LR_Multi_Varaible.ipynb

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  {
   "cell_type": "markdown",
   "id": "41122ddf",
   "metadata": {},
   "source": [
    "## Machine Learning With Python: Linear Regression Multiple Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6f61af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6867f3a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       NaN   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585fbcca",
   "metadata": {},
   "source": [
    "### Data Preprocessing: Fill NA values with median value of a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e4d867b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bedrooms.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c067ac7f",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
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      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       4.0   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000\n",
       "5  4100       6.0    8  810000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bedrooms = df.bedrooms.fillna(df.bedrooms.median())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cabd7771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df.drop('price', axis='columns'), df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25b59617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  112.06244194, 23388.88007794, -3231.71790863])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ffc838a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([498408.25158031])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3000,3,40]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b03c7ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([578876.03748933])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2500, 4, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51e314b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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