ClassificationAnalysis.ipynb

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   "source": [
    "# **Automatidata project**\n",
    "**Course 6 - The Nuts and bolts of machine learning**"
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    "The New York City Taxi & Limousine Commission (New York City TLC), was impressed with the work you have done and has requested that you build a machine learning model to predict if a customer will not leave a tip. They want to use the model in an app that will alert taxi drivers to customers who are unlikely to tip, since drivers depend on tips."
   ]
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   "cell_type": "markdown",
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   "source": [
    "# Build a machine learning model"
   ]
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    "<img src=\"images/Plan.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "\n",
    "## PACE: Plan \n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Plan stage.\n",
    "\n",
    "In this stage, consider the following questions:\n",
    "\n",
    "1.   What are you being asked to do?\n",
    "\n",
    "\n",
    "2.   What are the ethical implications of the model? What are the consequences of your model making errors?\n",
    "  *   What is the likely effect of the model when it predicts a false negative (i.e., when the model says a customer will give a tip, but they actually won't)?\n",
    "  \n",
    "  *   What is the likely effect of the model when it predicts a false positive (i.e., when the model says a customer will not give a tip, but they actually will)?  \n",
    "  \n",
    "  \n",
    "3.   Do the benefits of such a model outweigh the potential problems?\n",
    "  \n",
    "4.   Would you proceed with the request to build this model? Why or why not?\n",
    " \n",
    "5.   Can the objective be modified to make it less problematic?\n",
    " \n"
   ]
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    "1. Generate more revenues for taxi drivers\n",
    "2. Ethically this model might identify customers who wont tip at all. This might not be ethically correct.\n",
    "    * If we get a false positive that a customer is predicted generous when he isn't. The drivers would generate less revenue.\n",
    "    * If we get a false negative then the customer would give the tip and drivers would generate revenue.\n",
    "3. To make drivers happy the company is going for this model. Yes it might reveal that a customer wont tip he might not get pickups.\n",
    "4. Yes i would but with a few changes\n",
    "5. Yes to identify customer who will tip more instead of not tipping"
   ]
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   "source": [
    "Suppose you were to modify the modeling objective so, instead of predicting people who won't tip at all, you predicted people who are particularly generous&mdash;those who will tip 20% or more? Consider the following questions:\n",
    "\n",
    "1.  What features do you need to make this prediction?\n",
    "\n",
    "2.  What would be the target variable?  \n",
    "\n",
    "3.  What metric should you use to evaluate your model? Do you have enough information to decide this now?\n"
   ]
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   "source": [
    "1. Features like:\n",
    "    * Tip amount\n",
    "    * PickUp Time\n",
    "    * PickUp Location (might be)\n",
    "    * Payment through what mode\n",
    "    * Distance\n",
    "    * Duration\n",
    "    * No. of passengers"
   ]
  },
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   "source": [
    "\n",
    "**_Complete the following steps to begin:_**"
   ]
  },
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   "source": [
    "### **Task 1. Imports and data loading**\n",
    "\n",
    "Import packages and libraries needed to build and evaluate random forest and XGBoost classification models."
   ]
  },
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   "execution_count": 2,
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     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
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0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var 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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import packages and libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    "\n",
    "import shap\n",
    "shap.initjs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RUN THIS CELL TO SEE ALL COLUMNS \n",
    "# This lets us see all of the columns, preventing Juptyer from redacting them.\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "5weTXGKqa_iG"
   },
   "outputs": [],
   "source": [
    "# RUN THE CELL BELOW TO IMPORT YOUR DATA. \n",
    "\n",
    "# Load dataset into dataframe\n",
    "df0 = pd.read_csv('2017_Yellow_Taxi_Trip_Data.csv')\n",
    "\n",
    "# Import predicted fares and mean distance and duration from previous course\n",
    "nyc_preds_means = pd.read_csv('nyc_preds_means.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>03/25/2017 8:55:43 AM</td>\n",
       "      <td>03/25/2017 9:09:47 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
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       "      <td>N</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>04/11/2017 2:53:28 PM</td>\n",
       "      <td>04/11/2017 3:19:58 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>12/15/2017 7:26:56 AM</td>\n",
       "      <td>12/15/2017 7:34:08 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>05/07/2017 1:17:59 PM</td>\n",
       "      <td>05/07/2017 1:48:14 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>04/15/2017 11:32:20 PM</td>\n",
       "      <td>04/15/2017 11:49:03 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
       "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
       "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
       "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
       "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
       "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
       "0        2.76           0.0                    0.3         16.56  \n",
       "1        4.00           0.0                    0.3         20.80  \n",
       "2        1.45           0.0                    0.3          8.75  \n",
       "3        6.39           0.0                    0.3         27.69  \n",
       "4        0.00           0.0                    0.3         17.80  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the first few rows of df0\n",
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        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>mean_duration</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>predicted_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.616667</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>15.845642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_duration  mean_distance  predicted_fare\n",
       "0      22.847222       3.521667       16.434245\n",
       "1      24.470370       3.108889       16.052218\n",
       "2       7.250000       0.881429        7.053706\n",
       "3      30.250000       3.700000       18.731650\n",
       "4      14.616667       4.435000       15.845642"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the first few rows of `nyc_preds_means`\n",
    "nyc_preds_means.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join the two dataframes\n",
    "\n",
    "Join the two dataframes using a method of your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "    .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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>predicted_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>03/25/2017 8:55:43 AM</td>\n",
       "      <td>03/25/2017 9:09:47 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>04/11/2017 2:53:28 PM</td>\n",
       "      <td>04/11/2017 3:19:58 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>12/15/2017 7:26:56 AM</td>\n",
       "      <td>12/15/2017 7:34:08 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>05/07/2017 1:17:59 PM</td>\n",
       "      <td>05/07/2017 1:48:14 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30841670</td>\n",
       "      <td>2</td>\n",
