Springboard Decision Tree Specialty Coffee Case Study - Tier 3.ipynb

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   "source": [
    "# **Springboard Decision Tree Specialty Coffee Case Study - Tier 3**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# The Scenario\n",
    "\n",
    "Imagine you've just finished the Springboard Data Science Career Track course, and have been hired by a rising popular specialty coffee company - RR Diner Coffee - as a data scientist. Congratulations!\n",
    "\n",
    "RR Diner Coffee sells two types of thing:\n",
    "- specialty coffee beans, in bulk (by the kilogram only) \n",
    "- coffee equipment and merchandise (grinders, brewing equipment, mugs, books, t-shirts).\n",
    "\n",
    "RR Diner Coffee has three stores, two in Europe and one in the USA. The flagshap store is in the USA, and everything is quality assessed there, before being shipped out. Customers further away from the USA flagship store have higher shipping charges. \n",
    "\n",
    "You've been taken on at RR Diner Coffee because the company are turning towards using data science and machine learning to systematically make decisions about which coffee farmers they should strike deals with. \n",
    "\n",
    "RR Diner Coffee typically buys coffee from farmers, processes it on site, brings it back to the USA, roasts it, packages it, markets it, and ships it (only in bulk, and after quality assurance) to customers internationally. These customers all own coffee shops in major cities like New York, Paris, London, Hong Kong, Tokyo, and Berlin. \n",
    "\n",
    "Now, RR Diner Coffee has a decision about whether to strike a deal with a legendary coffee farm (known as the **Hidden Farm**) in rural China: there are rumours their coffee tastes of lychee and dark chocolate, while also being as sweet as apple juice. \n",
    "\n",
    "It's a risky decision, as the deal will be expensive, and the coffee might not be bought by customers. The stakes are high: times are tough, stocks are low, farmers are reverting to old deals with the larger enterprises and the publicity of selling *Hidden Farm* coffee could save the RR Diner Coffee business. \n",
    "\n",
    "Your first job, then, is ***to build a decision tree to predict how many units of the Hidden Farm Chinese coffee will be purchased by RR Diner Coffee's most loyal customers.*** \n",
    "\n",
    "To this end, you and your team have conducted a survey of 710 of the most loyal RR Diner Coffee customers, collecting data on the customers':\n",
    "- age\n",
    "- gender \n",
    "- salary \n",
    "- whether they have bought at least one RR Diner Coffee product online\n",
    "- their distance from the flagship store in the USA (standardized to a number between 0 and 11) \n",
    "- how much they spent on RR Diner Coffee products on the week of the survey \n",
    "- how much they spent on RR Diner Coffee products in the month preeding the survey\n",
    "- the number of RR Diner coffee bean shipments each customer has ordered over the preceding year. \n",
    "\n",
    "You also asked each customer participating in the survey whether they would buy the Hidden Farm coffee, and some (but not all) of the customers gave responses to that question. \n",
    "\n",
    "You sit back and think: if more than 70% of the interviewed customers are likely to buy the Hidden Farm coffee, you will strike the deal with the local Hidden Farm farmers and sell the coffee. Otherwise, you won't strike the deal and the Hidden Farm coffee will remain in legends only. There's some doubt in your mind about whether 70% is a reasonable threshold, but it'll do for the moment. \n",
    "\n",
    "To solve the problem, then, you will build a decision tree to implement a classification solution. \n",
    "\n",
    "\n",
    "-------------------------------\n",
    "As ever, this notebook is **tiered**, meaning you can elect that tier that is right for your confidence and skill level. There are 3 tiers, with tier 1 being the easiest and tier 3 being the hardest. This is ***tier 3***, so it will be challenging. \n",
    "\n",
    "**1. Sourcing and loading** \n",
    "- Import packages\n",
    "- Load data\n",
    "- Explore the data\n",
    "\n",
    " \n",
    "**2. Cleaning, transforming and visualizing**\n",
    "- Cleaning the data\n",
    "- Train/test split\n",
    "  \n",
    "  \n",
    "**3. Modelling** \n",
    "- Model 1: Entropy model - no max_depth\n",
    "- Model 2: Gini impurity model - no max_depth\n",
    "- Model 3: Entropy model - max depth 3\n",
    "- Model 4: Gini impurity model - max depth 3\n",
    "\n",
    "\n",
    "**4. Evaluating and concluding** \n",
    "- How many customers will buy Hidden Farm coffee?\n",
    "- Decision\n",
    "\n",
    "**5. Random Forest** \n",
    "- Import necessary modules\n",
    "- Model\n",
    "- Revise conclusion\n",
    "    "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "nciXGXtCDuVO"
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   "source": [
    "# 0. Overview\n",
    "\n",
    "This notebook uses decision trees to determine whether the factors of salary, gender, age, how much money the customer spent last week and during the preceding month on RR Diner Coffee products, how many kilogram coffee bags the customer bought over the last year, whether they have bought at least one RR Diner Coffee product online, and their distance from the flagship store in the USA, could predict whether customers would purchase the Hidden Farm coffee if a deal with its farmers were struck. "
   ]
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  {
   "cell_type": "markdown",
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   },
   "source": [
    "# 1. Sourcing and loading\n",
    "## 1a. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import tree, metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from io import StringIO  \n",
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZLgxyhNrDuVT"
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   "source": [
    "## 1b. Load data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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       "      <th>spent_week</th>\n",
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       "      <td>24</td>\n",
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       "      <td>20</td>\n",
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      "text/plain": [
       "   Age  Gender  num_coffeeBags_per_year  spent_week  spent_month  SlrAY  \\\n",
       "0   36  Female                        0          24           73  42789   \n",
       "1   24    Male                        0          44          164  74035   \n",
       "2   24    Male                        0          39          119  30563   \n",
       "3   20    Male                        0          30          107  13166   \n",
