12. Boston Housing.ipynb

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   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "execution_count": 8,
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    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
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       "      <th></th>\n",
       "      <th>RM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>MEDV</th>\n",
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       "      <th>0</th>\n",
       "      <td>6.575</td>\n",
       "      <td>4.98</td>\n",
       "      <td>15.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.421</td>\n",
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       "      <td>7.185</td>\n",
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       "      RM  LSTAT  PTRATIO      MEDV\n",
       "0  6.575   4.98     15.3  504000.0\n",
       "1  6.421   9.14     17.8  453600.0\n",
       "2  7.185   4.03     17.8  728700.0\n",
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     "metadata": {},
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   "source": [
    "df = pd.read_csv(\"data/proj12/housing.csv\")\n",
    "df.head()"
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   "execution_count": 27,
   "id": "75d40d56",
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       "RM         0\n",
       "LSTAT      0\n",
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       "            console.log([gd, 'removed!']);\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter_3d(df, x=\"LSTAT\", y=\"MEDV\", z=\"PTRATIO\", color_continuous_scale = [\"#FF0000\", \"#0000FF\", \"#FFFF00\", \n",
    "                                                  \"#00EFFF\", \"#FF00FF\", \"#FFFFFF\"])\n",
    "fig.update_traces(marker = dict(size = 3, line = dict(width = 1, color = \"#FFFFFF\")))\n",
    "fig.update_layout(template = \"plotly_dark\", font = dict(family = \"PT Sans\", size = 12, color = \"#97FFFF\"))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a9a35cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f07b262cfd0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(3, figsize=(15, 6))\n",
    "_.suptitle(\"Boston\")\n",
    "ax[0].scatter(df[\"RM\"], df[\"MEDV\"])\n",
    "ax[1].scatter(df[\"LSTAT\"], df[\"MEDV\"])\n",
    "ax[2].scatter(df[\"PTRATIO\"], df[\"MEDV\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6590975",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"MEDV\", axis=1)\n",
    "y = df[\"MEDV\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b233c38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "700df155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6944384608788422"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f909b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8396660959721389"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(X_train, y_train)\n",
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11603c20",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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