kurozumi/Jiyugaoka_LinearRegression.ipynb

## Jiyugaoka_LinearRegression.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# suumoから取ってきた自由が丘の賃貸料データを読み込む\n",
    "df = pd.read_csv(\"jiyugaoka.csv\", header=None)\n",
    "\n",
    "# Priceは賃貸料、Spaceは専有面積\n",
    "df.columns=[\"Price\",\"Space\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45000.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45000.0</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55000.0</td>\n",
       "      <td>18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55000.0</td>\n",
       "      <td>18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55000.0</td>\n",
       "      <td>18.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Price  Space\n",
       "0  45000.0  10.00\n",
       "1  45000.0  12.00\n",
       "2  55000.0  18.56\n",
       "3  55000.0  18.56\n",
       "4  55000.0  18.56"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 線形回帰モデルを生成\n",
    "lreg = LinearRegression()\n",
    "\n",
    "# Space（専有面積）を説明変数\n",
    "X = df.drop(\"Price\",1)\n",
    "\n",
    "# 賃貸料を目的変数\n",
    "Y = df.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# データを学習させる\n",
    "lreg.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.874990914238\n"
     ]
    }
   ],
   "source": [
    "# モデルの評価\n",
    "print(lreg.score(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 70040.66541169])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 専有面積が16.5平米の賃貸料を予測\n",
    "lreg.predict(16.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10c87b4a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KIAXmoYceZufOzBDdE4D30zEB8D2EmsCV7NjxEkcddRQXXng+Q4Y4IcncBZxP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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7bf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 専有面積と賃貸料の散布図\n",
    "plt.scatter(X,Y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"import pandas as pd\n",
	"\n",
	"import matplotlib.pyplot as plt\n",
	"%matplotlib inline\n",
	"\n",
	"import sklearn\n",
	"from sklearn.linear_model import LinearRegression"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# suumoから取ってきた自由が丘の賃貸料データを読み込む\n",
	"df = pd.read_csv(\"jiyugaoka.csv\", header=None)\n",
	"\n",
	"# Priceは賃貸料、Spaceは専有面積\n",
	"df.columns=[\"Price\",\"Space\"]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>Price</th>\n",
	" <th>Space</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <td>45000.0</td>\n",
	" <td>10.00</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <td>45000.0</td>\n",
	" <td>12.00</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <td>55000.0</td>\n",
	" <td>18.56</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <td>55000.0</td>\n",
	" <td>18.56</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <td>55000.0</td>\n",
	" <td>18.56</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" Price Space\n",
	"0 45000.0 10.00\n",
	"1 45000.0 12.00\n",
	"2 55000.0 18.56\n",
	"3 55000.0 18.56\n",
	"4 55000.0 18.56"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"df.head()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"# 線形回帰モデルを生成\n",
	"lreg = LinearRegression()\n",
	"\n",
	"# Space（専有面積）を説明変数\n",
	"X = df.drop(\"Price\",1)\n",
	"\n",
	"# 賃貸料を目的変数\n",
	"Y = df.Price"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# データを学習させる\n",
	"lreg.fit(X,Y)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"0.874990914238\n"
	]
	}
	],
	"source": [
	"# モデルの評価\n",
	"print(lreg.score(X, Y))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"array([ 70040.66541169])"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# 専有面積が16.5平米の賃貸料を予測\n",
	"lreg.predict(16.5)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.collections.PathCollection at 0x10c87b4a8>"
	]
	},
	"execution_count": 9,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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KIAXmoYceZufOzBDdE4D30zEB8D2EmsCV7NjxEkcddRQXXng+Q4Y4IcncBZxP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\nYII=\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x10c7bf940>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"# 専有面積と賃貸料の散布図\n",
	"plt.scatter(X,Y)"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
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