \n",
       "yr_renovated     0.092885  0.103917  -0.060618  0.014414  ... -0.224874   \n",
       "zipcode          0.030285  0.084827   0.003026 -0.184862  ... -0.346869   \n",
       "lat             -0.014274  0.006157  -0.014941  0.114084  ... -0.148122   \n",
       "long            -0.041910 -0.078400  -0.106500  0.198372  ...  0.409356   \n",
       "sqft_living15    0.086463  0.280439  -0.092824  0.713202  ...  0.326229   \n",
       "sqft_lot15       0.030703  0.072575  -0.003406  0.119248  ...  0.070958   \n",
       "year            -0.004165  0.001364  -0.045589 -0.030387  ...  0.003507   \n",
       "month            0.008132 -0.005638   0.021978  0.008376  ... -0.006226   \n",
       "dayofweek       -0.004668  0.006186   0.007011  0.002092  ...  0.007311   \n",
       "\n",
       "               yr_renovated   zipcode       lat      long  sqft_living15  \\\n",
       "price              0.126434 -0.053203  0.307003  0.021626       0.585379   \n",
       "bedrooms           0.018841 -0.152668 -0.008931  0.129473       0.391638   \n",
       "bathrooms          0.069322 -0.144088  0.049235  0.149157       0.497484   \n",
       "sqft_living        0.055363 -0.199430  0.052529  0.240223       0.756420   \n",
       "sqft_lot           0.007644 -0.129574 -0.085683  0.229521       0.144608   \n",
       "floors             0.015424 -0.022506  0.064603  0.088845       0.242730   \n",
       "waterfront         0.092885  0.030285 -0.014274 -0.041910       0.086463   \n",
       "view               0.103917  0.084827  0.006157 -0.078400       0.280439   \n",
       "condition         -0.060618  0.003026 -0.014941 -0.106500      -0.092824   \n",
       "grade              0.014414 -0.184862  0.114084  0.198372       0.713202   \n",
       "sqft_above         0.023285 -0.261190 -0.000816  0.343803       0.731870   \n",
       "sqft_basement      0.071323  0.074845  0.110538 -0.144765       0.200355   \n",
       "yr_built          -0.224874 -0.346869 -0.148122  0.409356       0.326229   \n",
       "yr_renovated       1.000000  0.064357  0.029398 -0.068372      -0.002673   \n",
       "zipcode            0.064357  1.000000  0.267048 -0.564072      -0.279033   \n",
       "lat                0.029398  0.267048  1.000000 -0.135512       0.048858   \n",
       "long              -0.068372 -0.564072 -0.135512  1.000000       0.334605   \n",
       "sqft_living15     -0.002673 -0.279033  0.048858  0.334605       1.000000   \n",
       "sqft_lot15         0.007854 -0.147221 -0.086419  0.254451       0.183192   \n",
       "year              -0.023707  0.001189 -0.029212  0.000270      -0.021734   \n",
       "month              0.012827 -0.000171  0.014961 -0.008134       0.002449   \n",
       "dayofweek         -0.000130  0.003339 -0.016213  0.003930       0.004286   \n",
       "\n",
       "               sqft_lot15      year     month  dayofweek  \n",
       "price            0.082447  0.003576 -0.010081  -0.001695  \n",
       "bedrooms         0.029244 -0.009838 -0.001533   0.009843  \n",
       "bathrooms        0.086952 -0.024469  0.009359   0.005006  \n",
       "sqft_living      0.183286 -0.029038  0.011810   0.005152  \n",
       "sqft_lot         0.718557  0.005468 -0.002369  -0.002758  \n",
       "floors          -0.010895 -0.022473  0.013963   0.004277  \n",
       "waterfront       0.030703 -0.004165  0.008132  -0.004668  \n",
       "view             0.072575  0.001364 -0.005638   0.006186  \n",
       "condition       -0.003406 -0.045589  0.021978   0.007011  \n",
       "grade            0.119248 -0.030387  0.008376   0.002092  \n",
       "sqft_above       0.194050 -0.023823  0.009872   0.006079  \n",
       "sqft_basement    0.017276 -0.015687  0.006035  -0.000683  \n",
       "yr_built         0.070958  0.003507 -0.006226   0.007311  \n",
       "yr_renovated     0.007854 -0.023707  0.012827  -0.000130  \n",
       "zipcode         -0.147221  0.001189 -0.000171   0.003339  \n",
       "lat             -0.086419 -0.029212  0.014961  -0.016213  \n",
       "long             0.254451  0.000270 -0.008134   0.003930  \n",
       "sqft_living15    0.183192 -0.021734  0.002449   0.004286  \n",
       "sqft_lot15       1.000000 -0.000085  0.003546   0.004076  \n",
       "year            -0.000085  1.000000 -0.782389   0.028662  \n",
       "month            0.003546 -0.782389  1.000000  -0.047837  \n",
       "dayofweek        0.004076  0.028662 -0.047837   1.000000  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "southwest-clarity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            1.000000\n",
       "bedrooms         0.308350\n",
       "bathrooms        0.518912\n",
       "sqft_living      0.702035\n",
       "sqft_lot         0.089661\n",
       "floors           0.244906\n",
