mod-3/week-2/day-4-Hyperparameter_Tuning/Hyperparameter_Tuning_Lecture-mmitchell.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning Lecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lesson Objectives:\n",
    "- Understand the difference between parameters and hyperparameters\n",
    "- Understand why we tune hyperparameters\n",
    "- Understand how hyperparameter tuning impacts the outcomes of your model\n",
    "- Apply hyperparameter tuning to your model using gridsearch and randomsearch\n",
    "- Select the \"best\" hyperparameters for your model\n",
    "- Use pipelines with hyperparameter searching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters vs Hyperparameters\n",
    "\n",
    "We talk a lot about parameters and hyperparameters, but what are they?  What is the difference?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![](images/meetupconsumercreditdefaultvers2all-11-638.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> Why do we tune hyperparameters?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We tune hyperparameters to find the set of hyperparameters that optimize the predictive ability (ex. accuracy, recall, r-squared value, RMSE, etc.)\n",
    "\n",
    "A big factor in whether a machine learning model will perform well is a lot of tweaking..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![Pile of data to stir (https://xkcd.com/1838/)](images/machine_learning_xkcd.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You can think of hyperparameters as little dials to adjust to make it easier for the machine learning model to learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![](images/dials.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "But how do we know what to adjust them to?!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does this fit into the CRISP-DM Model?\n",
    "\n",
    "Let's remind ourselves about the steps of the CRISP-DM Model.\n",
    "\n",
    "![](./images/new_crisp-dm.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First let's talk data!\n",
    "\n",
    "The [data](https://www.kaggle.com/jolasa/bay-area-bike-sharing-trips) we will be using today is taken from Kaggle. This dataset contains anonymized trips data of Lyft bike sharing system(Bay Wheels), in the Bay Area from January 2019 to May 2019.\n",
    "\n",
    "\n",
    "We are going to use this data to create a model the predicts if the the member is a \"brogrammer\".  Everyone who is a male and lives in SF AND rides an electric bike is a Brogrammer.  This target has already been created for you.\n",
    "\n",
    "![bikes](images/bikes.jpg) \n",
    "![the wiggle](images/wiggle.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's read in the data and do some EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from yellowbrick.classifier import ClassificationReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_duration_sec</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>trip_duration_min</th>\n",
       "      <th>bg</th>\n",
       "      <th>month_April</th>\n",
       "      <th>month_Februrary</th>\n",
       "      <th>month_January</th>\n",
       "      <th>month_March</th>\n",
       "      <th>month_May</th>\n",
       "      <th>user_type_Customer</th>\n",
       "      <th>user_type_Subscriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1128</td>\n",
       "      <td>124.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>877</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>18.80</td>\n",
       "      <td>Brogrammer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1388</td>\n",
       "      <td>243.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>3234</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>23.13</td>\n",
       "      <td>Brogrammer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>920</td>\n",
       "      <td>202.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3262</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>15.33</td>\n",
       "      <td>Brogrammer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>725</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2706</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>12.08</td>\n",
       "      <td>Brogrammer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>488</td>\n",
       "      <td>21.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>860</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>Brogrammer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trip_duration_sec  start_station_id  end_station_id  bike_id  \\\n",
       "4               1128             124.0             5.0      877   \n",
       "5               1388             243.0           247.0     3234   \n",
       "6                920             202.0           220.0     3262   \n",
       "7                725              44.0           121.0     2706   \n",
       "8                488              21.0            64.0      860   \n",
       "\n",
       "   member_birth_year  trip_duration_min          bg  month_April  \\\n",