       "      <td>04/15/2017 11:32:20 PM</td>\n",
       "      <td>04/15/2017 11:49:03 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>4.37</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>17.80</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>4.435000</td>\n",
       "      <td>15.845642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
       "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
       "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
       "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
       "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
       "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "4                1           4.37           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "4             4           112             2         16.5    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "4        0.00           0.0                    0.3         17.80   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  \n",
       "0      22.847222       3.521667       16.434245  \n",
       "1      24.470370       3.108889       16.052218  \n",
       "2       7.250000       0.881429        7.053706  \n",
       "3      30.250000       3.700000       18.731650  \n",
       "4      14.616667       4.435000       15.845642  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge datasets\n",
    "df = pd.concat([df0,nyc_preds_means],join='inner',axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EgPRBjizg1oo"
   },
   "source": [
    "<img src=\"images/Analyze.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Analyze**\n",
    "\n",
    "Consider the questions in your PACE Strategy Documentto reflect on the Analyze stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5VZowX9rhU1o"
   },
   "source": [
    "### **Task 2. Feature engineering**\n",
    "\n",
    "You have already prepared much of this data and performed exploratory data analysis (EDA) in previous courses. \n",
    "\n",
    "Call `info()` on the new combined dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "mBOSW8IDbO_d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22699 entries, 0 to 22698\n",
      "Data columns (total 21 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             22699 non-null  int64  \n",
      " 1   VendorID               22699 non-null  int64  \n",
      " 2   tpep_pickup_datetime   22699 non-null  object \n",
      " 3   tpep_dropoff_datetime  22699 non-null  object \n",
      " 4   passenger_count        22699 non-null  int64  \n",
      " 5   trip_distance          22699 non-null  float64\n",
      " 6   RatecodeID             22699 non-null  int64  \n",
      " 7   store_and_fwd_flag     22699 non-null  object \n",
      " 8   PULocationID           22699 non-null  int64  \n",
      " 9   DOLocationID           22699 non-null  int64  \n",
      " 10  payment_type           22699 non-null  int64  \n",
      " 11  fare_amount            22699 non-null  float64\n",
      " 12  extra                  22699 non-null  float64\n",
      " 13  mta_tax                22699 non-null  float64\n",
      " 14  tip_amount             22699 non-null  float64\n",
      " 15  tolls_amount           22699 non-null  float64\n",
      " 16  improvement_surcharge  22699 non-null  float64\n",
      " 17  total_amount           22699 non-null  float64\n",
      " 18  mean_duration          22699 non-null  float64\n",
      " 19  mean_distance          22699 non-null  float64\n",
      " 20  predicted_fare         22699 non-null  float64\n",
      "dtypes: float64(11), int64(7), object(3)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0D2RvXk0kwsx"
   },
   "source": [
    "You know from your EDA that customers who pay cash generally have a tip amount of $0. To meet the modeling objective, you'll need to sample the data to select only the customers who pay with credit card. \n",
    "\n",
    "Copy `df0` and assign the result to a variable called `df1`. Then, use a Boolean mask to filter `df1` so it contains only customers who paid with credit card."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "_pmNd78plQYr"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>extra</th>\n",
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       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>03/25/2017 8:55:43 AM</td>\n",
       "      <td>03/25/2017 9:09:47 AM</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
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       "      <td>N</td>\n",
       "      <td>100</td>\n",
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       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>16.56</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>04/11/2017 2:53:28 PM</td>\n",
       "      <td>04/11/2017 3:19:58 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>12/15/2017 7:26:56 AM</td>\n",
       "      <td>12/15/2017 7:34:08 AM</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>05/07/2017 1:17:59 PM</td>\n",
       "      <td>05/07/2017 1:48:14 PM</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>03/25/2017 8:34:11 PM</td>\n",
       "      <td>03/25/2017 8:42:11 PM</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
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       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>11.855376</td>\n",
       "      <td>2.052258</td>\n",
       "      <td>10.441351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID   tpep_pickup_datetime  tpep_dropoff_datetime  \\\n",
       "0    24870114         2  03/25/2017 8:55:43 AM  03/25/2017 9:09:47 AM   \n",
       "1    35634249         1  04/11/2017 2:53:28 PM  04/11/2017 3:19:58 PM   \n",
       "2   106203690         1  12/15/2017 7:26:56 AM  12/15/2017 7:34:08 AM   \n",
       "3    38942136         2  05/07/2017 1:17:59 PM  05/07/2017 1:48:14 PM   \n",
       "5    23345809         2  03/25/2017 8:34:11 PM  03/25/2017 8:42:11 PM   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  \n",
       "0      22.847222       3.521667       16.434245  \n",
       "1      24.470370       3.108889       16.052218  \n",
       "2       7.250000       0.881429        7.053706  \n",
       "3      30.250000       3.700000       18.731650  \n",
       "5      11.855376       2.052258       10.441351  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset the data to isolate only customers who paid by credit card\n",
    "df1 = df[df['payment_type']==1]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EcYudtSYyMcZ"
   },
   "source": [
    "##### **Target**\n",
    "\n",
    "Notice that there isn't a column that indicates tip percent, which is what you need to create the target variable. You'll have to engineer it. \n",
    "\n",
    "Add a `tip_percent` column to the dataframe by performing the following calculation:  \n",
    "<br/>  \n",
    "\n",
    "\n",
    "$$tip\\ percent = \\frac{tip\\ amount}{total\\ amount - tip\\ amount}$$  \n",
    "\n",
    "Round the result to three places beyond the decimal. **This is an important step.** It affects how many customers are labeled as generous tippers. In fact, without performing this step, approximately 1,800 people who do tip ≥ 20% would be labeled as not generous. \n",
    "\n",
    "To understand why, you must consider how floats work. Computers make their calculations using floating-point arithmetic (hence the word \"float\"). Floating-point arithmetic is a system that allows computers to express both very large numbers and very small numbers with a high degree of precision, encoded in binary. However, precision is limited by the number of bits used to represent a number, which is generally 32 or 64, depending on the capabilities of your operating system. \n",
    "\n",
    "This comes with limitations in that sometimes calculations that should result in clean, precise values end up being encoded as very long decimals.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "guanzJd8zBla"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\18343085.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['tip_percent'] = (df1['tip_amount']/(df1['total_amount']-df1['tip_amount'])).round(3)\n"
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      "text/plain": [
       "   Unnamed: 0  VendorID   tpep_pickup_datetime  tpep_dropoff_datetime  \\\n",
