Classification_EdTech_Lead_Conversion_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "34665454",
   "metadata": {},
   "source": [
    "# Decision Trees and Random Forest Project: Predicting Potential Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd38571",
   "metadata": {},
   "source": [
    "-------------------------------\n",
    "## Context\n",
    "-------------------------------\n",
    "\n",
    "The EdTech industry has experienced tremendous growth in the past decade. In fact, a recent forecast predicts that the Online Education market will be worth $286.62 billion by 2023, with a compound annual growth rate of 10.26% from 2018 to 2023. The rise of online education has been facilitated by several key features, including ease of information sharing, personalized learning experiences, and transparency of assessment, making it a preferred alternative to traditional education.\n",
    "\n",
    "The rapid growth of online education has led to the emergence of many new companies in the industry. With the availability and ease of use of digital marketing resources, these companies can reach a wider audience with their offerings. Customers who express interest in these offerings are referred to as \"leads.\" EdTech companies can obtain leads from various sources, such as:\n",
    "\n",
    "-- social media & other online platforms\n",
    "-- website browsing & brochure download\n",
    "-- email connection - request for more info\n",
    "\n",
    "Once these leads are obtained, the company focuses on nurturing them to convert them into paid customers. A representative from the organization reaches out to the lead by phone or email to provide further information and details about the product or service.\n",
    "\n",
    "-------------------------------\n",
    "## Objective\n",
    "-------------------------------\n",
    "ExtraaLearn is an initial stage startup that offers programs on cutting-edge technologies to students and professionals to help them upskill/reskill. With a large number of leads being generated on a regular basis, one of the issues faced by ExtraaLearn is to identify which of the leads are more likely to convert so that they can allocate the resources accordingly. You, as a data scientist at ExtraaLearn, have been provided the leads data to:\n",
    "* Analyze and build an ML model to help identify which leads are more likely to convert to paid customers. \n",
    "* Find the factors driving the lead conversion process.\n",
    "* Create a profile of the leads which are likely to convert.\n",
    "--------------------------\n",
    "## Data Dictionary\n",
    "--------------------------\n",
    "\n",
    "* **ID:** ID of the lead\n",
    "* **age:** Age of the lead\n",
    "* **current_occupation:** Current occupation of the lead. Values include 'Professional', 'Unemployed', and 'Student'\n",
    "* **first_interaction:** How did the lead first interact with ExtraaLearn? Values include 'Website' and 'Mobile App'\n",
    "* **profile_completed:** What percentage of the profile has been filled by the lead on the website/mobile app? Values include Low - (0-50%), Medium - (50-75%), High (75-100%)\n",
    "* **website_visits:** The number of times a lead has visited the website\n",
    "* **time_spent_on_website:** Total time (seconds) spent on the website.\n",
    "* **page_views_per_visit:** Average number of pages on the website viewed during the visits\n",
    "* **last_activity:** Last interaction between the lead and ExtraaLearn \n",
    "    * **Email Activity:** Seeking details about the program through email, Representative shared information with a lead like a brochure of the program, etc.\n",
    "    * **Phone Activity:** Had a phone conversation with a representative, had a conversation over SMS with a representative, etc.\n",
    "    * **Website Activity:** Interacted on live chat with a representative, updated profile on the website, etc.\n",
    "\n",
    "* **print_media_type1:** Flag indicating whether the lead had seen the ad of ExtraaLearn in the Newspaper\n",
    "* **print_media_type2:** Flag indicating whether the lead had seen the ad of ExtraaLearn in the Magazine\n",
    "* **digital_media:** Flag indicating whether the lead had seen the ad of ExtraaLearn on the digital platforms\n",
    "* **educational_channels:** Flag indicating whether the lead had heard about ExtraaLearn in the education channels like online forums, discussion threads, educational websites, etc.\n",
    "* **referral:** Flag indicating whether the lead had heard about ExtraaLearn through reference.\n",
    "* **status:** Flag indicating whether the lead was converted to a paid customer or not. The class 1 represents the paid customer and class 0 represents the unpaid customer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70119513",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Libraries for data manipulation and visualization\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Algorithms to use\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Metrics to evaluate the model\n",
    "from sklearn.metrics import confusion_matrix, classification_report, recall_score\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "# For hyperparameter tuning\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a7eccda",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = pd.read_csv(\"ExtraaLearn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a49f9899",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copying data to another variable to avoid any changes to the original data\n",
    "data = learn.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76064b32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4612 entries, 0 to 4611\n",
      "Data columns (total 15 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   ID                     4612 non-null   object \n",
      " 1   age                    4612 non-null   int64  \n",
      " 2   current_occupation     4612 non-null   object \n",
      " 3   first_interaction      4612 non-null   object \n",
      " 4   profile_completed      4612 non-null   object \n",
      " 5   website_visits         4612 non-null   int64  \n",
      " 6   time_spent_on_website  4612 non-null   int64  \n",
      " 7   page_views_per_visit   4612 non-null   float64\n",
      " 8   last_activity          4612 non-null   object \n",
      " 9   print_media_type1      4612 non-null   object \n",
      " 10  print_media_type2      4612 non-null   object \n",
      " 11  digital_media          4612 non-null   object \n",
      " 12  educational_channels   4612 non-null   object \n",
      " 13  referral               4612 non-null   object \n",
      " 14  status                 4612 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(10)\n",
      "memory usage: 540.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>current_occupation</th>\n",
       "      <th>first_interaction</th>\n",
       "      <th>profile_completed</th>\n",
       "      <th>website_visits</th>\n",
       "      <th>time_spent_on_website</th>\n",
       "      <th>page_views_per_visit</th>\n",
       "      <th>last_activity</th>\n",
       "      <th>print_media_type1</th>\n",
       "      <th>print_media_type2</th>\n",
       "      <th>digital_media</th>\n",
       "      <th>educational_channels</th>\n",
       "      <th>referral</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT001</td>\n",
       "      <td>57</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Website</td>\n",
       "      <td>High</td>\n",
       "      <td>7</td>\n",
       "      <td>1639</td>\n",
       "      <td>1.861</td>\n",
       "      <td>Website Activity</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT002</td>\n",
       "      <td>56</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Mobile App</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>0.320</td>\n",
       "      <td>Website Activity</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT003</td>\n",
       "      <td>52</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Website</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>330</td>\n",
       "      <td>0.074</td>\n",
       "      <td>Website Activity</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXT004</td>\n",
       "      <td>53</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Website</td>\n",
       "      <td>High</td>\n",
       "      <td>4</td>\n",
       "      <td>464</td>\n",
       "      <td>2.057</td>\n",
       "      <td>Website Activity</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EXT005</td>\n",
       "      <td>23</td>\n",
       "      <td>Student</td>\n",
       "      <td>Website</td>\n",
       "      <td>High</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>16.914</td>\n",
       "      <td>Email Activity</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  age current_occupation first_interaction profile_completed  \\\n",
       "0  EXT001   57         Unemployed           Website              High   \n",
       "1  EXT002   56       Professional        Mobile App            Medium   \n",
       "2  EXT003   52       Professional           Website            Medium   \n",
       "3  EXT004   53         Unemployed           Website              High   \n",
       "4  EXT005   23            Student           Website              High   \n",
       "\n",
       "   website_visits  time_spent_on_website  page_views_per_visit  \\\n",
       "0               7                   1639                 1.861   \n",
       "1               2                     83                 0.320   \n",
       "2               3                    330                 0.074   \n",
       "3               4                    464                 2.057   \n",
       "4               4                    600                16.914   \n",
       "\n",
       "      last_activity print_media_type1 print_media_type2 digital_media  \\\n",
       "0  Website Activity               Yes                No           Yes   \n",
       "1  Website Activity                No                No            No   \n",
       "2  Website Activity                No                No           Yes   \n",
       "3  Website Activity                No                No            No   \n",
       "4    Email Activity                No                No            No   \n",
       "\n",
       "  educational_channels referral  status  \n",
       "0                   No       No       1  \n",
       "1                  Yes       No       0  \n",
       "2                   No       No       0  \n",
       "3                   No       No       1  \n",