       "4             1973.0              18.80  Brogrammer            1   \n",
       "5             1997.0              23.13  Brogrammer            1   \n",
       "6             1976.0              15.33  Brogrammer            1   \n",
       "7             1973.0              12.08  Brogrammer            1   \n",
       "8             1986.0               8.13  Brogrammer            1   \n",
       "\n",
       "   month_Februrary  month_January  month_March  month_May  user_type_Customer  \\\n",
       "4                0              0            0          0                   0   \n",
       "5                0              0            0          0                   1   \n",
       "6                0              0            0          0                   0   \n",
       "7                0              0            0          0                   1   \n",
       "8                0              0            0          0                   0   \n",
       "\n",
       "   user_type_Subscriber  \n",
       "4                     1  \n",
       "5                     0  \n",
       "6                     1  \n",
       "7                     0  \n",
       "8                     1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike=pd.read_pickle('./data/bike.pkl')\n",
    "bike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brogrammer     706660\n",
       "Nice_person    240182\n",
       "Name: bg, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.bg.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_duration_sec</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>trip_duration_min</th>\n",
       "      <th>month_April</th>\n",
       "      <th>month_Februrary</th>\n",
       "      <th>month_January</th>\n",
       "      <th>month_March</th>\n",
       "      <th>month_May</th>\n",
       "      <th>user_type_Customer</th>\n",
       "      <th>user_type_Subscriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "      <td>946842.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>578.960272</td>\n",
       "      <td>138.468291</td>\n",
       "      <td>135.722913</td>\n",
       "      <td>3891.877124</td>\n",
       "      <td>1984.673319</td>\n",
       "      <td>9.649340</td>\n",
       "      <td>0.226212</td>\n",
       "      <td>0.176227</td>\n",
       "      <td>0.182460</td>\n",
       "      <td>0.241813</td>\n",
       "      <td>0.173288</td>\n",
       "      <td>0.098418</td>\n",
       "      <td>0.901582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>324.429294</td>\n",
       "      <td>112.670023</td>\n",
       "      <td>111.868923</td>\n",
       "      <td>1972.289709</td>\n",
       "      <td>10.045458</td>\n",
       "      <td>5.407152</td>\n",
       "      <td>0.418378</td>\n",
       "      <td>0.381013</td>\n",
       "      <td>0.386224</td>\n",
       "      <td>0.428182</td>\n",
       "      <td>0.378496</td>\n",
       "      <td>0.297879</td>\n",
       "      <td>0.297879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1878.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>327.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>2175.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>512.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>4560.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>8.530000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>769.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>5433.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>12.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1583.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>7196.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>26.380000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       trip_duration_sec  start_station_id  end_station_id        bike_id  \\\n",
       "count      946842.000000     946842.000000   946842.000000  946842.000000   \n",
       "mean          578.960272        138.468291      135.722913    3891.877124   \n",
       "std           324.429294        112.670023      111.868923    1972.289709   \n",
       "min            61.000000          3.000000        3.000000      11.000000   \n",
       "25%           327.000000         47.000000       44.000000    2175.000000   \n",
       "50%           512.000000        101.000000       98.000000    4560.000000   \n",
       "75%           769.000000        238.000000      223.000000    5433.000000   \n",
       "max          1583.000000        427.000000      427.000000    7196.000000   \n",
       "\n",
       "       member_birth_year  trip_duration_min    month_April  month_Februrary  \\\n",
       "count      946842.000000      946842.000000  946842.000000    946842.000000   \n",
       "mean         1984.673319           9.649340       0.226212         0.176227   \n",
       "std            10.045458           5.407152       0.418378         0.381013   \n",
       "min          1878.000000           1.020000       0.000000         0.000000   \n",
       "25%          1980.000000           5.450000       0.000000         0.000000   \n",