       "4   24  Female                        0          20           36  14244   \n",
       "\n",
       "   Distance  Online  Decision  \n",
       "0  0.003168       0       1.0  \n",
       "1  0.520906       0       NaN  \n",
       "2  0.916005       1       1.0  \n",
       "3  0.932098       1       NaN  \n",
       "4  0.965881       0       1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data to a variable called coffeeData\n",
    "coffeeData = pd.read_csv('data/RRDinerCoffeeData.csv')\n",
    "coffeeData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qnMVYf2NDuVW"
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   "source": [
    "## 1c. Explore the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U5Qm4obyDuVX"
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   "source": [
    "As we've seen, exploration entails doing things like checking out the **initial appearance** of the data with head(), the **dimensions** of our data with .shape, the **data types** of the variables with .info(), the **number of non-null values**, how much **memory** is being used to store the data, and finally the major summary statistcs capturing **central tendancy, dispersion and the null-excluding shape of the dataset's distribution**. \n",
    "\n",
    "How much of this can you do yourself by this point in the course? Have a real go. "
   ]
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      "text/plain": [
       "   Age  Gender  num_coffeeBags_per_year  spent_week  spent_month  SlrAY  \\\n",
       "0   36  Female                        0          24           73  42789   \n",
       "1   24    Male                        0          44          164  74035   \n",
       "2   24    Male                        0          39          119  30563   \n",
       "3   20    Male                        0          30          107  13166   \n",
       "4   24  Female                        0          20           36  14244   \n",
       "\n",
       "   Distance  Online  Decision  \n",
       "0  0.003168       0       1.0  \n",
       "1  0.520906       0       NaN  \n",
       "2  0.916005       1       1.0  \n",
       "3  0.932098       1       NaN  \n",
       "4  0.965881       0       1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call head() on your data \n",
    "coffeeData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rDshwA7tDuVa"
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    {
     "data": {
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       "(702, 9)"
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "# Call .shape on your data\n",
    "coffeeData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iCxo0tomDuVc"
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 702 entries, 0 to 701\n",
      "Data columns (total 9 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Age                      702 non-null    int64  \n",
      " 1   Gender                   702 non-null    object \n",
      " 2   num_coffeeBags_per_year  702 non-null    int64  \n",
      " 3   spent_week               702 non-null    int64  \n",
      " 4   spent_month              702 non-null    int64  \n",
      " 5   SlrAY                    702 non-null    int64  \n",
      " 6   Distance                 702 non-null    float64\n",
      " 7   Online                   702 non-null    int64  \n",
      " 8   Decision                 474 non-null    float64\n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 46.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Call info() on your data\n",
    "coffeeData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t0ZdmaUeDuVe"
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       "      <th>count</th>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>702.000000</td>\n",
       "      <td>474.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>34.243590</td>\n",
       "      <td>2.710826</td>\n",
       "      <td>32.853276</td>\n",
       "      <td>107.923077</td>\n",
       "      <td>43819.843305</td>\n",
       "      <td>4.559186</td>\n",
       "      <td>0.531339</td>\n",
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       "      <td>1.593629</td>\n",
       "      <td>15.731878</td>\n",
       "      <td>55.348485</td>\n",
       "      <td>26192.626943</td>\n",
       "      <td>3.116275</td>\n",
       "      <td>0.499373</td>\n",
       "      <td>0.480728</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1617.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.250000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>22812.250000</td>\n",
       "      <td>1.877812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>41975.000000</td>\n",
       "      <td>4.196167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>150.750000</td>\n",
       "      <td>60223.000000</td>\n",
       "      <td>6.712022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>182058.000000</td>\n",
       "      <td>10.986203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  num_coffeeBags_per_year  spent_week  spent_month  \\\n",
       "count  702.000000               702.000000  702.000000   702.000000   \n",
       "mean    34.243590                 2.710826   32.853276   107.923077   \n",
       "std     13.927945                 1.593629   15.731878    55.348485   \n",
       "min     16.000000                 0.000000    0.000000     0.000000   \n",
       "25%     23.000000                 1.000000   24.250000    62.000000   \n",
       "50%     28.000000                 3.000000   36.000000   113.500000   \n",
       "75%     46.000000                 4.000000   43.000000   150.750000   \n",
       "max     90.000000                 5.000000   62.000000   210.000000   \n",
       "\n",
       "               SlrAY    Distance      Online    Decision  \n",
       "count     702.000000  702.000000  702.000000  474.000000  \n",
       "mean    43819.843305    4.559186    0.531339    0.639241  \n",
       "std     26192.626943    3.116275    0.499373    0.480728  \n",
       "min      1617.000000    0.003168    0.000000    0.000000  \n",
       "25%     22812.250000    1.877812    0.000000    0.000000  \n",
       "50%     41975.000000    4.196167    1.000000    1.000000  \n",
       "75%     60223.000000    6.712022    1.000000    1.000000  \n",
       "max    182058.000000   10.986203    1.000000    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call describe() on your data to get the relevant summary statistics for your data \n",
    "coffeeData.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "INkujJ6QDuVg"
   },
   "source": [
    "# 2. Cleaning, transforming and visualizing\n",
    "## 2a. Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wIMW_rbdDuVh"
   },
   "source": [
