HOUSE PRICING-PREDICTION PROJECT.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "00c49e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c2fc191",
   "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>bedroom</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>79797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5490</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>34300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   area  bedroom  age  price\n",
       "0  1000        3   23  79797\n",
       "1  1500        4   23  50000\n",
       "2  2300        5   42  34000\n",
       "3  3540        4   12  45000\n",
       "4  4120        2   13  43000\n",
       "5  5490        3   20  34300"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e92ec9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df.bedroom.median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18191031",
   "metadata": {},
   "outputs": [
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       "3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "import math\n",
    "median_bedroom=math.floor(df.bedroom.median())\n",
    "median_bedroom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b242af2",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>79797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>50000</td>\n",
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       "      <td>2300</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>34000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>43000</td>\n",
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       "      <td>3</td>\n",
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       "      <td>34300</td>\n",
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      "text/plain": [
       "   area  bedroom  age  price\n",
       "0  1000        3   23  79797\n",
       "1  1500        4   23  50000\n",
       "2  2300        5   42  34000\n",
       "3  3540        4   12  45000\n",
       "4  4120        2   13  43000\n",
       "5  5490        3   20  34300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bedroom = df.bedroom.fillna(median_bedroom)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "681d7d74",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(df[['area','bedroom','age']],df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc218793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   -9.50681828, -7725.81851421,  -339.30519797])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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    "reg.coef_"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77d231c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110685.69470067005"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02810267",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\surya\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-233314.29946061])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3000,40,19]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af25c173",
   "metadata": {},
   "source": [
    "# y=mx+b"
   ]
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   "cell_type": "code",
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   "id": "b1f56ad3",
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    {
     "data": {
      "text/plain": [
       "-769826502.5253123"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-233314.29946061*3300+110685.69470067005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6e0c79f",
   "metadata": {},
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   "source": []
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