       "50%          1987.000000           8.530000       0.000000         0.000000   \n",
       "75%          1992.000000          12.820000       0.000000         0.000000   \n",
       "max          2001.000000          26.380000       1.000000         1.000000   \n",
       "\n",
       "       month_January    month_March      month_May  user_type_Customer  \\\n",
       "count  946842.000000  946842.000000  946842.000000       946842.000000   \n",
       "mean        0.182460       0.241813       0.173288            0.098418   \n",
       "std         0.386224       0.428182       0.378496            0.297879   \n",
       "min         0.000000       0.000000       0.000000            0.000000   \n",
       "25%         0.000000       0.000000       0.000000            0.000000   \n",
       "50%         0.000000       0.000000       0.000000            0.000000   \n",
       "75%         0.000000       0.000000       0.000000            0.000000   \n",
       "max         1.000000       1.000000       1.000000            1.000000   \n",
       "\n",
       "       user_type_Subscriber  \n",
       "count         946842.000000  \n",
       "mean               0.901582  \n",
       "std                0.297879  \n",
       "min                0.000000  \n",
       "25%                1.000000  \n",
       "50%                1.000000  \n",
       "75%                1.000000  \n",
       "max                1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a52fba8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike.bg.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2c01da58>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAFGCAYAAACc1G30AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXgklEQVR4nO3db0ydd/3/8dfFOYW6cw4SYkx+pH8CsyxhZ20DJ6yaU5yJFZ0xmlmlrUHNGheX1gVDDS0WKFlX2jQ9MRa3NvOGCRHboNOvdx22Q9oGmiviPKezjTrZLOuiMjPOSXeg57p+N76Bn/wELsZpuXo+ez5ujeu8Sd5Xd06fXBdwarmu6woAABS0Ir8XAAAA+SPoAAAYgKADAGAAgg4AgAEIOgAABgj6vcBKOY6jTCajNWvWyLIsv9cBAOCec11XMzMzCoVCKiqaf01esEHPZDK6ceOG32sAALDqqqurFYlE5h0r2KCvWbNG0v+eVHFxsc/bYCWSyaSi0ajfawAfOLz2Ctf09LRu3Lgx18D/VLBBn73NXlxcrJKSEp+3wUrx/w7wB6+9wrbQt5r5oTgAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAX7Xu4fBIHWPr9XuPf6r/m9wT2TO9Xs9woAPkC4QgcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAA3j+86kzMzM6ePCgbt68qaKiIj377LMKBoM6ePCgLMvSpk2b1NXVpaKiIvX29urixYsKBoNqb2/X5s2bNT4+nvcsAABYmmctX3nlFd25c0fnzp3Tvn379IMf/EA9PT1qaWlRf3+/XNfV4OCgUqmURkdHNTAwoEQioe7ubknKexYAAHjzDHplZaVyuZwcx1E6nVYwGFQqlVJ9fb0kqaGhQZcvX5Zt24rH47IsSxUVFcrlcpqcnMx7FgAAePO85f7AAw/o5s2b+tznPqd33nlHZ86c0dWrV2VZliQpFAppampK6XRaZWVlc583e9x13bxmvSSTyfd3xsAqsW3b7xWARfH8NI9n0H/yk58oHo+rtbVVb731lr7xjW9oZmZm7vFMJqPS0lKFw2FlMpl5xyORyLzvga9k1ks0GlVJSYn3mRai/mt+b4A81NXV+b0CsCDbtnl+FqhsNrvohaznLffS0lJFIhFJ0oc//GHduXNHNTU1GhkZkSQNDQ0pFouptrZWw8PDchxHExMTchxH5eXlec8CAABvnlfo3/zmN9Xe3q49e/ZoZmZG3/3udxWNRtXR0aFEIqGqqio1NjYqEAgoFoupqalJjuOos7NTktTW1pbXLAAA8Ga5ruv6vcRKzN52MPmWe6C1z+8VkIfcqWa/VwAWxC33wrVU+/glbwAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADBD0GnjppZf0y1/+UpKUzWb12muvqa+vT88995wCgYDi8bj2798vx3F05MgRXb9+XcXFxTp69Kg2btyosbGxvGYBAIA3z6A/8cQTeuKJJyRJ3d3d+vKXv6yuri6dPn1a69ev11NPPaVUKqWbN29qenpa58+f19jYmI4fP64XXngh79mHH374nv8hAABQ6JZ9y/2Pf/yj/vznP+vzn/+8pqentWHDBlmWpXg8ritXrsi2bW3fvl2StHXrViWTSaXT6bxnAQCAt2UH/ezZs9q3b5/S6bTC4fDc8VAopKmpqf86HggE7sosAADw5nnLXZLeffdd/fWvf9W2bduUTqeVyWTmHstkMiotLdV7770377jjOAqHw3nPekkmk8s5BWDV2bbt9wrAonh+mmdZQb969ao+8YlPSJLC4bDWrFmjN954Q+vXr9fw8LD279+vW7du6cKFC3r88cc1Njam6urquzLrJRqNqqSkJL8/hftV/zW/N0Ae6urq/F4BWJBt2zw/C1Q2m130QnZZQX/99de1bt26uY+7u7t14MAB5XI5xeNxbdmyRY888oguXbqkXbt2yXVdHTt27K7MAgAAb5bruq7fS6zE7FcpJl+hB1r7/F4BecidavZ7BWBBXKEXrqXaxxvLAABgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABgssZOnv2rH77299qZmZGu3fvVn19vQ4ePCjLsrRp0yZ1dXWpqKhIvb29unjxooLBoNrb27V582aNj4/nPQsAAJbmWcuRkRH9/ve/189+9jP19fXp1q1b6unpUUtLi/r7++W6rgYHB5VKpTQ6OqqBgQElEgl1d3dLUt6zAADAm2fQh4eHVV1drX379unb3/62HnvsMaVSKdXX10uSGhoadPnyZdm2rXg8LsuyVFFRoVwup8nJybxnAQCAN89b7u+8844mJiZ05swZ/f3vf9fTTz8t13VlWZYkKRQKaWpqSul0WmVlZXOfN3s831kvyWTy/Z0xsEps2/Z7BWBRPD/N4xn0srIyVVVVqbi4WFVVVSopKdGtW7fmHs9kMiotLVU4HFYmk5l3PBKJzPse+EpmvUSjUZWUlHifaSHqv+b3BshDXV2d3ysAC7Jtm+dngcpms4teyHrecq+rq9Pvfvc7ua6rt99+W7dv39bHP/5xjYyMSJKGhoYUi8VUW1ur4eFhOY6jiYkJOY6j8vJy1dTU5DULAAC8eV6hf+pTn9LVq1e1c+dOua6rzs5OrVu3Th0dHUokEqqqqlJjY6MCgYBisZiamprkOI46OzslSW1tbXnNAgAAb5bruq7fS6zE7G0Hk2+5B1r7/F4BecidavZ7BWBB3HIvXEu1j1/yBgDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAMHlDH3pS19SJBKRJK1bt05NTU167rnnFAgEFI/HtX//fjmOoyNHjuj69esqLi7W0aNHtXHjRo2NjeU1CwAAvHkGPZvNSpL6+vrmjn3xi1/U6dOntX79ej311FNKpVK6efOmpqendf78eY2Njen48eN64YUX1NXVldfsww8/fO/OHgAAQ3gG/U9/+pNu376tJ598Unfu3NF3vvMdTU9Pa8OGDZKkeDyuK1eu6B//+Ie2b98uSdq6dauSyaTS6XTeswQdAABvnkFfu3at9u7dq6985Sv629/+pm9961sqLS2dezwUCunNN99UOp1WOByeOx4IBP7r2EpmvSSTSe+zBHxg27bfKwCL4vlpHs+gV1ZWauPGjbIsS5WVlYpEIvr3v/8993gmk1Fpaanee+89ZTKZueOO4ygcDs87tpJZL9FoVCUlJd5nWoj6r/m9AfJQV1fn9wrAgmzb5vlZoLLZ7KIXsp4/5f7zn/9cx48flyS9/fbbun37th544AG98cYbcl1Xw8PDisViqq2t1dDQkCRpbGxM1dXVCofDWrNmTV6zAADAm+cV+s6dO3Xo0CHt3r1blmXp2LFjKioq0oEDB5TL5RSPx7VlyxY98sgjunTpknbt2iXXdXXs2DFJUnd3d16zAADAm+W6ruv3Eisxe9vB5FvugdY+7yHct3Knmv1eAVgQt9wL11Lt441lAAAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwAEEHAMAABB0AAAMQdAAADEDQAQAwwLKC/q9//Uuf/OQn9Ze//EXj4+PavXu39uzZo66uLjmOI0nq7e3Vzp07tWvXLr366quSdFdmAQCAN8+gz8zMqLOzU2vXrpUk9fT0qKWlRf39/XJdV4ODg0qlUhodHdXAwIASiYS6u7vvyiwAAFgez6CfOHFCu3bt0kc/+lFJUiqVUn19vSSpoaFBly9flm3bisfjsixLFRUVyuVympyczHsWAAAsz5JBf+mll1ReXq7t27fPHXNdV5ZlSZJCoZCmpqaUTqcVDofnZmaP5zsLAACWJ7jUg7/4xS9kWZauXLmi1157TW1tbZqcnJx7PJPJqLS0VOFwWJlMZt7xSCSioqKivGaXI5lMLmsOWG22bfu9ArAonp/mWTLoP/3pT+f+u7m5WUeOHNHJkyc1MjKiRx99VENDQ9q2bZs2bNigkydPau/evbp165Ycx1F5eblqamryml2OaDSqkpKS/P4U7lf91/zeAHmoq6vzewVgQbZt8/wsUNlsdtEL2SWDvpC2tjZ1dHQokUioqqpKjY2NCgQCisViampqkuM46uzsvCuzAABgeSzXdV2/l1iJ2a9STL5CD7T2+b0C8pA71ez3CsCCuEIvXEu1jzeWAQDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADBL0GcrmcDh8+rNdff12BQEA9PT1yXVcHDx6UZVnatGmTurq6VFRUpN7eXl28eFHBYFDt7e3avHmzxsfH854FAABL86zlhQsXJEnnzp3TM888o56eHvX09KilpUX9/f1yXVeDg4NKpVIaHR3VwMCAEomEuru7JSnvWQAA4M3zCv3Tn/60HnvsMUnSxMSEPvKRj+jixYuqr6+XJDU0NOjSpUuqrKxUPB6XZVmqqKhQLpfT5OSkUqlUXrM7duy4R6cOAIA5PIMuScFgUG1tbfrNb36jH/7wh7pw4YIsy5IkhUIhTU1NKZ1Oq6ysbO5zZo+7rpvXrJdkMrn8swVWkW3bfq8ALIrnp3mWFXRJOnHihA4cOKCvfvWrymazc8czmYxKS0sVDoeVyWTmHY9EIvO+B76SWS/RaFQlJSXLPY3C0n/N7w2Qh7q6Or9XABZk2zbPzwKVzWYXvZD1/B76r371K509e1aS9KEPfUiWZSkajWpkZESSNDQ0pFgsptraWg0PD8txHE1MTMhxHJWXl6umpiavWQAA4M3zCv0zn/mMDh06pK997Wu6c+eO2tvb9eCDD6qjo0OJREJVVVVqbGxUIBBQLBZTU1OTHMdRZ2enJKmtrS2vWQAA4M1yXdf1e4mVmL3tYPIt90Brn98rIA+5U81+rwAsiFvuhWup9vFL3gAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGICgAwBgAIIOAIABCDoAAAYg6AAAGCC41IMzMzNqb2/XzZs3NT09raefflof+9jHdPDgQVmWpU2bNqmrq0tFRUXq7e3VxYsXFQwG1d7ers2bN2t8fDzvWQAA4G3JYv76179WWVmZ+vv79eKLL+rZZ59VT0+PWlpa1N/fL9d1NTg4qFQqpdHRUQ0MDCiRSKi7u1uS8p4FAADLs+QV+mc/+1k1NjbOfRwIBJRKpVRfXy9Jamho0KVLl1RZWal4PC7LslRRUaFcLqfJycm8Z3fs2HGvzhsAAKMsGfRQKCRJSqfTeuaZZ9TS0qITJ07Isqy5x6emppROp1VWVjbv86ampuS6bl6zy5FMJt/H6QKrx7Ztv1cAFsXz0zxLBl2S3nrrLe3bt0979uzRF77wBZ08eXLusUwmo9LSUoXDYWUymXnHI5HIvO+Br2R2OaLRqEpKSpY1W3D6r/m9AfJQV1fn9wrAgmzb5vlZoLLZ7KIXskt+D/2f//ynnnzySX3ve9/Tzp07JUk1NTUaGRmRJA0NDSkWi6m2tlbDw8NyHEcTExNyHEfl5eV5zwIAgOVZ8gr9zJkzevfdd/X888/r+eeflyR9//vf19GjR5VIJFRVVaXGxkYFAgHFYjE1NTXJcRx1dnZKktra2tTR0bHiWQAAsDyW67qu30usxOxtB5NvuQda+/xeAXnInWr2ewVgQdxyL1xLtY9f9AYAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwAAEHQAAAxB0AAAMQNABADAAQQcAwADLCvof/vAHNTc3S5LGx8e1e/du7dmzR11dXXIcR5LU29urnTt3ateuXXr11Vfv2iwAAPDmGfQXX3xRhw8fVjablST19PSopaVF/f39cl1Xg4ODSqVSGh0d1cDAgBKJhLq7u+/KLAAAWB7PoG/YsEGnT5+e+ziVSqm+vl6S1NDQoMuXL8u2bcXjcVmWpYqKCuVyOU1OTuY9CwAAlscz6I2NjQoGg3Mfu64ry7IkSaFQSFNTU0qn0wqHw3Mzs8fznQUAAMsT9B6Zr6jo/30NkMlkVFpaqnA4rEwmM+94JBLJe3Y5ksnk+z0FYFXYtu33CsCieH6a530HvaamRiMjI3r00Uc1NDSkbdu2acOGDTp58qT27t2rW7duyXEclZeX5z27HNFoVCUlJe/7xAtC/zW/N0Ae6urq/F4BWJBt2zw/C1Q2m130QvZ9B72trU0dHR1KJBKqqqpSY2OjAoGAYrGYmpqa5DiOOjs778osAPgh0Nrn9wr3nsEXDLlTzX6v4AvLdV3X7yVWYvarFJOv0D8Qf6kY7IP6l4oJeO0VNpNfe0u1jzeWAQDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAADBP1e4D85jqMjR47o+vXrKi4u1tGjR7Vx40a/1wIA4L53X12hv/zyy5qentb58+fV2tqq48eP+70SAAAF4b66QrdtW9u3b5ckbd26VclkctFZ13UlSdPT06uymx/+T2iN3ysgD9ls1u8VsEK89gqbya+92ebNNvA/3VdBT6fTCofDcx8HAgHduXNHweB/rzkzMyNJunHjxqrtt9r+54ub/F4BeVjqC1Lc33jtFbYPwmtvZmZGa9eunXfsvgp6OBxWJpOZ+9hxnAVjLkmhUEjV1dVas2aNLMtarRUBAPCN67qamZlRKBT6r8fuq6DX1tbqwoULevzxxzU2Nqbq6upFZ4uKihSJRFZxOwAA/Pf/X5nPstyFbsT7ZPan3G/cuCHXdXXs2DE9+OCDfq8FAMB9774KOgAAWJn76tfWAADAyhB0AAAMQNABADAAQceqcxzH7xUAwDj31a+twVxvvvmmenp6lEwmFQwG5TiOqqurdejQIVVWVvq9HgAUPH7KHavi61//ulpbW7Vly5a5Y2NjYzp+/LjOnTvn42YAYAau0LEqpqen58Vc+t/36wdw7zU3N8+9XfYs13VlWRZfUBuEoGNVPPTQQzp06JC2b9+uSCSiTCajV155RQ899JDfqwHGO3DggA4fPqwf/ehHCgQCfq+De4Rb7lgVruvq5Zdflm3bc/8IT21trXbs2MF78QOr4Mc//rE2btyoHTt2+L0K7hGCDgCAAfi1NQAADEDQAQAwAEEHAMAABB0AAAMQdAAADPB/AdVfAlSBurCnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike.user_type_Customer.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23947828>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike.user_type_Subscriber.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23a46748>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike.trip_duration_min.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2233da58>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/+I+ONQYAAJxnKwjs3btXb731VuRCQgAAYGyw9a2BSZMmRT4SAAAAY4etIwJXXHGFvv71r+umm24a8jXC+vp6xxoDAADOsxUE5syZozlz5jjdCwAAiDJbQWDhwoX66KOP9J//+Z/Ky8vTsWPHhpw4CAAALk+2zhH44Q9/qOXLl2vDhg367LPPVFxcrDfeeMPp3gAAgMNsBYHnn39e//RP/xT5BcLXX39dzz33nNO9AQAAh9kKAnFxcfL5fJHbEydOVFycrbsCAIBRzNY5AtOnT9cLL7ygM2fO6NChQ9qxY4dmzJjhdG8AAMBhtt7W19TU6Pjx40pMTFRVVZV8Pp9qa2ud7g0AADjM1hGB8ePHa9WqVVq1apXT/QAAgCiyFQRmzJghl8s1ZNnv/d7vqbW11ZGmAABAdNgKAj/72c8i/x4cHNQ777yjAwcOONYUAACIji986n98fLzuuOMO/fjHP3aiHwAAEEW2jgjs2rUr8m/LsvSLX/xCHs/57zo4OKiqqiodPXpUAwMDWr58uf7gD/5Aa9askcvl0vTp01VbW6u4uDg1NjaqpaVFHo9HVVVVmjVrlrq6uoZdCwAALs5WEGhraxty+8orr9Tf/M3fnLf+zTffVGpqqjZv3qxPP/1UCxcu1IwZM1RRUaHZs2erpqZGzc3NSk9PV3t7u5qamnTs2DGtWLFCO3fuVH19/bBq582bN7ypAABgCFtB4Iv+yuDtt9+ugoKCyG23263Ozk7l5uZKkvLz87V3715NnTpVeXl5crlcSk9PVzAYVE9Pz7BrCQIAANhjKwjceuutn/vWgBT+mMDlcqm5uXnIcq/XK0nq6+vTypUrVVFRoU2bNkUew+v1qre3V319fUpNTR1yv97e3sjjXmqtHR0dHbbqRoLf74/aukYz5hDGHMKYQxhzwLlEc7+wFQTmz5+v+Ph4LVmyRB6PRz/4wQ/0H//xH/r2t7993vscO3ZMDz/8sEpLSzV//nxt3rw58rdAIKCUlBT5fD4FAoEhy5OTk4d8xn8ptXZkZmYqMTHRVu1w+P1+ZWdnO76e0Y45hDGHMOYQNmbnsOODWHdw2Rvp/aK/v/+8b4BtnVX33nvv6ZFHHtHEiROVlpamb37zm/qv//ovXX311br66qs/V//xxx+rvLxcq1evVmFhoSRp5syZkXMNWltblZOTo6ysLO3Zs0ehUEjd3d0KhUJKS0sbdi0AALDH1hEBSdq3b5/+5E/+RJK0e/fuyOH/c3nmmWd08uRJbdu2Tdu2bZMkfec739H69evV0NCgjIwMFRQUyO12KycnR0VFRQqFQqqpqZEkVVZWqrq6+pJrAQCAPS7LsqyLFXV0dKiyslIff/yxJCkjI0ObNm3S5MmTHW9wpJ09PMJHA9HFHMKYQxhzCBurc3Cv2h7rFi57wS1lI/p4F3rts3VEIDMzU//yL/+inp4eJSUlafz48SPaIAAAiA1b5wgcPXpUS5cuVXFxsQKBgO6991599NFHTvcGAAAcZvtniJctW6bx48drwoQJ+sY3vqHKykqnewMAAA6zFQQ+/fRT5eXlSZJcLpeWLFmivr4+RxsDAADOsxUEkpKS9Jvf/CZy4Z6f/vSnSkhIcLQxAADgPFsnC65du1YPPPCAfvWrX2nBggX67LPP9Ld/+7dO9wYAABxmKwh88sknevXVV/Xhhx8qGAwqIyODIwIAAIwBtj4a2Lx5s+Lj4zV9+nTNmDGDEAAAwBhh64jApEmTtHbtWn3lK19RUlJSZPldd93lWGMAAMB5FwwCx48f15e+9CVdeeWVkqT3339/yN8JAgAAXN4uGAQefPBBvf7666qvr9f3v/99lZeXR6svAAAQBRc8R+C3f4bgBz/4gePNAACA6LpgEDh73QBpaCgAAABjg61vDUhDQwEAABgbLniOwC9+8QvddtttksInDp79t2VZcrlcam5udr5DAADgmAsGgR/96EfR6gMAAMTABYPA1VdfHa0+AABADNg+RwAAAIw9BAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADOZoEHj//fdVVlYmSers7NScOXNUVlamsrIy/fCHP5QkNTY2qrCwUMXFxTp48KAkqaurSyUlJSotLVVtba1CodAXrgUAABd3wd8aGI7nn39eb775psaNGydJ+uCDD7R06VKVl5dHajo7O9Xe3q6mpiYdO3ZMK1as0M6dO1VfX6+KigrNnj1bNTU1am5uVnp6uu3aefPmObVZAACMKY4dEZg8ebK2bt0aud3R0aGWlhbdc889qqqqUl9fn/x+v/Ly8uRyuZSenq5gMKienh51dnYqNzdXkpSfn699+/Z9oVoAAGCPY0GgoKBAHs//H3CYNWuW/uqv/kovvviiJk2apKeeekp9fX3y+XyRGq/Xq97eXlmWJZfLNWTZF6kFAAD2OPbRwO+aN2+eUlJSIv+uq6vTbbfdpkAgEKkJBAJKTk5WXFzckGUpKSny+Xy2a+3o6OgY7ibZ5vf7o7au0Yw5hDGHMOYQxhxwLtHcL6IWBJYtW6bq6mrNmjVL+/fv1w033KCsrCxt3rxZy5Yt029+8xuFQiGlpaVp5syZamtr0+zZs9Xa2qo//uM/1uTJk23X2pGZmanExESHtzr8HzM7O9vx9Yx2zCGMOYQxh7AxO4cdH8S6g8veSO8X/f39530DHLUg8Pjjj6uurk7x8fGaMGGC6urq5PP5lJOTo6KiIoVCIdXU1EiSKisrVV1drYaGBmVkZKigoEBut9t2LQAAsMdlWZYV6yai6Wwq4ohAdDGHMOYQxhzCxuoc3Ku2x7qFy15wS9mIPt6FXvu4oBAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABvPEugEAwBfjXrU91i1gDOGIAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDBHg8D777+vsrIySVJXV5dKSkpUWlqq2tpahUIhSVJjY6MKCwtVXFysgwcPjlgtAAC4OMeCwPPPP6/HHntM/f39kqT6+npVVFRox44dsixLzc3N6uzsVHt7u5qamtTQ0KB169aNSC0AALDHsSAwefJkbd26NXK7s7NTubm5kqT8/Hzt27dPfr9feXl5crlcSk9PVzAYVE9Pz7BrAQCAPR6nHrigoEAfffRR5LZlWXK5XJIkr9er3t5e9fX1KTU1NVJzdvlwa+3o6OgY9jba5ff7o7au0Yw5hDGHMOYQxhxwLtHcLxwLAr8rLu7/Dz4EAgGlpKTI5/MpEAgMWZ6cnDzsWjsyMzOVmJg4nE2yxe/3Kzs72/H1jHbMIYw5hDGHsEuew44PRr4ZjCoj/f9Hf3//ed8AR+1bAzNnzlRbW5skqbW1VTk5OcrKytKePXsUCoXU3d2tUCiktLS0YdcCAAB7onZEoLKyUtXV1WpoaFBGRoYKCgrkdruVk5OjoqIihUIh1dTUjEgtAACwx2VZlhXrJqLp7OERPhqILuYQxhzCmEPYpc7BvWq7A91gNAluKRvRx7vQax8XFAIAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAM5on2Cu+66y4lJydLkq655hoVFRVpw4YNcrvdysvL0yOPPKJQKKTHH39cP//5z5WQkKD169drypQpOnDggO1aAABwcVENAv39/ZKk7du3R5YtWLBAW7du1aRJk3T//fers7NTR48e1cDAgF5++WUdOHBATzzxhJ5++mnV1tbargUAABcX1SDws5/9TKdOnVJ5ebnOnDmjFStWaGBgQJMnT5Yk5eXlaf/+/fqf//kfzZkzR5J04403qqOjQ319fbZrAQCAPVENAklJSVq2bJkWL16sDz/8UPfdd59SUlIif/d6vfr1r3+tvr4++Xy+yHK32/25ZReqPXPmjDyeC29aNAOD3++P2rpGM+YQxhzCmEMYc8C5RHO/iGoQmDp1qqZMmSKXy6WpU6cqOTlZJ06ciPw9EAgoJSVFp0+fViAQiCwPhULy+XxDll2o9mIhQJIyMzOVmJg4Qlt2fn6/X9nZ2Y6vZ7RjDmHMIYw5hF3yHHZ8MPLNYFQZ6f8/+vv7z/sGOKrfGnj11Vf1xBNPSJKOHz+uU6dOafz48frVr34ly7K0Z88e5eTkKCsrS62trZKkAwcO6LrrrpPP51N8fLytWgAAYE9UjwgUFhZq7dq1Kikpkcvl0saNGxUXF6dHH31UwWBQeXl5+spXvqI//MM/1N69e1VcXCzLsrRx40ZJ0rp162zXAgCAi4tqEEhISNCWLVs+t/yVV14ZcjsuLk7f/e53P1d344032q4FAAAXxwWFAAAwWNQvKAQAo5l71faLF40kTvxDjHFEAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwmCfWDYyEUCikxx9/XD//+c+VkJCg9evXa8qUKbFuC8A5uFdtD/9jxwexbQSApDFyROCdd97RwMCAXn75Za1atUpPPPFErFsCAOCyMCaOCPj9fs2ZM0eSdOONN6qjo+O8tZZlSZIGBgai0psk9ff3R21doxlzCHNyDtM2vO7YY4+UL3vjY90CMOqN9PPE2de8s6+Bv21MBIG+vj75fL7IbbfbrTNnzsjj+fzmDQ4OSpIOHz4ctf4uFExMwhzCnJzDGwumO/bYAKLHqeeJwcFBJSUlDVk2JoKAz+dTIBCI3A6FQucMAZLk9Xp13XXXKT4+Xi6XK1otAgAQM5ZlaXBwUF6v93N/GxNBICsrS7t379add96pAwcO6LrrrjtvbVxcnJKTk6PYHQAAsfe7RwLOclnn+sDgMnP2WwOHDx+WZVnauHGjpk2bFuu2AAAY9cZEEAAAAJdmTHx9EAAAXBqCAAAABiMIDMP777+vsrIySdKhQ4e0ZMkSlZSUaO3atQqFQpKk733ve7r77ru1aNEivf3225Kk06dPa8WKFSotLdV9992nnp6emG3DSPjtOXR2dqqwsFClpaWqq6uLzKGxsVGFhYUqLi7WwYMHJUldXV0qKSlRaWmpamtrI7WXKztz2LRpk4qKirRo0SK98sorkqSenh6Vl5ertLRUFRUVOnXqVMy2YSTYmYMknTp1SgsWLFBra6skM+fw2muvafHixbr77rv11FNPSTJzDvX19SosLNSSJUvk9/sljZ05DA4OavXq1SotLVVhYaGam5vP+9wXs+dJC5fkueees77xjW9YixcvtizLsh566CGrpaXFsizL+su//EurubnZ+uyzz6ybb77Z6u/vt06cOGHdcsstlmVZ1ve//33rySeftCzLsv75n//Zqquri81GjIDfncPChQstv99vWZZlNTQ0WLt27bI6OjqssrIyKxQKWUePHrXuvvtuy7Is64EHHrB+/OMfW5ZlWdXV1da//du/xWYjRoCdOezfv9966KGHLMuyrP7+fmvu3LnWiRMnrLq6Omvnzp2WZVnWs88+a/3d3/1dTLZhJNiZw1lr1qyxFixYYL377ruWZVnGzaGrq8sqLCy0Tp06ZQWDQeuv//qvrYGBAePmcOjQIWvx4sVWKBSyjhw5Yi1cuNCyrLGzP7z66qvW+vXrLcuyrJ6eHuvmm28+53NfLJ8nOSJwiSZPnqytW7dGbl9//fU6ceKELMtSIBCQx+PRuHHjlJ6erlOnTunUqVOR6xb89pUQ8/PztX///phsw0j43QBvqNYAAAReSURBVDkcP35cWVlZksJf6/T7/fL7/crLy5PL5VJ6erqCwaB6enrU2dmp3NxcSeE57Nu3LybbMBLszOGmm27Sxo0bIzXBYFAej+dz+8NYn4MUPlJ20003acaMGZFa0+awb98+ZWZmqrKyUn/2Z3+mrKwsxcfHGzeHiRMnKikpSQMDA+rr64tcA2aszOH222/XX/zFX0Ruu93ucz73xfJ5kiBwiQoKCoZctOjaa6/Vhg0bdMcdd+iTTz7R7NmzJUlf/vKX9fWvf10LFy7UvffeKyl8JcSz1zLwer3q7e2N/gaMkN+dw6RJk9Te3i5J2r17t06dOvW5Kz+e3WbLsiLhyIQ5JCYm6oorrtDg4KDWrFmjoqIieb1e4/aH/fv3q6urS0uWLBlyX9Pm8Omnn+qnP/2pNmzYoK1bt2r9+vU6efKkcXPweDyKi4vTHXfcoaVLl6q8vFzS2NkfvF6vfD6f+vr6tHLlSlVUVJzzuS+Wz5MEgRGyYcMGvfjii3rrrbd011136YknnlBra6v++7//W83NzWppadE777yjgwcPDrkSYiAQUEpKSoy7HzkbN27Us88+q/vvv19XXXWVrrzyys9d+TEQCCg5OVlxcXFDlo31OUjSZ599pm9961uaNm2aHnjgAUkybn949dVXdfjwYZWVlem9997T5s2bdejQIePmkJqaqtzcXPl8Pl111VWaNm2aPvzwQ+PmsGvXLk2YMEFvv/22mpub1djYqOPHj4+pORw7dkz33nuvFixYoPnz55/zuS+Wz5MEgRFyxRVXRNLcxIkTdfLkSV1xxRVKSkpSQkKCEhMTlZycrJMnTyorK0vvvvuuJKm1tVXZ2dmxbH1Evfvuu9q4caOee+45nThxQl/96leVlZWlPXv2KBQKqbu7W6FQSGlpaZo5c6ba2tokheeQk5MT4+5HzrnmcPr0af35n/+5Fi1apIcffjhSa9r+sGXLFr300kvavn275syZo9WrV+v66683bg5ZWVlqb29Xf3+//vd//1e//OUvNXnyZOPmkJKSovHjx8vtdsvr9SohIUGBQGDMzOHjjz9WeXm5Vq9ercLCQkk653NfLJ8nx8QlhkeD9evX69vf/rY8Ho/i4+NVV1ena665Rvv27dOSJUsUFxenrKwsffWrX1V2drYqKytVUlKi+Ph4bdmyJdbtj5gpU6bo/vvv17hx4zR79mzdfPPNkqScnBwVFRUpFAqppqZGklRZWanq6mo1NDQoIyNDBQUFsWx9RJ1rDn//93+vX//612pqalJTU5Ok8Duk5cuXq7KyUq+88oquvPJKI/aHczFxDosWLVJJSYksy9JDDz2k1NRU4+YQDAb17//+7youLlYwGNT8+fOVkZExZubwzDPP6OTJk9q2bZu2bdsmSfrOd76j9evXD3nuc7vdMXue5MqCAAAYjI8GAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACD/R+cWZIwc/O46AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike.member_birth_year.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> What other visualizations could/should we create?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start with a Baseline/Dummy Model\n",
    "\n",
    "First we are going to start with a dummy model to predict if the user was a brogrammer.  In our dummy model we classify everything as the majority class.  \n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set random state for our notebook\n",
    "import numpy as np\n",
    "seed = np.random.seed(107)\n",
    "\n",
    "#set brogrammer as the target and use all other features as \n",
    "y = bike['bg']\n",
    "#set features to all variables except brogrammer and trip in seconds\n",
    "X = bike.drop(['bg', 'trip_duration_sec'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split our data into test and train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=.3, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(662789, 12)\n",
      "(662789,)\n",
      "(284053, 12)\n",
      "(284053,)\n"
     ]
    }
   ],
   "source": [
    "#check out the shape of our data\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7463340520135368"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DummyClassifier to predict only target 0\n",
    "dummy = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "dummy.fit(X_train, y_train)\n",
    "dummy.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> What does the \"score\" from the dummy classifier tell us about the predictive quality of our model?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score from the classifier tells us that 75% of our training data was correctly classified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now  let's create a nice looking confusion matrix using the yellowbrick package\n",
    "[yellowbrick documentation](https://www.scikit-yb.org/en/latest/api/classifier/confusion_matrix.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
      "  YellowbrickWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7463340520135368"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(dummy)\n",
    "\n",
    "# To create the ConfusionMatrix, we need some test data. Score runs predict() on the data\n",
    "# and then creates the confusion_matrix from scikit-learn.\n",
    "cm.score(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> What does this confusion matrix tell us about what the dummy classifier did?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dummy classifier classified all test data as brogrammer.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your Turn: Fitting a Vanilla Model\n",
    "\n",
    "Now that we know what our dummy/baseline classifier does we are going to fit a vanilla decision tree model to predict if the user was a brogrammer.\n",
    "\n",
    "Do the following in your group then answer the questions:\n",
    "\n",
    "1. Create a vanilla decision tree model and fit it to your training data\n",
    "2. Print the accuracy score of this prediction\n",
    "3. Print a confusion matrix for your training data\n",
    "4. Print a classification report for the model\n",
    "\n",
    "**BONUS**:  Repeat the above steps using a Naive Bayes classifier. Compare and contrast the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999984912242056"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting vanilla model\n",
    "\n",
    "clf_DT = DecisionTreeClassifier()\n",
    "clf_DT.fit(X_train, y_train)\n",
    "clf_DT.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
      "  YellowbrickWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9999984912242056"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion matrix\n",
    "cm = ConfusionMatrix(clf_DT)\n",
    "cm.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Brogrammer       1.00      1.00      1.00    494662\n",
      " Nice_person       1.00      1.00      1.00    168127\n",