    "Some datasets don't require any cleaning, but almost all do. This one does. We need to replace '1.0' and '0.0' in the 'Decision' column by 'YES' and 'NO' respectively, clean up the values of the 'gender' column, and change the column names to words which maximize meaning and clarity. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EEXdBALeDuVi"
   },
   "source": [
    "First, let's change the name of `spent_week`, `spent_month`, and `SlrAY` to `spent_last_week` and `spent_last_month` and `salary` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rhxy23pbDuVi"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'num_coffeeBags_per_year', 'spent_week', 'spent_month',\n",
       "       'SlrAY', 'Distance', 'Online', 'Decision'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the names of our data's columns \n",
    "coffeeData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C_D1m6ryDuVk"
   },
   "outputs": [],
   "source": [
    "# Make the relevant name changes to spent_week and spent_per_week.\n",
    "coffeeData.columns = ['Age', 'Gender', 'num_coffeeBags_per_year', 'spent_last_week', 'spent_last_month',\n",
    "       'salary', 'Distance', 'Online', 'Decision']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3cmdaeg8DuVm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'num_coffeeBags_per_year', 'spent_last_week',\n",
       "       'spent_last_month', 'salary', 'Distance', 'Online', 'Decision'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the column names\n",
    "coffeeData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XJK-wbfODuVp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      702\n",
       "unique       9\n",
       "top       Male\n",
       "freq       355\n",
       "Name: Gender, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a closer look at the gender column. Its values need cleaning.\n",
    "coffeeData.Gender.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "88bITtgzDuVr"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male', 'female', 'F', 'f ', 'FEMALE', 'MALE', 'male',\n",
       "       'M'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the gender column's unique values \n",
    "coffeeData.Gender.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-eGzgGR1DuVt"
   },
   "source": [
    "We can see a bunch of inconsistency here.\n",
    "\n",
    "Use replace() to make the values of the `gender` column just `Female` and `Male`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h0FOX3I3DuVu"
   },
   "outputs": [],
   "source": [
    "# Replace all alternate values for the Female entry with 'Female'\n",
    "coffeeData.Gender = coffeeData.Gender.replace(['female','F','f','FEMALE','f '], 'Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5TThfHgCDuVw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male', 'MALE', 'male', 'M'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the unique values for the 'gender' column\n",
    "coffeeData.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4wRKBvSHDuVy"
   },
   "outputs": [],
   "source": [
    "# Replace all alternate values with \"Male\"\n",
    "coffeeData.Gender = coffeeData.Gender.replace(['MALE','male','M'],'Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nhBUkXIADuV0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the unique values of the column \"gender\"\n",
    "coffeeData.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AdhhO4WHDuV2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1., nan,  0.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the unique values of the column 'Decision'\n",
    "coffeeData.Decision.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m1JuiY-iDuV5"
   },
   "source": [
    "We now want to replace `1.0` and `0.0` in the `Decision` column by `YES` and `NO` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QsK6gYPgDuV5"
   },
   "outputs": [],
   "source": [
    "# Replace 1.0 and 0.0 by 'Yes' and 'No'\n",
    "coffeeData.Decision = coffeeData.Decision.replace([1.0],'YES')\n",
    "coffeeData.Decision = coffeeData.Decision.replace([0.0],'NO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YtHnTZuWDuV9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['YES', nan, 'NO'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that our replacing those values with 'YES' and 'NO' worked, with unique()\n",
    "coffeeData.Decision.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CKCt-fBlDuWB"
   },
   "source": [
    "## 2b. Train/test split\n",
    "To execute the train/test split properly, we need to do five things: \n",
    "1. Drop all rows with a null value in the `Decision` column, and save the result as NOPrediction: a dataset that will contain all known values for the decision \n",
    "2. Visualize the data using scatter and boxplots of several variables in the y-axis and the decision on the x-axis\n",
    "3. Get the subset of coffeeData with null values in the `Decision` column, and save that subset as Prediction\n",
    "4. Divide the NOPrediction subset into X and y, and then further divide those subsets into train and test subsets for X and y respectively\n",
    "5. Create dummy variables to deal with categorical inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ysHdkIaQDuWB"
   },
   "source": [
    "### 1. Drop all null values within the `Decision` column, and save the result as NoPrediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pvgH6SM4DuWC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     474\n",
       "unique      2\n",
       "top       YES\n",
       "freq      303\n",
       "Name: Decision, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NoPrediction will contain all known values for the decision\n",
    "# Call dropna() on coffeeData, and store the result in a variable NOPrediction \n",
    "# Call describe() on the Decision column of NoPrediction after calling dropna() on coffeeData\n",
    "NoPrediction = coffeeData.dropna()\n",
    "NoPrediction.Decision.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XfTK5Ec6DuWE"
   },
   "source": [
    "### 2. Visualize the data using scatter and boxplots of several variables in the y-axis and the decision on the x-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5O-M-t3tDuWE"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploring our new NOPrediction dataset\n",
    "# Make a boxplot on NOPrediction where the x axis is Decision, and the y axis is spent_last_week\n",
    "plt.figure()\n",
    "sns.boxplot(x = 'Decision', y = 'spent_last_week', data = NoPrediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mVqg_HI7DuWG"
   },
   "source": [
    "__Q__ Can you admissibly conclude anything from this boxplot? Write your answer here:\n",
    "\n",