       "4                   No       No       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.head(), data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed57750",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "* `age`, `website_visits`, `time_spent_on_website`, `page_views_per_visit`, and `status` are of numeric type while rest of the columns are of object type.\n",
    "\n",
    "* There are **no null values** in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "965a1c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicate values\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b73c81ae",
   "metadata": {},
   "source": [
    "- There are **no duplicate values** in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1160a9",
   "metadata": {},
   "source": [
    "## **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e070f54e",
   "metadata": {},
   "source": [
    "### **Univariate Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34b631c9",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>46.201214</td>\n",
       "      <td>13.161454</td>\n",
       "      <td>18.0</td>\n",
       "      <td>36.00000</td>\n",
       "      <td>51.000</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>63.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>website_visits</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>3.566782</td>\n",
       "      <td>2.829134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spent_on_website</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>724.011275</td>\n",
       "      <td>743.828683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>148.75000</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1336.75000</td>\n",
       "      <td>2537.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>page_views_per_visit</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>3.026126</td>\n",
       "      <td>1.968125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.07775</td>\n",
       "      <td>2.792</td>\n",
       "      <td>3.75625</td>\n",
       "      <td>18.434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>0.298569</td>\n",
       "      <td>0.457680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count        mean         std   min        25%  \\\n",
       "age                    4612.0   46.201214   13.161454  18.0   36.00000   \n",
       "website_visits         4612.0    3.566782    2.829134   0.0    2.00000   \n",
       "time_spent_on_website  4612.0  724.011275  743.828683   0.0  148.75000   \n",
       "page_views_per_visit   4612.0    3.026126    1.968125   0.0    2.07775   \n",
       "status                 4612.0    0.298569    0.457680   0.0    0.00000   \n",
       "\n",
       "                           50%         75%       max  \n",
       "age                     51.000    57.00000    63.000  \n",
       "website_visits           3.000     5.00000    30.000  \n",
       "time_spent_on_website  376.000  1336.75000  2537.000  \n",
       "page_views_per_visit     2.792     3.75625    18.434  \n",
       "status                   0.000     1.00000     1.000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the statistics summary for the dataset\n",
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791da3a5",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- The average age of ExtraaLearn leads is 46 years, with a median of 51. This suggests that there is potential interest from a broad range of age groups.\n",
    "- Website visits by leads vary greatly, from 0 to 30 with an average of around 3. It will be worth investigating if more website visits correlate with a higher chance of conversion to paid customers, as there may be many outliers.\n",
    "- Time spent on the website ranges from 0 to 40 minutes, with at least half of the leads spending less than 6 minutes on the site.\n",
    "- On average, leads view 3 pages per visit, with half of them viewing 2-4 pages. Outliers are expected in this category as well.\n",
    "- The conversion rate for leads to paid customers is around 30%, meaning that 70% of leads do not convert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01f6c840",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXT001     0.000217\n",
      "EXT2884    0.000217\n",
      "EXT3080    0.000217\n",
      "EXT3079    0.000217\n",
      "EXT3078    0.000217\n",
      "             ...   \n",
      "EXT1537    0.000217\n",
      "EXT1536    0.000217\n",
      "EXT1535    0.000217\n",
      "EXT1534    0.000217\n",
      "EXT4612    0.000217\n",
      "Name: ID, Length: 4612, dtype: float64\n",
      "--------------------------------------------------\n",
      "Professional    0.567216\n",
      "Unemployed      0.312446\n",
      "Student         0.120338\n",
      "Name: current_occupation, dtype: float64\n",
      "--------------------------------------------------\n",
      "Website       0.551171\n",
      "Mobile App    0.448829\n",
      "Name: first_interaction, dtype: float64\n",
      "--------------------------------------------------\n",
      "High      0.490893\n",
      "Medium    0.485906\n",
      "Low       0.023200\n",
      "Name: profile_completed, dtype: float64\n",
      "--------------------------------------------------\n",
      "Email Activity      0.493929\n",
      "Phone Activity      0.267563\n",
      "Website Activity    0.238508\n",
      "Name: last_activity, dtype: float64\n",
      "--------------------------------------------------\n",
      "No     0.892238\n",
      "Yes    0.107762\n",
      "Name: print_media_type1, dtype: float64\n",
      "--------------------------------------------------\n",
      "No     0.94948\n",
      "Yes    0.05052\n",
      "Name: print_media_type2, dtype: float64\n",
      "--------------------------------------------------\n",
      "No     0.885733\n",
      "Yes    0.114267\n",
      "Name: digital_media, dtype: float64\n",
      "--------------------------------------------------\n",
      "No     0.847138\n",
      "Yes    0.152862\n",
      "Name: educational_channels, dtype: float64\n",
      "--------------------------------------------------\n",
      "No     0.979835\n",
      "Yes    0.020165\n",
      "Name: referral, dtype: float64\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# making a list of all categorical variables\n",
    "cat_col = list(data.select_dtypes(\"object\").columns)\n",
    "\n",
    "# printing count of each unique value in each categorical column\n",
    "for column in cat_col:\n",
    "    print(data[column].value_counts(normalize = True))\n",
    "    print(\"-\" * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d473d9a",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "* Most of the leads are working professionals.\n",
    "* As expected, the majority of the leads interacted with ExtraaLearn from the website.\n",
    "* Almost an equal percentage of profile completions are categorized as high and medium that is 49.1% and 48.6%, respectively. Only **2.3%** of the profile completions are categorized as low.\n",
    "* Approx 49.4% of the leads had their last activity over email, followed by 26.8% having phone activity. This implies that the majority of the leads prefer to communicate via email.\n",
    "* We can observe that each ID has an equal percentage of values. Let's check the number of unique values in the ID column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "860f273d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4612"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of unique values\n",
    "data[\"ID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79299d15",
   "metadata": {},
   "source": [
    "* All the values in the ID column are unique.\n",
    "* We can drop this column as it would not add value to our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d85efad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping ID column\n",
    "data.drop([\"ID\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e44cf7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "ax = sns.countplot(x = 'status', data = data)\n",
    "\n",
    "# Annotating the exact count on the top of the bar for each category \n",
    "for p in ax.patches:\n",
    "    ax.annotate('{:.1f}'.format(p.get_height()), (p.get_x(), p.get_height() + 0.35))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b743b18",
   "metadata": {},
   "source": [
    "- The above plot shows that number of leads converted are significantly less than number of leads not converted which can be expected.\n",
    "- The plot indicates that **~30%** (1377/4612) of leads have been converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aef9006c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n",
      "Skew : -0.72\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "website_visits\n",
      "Skew : 2.16\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_spent_on_website\n",
      "Skew : 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "page_views_per_visit\n",
      "Skew : 1.27\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['age', 'website_visits', 'time_spent_on_website', 'page_views_per_visit']:\n",
    "    print(col)\n",
    "    \n",
    "    print('Skew :',round(data[col].skew(), 2))\n",
    "    \n",
    "    plt.figure(figsize = (15, 4))\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    \n",
    "    data[col].hist(bins = 10, grid = False)\n",
    "    \n",
    "    plt.ylabel('count')\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    \n",
    "    sns.boxplot(x = data[col])\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "716fe5a5",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- **All the distributions plotted are significantly skewed**, none of them tends to follow normal distribution.\n",
    "- Age distribution is right-skewed - that basically means **the majority of the leads belons to older age groups (50.. 60 y.o.)** while the average age of the lead is approx. 46 as mentioned above. It will be useful to check whether the core of this older audience is unemployed, or not (to understand the motivation behind this statistics).\n",
    "- **75% of the leads visited the website less than 5 times**. The distribution of website visits is left-skewed (the most skewed out of all the distributions analyzed, skew: 2.16) which implies the majority of the leads tend to visit the website less. Still, there is a huge numbers of leads having visited the website up to 30 times - all the leads who visited the website over approx. 10 times are considered to be outliers (IQR = 5 - 2 = 3, Q3 + 1.5 * IQR = 5 + 1.5 * 3 = 9.5).\n",