      "\n",
      "    accuracy                           1.00    662789\n",
      "   macro avg       1.00      1.00      1.00    662789\n",
      "weighted avg       1.00      1.00      1.00    662789\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Classification report\n",
    "y_pred=clf_DT.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> 1. What does the \"score\" from the decision tree classifier tell us about the predictive quality of our model?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A score of .99 indicates that 99% of the predictions were correct overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> 2. According to confusion matrix, how well did our decision tree classifier do?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly perfect predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\">3. According to classification report, how well did our decision tree classifier do?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No differences in prediction brogrammers or not.  100% correct!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-07T20:52:52.092683Z",
     "start_time": "2019-11-07T20:52:52.090712Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "## Now onto Grid Search: Find the best hyperparameters for us!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "A way for us to search over multiple hyperparameters for the given model to see if we can increase the accuracy of our model by \"tuning\" our hyperparameters in our decision tree model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5,\n",
       " 'max_features': 3,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 8}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first we set up a grid of hyperparameters we want to \"search over\"\n",
    "param_grid = {\n",
    "    'max_depth': [2, 5],\n",
    "    'max_features': [2, 3],\n",
    "    'min_samples_leaf': [3, 4],\n",
    "    'min_samples_split': [8, 10],\n",
    "}\n",
    "\n",
    "#then we conduct the \"search\"\n",
    "gs = GridSearchCV(estimator = clf_DT, param_grid = param_grid, \n",
    "                          cv = 5)\n",
    "gs.fit(X_train, y_train)\n",
    "#finally we find the best hyperparameters\n",
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  WOW that took a while!!  Is there an easier way than just waiting for this to run?\n",
    "\n",
    "## Luckily there is!  Let's check out the RandomizedSearchCV feature in sklearn\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 8,\n",
       " 'min_samples_leaf': 3,\n",
       " 'max_features': 2,\n",
       " 'max_depth': 2}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can use the same exact param_grid we have already defined\n",
    "param_grid = {\n",
    "    'max_depth': [2, 5],\n",
    "    'max_features': [2, 3],\n",
    "    'min_samples_leaf': [3, 4],\n",
    "    'min_samples_split': [8, 10],\n",
    "}\n",
    "\n",
    "#then we conduct the \"search\"\n",
    "rs = RandomizedSearchCV(estimator = clf_DT,  param_distributions = param_grid, \n",
    "                          cv = 5)\n",
    "rs.fit(X_train, y_train)\n",
    "#finally we find the best hyperparameters\n",
    "rs.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Great, that ran a bit faster.  But wait...why are our best params different now????\n",
    "\n",
    "In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting our Decision Tree Using Best Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7463340520135368"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save our best model from the grid search\n",
    "best_DT=gs.best_estimator_\n",
    "\n",
    "#fit the data using the best model and look at the score given test data\n",
    "best_DT.fit(X_train, y_train)\n",
    "best_DT.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> Our vanilla model had an accuracy score of 99%.  How did our tuned model do in comparison?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like our tuned model actually did worse than our vanilla model in overall accuracy. But it likely now isn't overfitting our data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
      "  YellowbrickWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7463340520135368"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(best_DT)\n",
    "\n",
    "# To create the ConfusionMatrix, we need some test data. Score runs predict() on the data\n",
    "# and then creates the confusion_matrix from scikit-learn.\n",
    "cm.score(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> According to confusion matrix, how well did our decision tree classifier do?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, this confusion matrix is the same as our dummy classifier! That's not what we want!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Brogrammer       0.75      1.00      0.85    494662\n",
      " Nice_person       0.00      0.00      0.00    168127\n",
      "\n",
      "    accuracy                           0.75    662789\n",
      "   macro avg       0.37      0.50      0.43    662789\n",