    "__A__ It appears as if those who spent more money last week in RR are more willing to buy this new chinese coffee. We could see that those who are willing spend around \\\\$40.00 last week whereas those who say are not willing to buy the new coffee spend around \\\\$25.00\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q-AyXWSxDuWH"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a scatterplot on NOPrediction, where x is distance, y is spent_last_month and hue is Decision \n",
    "plt.figure()\n",
    "sns.scatterplot(x = 'Distance',y = 'spent_last_month', hue = 'Decision', data=NoPrediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I_nr36yZDuWJ"
   },
   "source": [
    "__Q__ Can you admissibly conclude anything from this scatterplot? Remember: we are trying to build a tree to classify unseen examples. Write your answer here:\n",
    "\n",
    "__A__ The distance and the money spent last month does not appear to have any correlation; yet, it appears that a line near to $y = \\frac{25}{2}x$ divide those who are willing to buy the coffee from those who are not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oQBPyvehDuWK"
   },
   "source": [
    "### 3. Get the subset of coffeeData with null values in the Decision column, and save that subset as Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vcCednqUDuWL"
   },
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>num_coffeeBags_per_year</th>\n",
       "      <th>spent_last_week</th>\n",
       "      <th>spent_last_month</th>\n",
       "      <th>salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Online</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>164</td>\n",
       "      <td>74035</td>\n",
       "      <td>0.520906</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>107</td>\n",
       "      <td>13166</td>\n",
       "      <td>0.932098</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>17425</td>\n",
       "      <td>1.193188</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>153</td>\n",
       "      <td>84803</td>\n",
       "      <td>1.655096</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>122</td>\n",
       "      <td>42338</td>\n",
       "      <td>1.714179</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender  num_coffeeBags_per_year  spent_last_week  spent_last_month  \\\n",
       "1    24    Male                        0               44               164   \n",
       "3    20    Male                        0               30               107   \n",
       "7    24  Female                        0               20                34   \n",
       "11   24  Female                        0               40               153   \n",
       "12   21  Female                        0               38               122   \n",
       "\n",
       "    salary  Distance  Online Decision  \n",
       "1    74035  0.520906       0      NaN  \n",
       "3    13166  0.932098       1      NaN  \n",
       "7    17425  1.193188       0      NaN  \n",
       "11   84803  1.655096       1      NaN  \n",
       "12   42338  1.714179       1      NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get just those rows whose value for the Decision column is null  \n",
    "Prediction = coffeeData[coffeeData.Decision.isnull()]\n",
    "Prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aenlmp9NDuWM"
   },
   "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>Age</th>\n",
       "      <th>num_coffeeBags_per_year</th>\n",
       "      <th>spent_last_week</th>\n",
       "      <th>spent_last_month</th>\n",
       "      <th>salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>228.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.802632</td>\n",
       "      <td>2.960526</td>\n",
       "      <td>33.394737</td>\n",
       "      <td>110.407895</td>\n",
       "      <td>41923.741228</td>\n",
       "      <td>3.428836</td>\n",
       "      <td>0.570175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.302293</td>\n",
       "      <td>1.585514</td>\n",
       "      <td>15.697930</td>\n",
       "      <td>53.786536</td>\n",
       "      <td>27406.768360</td>\n",
       "      <td>2.153102</td>\n",
       "      <td>0.496140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1617.000000</td>\n",
       "      <td>0.010048</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>15911.500000</td>\n",
       "      <td>1.699408</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>40987.500000</td>\n",
       "      <td>3.208673</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>151.250000</td>\n",
       "      <td>58537.000000</td>\n",
       "      <td>5.261184</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>182058.000000</td>\n",
       "      <td>10.871566</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  num_coffeeBags_per_year  spent_last_week  spent_last_month  \\\n",
       "count  228.000000               228.000000       228.000000        228.000000   \n",
       "mean    31.802632                 2.960526        33.394737        110.407895   \n",
       "std     14.302293                 1.585514        15.697930         53.786536   \n",
       "min     16.000000                 0.000000         0.000000          0.000000   \n",
       "25%     22.000000                 2.000000        25.750000         65.000000   \n",
       "50%     25.000000                 3.000000        37.000000        113.500000   \n",
       "75%     39.000000                 4.000000        44.000000        151.250000   \n",
       "max     67.000000                 5.000000        62.000000        210.000000   \n",
       "\n",
       "              salary    Distance      Online  \n",
       "count     228.000000  228.000000  228.000000  \n",
       "mean    41923.741228    3.428836    0.570175  \n",
       "std     27406.768360    2.153102    0.496140  \n",
       "min      1617.000000    0.010048    0.000000  \n",
       "25%     15911.500000    1.699408    0.000000  \n",
       "50%     40987.500000    3.208673    1.000000  \n",
       "75%     58537.000000    5.261184    1.000000  \n",
       "max    182058.000000   10.871566    1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call describe() on Prediction\n",
    "Prediction.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zmN_BPwFDuWO"
   },
   "source": [
    "### 4. Divide the NOPrediction subset into X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "00WtPVYSDuWP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'num_coffeeBags_per_year', 'spent_last_week',\n",
       "       'spent_last_month', 'salary', 'Distance', 'Online', 'Decision'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the names of the columns of NOPrediction\n",
    "NoPrediction.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oPi2gMLfDuWQ"
   },
   "outputs": [],
   "source": [
    "# Let's do our feature selection.\n",
    "# Make a variable called 'features', and a list containing the strings of every column except \"Decision\"\n",
    "features = ['Age', 'Gender', 'num_coffeeBags_per_year', 'spent_last_week',\n",
