Starbucks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## Portfolio Exercise: Starbucks\n",
    "<br>\n",
    "\n",
    "<img src=\"https://opj.ca/wp-content/uploads/2018/02/New-Starbucks-Logo-1200x969.jpg\" width=\"200\" height=\"200\">\n",
    "<br>\n",
    "<br>\n",
    " \n",
    "#### Background Information\n",
    "\n",
    "The dataset you will be provided in this portfolio exercise was originally used as a take-home assignment provided by Starbucks for their job candidates. The data for this exercise consists of about 120,000 data points split in a 2:1 ratio among training and test files. In the experiment simulated by the data, an advertising promotion was tested to see if it would bring more customers to purchase a specific product priced at $10. Since it costs the company 0.15 to send out each promotion, it would be best to limit that promotion only to those that are most receptive to the promotion. Each data point includes one column indicating whether or not an individual was sent a promotion for the product, and one column indicating whether or not that individual eventually purchased that product. Each individual also has seven additional features associated with them, which are provided abstractly as V1-V7.\n",
    "\n",
    "#### Optimization Strategy\n",
    "\n",
    "Your task is to use the training data to understand what patterns in V1-V7 to indicate that a promotion should be provided to a user. Specifically, your goal is to maximize the following metrics:\n",
    "\n",
    "* **Incremental Response Rate (IRR)** \n",
    "\n",
    "IRR depicts how many more customers purchased the product with the promotion, as compared to if they didn't receive the promotion. Mathematically, it's the ratio of the number of purchasers in the promotion group to the total number of customers in the purchasers group (_treatment_) minus the ratio of the number of purchasers in the non-promotional group to the total number of customers in the non-promotional group (_control_).\n",
    "\n",
    "$$ IRR = \\frac{purch_{treat}}{cust_{treat}} - \\frac{purch_{ctrl}}{cust_{ctrl}} $$\n",
    "\n",
    "\n",
    "* **Net Incremental Revenue (NIR)**\n",
    "\n",
    "NIR depicts how much is made (or lost) by sending out the promotion. Mathematically, this is 10 times the total number of purchasers that received the promotion minus 0.15 times the number of promotions sent out, minus 10 times the number of purchasers who were not given the promotion.\n",
    "\n",
    "$$ NIR = (10\\cdot purch_{treat} - 0.15 \\cdot cust_{treat}) - 10 \\cdot purch_{ctrl}$$\n",
    "\n",
    "For a full description of what Starbucks provides to candidates see the [instructions available here](https://drive.google.com/open?id=18klca9Sef1Rs6q8DW4l7o349r8B70qXM).\n",
    "\n",
    "Below you can find the training data provided.  Explore the data and different optimization strategies.\n",
    "\n",
    "#### How To Test Your Strategy?\n",
    "\n",
    "When you feel like you have an optimization strategy, complete the `promotion_strategy` function to pass to the `test_results` function.  \n",
    "From past data, we know there are four possible outomes:\n",
    "\n",
    "Table of actual promotion vs. predicted promotion customers:  \n",
    "\n",
    "<table>\n",
    "<tr><th></th><th colspan = '2'>Actual</th></tr>\n",
    "<tr><th>Predicted</th><th>Yes</th><th>No</th></tr>\n",
    "<tr><th>Yes</th><td>I</td><td>II</td></tr>\n",
    "<tr><th>No</th><td>III</td><td>IV</td></tr>\n",
    "</table>\n",
    "\n",
    "The metrics are only being compared for the individuals we predict should obtain the promotion – that is, quadrants I and II.  Since the first set of individuals that receive the promotion (in the training set) receive it randomly, we can expect that quadrants I and II will have approximately equivalent participants.  \n",
    "\n",
    "Comparing quadrant I to II then gives an idea of how well your promotion strategy will work in the future. \n",
    "\n",
    "Get started by reading in the data below.  See how each variable or combination of variables along with a promotion influences the chance of purchasing.  When you feel like you have a strategy for who should receive a promotion, test your strategy against the test dataset used in the final `test_results` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Promotion</th>\n",
       "      <th>purchase</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30.443518</td>\n",
       "      <td>-1.165083</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
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       "      <td>3</td>\n",
       "      <td>32.159350</td>\n",
       "      <td>-0.645617</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30.431659</td>\n",
       "      <td>0.133583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.588914</td>\n",
       "      <td>-0.212728</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28.044331</td>\n",
       "      <td>-0.385883</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Promotion  purchase  V1         V2        V3  V4  V5  V6  V7\n",
       "0   1        No         0   2  30.443518 -1.165083   1   1   3   2\n",
       "1   3        No         0   3  32.159350 -0.645617   2   3   2   2\n",
       "2   4        No         0   2  30.431659  0.133583   1   1   4   2\n",
       "3   5        No         0   0  26.588914 -0.212728   2   1   4   2\n",