    "- The distribution of time spent on the website is also skewed to the left: median value ~ 6 minutes, **75% of the leads spent less than 22 minutes (1 340 sec) on the website while the rest 25% spent up to ~40 minutes**. Still, there are no outliers here.\n",
    "- In terms of page views there is also left-skewed distribution which is explainable: the majority of the leads do not dive deep when exploring the website. **The median nu. of pages per visit is ~3**. There are **numerous cases when page views quantity exceeds ~5, those will be treated as outliers** which can be seen from the boxplot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d7c33a",
   "metadata": {},
   "source": [
    "### **Bivariate Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72a81019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "sns.countplot(x = 'current_occupation', hue = 'status', data = data)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388a2a80",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- The plot shows that working professional leads are more likely to opt for a course offered by the organization and the students are least likely to be converted. \n",
    "- This shows that the currently offered programs are more oriented toward working professionals or unemployed personnel. The programs might be suitable for the working professionals who might want to transition to a new role or take up more responsibility in their current role. And also focused on skills that are in high demand making it more suitable for working professionals or currently unemployed leads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fa4dc95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "\n",
    "sns.boxplot(data[\"current_occupation\"], data[\"age\"])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "902235d2",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Professional</th>\n",
       "      <td>2616.0</td>\n",
       "      <td>49.347477</td>\n",
       "      <td>9.890744</td>\n",
       "      <td>25.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student</th>\n",
       "      <td>555.0</td>\n",
       "      <td>21.144144</td>\n",
       "      <td>2.001114</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unemployed</th>\n",
       "      <td>1441.0</td>\n",
       "      <td>50.140180</td>\n",
       "      <td>9.999503</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count       mean       std   min   25%   50%   75%   max\n",
       "current_occupation                                                           \n",
       "Professional        2616.0  49.347477  9.890744  25.0  42.0  54.0  57.0  60.0\n",
       "Student              555.0  21.144144  2.001114  18.0  19.0  21.0  23.0  25.0\n",
       "Unemployed          1441.0  50.140180  9.999503  32.0  42.0  54.0  58.0  63.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby([\"current_occupation\"])[\"age\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf446d0",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "* The range of age for students is 18 to 25 years.\n",
    "* The range of age for professionals is 25 to 60 years.\n",
    "* The range of age for unemployed leads is 32 to 63 years.\n",
    "* The average age of working professionals and unemployed leads is almost 50 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505f66dc",
   "metadata": {},
   "source": [
    "**It's important for the company to make a strong and convincing first impression on leads. We should investigate whether the channels used for the initial interaction have any effect on lead conversion.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f849b7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "sns.countplot(x = 'first_interaction', hue = 'status', data = data)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be861c9",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "* The website seems to be doing a good job as compared to mobile app as there is a huge difference in the number of conversions of the leads who first interacted with the company through website and those who interacted through mobile application.\n",
    "* Majority of the leads who interacted through websites were converted to paid customers, while only a small number of leads, who interacted through mobile app, converted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fc75f4",
   "metadata": {},
   "source": [
    "**We observed earlier that some leads spend more time on websites than others. Let's analyze if spending more time on websites results in conversion.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "091282e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "\n",
    "sns.boxplot(data = data, x = \"status\", y = \"time_spent_on_website\") # Write your code here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c6c977",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- We can observe huge difference of median time spent on website between leads converted to paid customers (~ 750 sec) and not (~ 450 sec). Those converted spent much more time on the website.\n",
    "- Thus, we can conclude **more time spent on website means higher probability of a lead to be converted to a paid customer**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04a0f82",
   "metadata": {},
   "source": [
    "**People browsing the website or the mobile app are generally required to create a profile by sharing their details before they can access more information. Let's see if the profile completion level has an impact on lead coversion**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a435d437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "sns.countplot(x = 'profile_completed', hue = 'status', data = data)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9935752b",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "* The leads whose profile completion level is high converted more in comparison to other levels of profile completion.\n",
    "* The medium and low levels of profile completion saw comparatively very less conversions.\n",
    "* The high level of profile completion might indicate a lead's intent to pursue the course which results in high conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4dbcf03",
   "metadata": {},
   "source": [
    "**Referrals from a converted lead can be a good source of income with a very low cost of advertisement. Let's see how referrals impact lead conversion status.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d63f3f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "sns.countplot(x = 'referral', hue = 'status', data = data)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b36cfe",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "* There are a very less number of referrals but the conversion is high. \n",
    "* Company should try to get more leads through referrals by promoting rewards for existing customer base when they refer someone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6cd5b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 7))\n",
    "\n",
    "sns.heatmap(data.corr(), annot = True, fmt = '.2f')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a40dc06",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- There are no strong correlations observed, except for one only: time spent on website and status (0.3). This implies **the higher the time spent on website is the higher the probability of conversion lead-to-paid customer may be expected**. \n",
    "- Slightly positive correlation (0.12) is also observed between age and status. Basically, the older the lead is the higher chances for their conversion are. Still, this figure is not that solid to make this kind of conclusion. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb89a481",
   "metadata": {},
   "source": [
    "## **Data preparation for modeling**\n",
    "\n",
    "- We want to predict which lead is more likely to be converted.\n",
    "- Before we proceed to build a model, we'll have to encode categorical features.\n",
    "- We'll split the data into train and test sets to be able to evaluate the model that we build on the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "428975a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating the target variable and other variables\n",
    "X = data.drop(columns = 'status')\n",
    "\n",
    "Y = data['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a311b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating dummy variables, drop_first=True is used to avoid redundant variables\n",
    "X = pd.get_dummies(X, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44c0dddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.30, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99c2ac76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the training set:  (3228, 16)\n",
      "Shape of the test set:  (1384, 16)\n",
      "Percentage of classes in the training set:\n",
      "0    0.704151\n",
      "1    0.295849\n",
      "Name: status, dtype: float64\n",
      "Percentage of classes in the test set:\n",
      "0    0.695087\n",
      "1    0.304913\n",
      "Name: status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of the training set: \", X_train.shape)   \n",
    "\n",
    "print(\"Shape of the test set: \", X_test.shape)\n",
    "\n",
    "print(\"Percentage of classes in the training set:\")\n",
    "\n",
    "print(y_train.value_counts(normalize = True))\n",
    "\n",
    "print(\"Percentage of classes in the test set:\")\n",
    "\n",
    "print(y_test.value_counts(normalize = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aeb8482",
   "metadata": {},
   "source": [
    "## **Building Classification Models**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69603e92",
   "metadata": {},
   "source": [
    "**Let's determine the appropriate model evaluation criterion for the problem at hand before proceeding with model training.**\n",
    "\n",
    "**Criteria for Model Evaluation**\n",
    "\n",
    "The model may make the following incorrect predictions:\n",
    "\n",
    "- Predicting that a lead will not convert to a paid customer when, in reality, the lead would have converted.\n",
    "- Predicting that a lead will convert to a paid customer when, in reality, the lead would not have converted.\n",
    "\n",
    "**Which case is more crucial?**\n",
    "\n",
    "Losing a potential customer is a significant loss for the organization. Thus, predicting that a lead will not convert and the lead would have converted is more important than predicting that a lead will convert but it does not.\n",
