Boston Housing - Feb 12 2020 .ipynb

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   "source": [
    "import pandas as pd"
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    {
     "name": "stdout",
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     "text": [
      "C:\\Users\\HP P DTS\\Desktop\n"
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   "source": [
    "cd Desktop/"
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     "name": "stdout",
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     "text": [
      "C:\\Users\\HP P DTS\\Desktop\\Python Classes\n"
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    "cd \"Python Classes\""
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     "name": "stdout",
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     "text": [
      "C:\\Users\\HP P DTS\\Desktop\\Python Classes\\DataSet\n"
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   "source": [
    "cd DataSet/"
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   "execution_count": 5,
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     "name": "stdout",
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     "text": [
      " Volume in drive C has no label.\n",
      " Volume Serial Number is 12AD-B436\n",
      "\n",
      " Directory of C:\\Users\\HP P DTS\\Desktop\\Python Classes\\DataSet\n",
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      "11-02-2020  20:26    <DIR>          .\n",
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing=pd.read_csv('USA_Housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.458574</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.800503</td>\n",
       "      <td>1.059034e+06</td>\n",
       "      <td>208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.642455</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.072174</td>\n",
       "      <td>1.505891e+06</td>\n",
       "      <td>188 Johnson Views Suite 079\\nLake Kathleen, CA...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.067179</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.159400</td>\n",
       "      <td>1.058988e+06</td>\n",
       "      <td>9127 Elizabeth Stravenue\\nDanieltown, WI 06482...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.240046</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.242831</td>\n",
       "      <td>1.260617e+06</td>\n",
       "      <td>USS Barnett\\nFPO AP 44820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.197226</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.109472</td>\n",
       "      <td>6.309435e+05</td>\n",
       "      <td>USNS Raymond\\nFPO AE 09386</td>\n",
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      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0      79545.458574             5.682861                   7.009188   \n",
       "1      79248.642455             6.002900                   6.730821   \n",
       "2      61287.067179             5.865890                   8.512727   \n",
       "3      63345.240046             7.188236                   5.586729   \n",
       "4      59982.197226             5.040555                   7.839388   \n",
       "\n",
       "   Avg. Area Number of Bedrooms  Area Population         Price  \\\n",
       "0                          4.09     23086.800503  1.059034e+06   \n",
       "1                          3.09     40173.072174  1.505891e+06   \n",
       "2                          5.13     36882.159400  1.058988e+06   \n",
       "3                          3.26     34310.242831  1.260617e+06   \n",
       "4                          4.23     26354.109472  6.309435e+05   \n",
       "\n",
       "                                             Address  \n",
       "0  208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...  \n",
       "1  188 Johnson Views Suite 079\\nLake Kathleen, CA...  \n",
       "2  9127 Elizabeth Stravenue\\nDanieltown, WI 06482...  \n",
       "3                          USS Barnett\\nFPO AP 44820  \n",
       "4                         USNS Raymond\\nFPO AE 09386  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n",
       "       'Avg. Area Number of Bedrooms', 'Area Population', 'Price', 'Address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(housing.drop(['Price', 'Address'],axis=1),housing['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1223846.97225061])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[79545.458574,5.682861,7.009188,4.09,23086.800503]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.15780494e+01, 1.65637027e+05, 1.20659949e+05, 1.65113905e+03,\n",
       "       1.52007439e+01])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2637299.033328599"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "file=open('housing_model','wb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "file1=open('housing_model','rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "md=pickle.load(file1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.15780494e+01, 1.65637027e+05, 1.20659949e+05, 1.65113905e+03,\n",
       "       1.52007439e+01])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2637299.033328599"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1223846.97225061])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.predict([[79545.458574,5.682861,7.009188,4.09,23086.800503]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib\n",
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['housing_joblib']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'housing_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "md1=joblib.load('housing_joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.15780494e+01, 1.65637027e+05, 1.20659949e+05, 1.65113905e+03,\n",
       "       1.52007439e+01])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2637299.033328599"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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