       "0    24870114         2  03/25/2017 8:55:43 AM  03/25/2017 9:09:47 AM   \n",
       "1    35634249         1  04/11/2017 2:53:28 PM  04/11/2017 3:19:58 PM   \n",
       "2   106203690         1  12/15/2017 7:26:56 AM  12/15/2017 7:34:08 AM   \n",
       "3    38942136         2  05/07/2017 1:17:59 PM  05/07/2017 1:48:14 PM   \n",
       "5    23345809         2  03/25/2017 8:34:11 PM  03/25/2017 8:42:11 PM   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  tip_percent  \n",
       "0      22.847222       3.521667       16.434245        0.200  \n",
       "1      24.470370       3.108889       16.052218        0.238  \n",
       "2       7.250000       0.881429        7.053706        0.199  \n",
       "3      30.250000       3.700000       18.731650        0.300  \n",
       "5      11.855376       2.052258       10.441351        0.200  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create tip % col\n",
    "df1['tip_percent'] = (df1['tip_amount']/(df1['total_amount']-df1['tip_amount'])).round(3)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bqb-SWfs-8Xn"
   },
   "source": [
    "Now create another column called `generous`. This will be the target variable. The column should be a binary indicator of whether or not a customer tipped ≥ 20% (0=no, 1=yes).\n",
    "\n",
    "1. Begin by making the `generous` column a copy of the `tip_percent` column.\n",
    "2. Reassign the column by converting it to Boolean (True/False).\n",
    "3. Reassign the column by converting Boolean to binary (1/0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "nqDSe0DSGwhB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15265 entries, 0 to 22698\n",
      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Unnamed: 0             15265 non-null  int64  \n",
      " 1   VendorID               15265 non-null  int64  \n",
      " 2   tpep_pickup_datetime   15265 non-null  object \n",
      " 3   tpep_dropoff_datetime  15265 non-null  object \n",
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      " 14  tip_amount             15265 non-null  float64\n",
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      " 19  mean_distance          15265 non-null  float64\n",
      " 20  predicted_fare         15265 non-null  float64\n",
      " 21  tip_percent            15262 non-null  float64\n",
      " 22  generous               15265 non-null  int64  \n",
      "dtypes: float64(12), int64(8), object(3)\n",
      "memory usage: 2.8+ MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\3648611487.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['generous'] = df1['tip_percent'].apply(lambda x: 1 if x >= 0.20 else 0)\n"
     ]
    }
   ],
   "source": [
    "# Create 'generous' col (target)\n",
    "#df1['generous'] = df1['tip_percent']\n",
    "df1['generous'] = df1['tip_percent'].apply(lambda x: 1 if x >= 0.20 else 0)\n",
    "df1.head()\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create day column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H27zUVIlkaxA"
   },
   "source": [
    "Next, you're going to be working with the pickup and dropoff columns.\n",
    "\n",
    "Convert the `tpep_pickup_datetime` and `tpep_dropoff_datetime` columns to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "OIycxWBMkafJ"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\2555743233.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['tpep_pickup_datetime'] = pd.to_datetime(df1['tpep_pickup_datetime'],format='%m/%d/%Y %I:%M:%S %p')\n",
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\2555743233.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['tpep_dropoff_datetime'] = pd.to_datetime(df1['tpep_dropoff_datetime'],format='%m/%d/%Y %I:%M:%S %p')\n"
     ]
    },
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       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 20:34:11</td>\n",
       "      <td>2017-03-25 20:42:11</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>11.855376</td>\n",
       "      <td>2.052258</td>\n",
       "      <td>10.441351</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  tip_percent  generous  \n",
       "0      22.847222       3.521667       16.434245        0.200         1  \n",
       "1      24.470370       3.108889       16.052218        0.238         1  \n",
       "2       7.250000       0.881429        7.053706        0.199         0  \n",
       "3      30.250000       3.700000       18.731650        0.300         1  \n",
       "5      11.855376       2.052258       10.441351        0.200         1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert pickup and dropoff cols to datetime\n",
    "df1['tpep_pickup_datetime'] = pd.to_datetime(df1['tpep_pickup_datetime'],format='%m/%d/%Y %I:%M:%S %p')\n",
    "df1['tpep_dropoff_datetime'] = pd.to_datetime(df1['tpep_dropoff_datetime'],format='%m/%d/%Y %I:%M:%S %p')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zpcM4FvNyPFY"
   },
   "source": [
    "Create a `day` column that contains only the day of the week when each passenger was picked up. Then, convert the values to lowercase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "abUvtMaYyWpD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\377259474.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['day'] = df1['tpep_pickup_datetime'].dt.day_name().str.lower()\n"
     ]
    },
    {
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       "      <td>0.238</td>\n",
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       "      <td>sunday</td>\n",
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      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  tip_percent  generous  \\\n",
       "0      22.847222       3.521667       16.434245        0.200         1   \n",
       "1      24.470370       3.108889       16.052218        0.238         1   \n",
       "2       7.250000       0.881429        7.053706        0.199         0   \n",
       "3      30.250000       3.700000       18.731650        0.300         1   \n",
       "5      11.855376       2.052258       10.441351        0.200         1   \n",
       "\n",
       "        day  \n",
       "0  saturday  \n",
       "1   tuesday  \n",
       "2    friday  \n",
       "3    sunday  \n",
       "5  saturday  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 'day' col\n",
    "df1['day'] = df1['tpep_pickup_datetime'].dt.day_name().str.lower()\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RZZhKnQrQgNM"
   },
   "source": [
    "\n",
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "\n",
    "To convert to day name, use `dt.day_name()` on the column.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create time of day columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HwslVt8Hpu7x"
   },
   "source": [
    "Next, engineer four new columns that represent time of day bins. Each column should contain binary values (0=no, 1=yes) that indicate whether a trip began (picked up) during the following times:\n",
    "\n",
    "`am_rush` = [06:00&ndash;10:00)  \n",
    "`daytime` = [10:00&ndash;16:00)  \n",
    "`pm_rush` = [16:00&ndash;20:00)  \n",
    "`nighttime` = [20:00&ndash;06:00)  \n",
    "\n",
    "To do this, first create the four columns. For now, each new column should be identical and contain the same information: the hour (only) from the `tpep_pickup_datetime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "x8LFySUyprau"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\607100940.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['am_rush'] = df1['tpep_pickup_datetime'].dt.hour.between(6, 9).astype(int)\n",
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\607100940.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['daytime'] = df1['tpep_pickup_datetime'].dt.hour.between(10, 15).astype(int)\n",
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\607100940.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['pm_rush'] = df1['tpep_pickup_datetime'].dt.hour.between(16, 19).astype(int)\n",
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\607100940.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['nighttime'] = ((df1['tpep_pickup_datetime'].dt.hour >= 20) | (df1['tpep_pickup_datetime'].dt.hour <= 5)).astype(int)\n"
     ]
    },
    {