      "weighted avg       0.56      0.75      0.64    662789\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=best_DT.predict(X_train)\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> According to classification report, how well did our decision tree classifier do?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, this confirms that we essentially went back to the dummy model where we classified everything as the majority class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipelines and hyperparameter searching!\n",
    "\n",
    "Yes, let's go back to that concept of a pipeline.  We established using pipelines is important when we our transforming our data and using cross validation (which our gridsearch and randomized search are using).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('standardscaler',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('decisiontreeclassifier',\n",
       "                                        DecisionTreeClassifier(ccp_alpha=0.0,\n",
       "                                                               class_weight=None,\n",
       "                                                               criterion='gini',\n",
       "                                                               max_depth=None,\n",
       "                                                               max_features=None,\n",
       "                                                               max_leaf_nodes=None,\n",
       "                                                               min_impurity_decrease=0.0,\n",
       "                                                               min_impurity_split=None,\n",
       "                                                               min...\n",
       "                                                               random_state=None,\n",
       "                                                               splitter='best'))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'decisiontreeclassifier__max_depth': [2, 5],\n",
       "                         'decisiontreeclassifier__max_features': [2, 3],\n",
       "                         'decisiontreeclassifier__min_samples_leaf': [3, 4],\n",
       "                         'decisiontreeclassifier__min_samples_split': [8, 10]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set up our pipe, even though ss isn't necessary here let's add it \n",
    "#just to see how the pipe works\n",
    "pipe = make_pipeline(StandardScaler(), DecisionTreeClassifier())\n",
    "\n",
    "\n",
    "#we can use the same exact param_grid we have already defined\n",
    "param_grid = {\n",
    "    'decisiontreeclassifier__max_depth': [2, 5],\n",
    "    'decisiontreeclassifier__max_features': [2, 3],\n",
    "    'decisiontreeclassifier__min_samples_leaf': [3, 4],\n",
    "    'decisiontreeclassifier__min_samples_split': [8, 10],\n",
    "}\n",
    "\n",
    "#now use our pipe in our grid search\n",
    "gridsearch_pipe = GridSearchCV(pipe, \n",
    "                          param_grid = param_grid,\n",
    "                          cv = 5)\n",
    "\n",
    "gridsearch_pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7463476309956865"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save our best model from the grid search\n",
    "best_pipe=gridsearch_pipe.best_estimator_\n",
    "\n",
    "#fit the data using the best model and look at the score given test data\n",
    "best_pipe.fit(X_train, y_train)\n",
    "best_pipe.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/yellowbrick/classifier/base.py:232: YellowbrickWarning: could not determine class_counts_ from previously fitted classifier\n",
      "  YellowbrickWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7463476309956865"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = ConfusionMatrix(best_pipe)\n",
    "\n",
    "# To create the ConfusionMatrix, we need some test data. Score runs predict() on the data\n",
    "# and then creates the confusion_matrix from scikit-learn.\n",
    "cm.score(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> Does this mean that hyperparameter tuning isn't good?  What can we do now?</span></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have lots of options to move forward and increase our classification.  We can:\n",
    "- **try a different classification model**\n",
    "- **tune other hyperparameters in our decision tree model**\n",
    "- **try balancing our classes**\n",
    "- add more data\n",
    "- add new features- maybe we don't have ones that are very predictive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\"><span style=\"color:red\"> Your Turn:  Now work in small groups to take one of the above next steps to increase the classification of brogrammers! </span></font>\n",
    "\n",
    "**When you have selected your VERY BEST Model then use the test data to see how it did!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7463431120248686"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_pipe.score(X_test, y_test)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}