    "\n",
    "**How to minimize losses?**\n",
    "\n",
    "The company would want to **maximize the Recall score** to minimize False Negatives. The higher the Recall score, the greater the chances of reducing the number of missed potential customers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "efc4dc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to print the classification report and get confusion matrix in a proper format\n",
    "\n",
    "def metrics_score(actual, predicted):\n",
    "    print(classification_report(actual, predicted))\n",
    "    \n",
    "    cm = confusion_matrix(actual, predicted)\n",
    "    \n",
    "    plt.figure(figsize = (8, 5))\n",
    "    \n",
    "    sns.heatmap(cm, annot = True,  fmt = '.2f', xticklabels = ['Not Converted', 'Converted'], yticklabels = ['Not Converted', 'Converted'])\n",
    "    \n",
    "    plt.ylabel('Actual')\n",
    "    \n",
    "    plt.xlabel('Predicted')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb43d6f",
   "metadata": {},
   "source": [
    "### **Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9878fd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the decision tree classifier on the training data\n",
    "d_tree =  DecisionTreeClassifier(random_state = 7)\n",
    "\n",
    "d_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ff1bd13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2273\n",
      "           1       1.00      1.00      1.00       955\n",
      "\n",
      "    accuracy                           1.00      3228\n",
      "   macro avg       1.00      1.00      1.00      3228\n",
      "weighted avg       1.00      1.00      1.00      3228\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the training data\n",
    "y_pred_train1 = d_tree.predict(X_train)\n",
    "\n",
    "metrics_score(y_train, y_pred_train1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5a2825",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- We can observe that the Decision Tree has given 100% score for all the metrics on the training dataset. The numbers for 'converted' / 'not converted' leads within the dataset corresponds to the actual figures' distribution. This may mean the model is overfitting, thus we need to check if that's the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2789dea4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.86      0.87       962\n",
      "           1       0.69      0.70      0.70       422\n",
      "\n",
      "    accuracy                           0.81      1384\n",
      "   macro avg       0.78      0.78      0.78      1384\n",
      "weighted avg       0.81      0.81      0.81      1384\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the testing data\n",
    "y_pred_test1 = d_tree.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df02ea5",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- **The Decision tree has proven to be overfitting the training data, it doesn't perform well on the test data** (the recall is 0.7 for test dataset while being 1.0 for the training data).\n",
    "- According to the model's precision on the test data a lead will convert with 31% (1 - 0.69) probability while in fact they will not. This implies **wrongly distributed business efforts put behind the leads who have less chances to get actually converted** instead of working with those who are more likely to become paid customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b91a3ca",
   "metadata": {},
   "source": [
    "### **Decision Tree - Hyperparameter Tuning**\n",
    "\n",
    "We will use the class_weight hyperparameter with the value equal to {0: 0.3, 1: 0.7} which is approximately the opposite of the imbalance in the original data. \n",
    "\n",
    "**This would tell the model that 1 is the important class here.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87eedcc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight={0: 0.3, 1: 0.7}, criterion='entropy',\n",
       "                       max_depth=3, min_samples_leaf=5, random_state=7)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier \n",
    "d_tree_tuned = DecisionTreeClassifier(random_state = 7, class_weight = {0: 0.3, 1: 0.7})\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {'max_depth': np.arange(2, 10), \n",
    "              'criterion': ['gini', 'entropy'],\n",
    "              'min_samples_leaf': [5, 10, 20, 25]\n",
    "             }\n",
    "\n",
    "# Type of scoring used to compare parameter combinations - recall score for class 1\n",
    "scorer = metrics.make_scorer(recall_score, pos_label = 1)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(d_tree_tuned, parameters, scoring = scorer, cv = 5)\n",
    "\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the classifier to the best combination of parameters\n",
    "d_tree_tuned = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data\n",
    "d_tree_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ef5ae5",
   "metadata": {},
   "source": [
    "We have tuned the model and fit the tuned model on the training data. Now, **let's check the model performance on the training and testing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7d7d0bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.77      0.85      2273\n",
      "           1       0.62      0.88      0.73       955\n",
      "\n",
      "    accuracy                           0.80      3228\n",
      "   macro avg       0.78      0.83      0.79      3228\n",
      "weighted avg       0.84      0.80      0.81      3228\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdYAAAE9CAYAAAC2mUMbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAuCUlEQVR4nO3dd5wV1f3/8dcbFFCUJr0oqGgssSLRKIrxq2LvitFYI4nBEhMbxq8a8yU/E2OKsQUrlmiIJbZExYaaqIiKUmyoBFc6CGID2f38/pgBrrDtLne4e+++nzzmcWfOlDOzXvezp8w5igjMzMysMJoV+wbMzMzKiQOrmZlZATmwmpmZFZADq5mZWQE5sJqZmRWQA6uZmVkBrVXsG6jJ13M/8HtAVhaG9Duv2LdgttpunXqfsrp2Q37fr91x48zuZ3U12sBqZmZNRFVlse+goBxYzcysuKKq2HdQUA6sZmZWXFUOrGZmZgUTLrGamZkVkEusZmZmBeQSq5mZWQG5V7CZmVkBlVmJ1SMvmZmZFZBLrGZmVlzuvGRmZlY4ft3GzMyskFxiNTMzKyCXWM3MzArIr9uYmZkVkEusZmZmBeQ2VjMzswJyidXMzKyAXGI1MzMrnAh3XjIzMyucMqsK9ljBZmZWXFVV+S91kHSLpNmSJq6UfqakdyRNkvTbnPRhkqak+/bNSd9R0oR039WSVFfeDqxmZlZcUZX/UrfbgEG5CZL2BA4BtomIrYDfpelbAoOBrdJzrpPUPD3temAI0DddvnHN6jiwmplZcVVV5r/UISKeA+avlHw6cEVELE6PmZ2mHwLcExGLI+JDYArQX1I3oE1EvBgRAdwOHFpX3g6sZmZWXNmUWKuzGTBA0suSxkjaKU3vAXyUc1xFmtYjXV85vVbuvGRmZsXVgNdtJA0hqaJdZkREjKjjtLWA9sDOwE7AKEkbA9W1m0Yt6XVmYmZmVlLSIFpXIF1ZBXB/Wq07VlIV0DFN75VzXE9gepres5r0Wrkq2MzMimvNVQX/A/gegKTNgBbAXOAhYLCklpL6kHRSGhsRM4BFknZOewOfADxYVyYusZqZWXFlMPKSpLuBgUBHSRXApcAtwC3pKzhLgBPT0uskSaOAycBSYGisGLXidJIexusA/0qXWjmwmplZcWUQWCPi2Bp2HV/D8cOB4dWkjwO2zidvB1YzMysqD2loZmZWSB6E38zMrIDKbKzgggdWSYfXtj8i7i90nmZmVsJcYq3TQelnZ+C7wNPp9p7As4ADq5mZreASa+0i4mQASY8AW6bvAZGOuXhtofMzM7MS5xJrvfVeFlRTs0jGaTQzM1vBJdZ6e1bS48DdJGMrDgaeyTA/MzMrRS6x1k9EnCHpMGD3NGlERDyQVX5mZlaiHFjz8hqwKCKelLSupPUjYlHGeZqZWSkps6rgzAbhl3QacC/wlzSpB8kAyGZmZitUVeW/NGJZzm4zFNgV+BQgIt4jeQXHzMxshTU3u80akWVV8OKIWJLMtAOS1qIeE8SamVkT08hLoPnKssQ6RtJFwDqS9gb+DjycYX5mZmZFl2VgvRCYA0wAfgT8MyJ+kWF+ZmZWilwVXG9nRsSfgBuXJUg6O00zMzNLuCq43k6sJu2kDPMzM7NSVGa9grOY3eZY4PvAxpIeytm1PjCv0PmZmVmJi/Lq15pFVfB/gBlAR+CqnPRFwJsZ5GdmZqWskZdA85XF7Db/lVQBfB4RYwp9fTMzKzMOrHWLiEpJX0hqGxELs8jDzMzKRCPv5ZuvLHsFfwVMkDQa+HxZYkSclWGeZmZWalxirbdH08XMzKxm7rxUPxExUtI6wIYR8U5W+ZiZWYkrsxJrlrPbHASMBx5Lt7db6fUbMzOzsnuPNcsBIi4D+gMLACJiPNAnw/zMzKwUZTCkoaRbJM2WNLGafedKCkkdc9KGSZoi6R1J++ak7yhpQrrvai2bWaYWWQbWpdX0CC6vinQzM1ttURV5L/VwGzBo5URJvYC9gWk5aVsCg4Gt0nOuk9Q83X09MATomy6rXHNlWQbWiZK+DzSX1FfSn0kGjzAzM1shg6rgiHgOmF/Nrj8A5/PNgt4hwD0RsTgiPgSmAP0ldQPaRMSLERHA7cChdeWdZWA9kyT6Lwb+CiwEfpphfmZmVooaUBUsaYikcTnLkLqykXQw8HFEvLHSrh7ARznbFWlaj3R95fRaZfm6zebpNHGeKs7MzGpWv6rdb4iIEcCI+h4vaV2SeLRPdbury6KW9FplWWL9vaS3Jf1K0lYZ5mNmZlaXTUg60L4haSrQE3hNUleSkmivnGN7AtPT9J7VpNcqs8AaEXsCA0kmOx+R9qq6OKv8zMysRK2B120iYkJEdI6I3hHRmyRo7hARM4GHgMGSWkrqQ9JJaWxEzAAWSdo57Q18AvBgXXllWWIlImZGxNXAj0neab0ky/zMzKwEZRBYJd0NvAhsLqlC0qk1HRsRk4BRwGSSsReGRkRluvt04CaSDk3vA/+qK+/M2lglbQEcAxxJMg/rPcDPs8qvqbj417/nuX+PpUP7dvzjzhsA+Pn//j+mTkva1xd99hnrr7ce9428lo9nzOLg7w+h94ZJTcY2W32LS88/ky+/+oqfXfxrKj6eQbNmzRi423c45/RTqs3vxtv/xv2PPE7zZs0Yds7p7PqdHQGY9PZ7XDz893y1eDEDdtmJYT/9MfV4vcusWle+cD1fffYlVVVVVC6t5PKDL+DoYSew3f/0Y+mSpcyeNpObz7uGLz/9gtbt1mPo9efRZ5tN+Pe9z3LnpTdVe83Wbdfj9Gt+RseenZlbMZvrhl7FF58mw5Yf8JPDGHD0XlRVVvHXX97CxOfGr8GntVVkMKRhRBxbx/7eK20PB4ZXc9w4YOt88s6y89KtwN3APhFRZ5201c+h++/N9484mIt+9bvlaVf9atjy9Sv/fCPrtV53+XavHt24b+S1q1zn5GOPoP+O2/L1119z6lnDeP7FVxiwy07fOOb9D//Lv54aw4N33sDsufP54dnDePSem2jevDm/+t01XHrBWWy71bc4/dxLeOGlcaucb5aP3xx7KZ99smj59qQX3uDe395JVWUVR114PAf+5HD+fsWdfL34ax646m56bL4hPTfbsMbr7X/6YUz+zwT+ef0D7H/6YRzwk8P4+xV30n3TnvQ/aDcu3uentOvcgfPuupQL9zyTaOSj+ZS1MvvZZ9nGunNE/MlBtbD6bfdt2rZZv9p9EcFjTz/H/nsPrPUa67RqRf8dtwVg7bXXZovNN2XWnLmrHPf08y+x31570KJFC3p278qGPbsz4a13mTN3Pp9//gXbbb0Fkjh40F48/fyLq/1sZrkmPf8GVZXJL9z3X3+X9l03AGDJl4t5b9zbfL3461rP337vnfj3vc8A8O97n2H7vfsn6fvsxNiHX2DpkqXMrZjN7P/OZOPtNs3wSaxOVZH/0ohlOVbwrpJGS3pX0geSPpT0QVb5Gbz6xkQ2aN+ejXqteM3q4xkzOfKkoZw09DxeHb/KyF58uugzxvz7Zb6z43ar7Js9Zx5du3Ravt2lc0dmz5nLrDlz6dK544r0Th2ZNWdeYR/GmpSI4Nw7LuHSh3/LHsfuvcr+AUftxYRnX8/rmm07tWPhnAUALJyzgDYd2wLQvssGzJ++4vs6f8Y82nfp0PCbt9WXwZCGxZRlVfDNwDnAq0BlHcdaAfxz9LPsv/cey7c7bdCe0fffTru2bZj09nucNexyHrzzBtZr3RqApUsrOf+y33DckQfTq0e3Va4X1byuJVR9uptXbTX8+ohfsGD2J6y/QRvOvfNSZrz/Me+OnQzAgUOPoLKykhf/8VxhMqvmu1pms5aVnkZeAs1Xlr2CF0bEvyJidkTMW7bUdkLuSBo33X53hrdWfpYureTJMf9h0F67L09r0aIF7dq2AWCrb/WlV49uTJ328fL9l/32T2zYszs/OOawaq/ZpVNHZs6as3x71uy5dOq0AV07dWLW7BVVx7PmzKVzxw0K/UjWhCyY/QkAi+Z9ymuPv8zG2yZVs7seMZBt99qREWf/Me9rLpyzgLad2gFJ6fXTucnQ5Z/MnEeH7iu+rx26bcCC2dWNfGdrSlRV5b00ZlkG1mckXSlpF0k7LFtqOyEiRkREv4jo98MTau3QZSt5adzrbLxRT7p2XlF1O/+TBVRWJpUFH308g2kfTV9eMr16xEg+++wLLjz7RzVec8/dduZfT41hyZIlVEyfybSK6Xx7i83o1LED6667Dm9MfIuI4KHHnmLP3XbO9gGtbLVYpyWtWrdavr71gG2peHcaW++xHfv9+FCu/uEVLPlqSd7XHf/kOHY9ck8Adj1yT14f/QoAr48eR/+DdmOtFmvRsWdnOvfuxgfjpxTugSx/ZdbGmmVV8HfSz345aQF8L8M8y955l17BK6+/yYIFn7LXocfzk1N/wBEH7cu/nhzDfv8z8BvHvjp+ItfcdAfN12pO82bNuOS8M2jbZn1mzp7DiJH30GejXhx18pkAHHvEQRx58CCeef4lJr39LmecdgKbbrwR+35vAAcf9yPWat6cX/zsJzRvnkz48L/nnrHidZudd3KPYGuwth3bccaI8wFo3rw5Lz34PBPHjOeKZ69h7RZrc+6dyevv77/+Lrf/IhnB7soXrqfVeuuw1tprsf0+/bnqB5czfUoFJ19xOs/c9QRTJ7zPo9ffz0+u/Tm7H70X86bP4bqfXAXA9Pc+4pVH/sPw0X+icmkld15yY6MvAZW9Rt5mmi9FI21c+HruB43zxszyNKTfecW+BbPVduvU+zLrSfH55cfl/fu+9SV3NdqeHVkOENEWuBRY1ug3Bri8mjlazcysKSuzGoMs21hvARYBR6fLpySDRpiZma3gNtZ62yQijsjZ/qWk8RnmZ2ZmpajM2lizLLF+KWm3ZRuSdgW+zDA/MzMrRS6x1tuPgdvTtlaAT4CTMszPzMxKULn1ys4ssEbEG8C2ktqk259mlZeZmVljUfDAKulnJKMu3QwrAqqkM4HmEfHHQudpZmYlrJFX7eYrixLrKUB1IyyNAF4B/phBnmZmVqocWOsUEbHK+GMRsVieCdvMzFZWZr2CM2ljldQlImatnJZFXmZmVuLKrMSaxes2VwKPStpD0vrpMhB4GPhdBvmZmVkJi6rIe2nMCl5