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       "      <th>nighttime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1</td>\n",
       "      <td>saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "      <td>0.238</td>\n",
       "      <td>1</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0</td>\n",
       "      <td>friday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1</td>\n",
       "      <td>sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 20:34:11</td>\n",
       "      <td>2017-03-25 20:42:11</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>11.855376</td>\n",
       "      <td>2.052258</td>\n",
       "      <td>10.441351</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1</td>\n",
       "      <td>saturday</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": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  tip_percent  generous  \\\n",
       "0      22.847222       3.521667       16.434245        0.200         1   \n",
       "1      24.470370       3.108889       16.052218        0.238         1   \n",
       "2       7.250000       0.881429        7.053706        0.199         0   \n",
       "3      30.250000       3.700000       18.731650        0.300         1   \n",
       "5      11.855376       2.052258       10.441351        0.200         1   \n",
       "\n",
       "        day  am_rush  daytime  pm_rush  nighttime  \n",
       "0  saturday        1        0        0          0  \n",
       "1   tuesday        0        1        0          0  \n",
       "2    friday        1        0        0          0  \n",
       "3    sunday        0        1        0          0  \n",
       "5  saturday        0        0        0          1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create 'am_rush' col\n",
    "df1['am_rush'] = df1['tpep_pickup_datetime'].dt.hour.between(6, 9).astype(int)\n",
    "\n",
    "# Create 'daytime' col\n",
    "df1['daytime'] = df1['tpep_pickup_datetime'].dt.hour.between(10, 15).astype(int)\n",
    "\n",
    "# Create 'pm_rush' col\n",
    "df1['pm_rush'] = df1['tpep_pickup_datetime'].dt.hour.between(16, 19).astype(int)\n",
    "\n",
    "# Create 'nighttime' col\n",
    "df1['nighttime'] = ((df1['tpep_pickup_datetime'].dt.hour >= 20) | (df1['tpep_pickup_datetime'].dt.hour <= 5)).astype(int)\n",
    "\n",
    "# df1['hour'] = df1['tpep_pickup_datetime'].dt.hour\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create `month` column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VrUmDy8U28bs"
   },
   "source": [
    "Now, create a `month` column that contains only the abbreviated name of the month when each passenger was picked up, then convert the result to lowercase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\3152299273.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['month'] = df1['tpep_pickup_datetime'].dt.month_name().str.lower()\n"
     ]
    }
   ],
   "source": [
    "# Create 'month' col\n",
    "df1['month'] = df1['tpep_pickup_datetime'].dt.month_name().str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qWbNVbngihE6"
   },
   "source": [
    "Examine the first five rows of your dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "jWxemeyl4vwQ"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>VendorID</th>\n",
       "      <th>tpep_pickup_datetime</th>\n",
       "      <th>tpep_dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>RatecodeID</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>extra</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>improvement_surcharge</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>mean_distance</th>\n",
       "      <th>predicted_fare</th>\n",
       "      <th>tip_percent</th>\n",
       "      <th>generous</th>\n",
       "      <th>day</th>\n",
       "      <th>am_rush</th>\n",
       "      <th>daytime</th>\n",
       "      <th>pm_rush</th>\n",
       "      <th>nighttime</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24870114</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 08:55:43</td>\n",
       "      <td>2017-03-25 09:09:47</td>\n",
       "      <td>6</td>\n",
       "      <td>3.34</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>100</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>16.56</td>\n",
       "      <td>22.847222</td>\n",
       "      <td>3.521667</td>\n",
       "      <td>16.434245</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1</td>\n",
       "      <td>saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35634249</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-04-11 14:53:28</td>\n",
       "      <td>2017-04-11 15:19:58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>186</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20.80</td>\n",
       "      <td>24.470370</td>\n",
       "      <td>3.108889</td>\n",
       "      <td>16.052218</td>\n",
       "      <td>0.238</td>\n",
       "      <td>1</td>\n",
       "      <td>tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>april</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106203690</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-12-15 07:26:56</td>\n",
       "      <td>2017-12-15 07:34:08</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>262</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>0.881429</td>\n",
       "      <td>7.053706</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0</td>\n",
       "      <td>friday</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>december</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38942136</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-05-07 13:17:59</td>\n",
       "      <td>2017-05-07 13:48:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>188</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27.69</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>18.731650</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1</td>\n",
       "      <td>sunday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23345809</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-03-25 20:34:11</td>\n",
       "      <td>2017-03-25 20:42:11</td>\n",
       "      <td>6</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>161</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>12.36</td>\n",
       "      <td>11.855376</td>\n",
       "      <td>2.052258</td>\n",
       "      <td>10.441351</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1</td>\n",
       "      <td>saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
       "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
       "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
       "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
       "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
       "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
       "\n",
       "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
       "0                6           3.34           1                  N   \n",
       "1                1           1.80           1                  N   \n",
       "2                1           1.00           1                  N   \n",
       "3                1           3.70           1                  N   \n",
       "5                6           2.30           1                  N   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
       "0           100           231             1         13.0    0.0      0.5   \n",
       "1           186            43             1         16.0    0.0      0.5   \n",
       "2           262           236             1          6.5    0.0      0.5   \n",
       "3           188            97             1         20.5    0.0      0.5   \n",
       "5           161           236             1          9.0    0.5      0.5   \n",
       "\n",
       "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
       "0        2.76           0.0                    0.3         16.56   \n",
       "1        4.00           0.0                    0.3         20.80   \n",
       "2        1.45           0.0                    0.3          8.75   \n",
       "3        6.39           0.0                    0.3         27.69   \n",
       "5        2.06           0.0                    0.3         12.36   \n",
       "\n",
       "   mean_duration  mean_distance  predicted_fare  tip_percent  generous  \\\n",