       "waterfront       0.266369\n",
       "view             0.397293\n",
       "condition        0.036362\n",
       "grade            0.667434\n",
       "sqft_above       0.605567\n",
       "sqft_basement    0.323816\n",
       "yr_built         0.054012\n",
       "yr_renovated     0.126434\n",
       "zipcode         -0.053203\n",
       "lat              0.307003\n",
       "long             0.021626\n",
       "sqft_living15    0.585379\n",
       "sqft_lot15       0.082447\n",
       "year             0.003576\n",
       "month           -0.010081\n",
       "dayofweek       -0.001695\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "contemporary-broadcasting",
   "metadata": {},
   "source": [
    "## Scatterplot Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-auditor",
   "metadata": {},
   "source": [
    "scatter plot matrix represents bi-variate or pairwise relationship between different combinations of variables\n",
    "Scatter plot matrix is also referred to as pair plot as it consists of scatter plots of different variables combined in pairs\n",
    "we don't do this if we got more than 10 cause it will plot 10^10 plots & histrograms, Histrogram are actually distribution \n",
    "of variable and they don't plot graph for x againt x \n",
    "\n",
    "\n",
    "[Features correlation:]\n",
    "Assess pairwise relationships between three or more variables. This is important to understand \n",
    "relationships between different features when building machine learning model\n",
    "\n",
    "[Multicollinearity:] \n",
    "Assess the collinearity / multi-collinearity by analyzing the correlation between two or more variables.\n",
    "Recall that multi-collinearity can result in two or more predictor variables that might be providing the same information about the response variable thereby leading to unreliable coefficients of the predictor variables (especially for linear models\n",
    "\n",
    "[Data is linearly separable?:]\n",
    "Assess whether the data is linearly separable or not. The data which is linearly separable can be\n",
    "separated using a linear line. The data which isn't linearly separable would need to be applied with kernel methods. Thus, \n",
    "it may help determine machine learning algorithm one would want to use.\n",
    "\n",
    "One can analyse the pairwise relationship at several stages of machine learning model pipeline including some of the following:\n",
    "Data analysis\n",
    "Before and after feature transformations\n",
    "Feature engineering\n",
    "Feature selection\n",
    "\n",
    "Option1: from pandas.plotting import scatter_matrix\n",
    "Option2: import seaborn as sns.pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "afraid-native",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Syed Muhammmad Hamza\\anaconda3\\envs\\PY37\\lib\\site-packages\\seaborn\\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\Syed Muhammmad Hamza\\anaconda3\\envs\\PY37\\lib\\site-packages\\seaborn\\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\Syed Muhammmad Hamza\\anaconda3\\envs\\PY37\\lib\\site-packages\\seaborn\\distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xb921ae288>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 588.75x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "vars=[\"price\",\"sqft_living\",\"sqft_above\",\"grade\"]\n",
    "sns.pairplot(data[vars], hue='grade')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "increasing-trust",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
       "       'waterfront', 'view', 'condition', 'grade', 'sqft_above',\n",
       "       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',\n",
       "       'sqft_living15', 'sqft_lot15', 'year', 'month', 'dayofweek'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "cooperative-palestinian",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x=data.drop(['price'],axis='columns')\n",
    "y=data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compatible-chrome",
   "metadata": {},
   "source": [
    "## Normalization using sklearn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indoor-forge",
   "metadata": {},
   "source": [
    "Normalization is good to use when you know that the distribution of your data does not follow a Gaussian distribution. This can be useful in algorithms that do not assume any distribution of the data like K-Nearest Neighbors and Neural Networks.\n",
    "In problems like our Regression we have Gaussain distribution A.K.A Normal distribution but just for demonstration here'show you will do it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "antique-chapel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# data normalization with sklearn\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\n# fit scaler on training data\\nnorm = MinMaxScaler().fit(x)\\n\\n# transform training data\\nX_norm = norm.transform(x)\\n'"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# data normalization with sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# fit scaler on training data\n",