       "4   8       Yes         0   3  28.044331 -0.385883   1   1   2   2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in packages\n",
    "from itertools import combinations\n",
    "\n",
    "from test_results import test_results, score\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score\n",
    "import statsmodels.stats.proportion as propstest\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "\n",
    "# load in the data\n",
    "train_data = pd.read_csv('./training.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             int64\n",
       "Promotion     object\n",
       "purchase       int64\n",
       "V1             int64\n",
       "V2           float64\n",
       "V3           float64\n",
       "V4             int64\n",
       "V5             int64\n",
       "V6             int64\n",
       "V7             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cells for you to work and document as necessary - \n",
    "# definitely feel free to add more cells as you need"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Determine how many more customers purchased product X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84534"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the number of observations\n",
    "num_obs = train_data['Promotion'].shape[0]\n",
    "num_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 84534 entries, 0 to 84533\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ID         84534 non-null  int64  \n",
      " 1   Promotion  84534 non-null  object \n",
      " 2   purchase   84534 non-null  int64  \n",
      " 3   V1         84534 non-null  int64  \n",
      " 4   V2         84534 non-null  float64\n",
      " 5   V3         84534 non-null  float64\n",
      " 6   V4         84534 non-null  int64  \n",
      " 7   V5         84534 non-null  int64  \n",
      " 8   V6         84534 non-null  int64  \n",
      " 9   V7         84534 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(1)\n",
      "memory usage: 6.4+ MB\n"
     ]
    }
   ],
   "source": [
    "#Reviewing the data in more detail\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of people who bought from the treatment group is 721\n",
      "The number of people who bought from the control group is 319\n"
     ]
    }
   ],
   "source": [
    "#number of people who bought and are in treatment group \n",
    "num_b_treat = train_data.loc[train_data['Promotion']== 'Yes','purchase'].sum()\n",
    "\n",
    "#number of people that bought who are in the control groups\n",
    "num_b_control = train_data.loc[train_data['Promotion'] == 'No','purchase'].sum()\n",
    "\n",
    "print ('The number of people who bought from the treatment group is %d' %num_b_treat)\n",
    "print ('The number of people who bought from the control group is %d' %num_b_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of people in the treatment group is 42364\n",
      "The number of people in the control group is 42170\n"
     ]
    }
   ],
   "source": [
    "#number of people in treatment group\n",
    "num_treat = train_data.loc[train_data['Promotion'] == 'Yes'].shape[0]\n",
    "#number of people in control group\n",
    "num_control = train_data.loc[train_data['Promotion'] == 'No'].shape[0]\n",
    "\n",
    "print ('The number of people in the treatment group is %d' %num_treat)\n",
    "print ('The number of people in the control group is %d' %num_control)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The incremental response rate is:0.009455\n"
     ]
    }
   ],
   "source": [
    "#calculating IRR\n",
    "\n",
    "irr = (num_b_treat/num_treat) - (num_b_control/num_control)\n",
    "print ('The incremental response rate is:%f' %irr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The net incremental revenue is -2334\n"
     ]
    }
   ],
   "source": [
    "#calculating NIR\n",
    "nir = (10*num_b_treat - 0.15*num_treat) - 10*num_b_control\n",
    "print ('The net incremental revenue is %d' %nir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling\n",
    "\n",
    "We will try to predict which customers will buy the product based on their features v1-v7.\n",
    "\n",
    "The process will go as follows:\n",
    "- Randomly extract 1000 customers from people who didn't purchase the product, and combine them with customers who bought the product, so that the two group are of the same size.\n",
    "- Convert categorical variables into dummy variables\n",
    "- Split the sample into train group and test group, with test_size = 0.3\n",
    "- Use the train group to train the Logistic Regression model\n",
    "- Use the model to predict the purchasing behavior of the test group\n",
    "- Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Promotion</th>\n",
       "      <th>purchase</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <td>162</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>352</th>\n",
       "      <td>534</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17.512709</td>\n",
       "      <td>1.259095</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "      <th>383</th>\n",
       "      <td>580</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.043736</td>\n",
       "      <td>-1.338239</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>639</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.901993</td>\n",