       "0      22.847222       3.521667       16.434245        0.200         1   \n",
       "1      24.470370       3.108889       16.052218        0.238         1   \n",
       "2       7.250000       0.881429        7.053706        0.199         0   \n",
       "3      30.250000       3.700000       18.731650        0.300         1   \n",
       "5      11.855376       2.052258       10.441351        0.200         1   \n",
       "\n",
       "        day  am_rush  daytime  pm_rush  nighttime     month  \n",
       "0  saturday        1        0        0          0     march  \n",
       "1   tuesday        0        1        0          0     april  \n",
       "2    friday        1        0        0          0  december  \n",
       "3    sunday        0        1        0          0       may  \n",
       "5  saturday        0        0        0          1     march  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop columns\n",
    "\n",
    "Drop redundant and irrelevant columns as well as those that would not be available when the model is deployed. This includes information like payment type, trip distance, tip amount, tip percentage, total amount, toll amount, etc. The target variable (`generous`) must remain in the data because it will get isolated as the `y` data for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15265 entries, 0 to 22698\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   generous         15265 non-null  int64  \n",
      " 1   mean_duration    15265 non-null  float64\n",
      " 2   mean_distance    15265 non-null  float64\n",
      " 3   predicted_fare   15265 non-null  float64\n",
      " 4   day              15265 non-null  object \n",
      " 5   month            15265 non-null  object \n",
      " 6   am_rush          15265 non-null  int32  \n",
      " 7   daytime          15265 non-null  int32  \n",
      " 8   pm_rush          15265 non-null  int32  \n",
      " 9   nighttime        15265 non-null  int32  \n",
      " 10  passenger_count  15265 non-null  int64  \n",
      " 11  VendorID         15265 non-null  int64  \n",
      " 12  PULocationID     15265 non-null  int64  \n",
      " 13  DOLocationID     15265 non-null  int64  \n",
      " 14  RatecodeID       15265 non-null  int64  \n",
      "dtypes: float64(3), int32(4), int64(6), object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Drop columns\n",
    "df_opt = df1[['generous','mean_duration','mean_distance','predicted_fare','day','month','am_rush','daytime','pm_rush','nighttime','passenger_count','VendorID','PULocationID','DOLocationID','RatecodeID']]\n",
    "df_opt.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BVs01W-Iitu7"
   },
   "source": [
    "Many of the columns are categorical and will need to be dummied (converted to binary). Some of these columns are numeric, but they actually encode categorical information, such as `RatecodeID` and the pickup and dropoff locations. To make these columns recognizable to the `get_dummies()` function as categorical variables, you'll first need to convert them to `type(str)`. \n",
    "\n",
    "1. Define a variable called `cols_to_str`, which is a list of the numeric columns that contain categorical information and must be converted to string: `RatecodeID`, `PULocationID`, `DOLocationID`.\n",
    "2. Write a for loop that converts each column in `cols_to_str` to string.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "FbB4AfATHqjC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15265 entries, 0 to 22698\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   generous         15265 non-null  int64  \n",
      " 1   mean_duration    15265 non-null  float64\n",
      " 2   mean_distance    15265 non-null  float64\n",
      " 3   predicted_fare   15265 non-null  float64\n",
      " 4   day              15265 non-null  object \n",
      " 5   month            15265 non-null  object \n",
      " 6   am_rush          15265 non-null  int32  \n",
      " 7   daytime          15265 non-null  int32  \n",
      " 8   pm_rush          15265 non-null  int32  \n",
      " 9   nighttime        15265 non-null  int32  \n",
      " 10  passenger_count  15265 non-null  int64  \n",
      " 11  VendorID         15265 non-null  object \n",
      " 12  PULocationID     15265 non-null  object \n",
      " 13  DOLocationID     15265 non-null  object \n",
      " 14  RatecodeID       15265 non-null  object \n",
      "dtypes: float64(3), int32(4), int64(2), object(6)\n",
      "memory usage: 1.6+ MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\2157311096.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_opt[i] = df_opt[i].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# 1. Define list of cols to convert to string\n",
    "cols_to_str = ['VendorID','DOLocationID','PULocationID','RatecodeID']\n",
    "\n",
    "# 2. Convert each column to string\n",
    "for i in cols_to_str:\n",
    "    df_opt[i] = df_opt[i].astype(str)\n",
    "\n",
    "df_opt.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6j6Nyb5RnsvC"
   },
   "source": [
    "\n",
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "\n",
    "To convert to string, use `astype(str)` on the column.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_5Ubw8O1pKRO"
   },
   "source": [
    "Now convert all the categorical columns to binary.\n",
    "\n",
    "1. Call `get_dummies()` on the dataframe and assign the results back to a new dataframe called `df2`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "H94yLzUMHqgB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15265, 353)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categoricals to binary\n",
    "df_opt = pd.get_dummies(df_opt)\n",
    "df_opt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nZfNE37b-LlJ"
   },
   "source": [
    "##### Evaluation metric\n",
    "\n",
    "Before modeling, you must decide on an evaluation metric. \n",
    "\n",
    "1. Examine the class balance of your target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "4mRefXCF-K_c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.526368\n",
       "0    0.473632\n",
       "Name: generous, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get class balance of 'generous' col\n",
    "df_opt['generous'].value_counts(' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TjgkLrOf_OrE"
   },
   "source": [
    "A little over half of the customers in this dataset were \"generous\" (tipped ≥ 20%). The dataset is very nearly balanced.\n",
    "\n",
    "To determine a metric, consider the cost of both kinds of model error:\n",
    "* False positives (the model predicts a tip ≥ 20%, but the customer does not give one)\n",
    "* False negatives (the model predicts a tip < 20%, but the customer gives more)\n",
    "\n",
    "False positives are worse for cab drivers, because they would pick up a customer expecting a good tip and then not receive one, frustrating the driver.\n",
    "\n",
    "False negatives are worse for customers, because a cab driver would likely pick up a different customer who was predicted to tip more&mdash;even when the original customer would have tipped generously.\n",
    "\n",
    "**The stakes are relatively even. You want to help taxi drivers make more money, but you don't want this to anger customers. Your metric should weigh both precision and recall equally. Which metric is this?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A f1 score is best for this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3n1eikFh8akS"
   },
   "source": [
    "<img src=\"images/Construct.png\" width=\"100\" height=\"100\" align=left>\n",
    "\n",
    "## PACE: **Construct**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Construct stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G5jzGjOS8iiv"
   },
   "source": [
    "### **Task 3. Modeling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nx41bVxX89Fe"
   },
   "source": [
    "##### **Split the data**\n",
    "\n",
    "Now you're ready to model. The only remaining step is to split the data into features/target variable and training/testing data. \n",
    "\n",
    "1. Define a variable `y` that isolates the target variable (`generous`).\n",
    "2. Define a variable `X` that isolates the features.\n",
    "3. Split the data into training and testing sets. Put 20% of the samples into the test set, stratify the data, and set the random state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "qLbapbSWDUL-"