    "       'spent_last_month', 'salary', 'Distance', 'Online']\n",
    "\n",
    "# Make an explanatory variable called X, and assign it: NoPrediction[features]\n",
    "x = NoPrediction[features]\n",
    "\n",
    "# Make a dependent variable called y, and assign it: NoPrediction.Decision\n",
    "y = NoPrediction.Decision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R-d3XOv8DuWW"
   },
   "source": [
    "### 5. Create dummy variables to deal with categorical inputs\n",
    "One-hot encoding replaces each unique value of a given column with a new column, and puts a 1 in the new column for a given row just if its initial value for the original column matches the new column. Check out [this resource](https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-to-use-it-e3c6186d008f) if you haven't seen one-hot-encoding before. \n",
    "\n",
    "**Note**: We will do this before we do our train/test split as to do it after could mean that some categories only end up in the train or test split of our data by chance and this would then lead to different shapes of data for our `X_train` and `X_test` which could/would cause downstream issues when fitting or predicting using a trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xXHGDZkZDuWW"
   },
   "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>Age</th>\n",
       "      <th>num_coffeeBags_per_year</th>\n",
       "      <th>spent_last_week</th>\n",
       "      <th>spent_last_month</th>\n",
       "      <th>salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Online</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "      <td>42789</td>\n",
       "      <td>0.003168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>119</td>\n",
       "      <td>30563</td>\n",
       "      <td>0.916005</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>14244</td>\n",
       "      <td>0.965881</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>14293</td>\n",
       "      <td>1.036346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>202</td>\n",
       "      <td>91035</td>\n",
       "      <td>1.134851</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  num_coffeeBags_per_year  spent_last_week  spent_last_month  salary  \\\n",
       "0   36                        0               24                73   42789   \n",
       "2   24                        0               39               119   30563   \n",
       "4   24                        0               20                36   14244   \n",
       "5   20                        0               23                28   14293   \n",
       "6   34                        0               55               202   91035   \n",
       "\n",
       "   Distance  Online  Gender_Female  Gender_Male  \n",
       "0  0.003168       0              1            0  \n",
       "2  0.916005       1              0            1  \n",
       "4  0.965881       0              1            0  \n",
       "5  1.036346       1              1            0  \n",
       "6  1.134851       0              1            0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encode all features in X.\n",
    "xd = pd.get_dummies(x)\n",
    "xd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jdn_dHlkDuWS"
   },
   "source": [
    "### 6. Further divide those subsets into train and test subsets for X and y respectively: X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RqbHQza-DuWT"
   },
   "outputs": [],
   "source": [
    "# Call train_test_split on X, y. Make the test_size = 0.25, and random_state = 246\n",
    "x_train, x_test, y_train, y_test = train_test_split(xd, y, test_size = 0.25, random_state = 246)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nqvyCX-6DuWb"
   },
   "source": [
    "# 3. Modelling\n",
    "It's useful to look at the scikit-learn documentation on decision trees https://scikit-learn.org/stable/modules/tree.html before launching into applying them. If you haven't seen them before, take a look at that link, in particular the section `1.10.5.` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RIm9IN5HDuWc"
   },
   "source": [
    "## Model 1: Entropy model - no max_depth\n",
    "\n",
    "We'll give you a little more guidance here, as the Python is hard to deduce, and scikitlearn takes some getting used to.\n",
    "\n",
    "Theoretically, let's remind ourselves of what's going on with a decision tree implementing an entropy model.\n",
    "\n",
    "Ross Quinlan's **ID3 Algorithm** was one of the first, and one of the most basic, to use entropy as a metric.\n",
    "\n",
    "**Entropy** is a measure of how uncertain we are about which category the data-points fall into at a given point in the tree. The **Information gain** of a specific feature with a threshold (such as 'spent_last_month <= 138.0') is the difference in entropy that exists before and after splitting on that feature; i.e., the information we gain about the categories of the data-points by splitting on that feature and that threshold. \n",
    "\n",
    "Naturally, we want to minimize entropy and maximize information gain. Quinlan's ID3 algorithm is designed to output a tree such that the features at each node, starting from the root, and going all the way down to the leaves, have maximial information gain. We want a tree whose leaves have elements that are *homogeneous*, that is, all of the same category. \n",
    "\n",
    "The first model will be the hardest. Persevere and you'll reap the rewards: you can use almost exactly the same code for the other models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8-gelLDgDuWc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Declare a variable called entr_model and use tree.DecisionTreeClassifier. \n",
    "entr_model = tree.DecisionTreeClassifier(criterion = 'entropy')\n",
    "\n",
    "# Call fit() on entr_model\n",
    "entr_model.fit(x_train, y_train)\n",
    "\n",
    "# Call predict() on entr_model with X_test passed to it, and assign the result to a variable y_pred \n",
    "y_pred = entr_model.predict(x_test)\n",
    "\n",
    "# Call Series on our y_pred variable with the following: pd.Series(y_pred)\n",
    "y_pred = pd.Series(y_pred)\n",
    "\n",
    "# Check out entr_model\n",
    "entr_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S4FbHfWnDuWe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we want to visualize the tree\n",
    "dot_data = StringIO()\n",
    "\n",
    "# We can do so with export_graphviz\n",
    "tree.export_graphviz(entr_model, out_file = dot_data, filled = True, rounded = True, special_characters = True, \n",
    "                    feature_names = x_train.columns, class_names = ['NO','YES']) \n",
    "\n",
    "# Alternatively for class_names use entr_model.classes_\n",
    "pl = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(pl.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dFbii4IADuWg"
   },
   "source": [