ijYjbJc0BLge2Jhl4fxJwaUT8q9D5mZlZiWvkgTJfmVQFpwHUQdTMzOrm91jNzMwKyCVWMzOzAiqzwJrZWMGS+tQnzczMmraIyHtpzLIchP++atLuzTA/MzMrRRkMwi/pFkmzJU3MSbtS0tuS3pT0gKR2OfuGSZoi6R1J++ak7yhpQrrv6vqMx1DwwCrpW5KOANpKOjxnOQloVej8zMysxGUzu81twKCV0kYDW0fENsC7wDAASVsCg4Gt0nOuk9Q8Ped6YAjQN11WvuYqsmhj3Rw4EGgHHJSTvgg4LYP8zMyshGXxXmpEPCep90ppT+RsvgQcma4fAtwTEYuBDyVNAfpLmgq0iYgXASTdDhxKHW+9ZPEe64PAg5J2WXYzZmZmNSpO56VTgL+l6z1IAu0yFWna1+n6yum1yrKN9aO0Dnu2pFmS7pPUM8P8zMysFFXlv0gaImlczjKkvtlJ+gWwFLhrWVI1h0Ut6bXK8nWbW4G/Akel28enaXtnmKeZmZWYhlQFR8QIklnT8iLpRJLmyr1iRffiCqBXzmE9gelpes9q0muVZYm1c0TcGhFL0+U2oFOG+ZmZWSnKpvPSKiQNAi4ADo6IL3J2PQQMltQyfS20LzA2ImYAiyTtnPYGPgF4sK58sgyscyQdL6l5uhwPzMswPzMzMwAk3Q28CGwuqULSqcA1wPrAaEnjJd0AEBGTgFHAZOAxYGhEVKaXOh24CZgCvE89huvNsir4FJKH+ANJnfR/0jQzM7MVMhgqOCKOrSb55lqOHw4MryZ9HMmEMvWWWWCNiGnAwVld38zMykNjnwYuXwUPrJIuqWV3RMSvCp2nmZmVsPKa3CaTEuvn1aS1Bk4FNgAcWM3MbDmXWOsQEVctW5e0PnA2cDJwD3BVTeeZmVkT5RJr3SR1AH4GHAeMBHaIiE+yyMvMzEpbOLDWTtKVwOEkL+5+OyI+K3QeZmZWRhxY6/RzYDFwMfCLnBl2RNJ5qU0GeZqZWYlyibUOEZHloBNmZlZuHFjNzMwKxyVWMzOzAnJgNTMzKyAHVjMzs0KK6qY9LV0OrGZmVlQusZqZmRVQVLnEamZmVjDlVmL1O6dmZmYF5BKrmZkVVbjzkpmZWeGUW1WwA6uZmRWVOy+ZmZkVUJTXPOcOrGZmVlwusZqZmRWQA6uZmVkBuSrYzMysgMqtxOoBIszMrKgilPdSF0m3SJotaWJOWgdJoyW9l362z9k3TNIUSe9I2jcnfUdJE9J9V0uqM3MHVjMzK6qoyn+ph9uAQSulXQg8FRF9gafSbSRtCQwGtkrPuU5S8/Sc64EhQN90Wfmaq6ixKljSn4Eaa74j4qy6Lm5mZlaXqgxGXoqI5yT1Xin5EGBguj4SeBa4IE2/JyIWAx9KmgL0lzQVaBMRLwJIuh04FPhXbXnX1sY6Lp+HMDMza4g1OKRhl4iYkeQZMyR1TtN7AC/lHFeRpn2drq+cXqsaA2tEjMz3js3MzPLVkM5LkoaQVNEuMyIiRjTwFqq7gaglvVZ19gqW1ImkqLwl0Gr5lSO+V9e5ZmZmdWnI6zZpEM03kM6S1C0trXYDZqfpFUCvnON6AtPT9J7VpNeqPp2X7gLeAvoAvwSmAq/U4zwzM7M6RZXyXhroIeDEdP1E4MGc9MGSWkrqQ9JJaWxabbxI0s5pb+ATcs6pUX3eY90gIm6WdHZEjAHGSBqT79OYmZlVJ4vOS5LuJumo1FFSBXApcAUwStKpwDTgKICImCRpFDAZWAoMjYjK9FKnk/QwXoek01KtHZegfoH16/RzhqQDSIrBPWs53szMrKgi4tgadu1Vw/HDgeHVpI8Dts4n7/oE1v+T1Bb4OfBnoA1wTj6ZmJmZ1aTJTXQeEY+kqwuBPbO9HTMza2qa3FjBkm6lmu7FEXFKJndkZmZNShZtrMVUn6rgR3LWWwGHUY/uxmZmZvXRFKuC78vdTntaPZnZHZmZWZPS5KqCq9EX2LDQN2JmZk1Tk6sKlrSIb7axziQZiSlT63QfkHUWZmvEg+13L/YtmDVqTbEqeP01cSNmZtY0lVuJtc4hDSU9VZ80MzOzhogGLI1ZbfOxtgLWJRkOqj0rRvlvA3RfA/dmZmZNQLmVWGurCv4R8FOSIPoqKwLrp8C12d6WmZk1FU2mjTUi/gT8SdKZEfHnNXhPZmbWhFQV+wYKrD7TxlVJardsQ1J7ST/J7pbMzKwpCZT30pjVJ7CeFhELlm1ExCfAaZndkZmZNSlVkf/SmNVngIhmkhSRjI0hqTnQItvbMjOzpqKqkZdA81WfwPo4ycSwN5D0cv4x9Zjo1czMrD4ae9VuvuoTWC8AhpDMoi7gdaBbljdlZmZWqupsY42IKuAl4AOgH8ns629lfF9mZtZEVDVgacxqGyBiM2AwcCwwD/gbQER4snMzMyuYplQV/DbwPHBQREwBkHTOGrkrMzNrMhp7CTRftVUFH0Eyk80zkm6UtBeU2Z8VZmZWdOVWFVxjYI2IByLiGOBbwLPAOUAXSddL2mcN3Z+ZmZW5JjdARER8HhF3RcSBQE9gPHBh1jdmZmZNQ5XyXxqz+rxus1xEzAf+ki5mZmarrdwGiKjPkIZmZmaZyWo+VknnSJokaaKkuyW1ktRB0mhJ76Wf7XOOHyZpiqR3JO3b0OdxYDUzs6LKovOSpB7AWUC/iNgaaE7yCumFwFMR0Rd4Kt1G0pbp/q2AQcB16RC+eXNgNTOzoqqS8l7qaS1gHUlrAesC04FDgJHp/pHAoen6IcA9EbE4Ij4EpgD9G/I8DqxmZlZUWVQFR8THwO+AacAMYGFEPAF0iYgZ6TEzgM7pKT2Aj3IuUZGm5c2B1czMiqohVcGShkgal7MMyb1m2nZ6CNAH6A60lnR8LbdRXTG4QRPU5dUr2MzMrNAa8vpMRIwARtRyyP8AH0bEHABJ9wPfBWZJ6hYRMyR1A2anx1cAvXLO70lSdZw3l1jNzKyoqlDeSz1MA3aWtK4ksWICmYeAE9NjTgQeTNcfAgZLaimpD9AXGNuQ53GJ1czMiqpB9a11XTPiZUn3Aq8BS0mmPB0BrEcyx/ipJMH3qPT4SZJGAZPT44dGRGVD8nZgNTOzospqJKWIuBS4dKXkxSSl1+qOHw4MX918XRVsZmZWQC6xmplZUTX22Wry5cBqZmZFlUUbazE5sJqZWVE19tlq8uXAamZmReWqYDMzswJyYDUzMyugcFWwmZlZ4bjEamZmVkAOrGZmZgXk123MzMwKyK/bmJmZFZCrgs3MzArIgdXMzKyA3MZqZmZWQG5jNTMzKyBXBZuZmRWQq4LNzMwKqKrMQmsmgVXSBGr5IyQitskiXzMzs2LLqsR6YPo5NP28I/08DvgiozzNzKwEuY21HiLivwCSdo2IXXN2XSjp38DlWeRrZmalp7wqgqFZxtdvLWm3ZRuSvgu0zjhPMzMrIVUNWBqzrDsvnQrcIqktyR8lC4FTMs7TzMxKiN9jzUNEvApsK6kNoIhYmGV+ZmZWesqtV3CmVcGSuki6GfhbRCyUtKWkU7PM08zMSks0YKkPSe0k3SvpbUlvSdpFUgdJoyW9l362zzl+mKQpkt6RtG9DnyfrNtbbgMeB7un2u8BPM87TzMxKSIZtrH8CHouIbwHbAm8BFwJPRURf4Kl0G0lbAoOBrYBBwHWSmjfkebIOrB0jYhTpzyEilgKVGedpZmYlpIrIe6lL2gS5O3AzQEQsiYgFwCHAyPSwkcCh6fohwD0RsTgiPgSmAP0b8jxZB9bPJW1AWnKXtDNJByYzMzMgs6rgjYE5wK2SXpd0k6TWQJeImAGQfnZOj+8BfJRzfkWalresA+vPgIeATdL3V28Hzso4TzMzKyENqQqWNETSuJxlyEqXXQvYAbg+IrYHPiet9q1BdX2TG9SrKuvXbSYBewCbk9z0O2QfzM3MrIQ0pFdwRIwARtRySAVQEREvp9v3kgTWWZK6RcQMSd2A2TnH98o5vycwPe8bI/sg92JELI2ISRExMSK+Bl7MOE8zMyshWVQFR8RM4CNJm6dJewGTSWpRT0zTTgQeTNcfAgZLaimpD9AXGNuQ58lqEP6uJHXT60janhVF7DbAulnkaWZmpSnDkZTOBO6S1AL4ADiZpEA5Kn31cxpwFEBETJI0iiT4LgWGRkSDOttmVRW8L3ASSVH6KlYE1k+BizLK08zMSlBkNEBERIwH+lWza68ajh8ODF/dfLMahH+kpDuAYyPirizyMDOz8tDYx/7NV2ZtrBFRBfwoq+ubmVl5yOI91mLKuvPSaEnnSuqVDiPVQVKHjPM0MzMrmqwD6ykkk50/B7yaLuMyzrPJuHHEVUyveIPxrz+1PO2IIw7kjfFPs+Srj9hxh22Wp2+0UU8WLZzCuFeeYNwrT3DtNVdUe8327dvx2D/v5q1JL/DYP++mXbu2y/ddcP4ZvD35BSZNfI599t4juwezJqfPj/Zj9zFXsvuY37LdDWfSrOXay/dtfPoBHDDrbtbusP7ytE3OOoSBL/2BPf59FR0HblPdJVm7XWv6j7qIgS/+nv6jLmKttq3zOt/WnKzGCi6WTANrRPSpZtk4yzybkttvH8UBBx73jbRJk97mqKNP4/nnX1rl+Pc/+C/9dtqHfjvtw9Azqn9P+oLzh/L0My+wxVa78fQzL3DB+UMB2GKLvhx99CFss933OODA4/jz1b+mWTO/kmyrr2XX9vT+4SBe2PcintvjfNSsGd0P3QWAVt070HGPb/PFR3OWH7/eZj3ofuguPLf7eYw99gq2/s0p0GzVd/s3OfMQ5j0/kWd3+Rnznp/IpmcenNf5tua4KjgPktaVdLGkEel2X0kHZplnU/L8Cy8z/5MF30h7++0pvPvu+w2+5kEH7cvtd/wdgNvv+DsHHzwIgIMP2pdRox5kyZIlTJ36Ee+/P5X+O23f4HzMcql5c5q3aoGaN6P5ui34auYnAGx5+Qm8dflfv1FE6TKoH9P/8SJVS5by5bQ5fPHhTNrtsOkq1+wyaEcq/vYcABV/e44u+/XL63xbc8ptovOsixy3AkuA76bbFcD/ZZyn1aBP7w15ZezjPP3kvey2a/VjS3fp3JGZM5OBSGbOnE3nThsA0L17Vz6qWDEIScXHM+jeo2v2N21lb/HMT/jg+kf43mvXsNeb17P00y+YO2YCnffdka9mzmfR5GnfOL5V1/Z89fG85dtfzZhPq67tV74sLTu1ZfHsBUkesxfQsmObvM63NSca8K8xy3pIw00i4hhJxwJExJeSXOdSBDNmzKbPJv2ZP/8Tdtj+29x37y1ss92eLFr0Wb3Or+4/W0Tj/nJbaVirbWu6DOrHMzudxdcLv2CHm86mx1ED2OiUfRh79K9XPaG6XyH5fBVX93wruMZeAs1X1iXWJZLWYcXsNpsAi2s6OHdQ5aqqzzO+taZlyZIlzJ+fVK+99voEPvhgKpv1XbW5e9bsuXTtmkz20LVrZ2bPSf6y//jjGfTq2X35cT17dGPG9Flr4M6t3HXcfWu+nDabJfMWEUsrmfnoK/Q6dg/W3bATA57+DXu+cjWtundgwOhf07JT26SE2WOD5ee36taBr2Z9ssp1F89ZSMvO7QBo2bkdi+d+ClDv823NKbcSa9aB9TLgMaCXpLtIJpU9v6aDI2JERPSLiH7NmrWu6TBrgI4dOyzvbNSnz4ZsumkfPvhw2irHPfLwE5zwg6MAOOEHR/Hww48D8PAjT3D00YfQokULevfuxaab9mHsK6+vuQewsvXVx3Npt0Nfmq3TAoCOA7Zm5qOv8ORWP+aZnc7imZ3O4qvp83l+74tYPGchsx5/le6H7kKzFmuxzoadaL1xVxa8NmWV6856/FV6HrM7AD2P2Z1Zj726PL0+59uaU25trJlWBUfEE5JeBXYmGdbw7IiYm2WeTcmdd1zLHrvvQseOHZj6wTh+efnvmP/JAv70h/+jU6cOPPTg7bzxxiT2P/A4BgzYmcsuPZelSyuprKxk6BnD+CTt+PSXG65kxIg7ePW1N/nNlddyz19v4OSTjuWjjz7mmGOTMT4mT36Xe+99mAlvPMPSykrOOvsXVFU19q+3lYIFr73PjEdeZsDoXxOVVSycMJVpdzxV4/GfvVPBjIdeYvfnf0csrWTihbdCVVKC+fbvT2PayKdY+MYHvP/nh9jhxrPp9f2BfPnxPF774R/rPN+Ko6rMmpWUZTuZpIeAu4GHIiKvut21WvQor5+0NVkPtt+92LdgttoOmHV3Zv1jjt/o8Lx/39/53/sbbX+drKuCrwIGAJMl/V3SkZJaZZynmZmVkHJ7jzXrquAxwBhJzYHvAacBt5BMH2dmZtboOyPlK+vXbUh7BR8EHAPsAIzMOk8zMysd5dZbI9PAKulvwHdIegZfCzybznpjZmYG0OirdvOVdYn1VuD7DZ2F3czMyp+rgvMQEY9J+q6k3rl5RcTtWeZrZmalo9yqMbOuCr4D2AQYDywrtQbgwGpmZkD5DY+adVVwP2DLKLefmpmZWQ2yDqwTga7AjIzzMTOzEuXOS/npSDI4xFhyBt+PiIMzztfMzEqE21jzc1nG1zczsxLnXsF5iIgxkroAO6VJYyNidpZ5mplZaSm3quBMxwqWdDQwFjgKOBp4WdKRWeZpZmalJSLyXhqzrAfh/wWwU0ScGBEnAP2B/804TzMzKyFZzscqqbmk1yU9km53kDRa0nvpZ/ucY4dJmiLpHUn7NvR5sg6szVaq+p23BvI0M7MSEg34l4ezgbdyti8EnoqIvsBT6TaStgQGA1sBg4Dr0glk8pZ1kHtM0uOSTpJ0EvAo8M+M8zQzsxKS1bRxknoCBwA35SQfworJYEYCh+ak3xMRiyPiQ2AKSS1r3jLpvCRpU6BLRJwn6XBgN0DAi8BdWeRpZmalqSFtppKGAENykkZExIiVDvsjcD6wfk5al4iYkeY7Q1LnNL0H8FLOcRVpWt6y6hX8R+AigIi4H7gfQFK/dN9BGeVrZmYlpiG9gtMgunIgXU7SgcDsiHhV0sB6XFLVZZP3jZFdYO0dEW+unBgR49IB+c3MzIDM3mPdFThY0v5AK6CNpDuBWZK6paXVbsCyfkAVQK+c83sC0xuScVZtrK1q2bdORnmamVkJqorIe6lLRAyLiJ4R0ZukU9LTEXE88BBwYnrYicCD6fpDwGBJLSX1AfqSvC6at6wC6yuSTls5UdKpwKsZ5WlmZiUoGrCshiuAvSW9B+ydbhMRk4BRwGTgMWBoQ+cSz6oq+KfAA5KOY0Ug7Qe0AA7LKE8zMytBWY+8FBHPAs+m6/OAvWo4bjgwfHXzyySwRsQs4LuS9gS2TpMfjYins8jPzMxKV7kNaZj1WMHPAM9kmYeZmZW2xj5EYb48CpKZmVkBZT1tnJmZWa1cFWxmZlZAno/VzMysgMqtjdWB1czMispVwWZmZgXkEquZmVkBucRqZmZWQO68ZGZmVkD1GVS/lDiwmplZUbnEamZmVkAusZqZmRWQS6xmZmYF5BKrmZlZAbnEamZmVkAusZqZmRWQS6xmZmYFFFFV7FsoKE90bmZmVkAusZqZWVF5rGAzM7MC8uw2ZmZmBeQSq5mZWQGVW4nVnZfMzKyoqiLyXuoiqZekZyS9JWmSpLPT9A6SRkt6L/1sn3POMElTJL0jad+GPo8Dq5mZFVU04F89LAV+HhFbADsDQyVtCVwIPBURfYGn0m3SfYOBrYBBwHWSmjfkeRxYzcysqCIi76Ue15wREa+l64uAt4AewCHAyPSwkcCh6fohwD0RsTgiPgSmAP0b8jxuYzUzs6LKuvOSpN7A9sDLQJeImAFJ8JXUOT2sB/BSzmkVaVreXGI1M7OiakiJVdIQSeNyliHVXVvSesB9wE8j4tNabkPV3VpDnsclVjMzK6qGDMIfESOAEbUdI2ltkqB6V0TcnybPktQtLa12A2an6RVAr5zTewLT874xXGI1M7Miy6KNVZKAm4G3IuL3ObseAk5M108EHsxJHyyppaQ+QF9gbEOexyVWMzMrqozaWHcFfgBMkDQ+TbsIuAIYJelUYBpwFEBETJI0CphM0qN4aERUNiRjB1YzMyuqLAaIiIgXqL7dFGCvGs4ZDgxf3bwdWM3MrKg80bmZmVkBeaJzMzOzAnKJ1czMrIA8CL+ZmZnVyCVWMzMrKrexmpmZFVC5VQU7sJqZWVE5sJqZmRVQeYVVULn9pWD1J2lIOpC1WUnzd9kaE/cKbtqqnWbJrAT5u2yNhgOrmZlZATmwmpmZFZADa9PmNikrF/4uW6PhzktmZmYF5BKrmZlZATmwrmGSQtJVOdvnSrqsjnMOlbRlLftPkDRR0iRJkyWdW8BbrjdJvSV9vwHn3SbpyCzuyRoHSV0l3SPp/fQ7+k9JmxXhPk6S1D3Pc3pLmpjVPVn5cWBd8xYDh0vqmMc5hwLVBlZJ+wE/BfaJiK2AHYCFq3mPeZO0FtAbyDuwWnmTJOAB4NmI2CQitgQuArqs4ftoDpwE5BVYzfLlwLrmLSXpaHHOyjskbSTpKUlvpp8bSvoucDBwpaTxkjZZ6bRhwLkRMR0gIr6KiBvT620n6aX0eg9Iap+mPyvpN5LGSnpX0oA0/WVJW+Xcz7OSdpTUWtItkl6R9LqkQ9L9J0n6u6SHgSeAK4AB6X2eI6m5pCvT896U9KP0PEm6Ji25PAp0LuQP2BqdPYGvI+KGZQkRMR54If1+TJQ0QdIxAJIGpt+9eyW9Lemu9Duzn6RRy66RHvdwur6PpBclvZZ+J9dL06dKukTSC8CxQD/grvQ7uk76/R4j6VVJj0vqlp63o6Q3JL0IDF1DPycrFxHhZQ0uwGdAG2Aq0BY4F7gs3fcwcGK6fgrwj3T9NuDIGq43H2hbw743gT3S9cuBP6brzwJXpev7A0+m6+cAv0zXuwHvpuu/Bo5P19sB7wKtSf76rwA6pPsGAo/k5D8EuDhdbwmMA/oAhwOjgeYkpYcFNT2fl9JfgLOAP1STfkTO96ALMC393g0kqXXpSfLH/4vAbiRDsE4DWqfnXw8cD3QEnstJvwC4JF2fCpyfk+ezQL90fW3gP0CndPsY4JZ0Pff/nSuBicX+OXopncUl1iKIiE+B20l+4eTaBfhrun4HyS+TBpHUFmgXEWPSpJHA7jmH3J9+vkpShQswCjgqXT8a+Hu6vg9woaTxJL+YWgEbpvtGR8T8Gm5jH+CE9LyXgQ2Avul93B0RlZGUtJ/O/wmtDOzGiu/BLGAMsFO6b2xEVEREFTAe6B0RS4HHgIPSpocDgAeBnUmaSv6dftdOBDbKyedvNeS/ObA1MDo972KgZzX/79xRiIe1psOD8BfPH4HXgFtrOaY+70JNAnYk/+C0OP2sJP0eRMTHkuZJ2obkr/cfpccIOCIi3sm9gKTvAJ/XkoeAMyPi8ZXO25/yG3fbajYJqK5zmmo5Z3HO+vLvKEmQHEpSU/NKRCxK23BHR8SxNVyrpu+ogEkRscs3EqV2+Ptpq8El1iJJS3mjgFNzkv8DDE7XjwNeSNcXAevXcKn/B/xWUlcASS0lnRURC4FPlrWfAj8gKRHU5R7gfJLq5Qlp2uPAmekvMCRtX8O5K9/n48DpktZOz9tMUmuSarvBaRtsN5I2OCtfTwMtJZ22LEHSTsAnwDHp96ATSU3G2Dqu9SxJB73TWFESfQnYVdKm6bXXraXHce539B2gk6Rd0vPWlrRVRCwAFkpaVmN0XL2f1AwH1mK7iqR9aJmzgJMlvUkSCM9O0+8Bzks7Dn2j81JE/BO4FnhS0iSSqt1lf92fSNLp6U1gO5J21rrcSxLcR+Wk/YqkPepNJa8d/KqGc98ElqadPs4BbgImA6+l5/0lvbcHgPeACSTtZPUJ+FaiIiKAw4C9lbxuMwm4jKTZ403gDZLge35EzKzjWpXAI8B+6ScRMYekvf/u9Lv+EvCtGi5xG3BDWvXbnKQk/RtJb5BUOX83Pe5k4Nq089KX+T6zNW0eecnMzKyAXGI1MzMrIAdWMzOzAnJgNTMzKyAHVjMzswJyYDUzMysgB1YzQFJlOn7sxHSs2XVX41rLZ+uRdJNqn5looJLxoPPNY6rym8jBzNYQB1azxJcRsV1EbA0sAX6cu1PJzCh5i4gfRsTkWg4ZyIp3J82sDDiwmq3qeWDTtDT5jKS/AhMaMltPOktLv3R9UDr7yhtKZi/qTRLAz0lLywMkdZJ0X5rHK5J2Tc/dQNIT6SAhf6H24QDNrIg8VrBZjnRw9/1IBnsH6A9sHREfShoCLIyInSS1JBn0/Qlge5IB3b9NMkvLZOCWla7bCbgR2D29VoeImC/pBuCziPhdetxfSWaCeUHShiTDQm4BXAq8EBGXSzqAZOYgM2uEHFjNEuukw9xBUmK9maSKdmxEfJim7wNss6z9lGTav2/M1gNMl1TdhAg7A88tu1YtMwL9D7BlOiwzQBtJ66d5HJ6e+6ikTxr2mGaWNQdWs8SXEbFdbkIa3HJnRlmd2XpUj2MgaZ7ZJSK+MT5tei8ef9SsBLiN1az+Vme2nheBPST1Sc/tkKavPCPQE8AZyzYkbZeuPkc6y4qk/YD2hXooMyssB1az+mvwbD3pDCxDgPvTmVSWTXn2MHDYss5LJDMc9Us7R01mRe/kXwK7S3qNpEp6WkbPaGarybPbmJmZFZBLrGZmZgXkwGpmZlZADqxmZmYF5MBqZmZWQA6sZmZmBeTAamZmVkAOrGZmZgXkwGpmZlZA/x/nASObBWNvgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the training data\n",