   },
   "outputs": [],
   "source": [
    "# Isolate target variable (y)\n",
    "y = df_opt.generous\n",
    "# Isolate the features (X)\n",
    "X = df_opt.drop(['generous'],axis=1)\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vynZs5het1b_"
   },
   "source": [
    "##### **Random forest**\n",
    "\n",
    "Begin with using `GridSearchCV` to tune a random forest model.\n",
    "\n",
    "1. Instantiate the random forest classifier `rf` and set the random state.\n",
    "\n",
    "2. Create a dictionary `cv_params` of any of the following hyperparameters and their corresponding values to tune. The more you tune, the better your model will fit the data, but the longer it will take. \n",
    " - `max_depth`  \n",
    " - `max_features`  \n",
    " - `max_samples` \n",
    " - `min_samples_leaf`  \n",
    " - `min_samples_split`\n",
    " - `n_estimators`  \n",
    "\n",
    "3. Define a set `scoring` of scoring metrics for GridSearch to capture (precision, recall, F1 score, and accuracy).\n",
    "\n",
    "4. Instantiate the `GridSearchCV` object `rf1`. Pass to it as arguments:\n",
    " - estimator=`rf`\n",
    " - param_grid=`cv_params`\n",
    " - scoring=`scoring`\n",
    " - cv: define the number of you cross-validation folds you want (`cv=_`)\n",
    " - refit: indicate which evaluation metric you want to use to select the model (`refit=_`)\n",
    "\n",
    "\n",
    "**Note:** `refit` should be set to `'f1'`.<font/>\n",
    "</details>\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Instantiate the random forest classifier\n",
    "rf = RandomForestClassifier(random_state=0)\n",
    "\n",
    "# 2. Create a dictionary of hyperparameters to tune \n",
    "cv_params = {\n",
    "    'max_depth':[None],\n",
    "    'max_features':[1],\n",
    "    'max_samples':[0.75],\n",
    "    'min_samples_leaf':[1],\n",
    "    'min_samples_split':[2],\n",
    "    'n_estimators':[300] \n",
    "    }\n",
    "\n",
    "# 3. Define a set of scoring metrics to capture\n",
    "scoring = ['accuracy','precision','recall','f1']\n",
    "\n",
    "# 4. Instantiate the GridSearchCV object\n",
    "rf_GSCV = GridSearchCV(estimator=rf, param_grid=cv_params, scoring=scoring, cv=5, refit='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wv_WvRA1RqTl"
   },
   "source": [
    "Now fit the model to the training data. Note that, depending on how many options you include in your search grid and the number of cross-validation folds you select, this could take a very long time&mdash;even hours. If you use 4-fold validation and include only one possible value for each hyperparameter and grow 300 trees to full depth, it should take about 5 minutes. If you add another value for GridSearch to check for, say, `min_samples_split` (so all hyperparameters now have 1 value except for `min_samples_split`, which has 2 possibilities), it would double the time to ~10 minutes. Each additional parameter would approximately double the time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "OXuBiTGi5ZHn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# rf_GSCV.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5wHi_YJduQOH"
   },
   "source": [
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "\n",
    "If you get a warning that a metric is 0 due to no predicted samples, think about how many features you're sampling with `max_features`. How many features are in the dataset? How many are likely predictive enough to give good predictions within the number of splits you've allowed (determined by the `max_depth` hyperparameter)? Consider increasing `max_features`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ChZsXw2sksDF"
   },
   "source": [
    "If you want, use `pickle` to save your models and read them back in. This can be particularly helpful when performing a search over many possible hyperparameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "YtAgrH0zy4CE"
   },
   "outputs": [],
   "source": [
    "import pickle \n",
    "\n",
    "# Define a path to the folder where you want to save the model\n",
    "# path = '/home/jovyan/work/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_pickle(path, model_object, save_name:str):\n",
    "    '''\n",
    "    save_name is a string.\n",
    "    '''\n",
    "    with open(path + save_name + '.pickle', 'wb') as to_write:\n",
    "        pickle.dump(model_object, to_write)\n",
    "\n",
    "# write_pickle('Pickle/',rf_GSCV,'RF_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_pickle(path, saved_model_name:str):\n",
    "    '''\n",
    "    saved_model_name is a string.\n",
    "    '''\n",
    "    with open(path + saved_model_name + '.pickle', 'rb') as to_read:\n",
    "        model = pickle.load(to_read)\n",
    "\n",
    "        return model\n",
    "rf_GSCV = read_pickle('Pickle/','RF_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QIaRiZW4hf-6"
   },
   "source": [
    "Examine the best average score across all the validation folds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "29kGUegqhviL"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72698044165293"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine best score\n",
    "rf_GSCV.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "heGb51fHh3E5"
   },
   "source": [
    "Examine the best combination of hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "FjgXbO7Kh8is"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None,\n",
       " 'max_features': 1,\n",
       " 'max_samples': 0.75,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 300}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_GSCV.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qZZnem5yiAau"
   },
   "source": [
    "Use the `make_results()` function to output all of the scores of your model. Note that it accepts three arguments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GeW48TS742jN"
   },
   "source": [
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "\n",
    "To learn more about how this function accesses the cross-validation results, refer to the [`GridSearchCV` scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html?highlight=gridsearchcv#sklearn.model_selection.GridSearchCV) for the `cv_results_` attribute.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "u-UodWEOedxz"
   },
   "outputs": [],
   "source": [
    "def make_results(model_name:str, model_object, metric:str):\n",
    "    '''\n",
    "    Arguments:\n",
    "    model_name (string): what you want the model to be called in the output table\n",
    "    model_object: a fit GridSearchCV object\n",
    "    metric (string): precision, recall, f1, or accuracy\n",
    "\n",
    "    Returns a pandas df with the F1, recall, precision, and accuracy scores\n",
    "    for the model with the best mean 'metric' score across all validation folds.\n",
    "    '''\n",
    "    \n",
    "    table = pd.DataFrame(columns = ['Model', 'Precision', 'Recall', 'F1', 'Accuracy'])\n",
    "    \n",
    "    # Create dictionary that maps input metric to actual metric name in GridSearchCV\n",
    "    metric_dict = {'precision': 'mean_test_precision',\n",
    "                     'recall': 'mean_test_recall',\n",
    "                     'f1': 'mean_test_f1',\n",
    "                     'accuracy': 'mean_test_accuracy',\n",
    "                     }\n",
    "    # Get all the results from the CV and put them in a df\n",
    "    cv_results = pd.DataFrame(model_object.cv_results_)\n",
    "    \n",
    "    for i in range(len(model_name)):\n",
    "        # Isolate the row of the df with the max(metric) score\n",
    "        best_estimator_results = cv_results.iloc[cv_results[metric_dict[metric[i]]].idxmax(), :]\n",
    "\n",
    "        # Extract Accuracy, precision, recall, and f1 score from that row\n",
    "        f1 = best_estimator_results.mean_test_f1\n",
    "        recall = best_estimator_results.mean_test_recall\n",
    "        precision = best_estimator_results.mean_test_precision\n",
    "        accuracy = best_estimator_results.mean_test_accuracy\n",
    "\n",
    "        # Create table of results\n",
    "        table.loc[len(table)]={'Model': model_name[i],\n",
    "                            'Precision': precision,\n",