    "## Model 1: Entropy model - no max_depth: Interpretation and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XRL0klK4DuWh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Entropy - no max depth\n",
      "Accuracy: 0.9915966386554622\n",
      "Balanced accuracy: 0.9878048780487805\n",
      "Precision score for \"Yes\" 0.9873417721518988\n",
      "Precision score for \"No\" 1.0\n",
      "Recall score for \"Yes\" 1.0\n",
      "Recall score for \"No\" 0.975609756097561\n"
     ]
    }
   ],
   "source": [
    "# Run this block for model evaluation metrics \n",
    "print(\"Model Entropy - no max depth\")\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_test,y_pred))\n",
    "print('Precision score for \"Yes\"' , metrics.precision_score(y_test,y_pred, pos_label = \"YES\"))\n",
    "print('Precision score for \"No\"' , metrics.precision_score(y_test,y_pred, pos_label = \"NO\"))\n",
    "print('Recall score for \"Yes\"' , metrics.recall_score(y_test,y_pred, pos_label = \"YES\"))\n",
    "print('Recall score for \"No\"' , metrics.recall_score(y_test,y_pred, pos_label = \"NO\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "icGOPFQ_DuWj"
   },
   "source": [
    "What can you infer from these results? Write your conclusions here:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MHipdpfxDuWk"
   },
   "source": [
    "## Model 2: Gini impurity model - no max_depth\n",
    "\n",
    "Gini impurity, like entropy, is a measure of how well a given feature (and threshold) splits the data into categories.\n",
    "\n",
    "Their equations are similar, but Gini impurity doesn't require logorathmic functions, which can be computationally expensive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FyBljeIpDuWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=44)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a variable called gini_model, and assign it exactly what you assigned entr_model with above, but with the\n",
    "# criterion changed to 'gini'\n",
    "gini_model = tree.DecisionTreeClassifier(criterion = 'gini', random_state = 44)\n",
    "\n",
    "# Call fit() on the gini_model as you did with the entr_model\n",
    "gini_model.fit(x_train, y_train)\n",
    "\n",
    "# Call predict() on the gini_model as you did with the entr_model \n",
    "ypre = gini_model.predict(x_test)\n",
    "\n",
    "# Turn y_pred into a series, as before\n",
    "ypre = pd.Series(ypre)\n",
    "\n",
    "# Check out gini_model\n",
    "gini_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d20SMUmgDuWo"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As before, but make the model name gini_model\n",
    "dot_data = StringIO()\n",
    "tree.export_graphviz(gini_model, out_file = dot_data, filled = True, rounded = True, special_characters = True,\n",
    "                    feature_names = x_train.columns, class_names = ['NO','YES'])\n",
    "\n",
    "# Alternatively for class_names use gini_model.classes_\n",
    "gr2 = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(gr2.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T0RadBy0DuWp"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Gini impurity model\n",
      "Accuracy: 0.9915966386554622\n",
      "Balanced accuracy: 0.9878048780487805\n",
      "Precision score 0.9873417721518988\n",
      "Recall score 0.975609756097561\n"
     ]
    }
   ],
   "source": [
    "# Run this block for model evaluation\n",
    "print(\"Model Gini impurity model\")\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_test,y_pred))\n",
    "print('Precision score' , metrics.precision_score(y_test,y_pred, pos_label = \"YES\"))\n",
    "print('Recall score' , metrics.recall_score(y_test,y_pred, pos_label = \"NO\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LR5LkC_yDuWs"
   },
   "source": [
    "__Q__ How do the results here compare to the previous model? Write your judgements here: \n",
    "\n",
    "__A__ Accuracy is practically identical in both models, and so is recall; yet, precision score is greater un the \"entropy model\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3PctBjTADuWs"
   },
   "source": [
    "## Model 3: Entropy model - max depth 3\n",
    "We're going to try to limit the depth of our decision tree, using entropy first.  \n",
    "\n",
    "As you know, we need to strike a balance with tree depth. \n",
    "\n",
    "Insufficiently deep, and we're not giving the tree the opportunity to spot the right patterns in the training data.\n",
    "\n",
    "Excessively deep, and we're probably going to make a tree that overfits to the training data, at the cost of very high error on the (hitherto unseen) test data. \n",
    "\n",
    "Sophisticated data scientists use methods like random search with cross-validation to systematically find a good depth for their tree. We'll start with picking 3, and see how that goes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cMiPjWClDuWt"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=44)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Made a model as before, but call it entr_model2, and make the max_depth parameter equal to 3. \n",
    "# Execute the fitting, predicting, and Series operations as before\n",
    "entr_model2 = tree.DecisionTreeClassifier(criterion = 'entropy', max_depth = 3, random_state =44)\n",
    "entr_model2.fit(x_train,y_train)\n",
    "y_pred = entr_model2.predict(x_test)\n",
    "y_pred = pd.Series(y_pred)\n",
    "entr_model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IRuw72VMDuWv"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As before, we need to visualize the tree to grasp its nature\n",
    "dot_data = StringIO()\n",
    "tree.export_graphviz(entr_model2, out_file = dot_data, filled = True, rounded = True, special_characters = True,\n",
    "                    feature_names = x_train.columns, class_names = ['NO', 'YES'])\n",
    "\n",
    "# Alternatively for class_names use entr_model2.classes_\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "29f4vmmMDuWy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Entropy model max depth 3\n",
      "Accuracy: 0.907563025210084\n",
      "Balanced accuracy: 0.8658536585365854\n",
      "Precision score for \"Yes\" 0.8764044943820225\n",
      "Recall score for \"No\" 0.7317073170731707\n"
     ]
    }
   ],
   "source": [
    "# Run this block for model evaluation \n",
    "print(\"Model Entropy model max depth 3\")\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_test,y_pred))\n",
    "print('Precision score for \"Yes\"' , metrics.precision_score(y_test,y_pred, pos_label = \"YES\"))\n",
    "print('Recall score for \"No\"' , metrics.recall_score(y_test,y_pred, pos_label = \"NO\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AG7S2YgUDuW4"
   },
   "source": [
    "__Q:__ So our accuracy decreased, but is this certainly an inferior tree to the max depth original tree we did with Model 1? Write your conclusions here:\n",
