Bike Sharing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"day.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_graph=pd.DataFrame(df.groupby('weekday')['cnt'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd06be10>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_graph.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_graph=pd.DataFrame(df.groupby('season')['cnt'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7955a70>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_graph.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a9ad50>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_graph_weather=pd.DataFrame(df.groupby('hum')['cnt'].sum())\n",
    "df_graph_weather.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x79f7ad0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_graph_weather=pd.DataFrame(df.groupby('temp')['cnt'].sum())\n",
    "df_graph_weather.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ae0690>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_graph_weather=pd.DataFrame(df.groupby('windspeed')['cnt'].sum())\n",
    "df_graph_weather.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['cnt','casual','registered','dteday','dteday','season','yr','mnth','holiday','weekday','workingday','weathersit'],axis =1)\n",
    "y = df['cnt']\n",
    "test_size = .25\n",
    "seed = 8\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y, test_size =test_size, random_state = seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>instant</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>354</td>\n",
       "      <td>0.385833</td>\n",
       "      <td>0.396454</td>\n",
       "      <td>0.595417</td>\n",
       "      <td>0.061571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>513</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.641425</td>\n",
       "      <td>0.697083</td>\n",
       "      <td>0.215171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>524</td>\n",
       "      <td>0.602500</td>\n",
       "      <td>0.569442</td>\n",
       "      <td>0.567083</td>\n",
       "      <td>0.157350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.537242</td>\n",
       "      <td>0.768333</td>\n",
       "      <td>0.133696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>177</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.637629</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>0.094533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      temp     atemp       hum  windspeed\n",
       "353      354  0.385833  0.396454  0.595417   0.061571\n",
       "512      513  0.690000  0.641425  0.697083   0.215171\n",
       "523      524  0.602500  0.569442  0.567083   0.157350\n",
       "488      489  0.560000  0.537242  0.768333   0.133696\n",
       "176      177  0.680000  0.637629  0.513333   0.094533"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(548, 5)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grace.zhao\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:657: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=2.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00378788 0.        ]\n"
     ]
    }
   ],
   "source": [
    "result = model_selection.cross_val_score(LogisticRegression(),X_train,y_train,cv=2,n_jobs=-1,scoring=\"accuracy\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
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 },
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