bikes-logistic-regression.ipynb

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   },
   "source": [
    "### A Logistic Regression Example from the book \"Principles of Data Science\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>holiday</th>\n",
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       "      <th>windspeed</th>\n",
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/bikeshare.csv'\n",
    "bikes = pd.read_csv(url)\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191.57413191254824\n"
     ]
    }
   ],
   "source": [
    "average_bike_rental = bikes['count'].mean()\n",
    "print(average_bike_rental)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.599853\nTrue     0.400147\nName: above_average, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes['above_average'] = bikes['count'] >= average_bike_rental\n",
    "bikes['above_average'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65319617927994122"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "features_cols = ['temp']\n",
    "x = bikes[features_cols]\n",
    "y = bikes['above_average']\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "logreg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
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  }
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