    "\n",
    "__A:__ Recall and precision are also lower. Yet this tree is simpler than the max depth original tree. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FZTl4W3wDuW4"
   },
   "source": [
    "## Model 4: Gini impurity  model - max depth 3\n",
    "We're now going to try the same with the Gini impurity model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FJK1bkG3DuW5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=1234)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As before, make a variable, but call it gini_model2, and ensure the max_depth parameter is set to 3\n",
    "gini_model2 = tree.DecisionTreeClassifier(criterion ='gini', random_state = 1234, max_depth = 3)\n",
    "\n",
    "# Do the fit, predict, and series transformations as before. \n",
    "gini_model2.fit(x_train, y_train)\n",
    "y_pr = gini_model2.predict(x_test)\n",
    "y_pr = pd.Series(y_pr)\n",
    "gini_model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_UIWhKZBDuW7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "tree.export_graphviz(gini_model2, out_file = dot_data, filled = True, rounded = True,\n",
    "                    special_characters = True, feature_names = x_train.columns, class_names = ['NO','YES'])\n",
    "\n",
    "# Alternatively for class_names use gini_model2.classes_\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R4t7e9-GDuW9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gini impurity  model - max depth 3\n",
      "Accuracy: 0.907563025210084\n",
      "Balanced accuracy: 0.8658536585365854\n",
      "Precision score 0.8764044943820225\n",
      "Recall score 0.7317073170731707\n"
     ]
    }
   ],
   "source": [
    "print(\"Gini impurity  model - max depth 3\")\n",
    "print(\"Accuracy:\", metrics.accuracy_score(y_test,y_pred))\n",
    "print(\"Balanced accuracy:\", metrics.balanced_accuracy_score(y_test,y_pred))\n",
    "print('Precision score' , metrics.precision_score(y_test,y_pred, pos_label = \"YES\"))\n",
    "print('Recall score' , metrics.recall_score(y_test,y_pred, pos_label = \"NO\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UzGThBXSDuW-"
   },
   "source": [
    "Now this is an elegant tree. Its accuracy might not be the highest, but it's still the best model we've produced so far. Why is that? Write your answer here: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jS1niFlCDuW_"
   },
   "source": [
    "# 4. Evaluating and concluding\n",
    "## 4a. How many customers will buy Hidden Farm coffee? \n",
    "Let's first ascertain how many loyal customers claimed, in the survey, that they will purchase the Hidden Farm coffee. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QPHXnHZvDuXC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YES    303\n",
       "NO     171\n",
       "Name: Decision, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call value_counts() on the 'Decision' column of the original coffeeData\n",
    "coffeeData.Decision.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kw_5g_ZEDuXD"
   },
   "source": [
    "Let's now determine the number of people that, according to the model, will be willing to buy the Hidden Farm coffee. \n",
    "1. First we subset the Prediction dataset into `new_X` considering all the variables except `Decision` \n",
    "2. Use that dataset to predict a new variable called `potential_buyers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>num_coffeeBags_per_year</th>\n",
       "      <th>spent_last_week</th>\n",
       "      <th>spent_last_month</th>\n",
       "      <th>salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Online</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>164</td>\n",
       "      <td>74035</td>\n",
       "      <td>0.520906</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>107</td>\n",
       "      <td>13166</td>\n",
       "      <td>0.932098</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>17425</td>\n",
       "      <td>1.193188</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  num_coffeeBags_per_year  spent_last_week  spent_last_month  \\\n",
       "1   24    Male                        0               44               164   \n",
       "3   20    Male                        0               30               107   \n",
       "7   24  Female                        0               20                34   \n",
       "\n",
       "   salary  Distance  Online Decision  \n",
       "1   74035  0.520906       0      NaN  \n",
       "3   13166  0.932098       1      NaN  \n",
       "7   17425  1.193188       0      NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mwypR8ymDuXE"
   },
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>num_coffeeBags_per_year</th>\n",
       "      <th>spent_last_week</th>\n",
       "      <th>spent_last_month</th>\n",
       "      <th>salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Online</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>164</td>\n",
       "      <td>74035</td>\n",
       "      <td>0.520906</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>107</td>\n",
       "      <td>13166</td>\n",
       "      <td>0.932098</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>17425</td>\n",
       "      <td>1.193188</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>153</td>\n",
       "      <td>84803</td>\n",
       "      <td>1.655096</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>122</td>\n",
       "      <td>42338</td>\n",
       "      <td>1.714179</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender  num_coffeeBags_per_year  spent_last_week  spent_last_month  \\\n",
       "1    24    Male                        0               44               164   \n",
       "3    20    Male                        0               30               107   \n",
       "7    24  Female                        0               20                34   \n",
       "11   24  Female                        0               40               153   \n",
       "12   21  Female                        0               38               122   \n",
       "\n",
       "    salary  Distance  Online  \n",
       "1    74035  0.520906       0  \n",
       "3    13166  0.932098       1  \n",
       "7    17425  1.193188       0  \n",
       "11   84803  1.655096       1  \n",
       "12   42338  1.714179       1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature selection\n",
    "# Make a variable called feature_cols, and assign it a list containing all the column names except 'Decision'\n",
    "new_X = Prediction.drop('Decision', axis = 1)\n",
    "\n",
    "# Make a variable called new_X, and assign it the subset of Prediction, containing just the feature_cols \n",
    "# _ _ _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0ajvbeRSDuXG"
   },
   "outputs": [],