    "y_pred_train2 = d_tree_tuned.predict(X_train)\n",
    "\n",
    "metrics_score(y_train, y_pred_train2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8a5c24",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- As expected, **the performance of the model on the training dataset has decreased. The tuned model seems not to overfit** the training data.\n",
    "- Still, it looks **capable of identifying the majority of the classes for the points correctly (converted / not converted)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38180aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.77      0.84       962\n",
      "           1       0.62      0.86      0.72       422\n",
      "\n",
      "    accuracy                           0.80      1384\n",
      "   macro avg       0.77      0.82      0.78      1384\n",
      "weighted avg       0.83      0.80      0.80      1384\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the testing data\n",
    "y_pred_test2 = d_tree_tuned.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d44711a",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- The **recall for the class 1 (converted) has decreased while the precision - increased**. \n",
    "- The **model doesn't seem to overfit that much which implies its performing now is more generalized** on both training and testing data. Though, it looks like there is still much room for improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d13733a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = list(X.columns)\n",
    "\n",
    "plt.figure(figsize = (20, 20))\n",
    "\n",
    "tree.plot_tree(d_tree_tuned, feature_names = features, filled = True, fontsize = 9, node_ids = True, class_names = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4947f13",
   "metadata": {},
   "source": [
    "**Note:** Blue leaves represent the converted leads, i.e., **y[1]**, while the orange leaves represent the not converted leads, i.e., **y[0]**. Also, the more the number of observations in a leaf, the darker its color gets.\n",
    "\n",
    "**Observations:**\n",
    "\n",
    "- The **first separation in the Decision tree is at first_interaction_website which makes this factor of the top priority** when trying to predict whether a lead is going to convert to a paid customer or not. Customers whose first interaction with ExtraaLearn was via website instead of mobile application are more likely to covert. \n",
    "- The **leads with shorter interaction with the website are less likely to convert into paid customers**. And vise versa, the leads who spent more time on the website are most likely to become real-life customers.\n",
    "- Still, **the leads interacting with the website longer (more than 416 sec, i.e. approx. 7 minutes) and whose age is below 25 y.o. are less likely to convert**. \n",
    "- **Those who spent less time on website but Medium-completed profile (50.. 75%) still have chances to be converted**. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06aca3e",
   "metadata": {},
   "source": [
    "**Let's look at the feature importance** of the tuned decision tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "96e1fbd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     Imp\n",
      "time_spent_on_website           0.348142\n",
      "first_interaction_Website       0.327181\n",
      "profile_completed_Medium        0.239274\n",
      "age                             0.063893\n",
      "last_activity_Website Activity  0.021511\n",
      "website_visits                  0.000000\n",
      "page_views_per_visit            0.000000\n",
      "current_occupation_Student      0.000000\n",
      "current_occupation_Unemployed   0.000000\n",
      "profile_completed_Low           0.000000\n",
      "last_activity_Phone Activity    0.000000\n",
      "print_media_type1_Yes           0.000000\n",
      "print_media_type2_Yes           0.000000\n",
      "digital_media_Yes               0.000000\n",
      "educational_channels_Yes        0.000000\n",
      "referral_Yes                    0.000000\n"
     ]
    }
   ],
   "source": [
    "# importance of features in the tree building\n",
    "\n",
    "print (pd.DataFrame(d_tree_tuned.feature_importances_, columns = [\"Imp\"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "133927ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAJcCAYAAABaCgp+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABiBElEQVR4nO3de7yvc53//8fToeSsqK8O2o2OkrYshBQlM501EdJB+SaVVPNVU7/K0GFiNJnOUhMdFONQSTOhELZDNra9ETWh6WCKklMovH5/XO+VT8s67tO61tqP++22buu63tf78Lren096fd7rfX12qgpJkiRJ02+l6Q5AkiRJUsfkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZOS5Pokdya5feDnkUuhz52WVoyTGO/gJF9bXuONJ8neSc6b7jgk9YvJuSRpKl5SVWsO/Px6OoNJssp0jr+4ZmrckpY9k3NJ0hJJsk6Sf09yQ5JfJflwkpXbtY2TnJnkd0luSnJsknXbta8CGwHfaavw706yQ5Jfjuj/L6vrbeX7xCRfS3IrsPd4408i9kryliQ/TXJbkg+1mC9IcmuS/0jyoFZ3hyS/TPL/tXu5PsleI+bhK0luTPLzJO9PslK7tneSeUmOSPJ74HjgSGCbdu9/aPVelOSyNvYvkhw80P+cFu/rkvxPi+F9A9dXbrH9rN3LJUke0649OckZSX6f5Jokrxxo98IkV7U2v0py4CRfeknLgMm5JGlJfRm4B3g8sDmwM/B/27UAHwUeCTwFeAxwMEBVvQb4H+5fjf+XSY73MuBEYF3g2AnGn4y/A7YAngm8GzgK2KvFuimw50Dd/wOsDzwKeB1wVJIntWufAtYB/gZ4DvBa4PUDbbcGrgUeDrwa2A+4oN37uq3OHa3dusCLgDcn2WVEvM8CngQ8DzgoyVNa+T+0WF8IrA28AfhjkjWAM4Cvt7H3BD6b5Kmt3b8Db6qqtdr9njnxlElaVkzOJUlT8a0kf2g/30ryCOAFwDuq6o6q+i1wBLAHQFX9d1WdUVV3V9WNwMfpEtclcUFVfauq7qNLQsccf5IOq6pbq+pK4Arg9Kq6tqpuAf6LLuEf9IF2Pz8Evgu8sq3U7w68t6puq6rrgX8FXjPQ7tdV9amquqeq7hwtkKo6u6oWVdV9VbUQ+AYPnK9DqurOqrocuBx4eiv/v8D7q+qa6lxeVb8DXgxcX1VHt7EvBU4Cdm3t/gxskmTtqrq5XZc0TdzzJkmail2q6vvDJ0m2AlYFbkgyXLwS8It2/eHAJ4HtgbXatZuXMIZfDBw/drzxJ+k3A8d3jnL+fwbOb66qOwbOf073V4H1gQe188Frjxoj7lEl2Ro4lG4F+0HAg4ETRlT734HjPwJrtuPHAD8bpdvHAlsPb51pVgG+2o5fAbwfODTJQuA9VXXBRLFKWjZcOZckLYlfAHcD61fVuu1n7aoa3jLxUaCAzapqbbrtHBloXyP6uwNYffikrUhvMKLOYJuJxl/a1mvbRIZtBPwauIluBfqxI679aoy4RzuHbuvJKcBjqmodun3pGaXeaH4BbDxG+Q8H5mfdtpXmzQBVdXFVvYxuy8u3gP+Y5HiSlgGTc0nSYquqG4DTgX9NsnaSldoDlcNbMdYCbgf+kORRwLtGdPEbuj3aw34CrNYejFyVbkX3wUsw/rJwSJIHJdmebsvICVV1L11S+5EkayV5LN0e8PG+tvE3wKOHHzht1gJ+X1V3tb9KvGoKcX0R+FCSJ6SzWZKHAacCT0zymiSrtp8tkzyl3cdeSdapqj8DtwL3TmFMSUuZybkkaUm9lm4LxlV0W1ZOBDZs1w4BngHcQrc/++QRbT8KvL/tYT+w7fN+C12i+Su6lfRfMr7xxl/a/reN8Wu6h1H3q6qr27W30cV7LXAe3Sr4l8bp60zgSuB/k9zUyt4CfDDJbcBBTG0V++Ot/ul0Sfa/Aw+pqtvoHpLdo8X9v8Bh3P+h5zXA9e3bb/aj++uGpGmSqtH+qiZJkgYl2QH4WlU9eppDkTSLuXIuSZIk9YTJuSRJktQTbmuRJEmSesKVc0mSJKkn/EeINCusv/76NWfOnOkOQ5IkaUKXXHLJTVU18t9wAEzONUvMmTOH+fPnT3cYkiRJE0ry87Guua1FkiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ5YZboDkJaGe2+4l5s/dPN0hyFJkmaw9T6w3nSH4Mq5JEmS1Bcm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTszo5T7Jukre040cmOXG6Y1pcSXZJssl0xzGWJHOSXDGF+vsleW073jvJI5dddJIkSTPDrE7OgXWBtwBU1a+ratfpDWeJ7AL0Njmfqqo6sqq+0k73BkzOJUnSCm+2J+eHAhsnWZDkhOGV3bZS+60k30lyXZL9k/xDksuSXJjkoa3exkm+l+SSJOcmefJYAyXZLckVSS5Pcs7AON9ufVyT5J8G6r86yY9abJ9PsnIrvz3JR1o/FyZ5RJJtgZcCh7f6G48Rw9zWZmGSbyZZr5WfneSwNt5Pkmw/zn38Z5LN2vFlSQ5qxx9K8n/b8buSXNzGOWSg+SpJvtzKT0yyeqt/aJKrWvnHWtnBSQ5MsiswBBzb7u0hSbZI8sM276cl2XCMWPdNMj/J/JvuuGmsW5IkSZoxZnty/h7gZ1U1F3jXiGubAq8CtgI+AvyxqjYHLgBe2+ocBbytqrYADgQ+O85YBwF/W1VPp0ukh20F7AXMBXZLMpTkKcDuwHYttntbHYA1gAtbP+cAb6yq84FTgHdV1dyq+tkYMXwF+Meq2gxYBPzTwLVVqmor4B0jykc6B9g+ydrAPcB2rfxZwLlJdgae0O5rLrBFkme3Ok8Cjmrj3wq8pX3QeTnw1Fb+4cHBqupEYD6wV5uLe4BPAbu2ef8S3evzAFV1VFUNVdXQ+musP84tSZIkzQyrTHcA0+isqroNuC3JLcB3WvkiYLMkawLbAickGW7z4HH6mwcck+Q/gJMHys+oqt8BJDmZLsm9B9gCuLj1/RDgt63+n4BT2/ElwPMnczNJ1gHWraoftqIvAycMVBmO6RJgzjhdnQscAFwHfBd4flsBn1NV1yR5I7AzcFmrvyZdsv4/wC+qal4r/1rr59+Au4AvJvnuwL2N5Ul0H5zOaHOzMnDDBG0kSZJmhRU5Ob974Pi+gfP76OZlJeAPbTV3QlW1X5KtgRcBC5IMt6uRVYEAX66q947S1Z+rarjNvSy912j4/ibq82K6bSbXAmcA6wNvpEvqoYv9o1X1+cFGSeYwyr1W1T1JtgKeB+wB7A88d5zxA1xZVdtMdEOSJEmzzWzf1nIbsNbiNKyqW4HrkuwGkM7Tx6qfZOOquqiqDgJuAh7TLj0/yUOTPITuoc55wA+AXZM8vLV9aJLHLsm9VNUtwM0D+8lfA/xwrPrj9PMn4BfAK4EL6VbSD2y/AU4D3tD+skCSRw3fB7BRkuGkek/gvFZvnar6T7otNXMnuLdrgA2G+0myapKnTvU+JEmSZqJZvXJeVb9LMq89CPrjxehiL+BzSd4PrAocB1w+Rt3DkzyBbuX3B63eXOA84KvA44GvV9V8gNbn6UlWAv4MvBX4+TixHAd8IckBdPuxR9t3/jrgyLYN5Vrg9VO410HnAs+rqj8mORd4dCujqk5ve+YvaNtObgdeTbci/2PgdUk+D/wU+BywDvDtJKvRzc07RxnvmBb3ncA2wK7AJ9tWnVXotsZcuZj3IkmSNGPk/h0UWtqS7A0MVdX+0x3LbLf5ozavM/c7c7rDkCRJM9h6H1hvuYyT5JKqGhrt2mzf1iJJkiTNGLN6W8uykOR9wG4jik+oqgd83V9VHUO3ZWNpx/AZ7v+Kw2GfqKqjp9DH3wKHjSi+rqpevqTxSZIkafGYnE9RS8JH/d7t5RjDW5dCH6fRPdwpSZKknnBbiyRJktQTJueSJElST5icS5IkST3hnnPNCitvuPJy+/ojSZKkZcWVc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSf8thbNCvfecC83f+jm6Q5DkiQtAb95zZVzSZIkqTdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknpgVyXmSA5L8OMnNSd4zhXZzkrxqgjpDST45QZ11k7xlsuMuriRzk7xw4PylU7nfSfT/zSS7DJxfk+T9A+cnJfn7cdpfn2T9SY71l3lNskOSbZcgdEmSpFlhViTnwFuAF1bVelV16MiLSVYZo90cYNzkvKrmV9UBE4y/both0tKZ6vzPBf6SnFfVKaPd7xI4H9gWIMnDgNuBbQaub9PqLLER87rD8LiSJEkrshmfnCc5Evgb4JQk70zy6VZ+TJKPJzkLOCzJc5IsaD+XJVkLOBTYvpW9c4z+d0hyajs+OMmXkpyd5Nokw8nlocDGrZ/DW913Jbk4ycIkh7SyOW2F/7PApcBjknwuyfwkVw7Xa3W3THJ+ksuT/CjJOsAHgd3bOLsn2Xvgfh+b5AdtvB8k2WhgHj7Z+ro2ya7jTOc87k+StwVOBTZoHyQeB9xZVf+bZOckFyS5NMkJSdYc6ONdLd4fJXl8i2G3JFe0ezlncF6TzAH2A97Z7mv7JBu0VfqL2892Y7w2+7a5m3/THTeNc1uSJEkzw1gryjNGVe2X5O+AHYEXj7j8RGCnqro3yXeAt1bVvJZM3gW8Bziwqka2G8+T21hrAdck+VzrZ9OqmguQZGfgCcBWQOg+ODwb+B/gScDrq+otre77qur3SVYGfpBkM+Bq4Hhg96q6OMnawB+Bg4Chqtq/td17IK5PA1+pqi8neQPwSWCXdm1D4Fkt9lOAE8e4t0uATZM8iC45/yHdB5+nAJsD89q2lfe3eb0jyT8C/0D3wQHg1qraKslrgX+je00OAv62qn6VZN3BAavq+vYB6/aq+li7r68DR1TVee1DxmktBka0PQo4CmDzR21eY9yTJEnSjDHjk/MJnFBV97bjecDHkxwLnFxVv0yyOH1+t6ruBu5O8lvgEaPU2bn9XNbO16RL1v8H+HlVXThQ95VJ9qV7LTYENgEKuKGqLgaoqlsBJoh3G2B4P/hXgX8ZuPatqroPuCrJaPHSxrk7yZXAM4Bntj7+hi5R35xuS8szW4zzWjwPAi4Y6OYbA7+PaMfzgGOS/Adw8ng30ewEbDJwv2snWauqbptEW0mSpBlrtifndwwfVNWhSb5Lt2f7wiQ7LWafdw8c38vocxjgo1X1+b8q7LZw3DFw/jjgQGDLqro5yTHAaq39kq4ED7YfjHmiTyTnA88G1moxXQjsT5ecH0m3T/+MqtpzEuMW/OWvG1sDLwIWJJk7QQwrAdtU1Z0T1JMkSZpVZvye88lKsnFVLaqqw4D5dFs8bqPbnrKkRvZzGvCG4b3YSR6V5OGjtFubLlm/pa1ov6CVXw08MsmWrf1a6R5qHS/e84E92vFewHmLeS/zgDcBl7fzhXSr5RsBVwIXAtsN7CdfPckTB9rvPvD7glZn46q6qKoOAm4CHjNizJH3dTrdBwJa+7mLeS+SJEkzygqTnAPvGH4oEbgT+C+6xPOe9qDiqA+ETkZV/Y5um8cVSQ6vqtOBrwMXJFlEt8f7AUl1VV1Ot/XlSuBLdIkxVfUnuuT2Uy3eM+hW1M+i2+6xIMnuI7o7AHh9koXAa4C3L+btnE+3leWCFss9wG+B+VV1X1XdCOwNfKONdSHdB51hD05yURt/eE4PT7IoyRXAOdyf+A/7DvDy4QdC270MtYdbr6J7YFSSJGnWS5XP0Wnm2/xRm9eZ+5053WFIkqQlsN4H1pvuEJaLJJdU1dBo11aklXNJkiSp12b7A6GTluRvgcNGFF9XVS+fjniWpSRPo/tGl0F3V9XW0xGPJEmSOibnTVWdRvcg56xXVYvo/rVRSZIk9YjbWiRJkqSeMDmXJEmSesJtLZoVVt5w5RXmCW9JkjR7uXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hN/Wolnh3hvu5eYP3TzdYUiSJsFv15LG5sq5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST8za5DzJbkl+nOSsJENJPtnK907y6emObyJJbp/g+rpJ3rIY/R6c5MBxrh+T5I9J1hoo+0SSSrL+FMb5yzwn2S/Ja6caqyRJ0opmlekOYEkkWbmq7h3j8j7AW6rqrHY+fzmFtbysC7wF+Owy6Pu/gZcBX0uyErAj8KvF7ayqjlxagUmSJM1mvV05TzInydVJvpxkYZITk6ye5PokByU5D9gtyZ5JFiW5Islhre1BwLOAI5McnmSHJKeOMsYGSU5KcnH72W6ceNZMcnQba2GSV7TyB4zfym9PcliSS5J8P8lWSc5Ocm2Sl7Y6eyf5dpLvJbkmyT+NMfa7WnwLkxzSig8FNk6yIMnh49Qjyfta/98HnjSJ6f8GsHs73gGYB9wz0N+rk/yojf35JCu38tcn+UmSHwLbDdT/y2p9m4Ohdrx+kusH5uJbSb6T5Lok+yf5hySXJbkwyUNHmZd9k8xPMv+mO26axG1JkiT1W2+T8+ZJwFFVtRlwK91KMcBdVfUs4BzgMOC5wFxgyyS7VNUH6VbK96qqd43T/yeAI6pqS+AVwBfHqfsB4JaqelqL58wkjxxt/FZ/DeDsqtoCuA34MPB84OXABwf63QrYq7XfbThxHZZkZ+AJrd5cYIskzwbeA/ysquZW1bvGqpdkC2APYHPg74Etx7nHYT8FNkiyHrAncNxAPE+hS9y3q6q5wL3AXkk2BA6hS8qfD2wyiXFG2hR4VbuHjwB/rKrNgQuAB2yLqaqjqmqoqobWX2PSO24kSZJ6q+/bWn5RVfPa8deAA9rx8e33lnQJ8I0ASY4Fng18a5L97wRskmT4fO0ka1XVbWPU3WP4pKpubknyWOP/Cfheq74IuLuq/pxkETBnoN8zqup3rf3JdCv+g1twdm4/l7XzNemS8P8ZEd9Y9dYCvllVf2xjnDLOfAw6ud3v1sCbBsqfB2wBXNzm7SHAb1u9wbk4HnjiJMcadlab+9uS3AJ8p5UvAjabYl+SJEkzTt+T8xrj/I72OyyZlYBtqurOSdTNKPGMN/6fq2q4/n3A3QBVdV+SwXkf6x4Hx/hoVX3+rwqTOZOs945R+pyM44BLgS+3mAfH+XJVvXfEOLtMcpx7uP8vNquNuHb3wPF9A+f30f/3qiRJ0hLr+7aWjZJs0473BM4bcf0i4Dlt7/LKrc4Pp9D/6cD+wydJ5k6h7npLYXyA5yd5aJKHALvQ7e8edBrwhiRrtnEfleThdFtl1ppEvXOAlyd5SLpvYHnJZIKqqv8B3scDHzj9AbBr65sW+2Pp5mKHJA9Lsiqw2xhdX0+38g6w62RikSRJWlH0PTn/MfC6JAuBhwKfG7xYVTcA7wXOAi4HLq2qb0+h/wOAofYA5VXAfuPU/TCwXnvw83Jgx6UwPnQfOL4KLABOqqq/+laZqjod+DpwQdsScyKwVtsKM6/Fc/g49S6l2wa0ADgJOHeygVXV56vqZyPKrgLeD5zeXpczgA3bXBxMtz/8+3Sr7qP5GPDmJOcDbhSXJEkakPt3XvRL27ZxalVtOt2xLCtJ9gaGqmr/iepqfJs/avM6c78zpzsMSdIkrPeB9aY7BGlaJbmkqoZGu9b3lXNJkiRphdHbh+yq6nq6r9ZbrpK8Hnj7iOJ5VfXWpT1WVR0DHLO0+52MJJ9h4LvIm09U1dHTEY8kSZJ6nJxPl5aczvoEdVl82JAkSdKScVuLJEmS1BMm55IkSVJPuK1Fs8LKG67s0/+SJGnGc+VckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSf8KkXNCvfecC83f+jm6Q5jhefXWUqStGRcOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3MtF0m+leSSJFcm2beV7ZPkJ0nOTvKFJJ9u5RskOSnJxe1nu+mNXpIkaflYZboD0ArjDVX1+yQPAS5O8l3gA8AzgNuAM4HLW91PAEdU1XlJNgJOA54yssOW5O8L8Oh1Hr0cbkGSJGnZMjnX8nJAkpe348cArwF+WFW/B0hyAvDEdn0nYJMkw23XTrJWVd022GFVHQUcBbD5ozavZRy/JEnSMmdyrmUuyQ50Cfc2VfXHJGcD1zDKanizUqt753IJUJIkqSfcc67lYR3g5paYPxl4JrA68Jwk6yVZBXjFQP3Tgf2HT5LMXZ7BSpIkTReTcy0P3wNWSbIQ+BBwIfAr4J+Bi4DvA1cBt7T6BwBDSRYmuQrYb/mHLEmStPy5rUXLXFXdDbxgZHmS+VV1VFs5/ybdijlVdROw+/KNUpIkafq5cq7pdHCSBcAVwHXAt6Y1GkmSpGnmyrmmTVUdON0xSJIk9Ykr55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk/4bS2aFVbecGXW+8B60x2GJEnSEnHlXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gm/rUWzwr033MvNH7p5WmPw22IkSdKScuVckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeqJ5ZqcJ7l9Mdu9I8nqSzGOuUleOHD+0iTvmaDNB5PstCTxJDkiyTsGzk9L8sWB839N8g/jtD87ydAkx3pkkhPb8V/d7xRjfmeSu5KsM4m6/9+I8/MnqD+U5JPteIck2y5OjJIkSbPFTFk5fwew1JJzYC7wl2S1qk6pqkPHa1BVB1XV95cwnvOBbQGSrASsDzx14Pq2wLzF6PcBqurXVbVrO53LwP1O0Z7AxcDLJ1H3r5Lzqho32a6q+VV1QDvdgTY3kiRJK6ppSc6TrJnkB0kuTbIoycta+RpJvpvk8iRXJNk9yQHAI4Gzkpw1Tp+fSzI/yZVJDhko3zLJ+a3PH7UV4A8CuydZ0MbYO8mnk6yT5PqWOJNk9SS/SLJqkmOS7DoyniT7JDliYLw3Jvn4GGHO4/4E9KnAFcBtSdZL8mDgKcBlSbZI8sMkl7TV9Q0H+nh1u58rkmzVxnxOu5cFSS5LslaSOa3Og0a53zWSfCnJxa3+y8aY042BNYH30yXpg6/f0e21W5jkFUkOBR7Sxji21bu9/T5+xF8qjmltdkhyapI5wH7AO1v77ZNcl2TVVn/t9rqsOtbrL0mSNBusMk3j3gW8vKpuTbI+cGGSU4C/A35dVS8CSLJOVd3StnrsWFU3jdPn+6rq90lWBn6QZDPgauB4YPequjjJ2sAfgYOAoarav42zN0Ab63LgOcBZwEuA06rqz0lodT45GE+SNYCFSd5dVX8GXg+8abQAq+rXSe5JshFdkn4B8ChgG+AWYCFQwKeAl1XVjUl2Bz4CvKF1s0ZVbZvk2cCXgE2BA4G3VtW8JGu2+R0e809JRt7vPwNnVtUbkqwL/CjJ96vqjhEh7wl8AzgXeFKSh1fVb4EPALdU1dNaf+tV1UlJ9q+quaPc+nHA7sB/tg8LzwPeDGzdYrw+yZHA7VX1sdbn2cCLgG8BewAntfn9iyT7AvsCPHqdR4825ZIkSTPKdG1rCfDPSRYC36dLUB8BLAJ2SnJYku2r6pYp9PnKJJcCl9GtSm8CPAm4oaouBqiqW6vqngn6OZ4ukYQuKTx+vMotoT0TeHGSJwOrVtWicZoMr54PJ+cXDJyf32LeFDgjyQK6VevBzPMbbdxzgLVbcj0P+Hhb1V93Eve4M/Ce1v/ZwGrARqPU2wM4rqruA04GdmvlOwGfGZiDmycY77+A57a/DrwAOKeq7pygzRfpPujQfh89skJVHVVVQ1U1tP4a60/QnSRJUv9N18r5XsAGwBZtVfp6YLWq+kmSLej2R380yelV9cGJOkvyOLrV4y2r6uYkx9AlnKFbiZ6KU9rYDwW2oEu8J/JFuv3WVzNKEjnC8L7zp9Fta/kF8P+AW+lWwgNcWVXbjNF+5P1UVR2a5Lt083ZhugdX73pg078I8IqqumbMCt1fHp5A9yEB4EHAtXRJ+ZTmtaruaivhf0v3wecbk2gzr23NeQ6wclVdMdnxJEmSZqrpWjlfB/htS8x3BB4L3TeMAH+sqq8BHwOe0erfBqw1Tn9rA3cAtyR5BN3qLHTJ8iOTbNn6XyvJKuP1V1W3Az8CPgGcWlX3jlLtr9pX1UXAY4BXMXHiOQ94MfD7qrq3qn4PrEu3teUC4BpggyTbtJhXTTL40OjurfxZdFtLbkmycVUtqqrDgPnAk8eLFzgNeFta1p1k81Hi3BM4uKrmtJ9HAo9K8ljgdGD/4YpJ1muHfx5nX/hxdCvg27fxRxrtNfkK3XxO9IFHkiRpVpiu5PxYYCjJfLpV9Ktb+dPo9j8vAN4HfLiVHwX8V8Z4ILSqLqfbznIl3erzvFb+J7pk9lNtL/kZdCvqZwGbDD8gOUqXxwOvZuwtLaPF8x/AvEls8VhE9y0tF44ou6Wqbmox7woc1mJewF9/i8nN6b6i8Ehgn1b2jvbw5+XAnXTbSAaNvN8PAavS7ZW/op2PtAfwzRFl32zlHwbWGxhzx3b9qNbnsaP0dzrwbOD77R5H+g7w8uEHQlvZscB6TGKlXZIkaTZI1VR3fWg0SU4FjqiqH0x3LLNFkl3pHox9zUR1N3/U5nXmfpPZgbTsrPeB9SauJEmSVnhJLqmqUf/tmunacz5rDH/bCXC5ifnSk+RTdNuTFvf72SVJkmacGZecJ7kIePCI4tdM8A0py0xV/QF44mBZkocBoyXqz6uq3y2PuGa6qnrbdMcgSZK0vM245Lyqtp7uGCbSEvC50x2HJEmSZpbpeiBUkiRJ0ggm55IkSVJPmJxLkiRJPTHj9pxLo1l5w5X9KkNJkjTjuXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hcr6CSXJAkh8nOXYZ9X/7GOWrJbk6ydMGyt6d5MhlEYckSdJMtMp0B6ClL0mAVNV9o1x+C/CCqrpukn2tUlX3jHU+WVV1V5J3AJ9N8mzgkcCbgKGp9iVJkjRbuXI+SySZ01bEPwtcCnwgycVJFiY5pNU5Evgb4JQk70yyRpIvtXqXJXlZq7d3khOSfAc4fZTzNZP8IMmlSRYNt5tIVX0PuAF4LXAEcDCwSpKTWgwXJ9muxfCcJAvaz2VJ1hrlnvdNMj/J/BtvvHFJp1CSJGnapaqmOwYtBUnmANcC2wJrA7vSrUwHOAX4l6o6J8n1wFBV3ZTkn4GrquprSdYFfgRsDuwGfBjYrKp+n2TvEeerAKtX1a1J1gcuBJ5QVZXk9qpac5w4H9nG+WlV7Zjk68Bnq+q8JBsBp1XVU9oHgUOral6SNYG7xluxHxoaqvnz5y/u9EmSJC03SS6pqlF3D7itZXb5eVVdmORjwM7AZa18TeAJwDkj6u8MvDTJge18NWCjdnxGVf1+oO7geYB/bttT7gMeBTwC+N+JAqyqXyc5Ezi1Fe0EbNLtxAFg7bZKPg/4eNsbf3JV/XKiviVJkmY6k/PZ5Y72O8BHq+rzE9QP8IqquuavCpOtB/oa2TfAXsAGwBZV9ee2Gr/aFOK8r/1At7Vqm6q6c0SdQ5N8F3ghcGGSnarq6imMIUmSNOO453x2Og14Q9sOQpJHJXn4GPXe1h4gJcnmk+x/HeC3LTHfEXjsEsR6OrD/8EmSue33xlW1qKoOA+YDT16CMSRJkmYEk/NZqKpOB74OXJBkEXAi8IAHKoEPAasCC5Nc0c4n41hgKMl8ulX0JVnRPqD1tTDJVcB+rfwdSa5IcjlwJ/BfSzCGJEnSjOADoZoVfCBUkiTNFOM9EOrKuSRJktQTPhCqpa79K6BfHVF8d1VtPR3xSJIkzRQm51rqqmoRMHe645AkSZpp3NYiSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPzKrkPMneST69lPvcJckmA+cfTLLTUh5jhySnLka725dmHFMc++AkB06h/lOT/CTJQwbKvptkj2UToSRJ0swzq5LzZWQX4C/JeVUdVFXfn75wZqaquhI4GXgfdB96gFWr6rjpjEuSJKlPZlRynuTVSX6UZEGSzydZOcnr24rsD4HtBuoek2TXgfPbB47fnWRRksuTHNrK3pjk4lZ2UpLVk2wLvBQ4vI258WC/SZ6X5LLW15eSPLiVX5/kkCSXtmtPbuVbJTm/tTk/yZMmed9rJjm69bUwySsGrn2kxXxhkke0spckuaiN8/2B8oNbnGcnuTbJAa18TpIfJ/lCkiuTnD68wt3u+XtJLkly7vC9jIjvgCRXtdjGS7Y/COyWZC5wKPDWJFsk+WHr/7QkG062zyT7JpmfZP6NN944mamUJEnqtRmTnCd5CrA7sF1VzQXuBV4NHEKXlD+fgRXucfp5Ad1q+NZV9XTgX9qlk6tqy1b2Y2CfqjofOAV4V1XNraqfDfSzGnAMsHtVPQ1YBXjzwFA3VdUzgM8Bw9s/rgaeXVWbAwcB/zzJ2/8AcEtVPa2qNgPObOVrABe2mM8B3tjKzwOe2cY5Dnj3QF9PBv4W2Ar4pySrtvInAJ+pqqcCfwCGPwAcBbytqrZo9/HZUeJ7D7B5i22/sW6iqv7Y+jinxXU98Clg19b/l4CPTLbPqjqqqoaqamiDDTYYa1hJkqQZY5XpDmAKngdsAVycBOAhwLbA2VV1I0CS44EnTtDPTsDRLVGkqn7fyjdN8mFgXWBN4LQJ+nkScF1V/aSdfxl4K/Bv7fzk9vsS4O/b8TrAl5M8AShgODGeyE7AX/ZmV9XN7fBPwPBe9UvoPqAAPBo4vq1CPwi4bqCv71bV3cDdSX4LPKKVX1dVCwb6mpNkTbo5PqHNOcCDR4lvIXBskm8B3xrvRqrqO0n+QJfkPwnYFDij9b8ycMNU+5QkSZotZszKORDgy20Fe25VPQk4mC7JHc09tPtLl/k9aKCf0docA+zfVsEPAVabRDzjubv9vpf7PwR9CDirqjYFXjKJMQbHGi3mP1fVcPngOJ8CPt3u5U0jxrl74HiwzWjlKwF/GJjzuVX1lFHieBHwGboPT5ckmehD333tJ8CVA30/rap2Xsw+JUmSZryZlJz/ANg1ycMBkjwUuAzYIcnD2vaM3QbqX0+X2AG8jPtXqU8H3pBk9YF+ANYCbmj97DXQz23t2khX060uP76dvwb44QT3sA7wq3a89wR1B50O7D98kmS9KYzzuimM81eq6lbguiS7tXGT5OmDdZKsBDymqs6i2z6zLt1fHibjGmCDJNu0vlZN960uS9KnJEnSjDVjkvOqugp4P3B6koXAGcCGdKvnFwDfBy4daPIF4DlJfgRsDdzR+vke3T7y+UkWcP9+8A8AF7V+rx7o5zjgXe3hyo0H4rkLeD3dlo9FdCvBR05wG/8CfDTJPLotHJP1YWC9JFckuRzYcYL6B7e4zgVumsI4o9kL2KeNeyXdB51BKwNfa3NwGXBEVf1hMh1X1Z+AXYHDWv8L6LbRLHafkiRJM1nu3xUhzVxDQ0M1f/786Q5DkiRpQkkuqaqh0a7NmJVzSZIkabbzIbseSfJ64O0jiudV1VunI57FNVvuQ5IkaXkzOe+RqjoaOHq641hSs+U+JEmSlje3tUiSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm58tQkoOTHJjkg0l2mqDuS5O8px3vkmSTyfa/tOKdYKy9k3y6He+X5LVTbL9zkguSpJ2vnGRBkm2XRbySJEkzkcn5clBVB1XV9yeoc0pVHdpOdwEmTM6nS1UdWVVfmWKb04GfA/u0orcBF1fV+Us7PkmSpJnK5HwpS/K+JNck+T7wpFZ2TJJd2/ELk1yd5Lwkn0xyaivfO8mn20ryS4HD28ryxknemOTiJJcnOSnJ6pOM5ewkRyQ5J8mPk2yZ5OQkP03y4YF6r07yozbe55Os3Mpfn+QnSX4IbDdQ/y8r9lOM7Z3Ae5M8Fdgf+MeBFfVLk5yQZM3W76FJrkqyMMnHxri/fZPMTzL/xhtvnMyUSJIk9ZrJ+VKUZAtgD2Bz4O+BLUdcXw34PPCCqnoWsMHIPtpK8inAu6pqblX9DDi5qrasqqcDP+b+1efJ+FNVPRs4Evg28FZgU2DvJA9L8hRgd2C7qpoL3AvslWRD4BC6pPz5jL2SP+nYquoG4N+AC4AP073/3g/sVFXPAOYD/5DkocDLgadW1Wat7mj9HVVVQ1U1tMEGD5hKSZKkGcfkfOnaHvhmVf2xqm6lS7IHPRm4tqqua+ffmGS/myY5N8kiYC/gqVOIaTiGRcCVVXVDVd0NXAs8BngesAVwcZIF7fxvgK2Bs6vqxqr6E3D8UortM8DKVXUM8Ey6pH9eG/t1wGOBW4G7gC8m+Xvgj1O4X0mSpBlrlekOYBaqca5lMfs8Btilqi5PsjewwxTa3t1+3zdwPHy+Sovpy1X13r8KNNmF8e9lsWKrqvuSDPcb4Iyq2nNkvSRb0X1Q2INuC8xzJxGLJEnSjObK+dJ1DvDyJA9JshbwkhHXrwb+Jsmcdr77GP3cBqw1cL4WcEOSVelWp5emHwC7Jnk4QJKHJnkscBGwQ9v6siqw2xjtlyS2C4Htkjy+jb16kie2fefrVNV/Au8A5k71piRJkmYiV86Xoqq6NMnxwAK6byY5d8T1O5O8BfhekpuAH43R1XHAF5IcAOwKfIAuWf453faUtcZotzgxX5Xk/cDpSVYC/gy8taouTHIw3f7wG4BLgZVH6WKxY6uqG9tq+zeSPLgVv5/uw8m32x790D1IKkmSNOulajI7F7S0JFmzqm5v3/f9GeCnVXXEdMc10w0NDdX8+fOnOwxJkqQJJbmkqoZGu+a2luXvje3hxyuBdei+vUWSJElyW8vy1lbJl+pKeZLPMPA95M0nquropTnOFOJ5Hw/co35CVX1kOuKRJEmaKUzOZ4Gqeut0xzCoJeEm4pIkSVPkthZJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZPzEZJ8MMlOE9TZIcm2yzGmHZKc2o5fmuQ9i9nHUo85yfOTXJJkUfv93HHqfj3JmwfOt06yMMkqSzsuSZKkmcikaECSlavqoElU3QG4HTh/2Ub0QFV1CnDKYjTdgWUT803AS6rq10k2BU4DHjVG3XcCFyQ5Efgd8GngLVV1z1KOSZIkaUZaYVbOk8xJcnWSL7fV2hOTrJ7k+iQHJTkP2C3JMUl2bW2uT3JIkkvbyvCTk8wB9gPemWRBku3HGO+YJJ9LclaSa5M8J8mXkvw4yTED9XZOckEb44Qka7byv2vxngf8/UD9vZN8uh2/JMlFSS5L8v0kjxjr3kfGnOS6JKu262u3e101ydlJ/i3J+UmuSLJVq7NGi//iNt7LAKrqsqr6dRvqSmC1JA8eLY6q+g3wMeBfWjwL6ZL1w1u/C5O8qY23YZJzWrxXjDbPSfZNMj/J/BtvvHG0ISVJkmaUFSY5b54EHFVVmwG3Am9p5XdV1bOq6rhR2txUVc8APgccWFXXA0cCR1TV3Ko6d5zx1gOeS7di/B3gCOCpwNOSzE2yPvB+YKc2xnzgH5KsBnwBeAmwPfB/xuj/POCZVbU5cBzw7tEqjRHz2cCLWpU9gJOq6s/tfI2q2rbNz5da2fuAM6tqS2BH4PAka4wY6hXAZVV19zhzciSwCfCuFu8+wC2t3y2BNyZ5HPAq4LSqmgs8HVgwyn0dVVVDVTW0wQYbjDOkJEnSzLCibWv5RVXNa8dfAw5ox8eP0+bk9vsSBlawJ+k7VVVJFgG/qapFAEmuBOYAj6ZLVOclAXgQcAHwZOC6qvppq/81YN9R+n80cHySDVvb66YQ2xfpkuNvAa8H3jhw7RsAVXVOW1VfF9gZeGmSA1ud1YCNgB+3GJ8KHNbqjamq7kvyeWCoqn6XZGdgs+G/VgDrAE8ALga+1Fb3v1VVC6Zwb5IkSTPSipac1xjnd4zTZngV+F6mPl/Dbe8bOB4+X6X1eUZV7TnYKMncUWIdzaeAj1fVKUl2AA6ebGBVNa9t9XkOsHJVXTF4eWR1IMArquqakX0leTTwTeC1VfWzSQx/X/uh9fu2qjptlH6fTbe6/9Ukh1fVVybRtyRJ0oy1om1r2SjJNu14T7ptIYvjNmCtpRDPhcB2SR4P0PbAPxG4Gnhcko1bvT3HaL8O8Kt2/LoJxhot5q/QrZIfPaJ89xbPs+i2nNxC96Dn29KW+JNs3n6vC3wXeO/AXyWm4jTgzQP735/Y9rc/FvhtVX0B+HfgGYvRtyRJ0oyyoiXnPwZel2Qh8FC6feSL4zvAy8d7IHQyqupGYG/gGy2mC4EnV9VddNtYvtseCP35GF0cDJyQ5Fy6b02ZaszH0u2L/8aIujcnOZ9uf/g+rexDwKrAwiRXtHOA/YHHAx9ofS9I8vAJYhn0ReAq4NLW7+fp/qqwA7AgyWV0e9k/MYU+JUmSZqRUTWb3xMzXvrHk1KradLpj6Yu2z/tlVfWagbKz6R58nT9tgS2GoaGhmj9/RoUsSZJWUEkuqaqh0a6taHvO1ST5FPAC4IXTHYskSZI6K0xy3r5OcKmvmid5H7DbiOITquojS3usScbzeuDtI4rnVdVbBwuq6m2jta+qHZZSHBcBI7/v/DXD31gjSZKkB1phtrVodnNbiyRJminG29ayoj0QKkmSJPWWybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icj5Dkg0l2mqDODkm2XY4x7ZDk1Hb80iTvWcw+lnrMSR6W5Kwktyf59AR1v57kzQPnWydZmGSVpR2XJEnSTGRSNCDJylV10CSq7