       "      <td>-0.905350</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      ID Promotion  purchase  V1         V2        V3  V4  V5  V6  V7\n",
       "107  162       Yes         1   1  33.030562 -0.732194   2   1   1   1\n",
       "328  503       Yes         1   3  31.760033  1.518828   2   1   2   2\n",
       "352  534       Yes         1   2  17.512709  1.259095   2   3   4   2\n",
       "383  580       Yes         1   1  31.043736 -1.338239   2   1   2   1\n",
       "423  639        No         1   1  34.901993 -0.905350   2   3   2   2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomly select 1000 rows from not purchased dataset\n",
    "# Combine it with all the rows from purchased dataset\n",
    "pur = train_data[train_data['purchase'] == 1]\n",
    "not_pur = train_data[train_data['purchase'] == 0].sample(1000)\n",
    "sample = pd.concat([pur, not_pur], axis = 0)\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>503</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>31.760033</td>\n",
       "      <td>1.518828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>534</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>17.512709</td>\n",
       "      <td>1.259095</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>580</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>31.043736</td>\n",
       "      <td>-1.338239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>639</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>34.901993</td>\n",
       "      <td>-0.905350</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID Promotion  purchase         V2        V3  V1_0  V1_1  V1_2  V1_3  \\\n",
       "107  162       Yes         1  33.030562 -0.732194     0     1     0     0   \n",
       "328  503       Yes         1  31.760033  1.518828     0     0     0     1   \n",
       "352  534       Yes         1  17.512709  1.259095     0     0     1     0   \n",
       "383  580       Yes         1  31.043736 -1.338239     0     1     0     0   \n",
       "423  639        No         1  34.901993 -0.905350     0     1     0     0   \n",
       "\n",
       "     V4_1  ...  V5_1  V5_2  V5_3  V5_4  V6_1  V6_2  V6_3  V6_4  V7_1  V7_2  \n",
       "107     0  ...     1     0     0     0     1     0     0     0     1     0  \n",
       "328     0  ...     1     0     0     0     0     1     0     0     0     1  \n",
       "352     0  ...     0     0     1     0     0     0     0     1     0     1  \n",
       "383     0  ...     1     0     0     0     0     1     0     0     1     0  \n",
       "423     0  ...     0     0     1     0     0     1     0     0     0     1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting categorical variables into dummy variables\n",
    "cat_var = train_data.select_dtypes('int').columns\n",
    "cat_var = cat_var.drop(['ID', 'purchase'])\n",
    "\n",
    "features_df = pd.get_dummies(data = sample, columns = cat_var)\n",
    "features_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split them into train data and test data\n",
    "x = features_df.drop(['ID','Promotion','purchase'], axis =1)\n",
    "y = features_df['purchase']\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model training with logistic regression\n",
    "lgr = LogisticRegression()\n",
    "\n",
    "lgr.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The precision score of the model is 0.6181\n",
      "The recall score of the model is 0.5859\n"
     ]
    }
   ],
   "source": [
    "# Testing the model with test_x to predict pred_y\n",
    "pred_y = lgr.predict(test_x)\n",
    "\n",
    "# Compute the precision rate and recall rate\n",
    "precision = precision_score(test_y, pred_y)\n",
    "recall = recall_score(test_y, pred_y)\n",
    "\n",
    "print('The precision score of the model is %.4f' %precision)\n",
    "print('The recall score of the model is %.4f' %recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_No</th>\n",
       "      <th>act_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pred_No</th>\n",
       "      <td>168</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pred_Yes</th>\n",
       "      <td>118</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          act_No  act_Yes\n",
       "pred_No      168      135\n",
       "pred_Yes     118      191"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use a confusion matrix to show the comparison of the predicted promotion and the actual promotion\n",
    "con_mat = pd.DataFrame(confusion_matrix(pred_y, test_y), index = ['pred_No', 'pred_Yes'], columns = ['act_No', 'act_Yes'])\n",
    "con_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def promotion_strategy(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    \n",
    "    x = pd.get_dummies(data = df, columns = cat_var) \n",
    "    promotion = lgr.predict(x)\n",
    "    \n",
    "    my_map = {0: \"No\", 1: \"Yes\"}\n",
    "    promotion = np.array([my_map[i] for i in promotion])\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0179.\n",
      "\n",
      "Your nir with this strategy is 249.85.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.017867227786427138, 249.85000000000014)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This will test your results, and provide you back some information \n",
    "# on how well your promotion_strategy will work in practice\n",
    "\n",
    "test_results(promotion_strategy)"
   ]
  }
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