   "source": [
    "# Call get_dummies() on the Pandas object pd, with new_X plugged in, to one-hot encode all features in the training set\n",
    "new_X = pd.get_dummies(new_X)\n",
    "\n",
    "# Make a variable called potential_buyers, and assign it the result of calling predict() on a model of your choice; \n",
    "# don't forget to pass new_X to predict()\n",
    "potential_buyers = gini_model2.predict(new_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-MO_KWT9DuXI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['NO', 'YES'], dtype=object), array([ 45, 183]))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's get the numbers of YES's and NO's in the potential buyers \n",
    "# Call unique() on np, and pass potential_buyers and return_counts=True \n",
    "np.unique(potential_buyers, return_counts = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3CB7HbiTDuXJ"
   },
   "source": [
    "The total number of potential buyers is 303 + 183 = 486"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9EGFQcTuDuXK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of surveyed people 702\n"
     ]
    }
   ],
   "source": [
    "# Print the total number of surveyed people \n",
    "print('Total number of surveyed people', coffeeData.Age.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "22_KRMX4DuXM"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6923076923076923"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's calculate the proportion of buyers\n",
    "486 / 702"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xLMCDMpmDuXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.23"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the percentage of people who want to buy the Hidden Farm coffee, by our model \n",
    "round(100*486/702, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9bRDyi6rDuXQ"
   },
   "source": [
    "## 4b. Decision\n",
    "Remember how you thought at the start: if more than 70% of the interviewed customers are likely to buy the Hidden Farm coffee, you will strike the deal with the local Hidden Farm farmers and sell the coffee. Otherwise, you won't strike the deal and the Hidden Farm coffee will remain in legends only. Well now's crunch time. Are you going to go ahead with that idea? If so, you won't be striking the deal with the Chinese farmers. \n",
    "\n",
    "They're called `decision trees`, aren't they? So where's the decision? What should you do? (Cue existential cat emoji). \n",
    "\n",
    "Ultimately, though, we can't write an algorithm to actually *make the business decision* for us. This is because such decisions depend on our values, what risks we are willing to take, the stakes of our decisions, and how important it us for us to *know* that we will succeed. What are you going to do with the models you've made? Are you going to risk everything, strike the deal with the *Hidden Farm* farmers, and sell the coffee? \n",
    "\n",
    "The philosopher of language Jason Stanley once wrote that the number of doubts our evidence has to rule out in order for us to know a given proposition depends on our stakes: the higher our stakes, the more doubts our evidence has to rule out, and therefore the harder it is for us to know things. We can end up paralyzed in predicaments; sometimes, we can act to better our situation only if we already know certain things, which we can only if our stakes were lower and we'd *already* bettered our situation. \n",
    "\n",
    "Data science and machine learning can't solve such problems. But what it can do is help us make great use of our data to help *inform* our decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "crWWrpdox8i-"
   },
   "source": [
    "## 5. Random Forest\n",
    "You might have noticed an important fact about decision trees. Each time we run a given decision tree algorithm to make a prediction (such as whether customers will buy the Hidden Farm coffee) we will actually get a slightly different result. This might seem weird, but it has a simple explanation: machine learning algorithms are by definition ***stochastic***, in that their output is at least partly determined by randomness. \n",
    "\n",
    "To account for this variability and ensure that we get the most accurate prediction, we might want to actually make lots of decision trees, and get a value that captures the centre or average of the outputs of those trees. Luckily, there's a method for this, known as the ***Random Forest***. \n",
    "\n",
    "Essentially, Random Forest involves making lots of trees with similar properties, and then performing summary statistics on the outputs of those trees to reach that central value. Random forests are hugely powerful classifers, and they can improve predictive accuracy and control over-fitting. \n",
    "\n",
    "Why not try to inform your decision with random forest? You'll need to make use of the RandomForestClassifier function within the sklearn.ensemble module, found [here](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fI4fiVWq0IH9"
   },
   "source": [
    "### 5a. Import necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vSRTnHnD0D-O"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jKmFvvjb0WB9"
   },
   "source": [
    "### 5b. Model\n",
    "You'll use your X_train and y_train variables just as before.\n",
    "\n",
    "You'll then need to make a variable (call it firstRFModel) to store your new Random Forest model. You'll assign this variable the result of calling RandomForestClassifier().\n",
    "\n",
    "Then, just as before, you'll call fit() on that firstRFModel variable, and plug in X_train and y_train.\n",
    "\n",
    "Finally, you should make a variable called y_pred, and assign it the result of calling the predict() method on your new firstRFModel, with the X_test data passed to it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OQEeTiRG1aSm"
   },
   "outputs": [],
   "source": [
    "# Plug in appropriate max_depth and random_state parameters \n",
    "firstRFModel = RandomForestClassifier(max_depth = 3, random_state = 44)\n",
    "\n",
    "# Model and fit\n",
    "firstRFModel.fit(x_train, y_train)\n",
    "ypred = firstRFModel.predict(new_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['NO', 'YES'], dtype=object), array([ 38, 190]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(ypred, return_counts = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.22792022792022"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*(303 + 190) / 702"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sCIt6pyn1zpb"
   },
   "source": [
    "### 5c. Revise conclusion\n",
    "\n",
    "Has your conclusion changed? Or is the result of executing random forest the same as your best model reached by a single decision tree? \n",
    "\n",
    "We can see tha"
   ]
  }
 ],
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