    "norm = MinMaxScaler().fit(x)\n",
    "\n",
    "# transform training data\n",
    "X_norm = norm.transform(x)\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coordinated-casting",
   "metadata": {},
   "source": [
    "## Standardization using sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vertical-sterling",
   "metadata": {},
   "source": [
    "Standardization, on the other hand, can be helpful in cases where the data follows a Gaussian distribution. However, this does not have to be necessarily true. Also, unlike normalization, standardization does not have a bounding range. So, even if you have outliers in your data, they will not be affected by standardization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continuing-brunswick",
   "metadata": {},
   "source": [
    "However, at the end of the day, the choice of using normalization or standardization will depend on your problem and the machine learning algorithm you are using. There is no hard and fast rule to tell you when to normalize or standardize your data. You can always start by fitting your model to raw, normalized and standardized data and compare the performance for best results.\n",
    "\n",
    "\n",
    "clearly in our problem there is no effect at all of standardization and Normalization that just points to the fact our data does not need Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "understood-accused",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data standardization with  sklearn\n",
    "std = StandardScaler()\n",
    "x = std.fit_transform(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "correct-grove",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    221900.0\n",
       "1    538000.0\n",
       "2    180000.0\n",
       "3    604000.0\n",
       "4    510000.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "academic-guidance",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "likely-ratio",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x=x.drop('date',axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "canadian-flash",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "running-merchandise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "needed-poland",
   "metadata": {},
   "source": [
    "## LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "framed-commercial",
   "metadata": {},
   "outputs": [],
   "source": [
    "model =linear_model.LinearRegression()\n",
    "model.fit(x_train,y_train)\n",
    "y_prid=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "polish-chapel",
   "metadata": {},
   "source": [
    "Accuracy (e.g. classification accuracy) is a measure for classification, not regression.\n",
    "We cannot calculate accuracy for a regression model.\n",
    "The skill or performance of a regression model must be reported as an error in those predictions.\n",
    "\n",
    "This makes sense if you think about it. If you are predicting a numeric value like a height or a dollar amount, you don’t want to know if the model predicted the value exactly (this might be intractably difficult in practice); instead, we want to know how close the predictions were to the expected values.\n",
    "\n",
    "There are three error metrics that are commonly used for evaluating and reporting the performance of a regression model; they are:\n",
    "\n",
    "1)Mean Squared Error (MSE).\n",
    "2)Root Mean Squared Error (RMSE).\n",
    "3)Mean Absolute Error (MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "precise-submission",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy :  70.29292527894575\n",
      "Test Accuracy :  69.80990801636075\n",
      "Mean Squared Error 35903346291.52909\n",
      "Root Mean Squared Error 189481.78353480075\n",
      "Mean Absolute Error 121243.6897093018\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Accuracy : \",(model.score(x_train,y_train))*100)\n",
    "print(\"Test Accuracy : \",(model.score(x_test,y_test))*100)\n",
    "#rms = mean_squared_error(y_actual, y_predicted, squared=False)\n",
    "print(\"Mean Squared Error\",mean_squared_error(y_test, y_prid))\n",
    "print(\"Root Mean Squared Error\",mean_squared_error(y_test, y_prid,squared=False))\n",
    "print(\"Mean Absolute Error\",mean_absolute_error(y_test, y_prid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "breeding-inspection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb940f8288>]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(y_test,y_prid)\n",
