Day 47 of 75 DataSci.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2742b2b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "id": "dc6d489f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('homeprices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90f45c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
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   "execution_count": 4,
   "id": "054afd86",
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   "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",
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       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2fe0108",
   "metadata": {},
   "outputs": [],
   "source": [
    "med=df['bedrooms'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ca19822",
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   "outputs": [
    {
     "data": {
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       "4.0"
      ]
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "med"
   ]
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   "execution_count": 7,
   "id": "31c45eb3",
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   "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",
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       "      <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",
       "    </tr>\n",
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       "</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       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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.bedrooms=df.bedrooms.fillna(med)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f6f0190",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b2bf2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg =linear_model.LinearRegression()"
   ]
  },
  {
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   "execution_count": 10,
   "id": "eaf915ed",
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       "LinearRegression()"
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    "reg"
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      ],
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       "LinearRegression()"
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     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "reg.fit(df[['area','bedrooms','age']],df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34a73a3a",
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    {
     "data": {
      "text/plain": [
       "array([  112.06244194, 23388.88007794, -3231.71790863])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "reg.coef_\n"
   ]
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   "id": "07844888",
   "metadata": {},
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       "221323.0018654043"
      ]
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     "execution_count": 13,
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   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adfedb85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the area: 5000\n",
      "Enter the bedrooms: 3\n",
      "Enter the age: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Acer\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([813021.2369063])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=int(input(\"Enter the area: \"))\n",
    "b=int(input(\"Enter the bedrooms: \"))\n",
    "c=int(input(\"Enter the age: \"))\n",
    "reg.predict([[a,b,c]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eb72a06",
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