supervised 1 .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "782a291d-7c81-4b40-9cd5-6594884897f9",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>263000018</td>\n",
       "      <td>20140521T000000</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530</td>\n",
       "      <td>1131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1530</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530</td>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21609</th>\n",
       "      <td>6600060120</td>\n",
       "      <td>20150223T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310</td>\n",
       "      <td>5813</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2310</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830</td>\n",
       "      <td>7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21610</th>\n",
       "      <td>1523300141</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>402101.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21611</th>\n",
       "      <td>291310100</td>\n",
       "      <td>20150116T000000</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600</td>\n",
       "      <td>2388</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98027</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410</td>\n",
       "      <td>1287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21612</th>\n",
       "      <td>1523300157</td>\n",
       "      <td>20141015T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020</td>\n",
       "      <td>1076</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1020</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21613 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date     price  bedrooms  bathrooms  \\\n",
       "0      7129300520  20141013T000000  221900.0         3       1.00   \n",
       "1      6414100192  20141209T000000  538000.0         3       2.25   \n",
       "2      5631500400  20150225T000000  180000.0         2       1.00   \n",
       "3      2487200875  20141209T000000  604000.0         4       3.00   \n",
       "4      1954400510  20150218T000000  510000.0         3       2.00   \n",
       "...           ...              ...       ...       ...        ...   \n",
       "21608   263000018  20140521T000000  360000.0         3       2.50   \n",
       "21609  6600060120  20150223T000000  400000.0         4       2.50   \n",
       "21610  1523300141  20140623T000000  402101.0         2       0.75   \n",
       "21611   291310100  20150116T000000  400000.0         3       2.50   \n",
       "21612  1523300157  20141015T000000  325000.0         2       0.75   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  grade  \\\n",
       "0             1180      5650     1.0           0     0  ...      7   \n",
       "1             2570      7242     2.0           0     0  ...      7   \n",
       "2              770     10000     1.0           0     0  ...      6   \n",
       "3             1960      5000     1.0           0     0  ...      7   \n",
       "4             1680      8080     1.0           0     0  ...      8   \n",
       "...            ...       ...     ...         ...   ...  ...    ...   \n",
       "21608         1530      1131     3.0           0     0  ...      8   \n",
       "21609         2310      5813     2.0           0     0  ...      8   \n",
       "21610         1020      1350     2.0           0     0  ...      7   \n",
       "21611         1600      2388     2.0           0     0  ...      8   \n",
       "21612         1020      1076     2.0           0     0  ...      7   \n",
       "\n",
       "       sqft_above  sqft_basement  yr_built  yr_renovated  zipcode      lat  \\\n",
       "0            1180              0      1955             0    98178  47.5112   \n",
       "1            2170            400      1951          1991    98125  47.7210   \n",
       "2             770              0      1933             0    98028  47.7379   \n",
       "3            1050            910      1965             0    98136  47.5208   \n",
       "4            1680              0      1987             0    98074  47.6168   \n",
       "...           ...            ...       ...           ...      ...      ...   \n",
       "21608        1530              0      2009             0    98103  47.6993   \n",
       "21609        2310              0      2014             0    98146  47.5107   \n",
       "21610        1020              0      2009             0    98144  47.5944   \n",
       "21611        1600              0      2004             0    98027  47.5345   \n",
       "21612        1020              0      2008             0    98144  47.5941   \n",
       "\n",
       "          long  sqft_living15  sqft_lot15  \n",
       "0     -122.257           1340        5650  \n",
       "1     -122.319           1690        7639  \n",
       "2     -122.233           2720        8062  \n",
       "3     -122.393           1360        5000  \n",
       "4     -122.045           1800        7503  \n",
       "...        ...            ...         ...  \n",
       "21608 -122.346           1530        1509  \n",
       "21609 -122.362           1830        7200  \n",
       "21610 -122.299           1020        2007  \n",
       "21611 -122.069           1410        1287  \n",
       "21612 -122.299           1020        1357  \n",
       "\n",
       "[21613 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"kc_house_data.csv\",)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6317fcf0-46a1-42fb-ba57-a0c7fbfa7454",
   "metadata": {},
   "outputs": [],
   "source": [
    "the most important features are : the rooms, the price, the floors, all the sqft, yr _renovated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2bd90693-9a1b-4b3e-af6c-e310ec245f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df[\"price\"]\n",
    "y=df[\"sqft_living\"]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7154607-f1be-4176-9d0e-51eaeca96146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 419107.76546734595\n",
      "R squared 0.49875223400847035\n"
     ]
    }
   ],
   "source": [
    "#Importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "#extract x and y from our data\n",
    "x=df[\"price\"].values[:,np.newaxis]\n",
    "y=df[\"sqft_living\"].values\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.35,random_state=40) #splitting data with test size of 35%\n",
    "\n",
    "model=LinearRegression()   #build linear regression model\n",
    "model.fit(x_train,y_train)  #fitting the training data\n",
    "predicted=model.predict(x_test) #testing our model’s performance\n",
    "\n",
    "\n",
    "print(\"MSE\", mean_squared_error(y_test,predicted))\n",
    "print(\"R squared\", metrics.r2_score(y_test,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d362eefb-29fa-47cc-a808-ae88aba0e0f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color=\"r\")\n",
    "plt.title(\"Linear Regression\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.xlabel(\"sqft_living\")\n",
    "plt.plot(x,model.predict(x),color=\"k\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da58ddb7-a311-4c66-890b-1ac558e5e87c",
   "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.scatter(x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d27f6fec-5599-4223-b0ee-78fea5985539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 62886120082.13324\n",
      "R squared 0.4997824109184996\n"
     ]
    }
   ],
   "source": [
    "#extract x and y from our data\n",
    "x=df[[\"sqft_living\",\"floors\"]]  #we have more than one input\n",
    "y=df[\"price\"].values\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.35,random_state=40) #splitting data with test size of 35%\n",
    "\n",
    "model=LinearRegression() #build linear regression model\n",
    "model.fit(x_train,y_train) #fitting the training data\n",
    "predicted=model.predict(x_test) #testing our model’s performance\n",
    "\n",
    "print(\"MSE\", mean_squared_error(y_test,predicted))\n",
    "print(\"R squared\", metrics.r2_score(y_test,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3e1c422b-5ff4-4f38-878b-5d34a2731f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures \n",
    "# create matrix and vectors\n",
    "x = [[0.44, 0.68], [0.99, 0.23]]\n",
    "y = [109.85, 155.72]\n",
    "\n",
    "lg = LinearRegression() \n",
    "poly = PolynomialFeatures(degree = 2) "
   ]
  }
 ],
 "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": "3.8.8"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}