    "                            'Recall': recall,\n",
    "                            'F1': f1,\n",
    "                            'Accuracy': accuracy,\n",
    "                            }\n",
    "\n",
    "    return table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cI84Xo37ZLy0"
   },
   "source": [
    "Call `make_results()` on the GridSearch object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "qAYb2QigiT_h"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RF_F1</td>\n",
       "      <td>0.685592</td>\n",
       "      <td>0.773801</td>\n",
       "      <td>0.72698</td>\n",
       "      <td>0.694071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RF_AC</td>\n",
       "      <td>0.685592</td>\n",
       "      <td>0.773801</td>\n",
       "      <td>0.72698</td>\n",
       "      <td>0.694071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RF_RC</td>\n",
       "      <td>0.526368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.68970</td>\n",
       "      <td>0.526368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF_PC</td>\n",
       "      <td>0.685592</td>\n",
       "      <td>0.773801</td>\n",
       "      <td>0.72698</td>\n",
       "      <td>0.694071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model  Precision    Recall       F1  Accuracy\n",
       "0  RF_F1   0.685592  0.773801  0.72698  0.694071\n",
       "1  RF_AC   0.685592  0.773801  0.72698  0.694071\n",
       "2  RF_RC   0.526368  1.000000  0.68970  0.526368\n",
       "3  RF_PC   0.685592  0.773801  0.72698  0.694071"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_rf = make_results(['RF_F1','RF_AC','RF_RC','RF_PC'],rf_GSCV,['f1','accuracy','recall','precision'])\n",
    "out_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SB-yhW9uu7dO"
   },
   "source": [
    "Your results should produce an acceptable model across the board. Typically scores of 0.65 or better are considered acceptable, but this is always dependent on your use case. Optional: try to improve the scores. It's worth trying, especially to practice searching over different hyperparameters.\n",
    "\n",
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "\n",
    "For example, if the available values for `min_samples_split` were [2, 3, 4] and GridSearch identified the best value as 4, consider trying [4, 5, 6] this time.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use your model to predict on the test data. Assign the results to a variable called `rf_preds`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "    \n",
    "You cannot call `predict()` on the GridSearchCV object directly. You must call it on the `best_estimator_`.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, you will use several models to predict on the test data. Remember that this decision comes with a trade-off. What is the benefit of this? What is the drawback?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ycwjBHJjiT9J"
   },
   "source": [
    "The benefit of using multiple models to predict on the test data is that you can compare models using data that was not used to train/tune hyperparameters. This reduces the risk of selecting a model based on how well it fit the training data.\n",
    "\n",
    "The drawback of using the final test data to select a model is that, by using the unseen data to make a decision about which model to use, you no longer have a truly unbiased idea of how your model would be expected to perform on new data. In this case, think of final model selection as another way of \"tuning\" your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get scores on test data\n",
    "y_preds_rf = rf_GSCV.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the below `get_test_scores()` function you will use to output the scores of the model on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_scores(model_name:str, preds, y_test_data):\n",
    "    '''\n",
    "    Generate a table of test scores.\n",
    "\n",
    "    In:\n",
    "    model_name (string): Your choice: how the model will be named in the output table\n",
    "    preds: numpy array of test predictions\n",
    "    y_test_data: numpy array of y_test data\n",
    "\n",
    "    Out:\n",
    "    table: a pandas df of precision, recall, f1, and accuracy scores for your model\n",
    "    '''\n",
    "    accuracy = accuracy_score(y_test_data, preds)\n",
    "    precision = precision_score(y_test_data, preds)\n",
    "    recall = recall_score(y_test_data, preds)\n",
    "    f1 = f1_score(y_test_data, preds)\n",
    "\n",
    "    table = pd.DataFrame({'Model': [model_name],\n",
    "                        'Precision': [precision],\n",
    "                        'Recall': [recall],\n",
    "                        'F1': [f1],\n",
    "                        'Accuracy': [accuracy]\n",
    "                        })\n",
    "\n",
    "    return table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FDRAL7zQx21J"
   },
   "source": [
    "1. Use the `get_test_scores()` function to generate the scores on the test data. Assign the results to `rf_test_scores`.\n",
    "2. Call `rf_test_scores` to output the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### RF test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "Iil1LjabiT5x"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RF_F1</td>\n",
       "      <td>0.685592</td>\n",
       "      <td>0.773801</td>\n",
       "      <td>0.726980</td>\n",
       "      <td>0.694071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RF Test</td>\n",
       "      <td>0.690800</td>\n",
       "      <td>0.789670</td>\n",
       "      <td>0.736934</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model  Precision    Recall        F1  Accuracy\n",
       "0    RF_F1   0.685592  0.773801  0.726980  0.694071\n",
       "0  RF Test   0.690800  0.789670  0.736934  0.703243"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Get scores on test data\n",
    "results_rf = get_test_scores('RF Test', y_preds_rf,y_test)\n",
    "results_rf = pd.concat([out_rf.iloc[0:1,:],results_rf],axis=0)\n",
    "results_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E4JiP5VRz2un"
   },
   "source": [
    "**Question:** How do your test results compare to your validation results?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dE6oXEJJiT2R"
   },
   "source": [
    "Scores were a little bit better on test data, overall they are comparable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### **XGBoost**\n",
    "\n",
    " Try to improve your scores using an XGBoost model.\n",
    "\n",
    "1. Instantiate the XGBoost classifier `xgb` and set `objective='binary:logistic'`. Also set the random state.\n",
    "\n",
    "2. Create a dictionary `cv_params` of the following hyperparameters and their corresponding values to tune:\n",
    " - `max_depth`\n",
    " - `min_child_weight`\n",
    " - `learning_rate`\n",
    " - `n_estimators`\n",
    "\n",
    "3. Define a set `scoring` of scoring metrics for grid search to capture (precision, recall, F1 score, and accuracy).\n",
    "\n",
    "4. Instantiate the `GridSearchCV` object `xgb1`. Pass to it as arguments:\n",
    " - estimator=`xgb`\n",
    " - param_grid=`cv_params`\n",
    " - scoring=`scoring`\n",
    " - cv: define the number of cross-validation folds you want (`cv=_`)\n",
    " - refit: indicate which evaluation metric you want to use to select the model (`refit='f1'`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Instantiate the XGBoost classifier\n",
    "xgb = XGBClassifier(objective='binary:logistic', random_state=0,n_jobs=-1)\n",
    "# 2. Create a dictionary of hyperparameters to tune\n",
    "cv_params = {'max_depth':[6,8,10],\n",
    "         'min_child_weight':[2],\n",
    "         'learning_rate':[0.05,0.1,0.2],\n",
    "         'n_estimators':[300,500]}\n",
    "\n",
    "# 3. Define a set of scoring metrics to capture\n",
    "scoring = ['accuracy','precision','recall','f1']\n",
    "\n",
    "# 4. Instantiate the GridSearchCV object\n",
    "xgb_GSCV = GridSearchCV(estimator=xgb, param_grid=cv_params, scoring=scoring, cv=4, refit='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now fit the model to the `X_train` and `y_train` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 359 ms\n",
