데이터모형구축작업1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 모형 구축 작업\n",
    "\n",
    "## - 분류 : LogisticRegresion, SVC\n",
    "\n",
    "## - 예측 : Linear_regression, SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예측 - california 주택 가격"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "dataset = datasets.fetch_california_housing()\n",
    "df = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
    "df['target'] = dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
      "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
      "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
      "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
      "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
      "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
      "\n",
      "   Longitude  target  \n",
      "0    -122.23   4.526  \n",
      "1    -122.22   3.585  \n",
      "2    -122.24   3.521  \n",
      "3    -122.25   3.413  \n",
      "4    -122.25   3.422  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 결측, 이상 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedInc        0\n",
      "HouseAge      0\n",
      "AveRooms      0\n",
      "AveBedrms     0\n",
      "Population    0\n",
      "AveOccup      0\n",
      "Latitude      0\n",
      "Longitude     0\n",
      "target        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum()) #결측값 없음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns: #이상치 \n",
    "    data = df[col]\n",
    "    q75 = np.quantile(data,0.75)\n",
    "    q25 = np.quantile(data,0.25)\n",
    "    IQR = q75 - q25\n",
    "    \n",
    "    lower = q25 - IQR*1.5\n",
    "    upper = q75 + IQR*1.5\n",
    "    \n",
    "    lower_list = data[data<lower].index.tolist()\n",
    "    upper_list = data[data>upper].index.tolist()\n",
    "    \n",
    "    #print(lower_list)\n",
    "    #print(upper_list)\n",
    "    \n",
    "    for i in lower_list:\n",
    "        df[col][i] = lower\n",
    "    \n",
    "    for i in upper_list:\n",
    "        df[col][i] = upper\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "for count,i in enumerate(df.columns):\n",
    "    sns.boxplot(df[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['target']\n",
    "df = df.drop('target',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_target = MinMaxScaler().fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for num,name in enumerate(df.columns):\n",
    "    df[name] = std_target[:,num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.539668</td>\n",
       "      <td>0.784314</td>\n",
       "      <td>0.043512</td>\n",
       "      <td>0.020469</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.567481</td>\n",
       "      <td>0.211155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.538027</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.038224</td>\n",
       "      <td>0.018929</td>\n",
       "      <td>0.067210</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.565356</td>\n",
       "      <td>0.212151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.466028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052756</td>\n",
       "      <td>0.021940</td>\n",
       "      <td>0.013818</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>0.210159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.354699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035241</td>\n",
       "      <td>0.021929</td>\n",
       "      <td>0.015555</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>0.209163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.230776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038534</td>\n",
       "      <td>0.022166</td>\n",
       "      <td>0.015752</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>0.564293</td>\n",
       "      <td>0.209163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>0.073130</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.029769</td>\n",
       "      <td>0.023715</td>\n",
       "      <td>0.023599</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.737513</td>\n",
       "      <td>0.324701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>0.141853</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.037344</td>\n",
       "      <td>0.029124</td>\n",
       "      <td>0.009894</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.738576</td>\n",
       "      <td>0.312749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>0.082764</td>\n",
       "      <td>0.313725</td>\n",
       "      <td>0.030904</td>\n",
       "      <td>0.023323</td>\n",
       "      <td>0.028140</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>0.732200</td>\n",
       "      <td>0.311753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>0.094295</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.031783</td>\n",
       "      <td>0.024859</td>\n",
       "      <td>0.020684</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.732200</td>\n",
       "      <td>0.301793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>0.130253</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.031252</td>\n",
       "      <td>0.024573</td>\n",
       "      <td>0.038790</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.725824</td>\n",
       "      <td>0.309761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  \\\n",
       "0      0.539668  0.784314  0.043512   0.020469    0.008941  0.001499   \n",
       "1      0.538027  0.392157  0.038224   0.018929    0.067210  0.001141   \n",
       "2      0.466028  1.000000  0.052756   0.021940    0.013818  0.001698   \n",
       "3      0.354699  1.000000  0.035241   0.021929    0.015555  0.001493   \n",
       "4      0.230776  1.000000  0.038534   0.022166    0.015752  0.001198   \n",
       "...         ...       ...       ...        ...         ...       ...   \n",
       "20635  0.073130  0.470588  0.029769   0.023715    0.023599  0.001503   \n",
       "20636  0.141853  0.333333  0.037344   0.029124    0.009894  0.001956   \n",
       "20637  0.082764  0.313725  0.030904   0.023323    0.028140  0.001314   \n",
       "20638  0.094295  0.333333  0.031783   0.024859    0.020684  0.001152   \n",
       "20639  0.130253  0.294118  0.031252   0.024573    0.038790  0.001549   \n",
       "\n",
       "       Latitude  Longitude  \n",
       "0      0.567481   0.211155  \n",
       "1      0.565356   0.212151  \n",
       "2      0.564293   0.210159  \n",
       "3      0.564293   0.209163  \n",
       "4      0.564293   0.209163  \n",
       "...         ...        ...  \n",
       "20635  0.737513   0.324701  \n",
       "20636  0.738576   0.312749  \n",
       "20637  0.732200   0.311753  \n",
       "20638  0.732200   0.301793  \n",
       "20639  0.725824   0.309761  \n",
       "\n",
       "[20640 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(df,target,test_size = 0.3,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import svm\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, mean_squared_log_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6132719413179822"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = LinearRegression()\n",
    "model1.fit(x_train,y_train)\n",
    "model1.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.746601587113346"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model1.predict(x_test)\n",
    "MSE = mean_squared_error(y_test, pred)\n",
    "RMSE = np.sqrt(MSE)\n",
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poly\n",
      "0.6214232075741655\n",
      "0.6245488812110718\n",
      "rbf\n",
      "0.6615567703624965\n"
     ]
    }
   ],
   "source": [
    "kernel = ['poly','rbf']\n",
    "for kn in kernel:\n",
    "    model2 = svm.SVR(kernel=kn)\n",
    "    model2.fit(x_train,y_train)\n",
    "    print(kn)\n",
    "    print(model2.score(x_train,y_train)) #r2 score\n",
    "    pred = model2.predict(x_test)\n",
    "    print(mean_squared_error(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}