    "plt.plot(y_test,y_test,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-seeker",
   "metadata": {},
   "source": [
    "## RegressionTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "aging-israeli",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "  \n",
    "# create a regressor object\n",
    "regressor = DecisionTreeRegressor(random_state = 0) \n",
    "  \n",
    "# fit the regressor with X and Y data\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "fallen-mitchell",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prid=regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "worldwide-alexandria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy :  99.99999986969239\n",
      "Test Accuracy :  77.23649671365244\n",
      "Mean Squared Error 27071329949.60745\n",
      "Root Mean Squared Error 164533.67421171707\n",
      "Mean Absolute Error 95349.5468424705\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Train Accuracy : \",(regressor.score(x_train,y_train))*100)\n",
    "print(\"Test Accuracy : \",(regressor.score(x_test,y_test))*100)\n",
    "#rms = mean_squared_error(y_actual, y_predicted, squared=False)\n",
    "print(\"Mean Squared Error\",mean_squared_error(y_test, y_prid))\n",
    "print(\"Root Mean Squared Error\",mean_squared_error(y_test, y_prid,squared=False))\n",
    "print(\"Mean Absolute Error\",mean_absolute_error(y_test, y_prid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "amateur-monthly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb940a0e08>]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(y_test,y_prid)\n",
    "plt.plot(y_test,y_test,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iraqi-catalog",
   "metadata": {},
   "source": [
    "## Ensemble Learning Bagging(RandomForestRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "secure-maria",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "model.fit(x_train,y_train)\n",
    "y_prid=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "realistic-alarm",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy :  98.24784452226893\n",
      "Test Accuracy :  89.29150356935301\n",
      "Mean Squared Error 12735001132.804628\n",
      "Root Mean Squared Error 112849.46226192055\n",
      "Mean Absolute Error 64507.989730896756\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"Train Accuracy : \",(model.score(x_train,y_train))*100)\n",
    "print(\"Test Accuracy : \",(model.score(x_test,y_test))*100)\n",
    "#rms = mean_squared_error(y_actual, y_predicted, squared=False)\n",
    "print(\"Mean Squared Error\",mean_squared_error(y_test, y_prid))\n",
    "print(\"Root Mean Squared Error\",mean_squared_error(y_test, y_prid,squared=False))\n",
    "print(\"Mean Absolute Error\",mean_absolute_error(y_test, y_prid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "bulgarian-credit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xba26c3bc8>]"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(y_test,y_prid)\n",
    "plt.plot(y_test,y_test,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "periodic-mailman",
   "metadata": {},
   "source": [
    "## Ensemble Learning Boosting(XGboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "least-receipt",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "removed-extraction",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:47:42] WARNING: d:\\bld\\xgboost-split_1615294821523\\work\\src\\objective\\regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "xg_reg.fit(x_train,y_train)\n",
    "y_prid = xg_reg.predict(x_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "prospective-screw",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy :  96.36776151025393\n",
      "Test Accuracy :  90.39554109286699\n",
      "Mean Squared Error 11422032575.204823\n",
      "Root Mean Squared Error 106873.90970299918\n",
      "Mean Absolute Error 62146.428601376356\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Accuracy : \",(xg_reg.score(x_train,y_train))*100)\n",
    "print(\"Test Accuracy : \",(xg_reg.score(x_test,y_test))*100)\n",
    "#rms = mean_squared_error(y_actual, y_predicted, squared=False)\n",
    "print(\"Mean Squared Error\",mean_squared_error(y_test, y_prid))\n",
    "print(\"Root Mean Squared Error\",mean_squared_error(y_test, y_prid,squared=False))\n",
    "print(\"Mean Absolute Error\",mean_absolute_error(y_test, y_prid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dimensional-bacteria",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "pursuant-conspiracy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xba27378c8>]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(y_test,y_prid)\n",
    "plt.plot(y_test,y_test,color='red')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}