      "Wall time: 1.32 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#==> ENTER YOUR CODE HERE\n",
    "#xgb_GSCV.fit(X_train, y_train, verbose=1)\n",
    "#write_pickle('Pickle/',xgb_GSCV,'xgb_model')\n",
    "xgb_GSCV = read_pickle('Pickle/','xgb_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the best score from this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73603875973445"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine best score\n",
    "xgb_GSCV.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2bB-QyGz0RcU"
   },
   "source": [
    "And the best parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "JiLja3YViTzj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.05,\n",
       " 'max_depth': 6,\n",
       " 'min_child_weight': 2,\n",
       " 'n_estimators': 300}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine best parameters\n",
    "xgb_GSCV.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eTE2QdNP0eEP"
   },
   "source": [
    "##### XGB CV Results\n",
    "\n",
    "Use the `make_results()` function to output all of the scores of your model. Note that it accepts three arguments. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "L4TSYXJWiTxs"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GB_F1</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GB_AC</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GB_RC</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GB_PC</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model  Precision    Recall        F1  Accuracy\n",
       "0  GB_F1   0.692073  0.786092  0.736039  0.703243\n",
       "1  GB_AC   0.692073  0.786092  0.736039  0.703243\n",
       "2  GB_RC   0.692073  0.786092  0.736039  0.703243\n",
       "3  GB_PC   0.692073  0.786092  0.736039  0.703243"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call 'make_results()' on the GridSearch object\n",
    "out_xgb = make_results(['GB_F1','GB_AC','GB_RC','GB_PC'],xgb_GSCV,['f1','accuracy','recall','precision'])\n",
    "out_xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wR1QdIAX1dKX"
   },
   "source": [
    "Use your model to predict on the test data. Assign the results to a variable called `xgb_preds`.\n",
    "\n",
    "<details>\n",
    "  <summary><h5>HINT</h5></summary>\n",
    "    \n",
    "You cannot call `predict()` on the GridSearchCV object directly. You must call it on the `best_estimator_`.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "5Y2giCN32Dwc"
   },
   "outputs": [],
   "source": [
    "# Get scores on test data\n",
    "y_preds_xgb = xgb_GSCV.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cEwnNMMP2Nbb"
   },
   "source": [
    "###### XGB test results\n",
    "\n",
    "1. Use the `get_test_scores()` function to generate the scores on the test data. Assign the results to `xgb_test_scores`.\n",
    "2. Call `xgb_test_scores` to output the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "g7jShC2TiTvx"
   },
   "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>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GB_F1</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GB Test</td>\n",
       "      <td>0.696189</td>\n",
       "      <td>0.807094</td>\n",
       "      <td>0.747550</td>\n",
       "      <td>0.713069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model  Precision    Recall        F1  Accuracy\n",
       "0    GB_F1   0.692073  0.786092  0.736039  0.703243\n",
       "0  GB Test   0.696189  0.807094  0.747550  0.713069"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get scores on test data\n",
    "results_xgb = get_test_scores('GB Test', y_preds_xgb,y_test)\n",
    "results_xgb = pd.concat([out_xgb.iloc[0:1,:],results_xgb],axis=0)\n",
    "results_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\4283838200.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  results['Model'].iloc[0]='RF CV'\n",
      "C:\\Users\\nitin\\AppData\\Local\\Temp\\ipykernel_14156\\4283838200.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  results['Model'].iloc[2]='GB CV'\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RF CV</td>\n",
       "      <td>0.685592</td>\n",
       "      <td>0.773801</td>\n",
       "      <td>0.726980</td>\n",
       "      <td>0.694071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RF Test</td>\n",
       "      <td>0.690800</td>\n",
       "      <td>0.789670</td>\n",
       "      <td>0.736934</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GB CV</td>\n",
       "      <td>0.692073</td>\n",
       "      <td>0.786092</td>\n",
       "      <td>0.736039</td>\n",
       "      <td>0.703243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GB Test</td>\n",
       "      <td>0.696189</td>\n",
       "      <td>0.807094</td>\n",
       "      <td>0.747550</td>\n",
       "      <td>0.713069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model  Precision    Recall        F1  Accuracy\n",
       "0    RF CV   0.685592  0.773801  0.726980  0.694071\n",
       "0  RF Test   0.690800  0.789670  0.736934  0.703243\n",
       "0    GB CV   0.692073  0.786092  0.736039  0.703243\n",
       "0  GB Test   0.696189  0.807094  0.747550  0.713069"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.concat([results_rf,results_xgb],axis=0)\n",
    "results['Model'].iloc[0]='RF CV'\n",
    "results['Model'].iloc[2]='GB CV'\n",
    "\n",
    "import dataframe_image as dfi\n",
    "results.dfi.export('Figures/Evaluation Metrics.png',dpi=300)\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "saM8YwbAyi-F"
   },
   "source": [
    "**Question:** Compare these scores to the random forest test scores. What do you notice? Which model would you choose?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GB povide a little better predictions, but still both results are comparable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eCNH80Ku9TpO"
   },
   "source": [
    "Plot a confusion matrix of the model's predictions on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "5iUyZWjWvqOd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate array of values for confusion matrix\n",
    "cm = confusion_matrix(y_test,y_preds_xgb)\n",
    "\n",
    "# Plot confusion matrix\n",
    "ax = sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"viridis\", linewidths=.5)\n",
    "# Add labels and a title if needed\n",
    "ax.set_xlabel('Predicted Labels')\n",
    "ax.set_ylabel('True Labels')\n",
    "ax.set_title('Confusion Matrix')\n",
    "plt.savefig('Figures/Confusion Matrix.png', format='png', dpi=300, bbox_inches='tight') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dW-3_eWW-k2u"
   },
   "source": [
    "**Question:** What type of errors are more common for your model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "False positives are more common, compared to false negatives (~2X). This means that drivers would see more often a case where customer won't tip generously, when model says they would. This would be bad for the drivers as they would expect a tip."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNexnwvy09PK"
   },
   "source": [
    "##### Feature importance\n",
    "\n",
    "Use the `feature_importances_` attribute of the best estimator object to inspect the features of your final model. You can then sort them and plot the most important ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "id": "kz5T1gHc1R2x"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|===================| 2997/3053 [00:26<00:00]        "
     ]
    }
   ],
   "source": [
    "explainer = shap.Explainer(xgb_GSCV.best_estimator_, X_train)\n",
    "shap_values = explainer(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show=True\n",
    "shap.summary_plot(shap_values, X_test, show=show)\n",
    "if show == False:\n",
    "    plt.savefig('Figures/Feature summary.png', format='png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()\n",
    "shap.dependence_plot(\"predicted_fare\", shap_values.values, X_test, show=show)\n",
    "if show == False:\n",
    "    plt.savefig('Figures/Feature Dependence.png', format='png', dpi=300, bbox_inches='tight')\n",
    "    plt.close()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": [
    {
     "file_id": "1oNheYh5WbljxkvoK_BMkQTey2DWnFXMs",
     "timestamp": 1663785370813
    }
   ]
  },
  "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}