Pickel, Joblib - model save and reterieve Feb 12 2020.ipynb

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   "source": [
    "import pandas as pd"
   ]
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "house=pd.read_csv(r'C:\\Users\\HP P DTS\\Desktop\\Python Classes\\DataSet\\House_Price_Mult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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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",
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       "      <td>3600</td>\n",
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       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Area  Bedroom  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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "house['Bedroom']=house['Bedroom'].replace(np.NaN,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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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>3.0</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>3100</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>850000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3800</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>750000</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>580000</td>\n",
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       "      <td>3700</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>625000</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10</td>\n",
       "      <td>500000</td>\n",
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      "text/plain": [
       "   Area  Bedroom  Age   Price\n",
       "0  2600      3.0   20  550000\n",
       "1  3000      4.0   15  565000\n",
       "2  3200      3.0   18  610000\n",
       "3  3600      3.0   30  595000\n",
       "4  4000      5.0    8  760000\n",
       "5  3100      6.0    6  850000\n",
       "6  3800      4.0   10  750000\n",
       "7  2800      3.0    2  580000\n",
       "8  3700      4.0    6  625000\n",
       "9  2500      3.0   10  500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Area', 'Bedroom', 'Age', 'Price'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(house.drop('Price',axis=1),house['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.01125077e+01,  8.11479440e+04, -1.47406365e+02])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137816.99265206116"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E:\\\\housemult']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'E:\\housemult')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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