gDcDpy/bCN6oKo6BThlMZruwLKJ+S7gA8Cm7Wc87wQuSHIi8Dvg08BbquqepRyTJEnSjLTCrJwnmZPk6iRfbqu1JyZZPcn1SQ5Kch6wW5Jjkuza2lyf5JAklyZZlOTJSeYA+wHvTLIgyfZjjHdMks+1VeVrkzwnyZeS/DjJMQP1dk5yQRvjhCRrtvK/a/GeB/z9QP29h1eok7wkyUVJLkvy/SSPGOveR8ac5Lokq7bra7d7XTXJ2Un+Lcn5Sa5IslWrs0aL/+I23ssAquqOqjqPLkkfV1X9BvgY8C8tnoV0yfrhrd+FSd7UxtswyTkt3itGm+ck+yaZn2T+jTfeONHwkiRJvbfCJOfNk4Cjqmoz4FbgLa38rqp6VlUdN0qbm6rqGcDngAOr6nrgSOCIqppbVeeOM956wHPpVoy/AxwBPBV4WpK5SdYH3g/s1MaYD/xDktWALwAvAbYH/s8Y/Z8HPLOqNgeOA949WqUxYj4beFGrsgdwUlX9uZ2vUVXbtvn5Uit7H3BmVW0J7AgcnmSNce59LEcCmwDvavHuA9zS+t0SeGOSxwGvAk6rqrnA04EFo9zXUVU1VFVDG2ywwWKEIkmS1C8r2raWX1TVvHb8NeCAdnz8OG1Obr8vYWAFe5K+U1WVZBHwm6paBJDkSmAO8Gi6RHVeEoAHARcATwauq6qftvpfA/Ydpf9HA8cn2bC1vW4KsX2RLjn+FvB64I0D174BUFXntFX1dYGdgZcmObDVWQ3YCPjxFMakqu5L8nlgqKp+l2RnYLPhv1YA6wBPAC4GvtRW979VVQumMo4kSdJMtKIl5zXG+R3jtLm7/b6Xqc/XcNv7Bo6Hz1dpfZ5RVXsONkoyd5RYR/Mp4ONVdUqSHYCDJxtYVc1rW32eA6xcVVcMXh5ZHQjwiqq6ZrJjjOO+9kPr921VddrISkmeTbe6/9Ukh1fVV5bC2JIkSb21om1r2SjJNu14T7ptIYvjNmCtpRDPhcB2SR4P0PbAPxG4Gnhcko1bvT3HaL8O8Kt2/LoJxhot5q/QrZIfPaJ89xbPs+i2nNwCnAa8LW2JP8nmE4w3WacBbx7Y//7Etr/9scBvq+oLwL8Dz1hK40mSJPXWipac/xh4XZKFwEPp9pEvju8ALx/vgdDJqKobgb2Bb7SYLgSeXFV30W1j+W57IPTnY3RxMHBCknOBmxYj5mPp9sV/Y0Tdm5OcT7c/fJ9W9iFgVWBhkivaOdA9OAt8HNg7yS+TbDJBLIO+CFwFXNr6/TzdXxV2ABYkuQx4BfCJKfQpSZI0I6VqMrsnZr72jSWnVtVEX/e3wmj7vF9WVa8ZKDub7sHX+dMW2GIYGhqq+fNnVMiSJGkFleSSqhoa7dqKtudcTZJPAS8AXjjdsUiSJKmzwiTn7esEl/qqeZL3AbuNKD6hqj6ytMeaZDyvB94+onheVb11sKCq3jZa+6raYSnFcRHw4BHFrxn+xhpJkiQ90AqzrUWzm9taJEnSTDHetpYV7YFQSZIkqbdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6onfJeZLbF7PdO5KsvhTjmJvkhQPnL03yngnafDDJTksaT5J7kyxIckWSE5KsnmROkisWp78lkeTlSSrJkydR96/uOcl/Jll3nPqPTHJiO/6r+ZYkSVoR9S45XwLvAJZacg7MBf6SLFbVKVV16HgNquqgqvr+UojnzqqaW1WbAn8C9lvMfpaGPYHzgD0mUfcdDNxzVb2wqv4wVuWq+nVV7dpO5zIw35IkSSui3ibnSdZM8oMklyZZlORlrXyNJN9NcnlbWd49yQHAI4Gzkpw1Tp+fSzI/yZVJDhko3zLJ+a3PHyVZB/ggsHtbwd49yd5JPp1knSTXJ1mptV09yS+SrJrkmCS7jownyT5JjhgY741JPj7JqTgXeHw7XjnJF1r8pyd5SOtvbpILkyxM8s0k67Xys5Mc1u7pJ0m2b+UrJzk8ycWtzZvGeg2A7YB9GEjOW/uPtddlYZK3jfYatHlav8XwloH2Byf5f8N/DUjyoFHm+6dJNmj1V0ry30nWHxHfvu31nH/jjTdOcjolSZL6q7fJOXAX8PKqegawI/CvSQL8HfDrqnp6W1n+XlV9Evg1sGNV7ThOn++rqiFgM+A5STZrieHxwNur6unATsAdwEHA8W0F+/jhDqrqFuBy4Dmt6CXAaVX154E6I+M5DnhpklVbldcDR080AUlWAV4ALGpFTwA+U1VPBf4AvKKVfwX4x6rarNX9p4FuVqmqrehWtYfL9wFuqaotgS2BNyZ53Cgh7EI3vz8Bfp/kGa18X+BxwOZtzGMneA2OA3YfOH8lcMLwSVX9iQfO99eAvVqVnYDLq+qmwU6r6qiqGqqqoQ022GCU8CVJkmaWPifnAf45yULg+8CjgEfQJZ87tdXY7VuyPFmvTHIpcBnwVGAT4EnADVV1MUBV3VpV90zQz/Hcn2zu0c7HVFV3AGcCL257t1etqkXjNHlIkgXAfOB/gH9v5ddV1YJ2fAkwp63yr1tVP2zlXwaePdDXyYP12/HOwGvbGBcBD6NL/Efaky6xpv3esx3vBBw5PE9V9ftx7oWqugx4eNtj/nTg5qr6n/HaAF8CXtuO38AkPsxIkiTNdKtMdwDj2AvYANiiqv6c5Hpgtar6SZIt6PYnfzTJ6VX1wYk6ayvDBwJbVtXNSY4BVqP7EFBTjO2UNvZDgS3oEu+JfBH4/4CrmTjRvLOq5g4WdH804O6BonuBh0xi3OE293L/6x3gbVV12liNkjwMeC6waZICVgYqybtZvDk7EdgV+D/cn/CPqap+keQ3SZ4LbM39q+iSJEmzVp9XztcBftsS8x2Bx0L3DR/AH6vqa8DHgOGtFrcBa43T39p021VuSfIIuu0i0CXLj0yyZet/rbadZMz+qup24EfAJ4BTq+reUar9Vfuqugh4DPAq4BsT3Puktb8c3Dy8nxx4DfDDcZoAnAa8eXibTZInJlljRJ1dga9U1WOrak5VPQa4DngWcDqwX5sn2ocUGP81OI7urwy70iXqI43W9ot021v+Y4w5liRJmlX6nJwfCwwlmU+3anp1K38a8KO2JeN9wIdb+VHAf431QGhVXU63neVKui0T81r5n+i2qHwqyeXAGXQr6mcBmww/oDhKl8cDr2bsLS2jxfMfwLyqunmCe5+q1wGHty1Ac+kerhzPF4GrgEvTfT3j53ngX1H2BL45ouwkug8XX6TbbrOwzdmr2vUxX4OqupIu+f5VVd0wSkyjzfcpwJq4pUWSJK0gUjXV3QlaXElOBY6oqh9MdywzQZIhuvnafqK6Q0NDNX/+/OUQlSRJ0pJJckn7kpIH6PPK+ayRZN0kP6HbS25iPgnp/sGnk4D3TncskiRJy0ufHwhdbEkuAh48ovg1E3xDyjLT/iGeJw6WtQcuR0vUn1dVv1secfVZ+wefxv1HnyRJkmabWZmcV9XW0x3DRFoCPne645AkSVJ/uK1FkiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmfpCS7JflxkrOSDCX5ZCvfO8mnpzu+iSS5fYLr6yZ5y2L0e3CSA8e5fkySXafaryRJ0opolekOoE+SrFxV945xeR/gLVV1Vjufv5zCWl7WBd4CfHaa45AkSVphrTAr50nmJLk6yZeTLExyYpLVk1yf5KAk5wG7JdkzyaIkVyQ5rLU9CHgWcGSSw5PskOTUUcbYIMlJSS5uP9uNE8+aSY5uYy1M8opW/oDxW/ntSQ5LckmS7yfZKsnZSa5N8tJWZ+8k307yvSTXJPmnMcZ+V4tvYZJDWvGhwMZJFiQ5fJx6JHlf6//7wJOm9kpAktUG7v2yJDu28v9Mslk7vqzNO0k+lOT/jtLPvknmJ5l/4403TjUMSZKk3lnRVs6fBOxTVfOSfIlupRjgrqp6VpJHAhcCWwA3A6cn2aWqPpjkucCBVTU/yQ5j9P8J4IiqOi/JRsBpwFPGqPsB4JaqehpAkvXa+IeNMv63gDWAs6vqH5N8E/gw8HxgE+DLwCmt362ATYE/Ahcn+W5V/WWVP8nOwBNavQCnJHk28B5g06qaO0G9O4A9gM3p3j+XApeMcY9jeStAVT0tyZPbfT4ROAfYPsn1wD3A8IebZwFfG9lJVR0FHAUwNDRUU4xBkiSpd1a05PwXVTWvHX8NOKAdH99+b0mXAN8IkORY4NnAtybZ/07AJkmGz9dOslZV3TZG3T2GT6rq5pb8jjX+n4DvteqLgLur6s9JFgFzBvo9o6p+19qfTJfYDm7B2bn9XNbO16RLwv9nRHxj1VsL+GZV/bGNcQpT9yzgU+2+r07yc+CJwLl0r8l1wHeB5ydZHZhTVdcsxjiSJEkzyoqWnI9cXR0+v6P9DktmJWCbqrpzEnUzSjzjjf/nqhqufx9wN0BV3Zdk8HUc6x4Hx/hoVX3+rwqTOZOs945R+pyqse7zYmAIuBY4A1gfeCNTX5mXJEmakVaYPefNRkm2acd7AueNuH4R8Jwk6ydZudX54RT6Px3Yf/gkydwp1F1vKYwP3WrzQ5M8BNgFmDfi+mnAG5Ks2cZ9VJKHA7fRrYpPVO8c4OVJHpJkLeAlU4yP1sderd8nAhsB11TVn4BfAK+k2150LnBg+y1JkjTrrWjJ+Y+B1yVZCDwU+Nzgxaq6AXgvcBZwOXBpVX17Cv0fAAy1ByivAvYbp+6HgfXag5+XAzsuhfGh+8DxVWABcNLgfnOAqjod+DpwQdsScyKwVtsKM6/Fc/g49S6l2wa0ADiJySXOn0/yy/ZzAd03wqzc+j0e2Luq7m51zwV+07bNnAs8epJjSJIkzXi5f6fE7Na2bZxaVZtOdyzLSpK9gaGq2n+iurPN0NBQzZ8/277dUpIkzUZJLqmqodGurWgr55IkSVJvrTAPhFbV9XRfMbhcJXk98PYRxfOq6q1Le6yqOgY4Zmn3OxlJPsP9X3047BNVdfR0xCNJkjQTrTDJ+XRpyemsT1CXxYcNSZKkFY3bWiRJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknep+cJ3lHktWnO47JSjInyasGzoeSfHIp93/FiLKDkxy4tMaYYjw7JDl1GY9xfZL1l+UYkiRJfbDMkvMkK493PgXvAGZMcg7MAf6SnFfV/Ko6YPrCkSRJ0kwxqeQ8yWuTLExyeZKvJjkmya4D129vv3dIclaSrwOLRjlfOcnhSS5u/b1poN3ZSU5McnWSY9M5AHgkcFaSs8aJb88ki5JckeSwgfK/S3Jpi/sHrWzNJEe3+guTvGLwHtrxrkmOacfHJDkyyblJfpLkxa18Tiu7tP1s25ofCmyfZEGSdw6uLCd5aJJvtXEvTLJZKz84yZfaHFzb7nuxtD4OS/KjFu/2rXy8uf9hkv9o9Q9NsldrvyjJxuPNw4ixH3B/SVZK8tMkG7Q6KyX57yTrJ9kgyUktpouTbNfqPCzJ6UkuS/J5IGPc675J5ieZf+ONNy7ulEmSJPXGKhNVSPJU4H3AdlV1U5KHAh8fp8lWwKZVdV2SHUac7wvcUlVbJnkwMC/J6a3d5sBTgV8D89p4n0zyD8COVXXTGPE9EjgM2AK4GTg9yS6tjy8Az25jP7Q1+UCL4Wmt/XoTzQHdavhzgI3pPig8Hvgt8PyquivJE4BvAEPAe4ADq2o4id9hoJ9DgMuqapckzwW+Asxt154M7AisBVyT5HNV9edJxDaaVapqqyQvBP4J2AnYh7Hn/unAU4DfA9cCX2zt3w68je6vF2PNw6AH3F9VzU3yNWAv4N9aLJe399LXgSOq6rwkGwGntTj+CTivqj6Y5EXAvqPdZFUdBRwFMDQ0VIs5V5IkSb0xYXIOPBc4cTg5rqrfJ6MuZA77UVVdN8b5zsBmuX/VfR3gCcCfWr1fAiRZQJcInjeJ+LYEzq6qG1vbY4FnA/cC5wyPXVW/b/V3AvYYblxVN09ijP+oqvuAnya5li6Rvg74dJK5bawnTqKfZwGvaOOe2VaI12nXvltVdwN3J/kt8Ajgl6P0MVYSOlh+cvt9Cd08wvhzf3FV3QCQ5GfAcNK+iO4Dw7DR5mEy9/cl4Nt0yfkbgKNb/Z2ATQbeT2snWYvu9fv71s93k0zmNZIkSZrxJpOchwcmhPfQtsSky6weNHDtjhF1B88DvK2qTvurAbrV5bsHiu6dZGzDfY5VPloiO1b5YNlq41wbPn8n8Bu6VeeVgLsmjHT0WIf7nuz9/w4Yudr/ULoPC8OG+xrsZ7Jzf9/A+X0j4hhtHv6qu1Hirar6RZLftNX0relW0aGbt22q6s4RMY3WtyRJ0qw3mT3nPwBemeRh0O0rBq6n20YC8DJg1UmOdxrw5iSrtr6emGSNCdrcRrfVYywXAc9pe5hXBvYEfghc0MofNxA3dKvC+w83HtjW8pskT0myEvDyEWPs1vZKbwz8DXAN3crzDW0l+TXA8AOv48V7Di0xbUnxTVV16zj39gBVdTtwQ5LnDdzX3zHxXxkWZ+5HGm0eBo13f18Evka3+n5vKxv5WswdpZ8X8MAPI5IkSbPShKvTVXVlko8AP0xyL3AZ8I/At5P8iC55H7laPpYv0m2zuLStuN8I7DJBm6OA/0pyQ1XtOPJiVd2Q5L3AWXQrt/9ZVd+G7oFB4OSWcP8WeD7wYeAz6b6O8F66fdIn0+0VPxX4BXAFsObAMNfQJfyPAPZr+8w/C5yUZLc29vAcLATuSXI5cAzdfA07GDg6yULgj8DrJrj3sby23cO/tvNDqupnE7RZnLkfabR5GLx+MGPf3yl021mOHig7oN3HQrr34jnAfnSvyTeSXNrG+58pxilJkjQjpcrdA+NJ960tp1bVidMdy3Ra0nlIMkT38Of2SzWwZmhoqObPn78supYkSVqqklxSVUOjXZvsvm5psSV5D/Bm7t9rLkmSpFHMqJXzJBcBDx5R/JqqWjQd8SxLbY//D0a59Lyq+t3yjqfvXDmXJEkzxaxZOa+qrac7huWlJeBzpzsOSZIkLT+T+hdCJUmSJC17JueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkSZLUEybnkiRJUk+YnEuSJEk9YXIuSZIk9YTJuSRJktQTJueSJElST5icS5IkST1hci5JkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPbFCJ+dJ3pFk9emOY7KSzEnyqoHzoSSfXMpjvC/JlUkWJlmQZOtWvlhzleT2JYhl7ySPXNz2kiRJM82MTM6TrDze+RS8A5gxyTkwB/hLcl5V86vqgKXVeZJtgBcDz6iqzYCdgF+0y+9g+c/V3oDJuSRJWmFMe3Ke5LVtlfbyJF9NckySXQeu395+75DkrCRfBxaNcr5yksOTXNz6e9NAu7OTnJjk6iTHpnMAXeJ3VpKzxolvzySLklyR5LCB8r9LcmmL+wetbM0kR7f6C5O8YvAe2vGuSY5px8ckOTLJuUl+kuTFrXxOK7u0/Wzbmh8KbN9WtN/Z7u3U1uahSb7Vxr0wyWat/OAkX2pzcG2777FsCNxUVXcDVNVNVfXr0eZqnHt6XJIL2uvwoRFz+a6B1+eQgXv9cZIvtBX705M8pL0HhoBj2/0+ZJTXZt8k85PMv/HGG8e5LUmSpJlhWpPzJE8F3gc8t6qeDrx9giZbAe+rqk1GOd8HuKWqtgS2BN6Y5HGt3uZ0K7+bAH8DbFdVnwR+DexYVTuOEd8jgcOA5wJzgS2T7JJkA+ALwCta3Lu1Jh9oMTytrTyfOYlpmAM8B3gRcGSS1YDfAs+vqmcAuwPDW1feA5xbVXOr6ogR/RwCXNbG/f+ArwxcezLwt22+/inJqmPEcjrwmPZB4bNJngMwmbka8Angc+11+N/hwiQ7A09oMcwFtkjy7Hb5CcBnquqpwB/o5vVEYD6wV7vfO0cOVFVHVdVQVQ1tsMEGE4QlSZLUf9O9cv5c4MSqugmgqn4/Qf0fVdV1Y5zvDLw2yQLgIuBhdEnfcL1fVtV9wAK6hHgytgTOrqobq+oe4Fjg2cAzgXOGxx6IeyfgM8ONq+rmSYzxH1V1X1X9FLiWLpFeFfhCkkXACXQfKibyLOCrbdwzgYclWadd+25V3d3m+bfAI0broKpuB7YA9gVuBI5Psvckxh60HfCNdvzVgfKd289lwKV09zn8+lxXVQva8SVM/vWRJEmaVVaZ5vED1Iiye2gfGpIEeNDAtTtG1B08D/C2qjrtrwZIdgDuHii6l8nfd8YpHxn3eOWDZauNc234/J3Ab4Cn083FXRNGOnqsw31P+v6r6l7gbODs9uHgdcAx4/QNE9/TcHwfrarP/1VhMmeU+B6whUWSJGlFMN0r5z8AXpnkYdDtmwaup1u9BXgZ3SryZJwGvHl4y0aSJyZZY4I2twFrjXP9IuA5SdZvD53uCfwQuKCVP24gbui2hew/3DjJeu3wN0mekmQl4OUjxtgtyUpJNqbbcnMNsA5wQ1vpfw0w/MDrePGeA+zVxt2Bbu/4rePc2wMkeVKSJwwUzQV+PsbYY93TPGCPdrzXQPlpwBuSrNnGelSSh08Q0kSvjyRJ0qwyrSvnVXVlko8AP0xyL92Wh38Evp3kR3TJ+8jV8rF8kW47xKVtxf1GYJcJ2hwF/FeSG0bbS11VNyR5L3AW3crvf1bVt6F7GBE4uSWnvwWeD3wY+EySK+hWgA8BTqbbK34q3TefXAGsOTDMNXQJ/yOA/arqriSfBU5Kslsbe3gOFgL3JLmcbjX7soF+DgaOTrIQ+CPdivdUrQl8Ksm6dH/B+G+6LS7wwLka657eDnw9yduBk4Y7rqrTkzwFuKB7ebgdeHWbp7EcQ7cP/05gm9H2nUuSJM0mqRptB4KWh/YNJ6e2hx+1BIaGhmr+/PnTHYYkSdKEklxSVUOjXZvubS2SJEmSmul+ILQ3klwEPHhE8WuqatGyGrOq9l5WfY+n7fH/wSiXnldVv1ve8UiSJKljct5U1dbTHcPy0hLwudMdhyRJkv6a21okSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk/OeSjKU5JPTHceykGS/JK8d5/pLk7ynHe+SZJPlF50kSdL0WWW6A9Doqmo+MH+645iMJKtU1T2TrV9VR05w/RTglHa6C3AqcNViByhJkjRDuHI+IMmcJFcn+XKShUlOTLJ6koOSXJzkiiRHJUmrv2Wrd0GSw5Nc0cpXbucXt+tvGmfM45O8cOD8mCSvSLJDklNb2RpJvtT6uyzJy1r5fybZrB1fluSgdvyhJP83yYZJzkmyoMW+/Thx3J7kX5NcmuQHSTZo5Rsn+V6SS5Kcm+TJA3F+PMlZwGGj9LdSkuuTrDtQ9t9JHpHk4CQHtrIDklzV5um4VrZ3kk8n2RZ4KXB4u4eNR4yxb5L5SebfeOONY7+wkiRJM4TJ+QM9CTiqqjYDbgXeAny6qrasqk2BhwAvbnWPBvarqm2Aewf62Ae4paq2BLYE3pjkcWOMdxywO0CSBwHPA/5zRJ33AWe2/nakS1bXAM4Btk+yNnAPsF2r/yzgXOBVwGlVNRd4OrBgnPteA7i0qp4B/BD4p1Z+FPC2qtoCOBD47ECbJwI7VdX/G9lZVd0HfBt4ebu3rYHrq+o3I6q+B9i8zfd+I/o4n24F/V1VNbeqfjbi+lFVNVRVQxtssME4tyZJkjQzmJw/0C+qal47/hpdortjkouSLAKeCzy1rQiv1RJIgK8P9LEz8NokC4CLgIcBTxhjvP8CnpvkwcALgHOq6s4RdXYG3tP6OxtYDdiILgF/dovxu8CaSVYH5lTVNcDFwOuTHAw8rapuG+e+7wOOH7zvJGsC2wIntLE/D2w40OaEqrqXsR1P++AB7DHQ/6CFwLFJXk33AUOSJGmF5Z7zB6pRzj8LDFXVL1qiuxqQcfoI3WrzaRMOVnVXkrOBv6VLZL8xRn+vaAn3/YXdSvsQcC1wBrA+8Ebgktb3OUmeDbwI+GqSw6vqKxPFNBwa3Ye3P7SV99HcMUEfFwCPb1tkdgE+PEqdF9F9wHgp8IEkT51kfJIkSbOOK+cPtFGSbdrxnsB57fimtpK8K0BV3QzcluSZ7foeA32cBrw5yaoASZ7YtqGM5Tjg9cD2re1IpwFvG9jrvnmL4U/AL4BXAhfSraQf2H6T5LHAb6vqC8C/A88YJ4aVhu+NbjvMeVV1K3Bdkt1af0ny9HH6+CtVVcA3gY8DP66q3w1eT7IS8JiqOgt4N7AusOaIbm4D1prsmJIkSTOZyfkD/Rh4XZKFwEOBzwFfABYB36LbKjJsH+CoJBfQrW7f0sq/SPftIpe2h0Q/z/h/pTidbvX4+y3hHulDwKrAwtbfhwaunQv8pqr+2I4f3X4D7AAsSHIZ8ArgE+PEcAfddp1L6LbufLCV7wXsk+Ry4ErgZeP0MZrjgVcz+paWlYGvte1ClwFHVNUfRtQ5DnhXe+B145EdSJIkzSbpFjcF3be1AKe2Bz8nU3/Nqrq9Hb8H2LCq3r4MQ1xmktxeVSNXrWeMoaGhmj9/RnzzpCRJWsEluaSqhka75p7zJfOiJO+lm8efA3tPbziSJEmayUzOB1TV9cCkVs1b/eMZfbvGAyR5GvDVEcV3V9XWkw5wKUhyEfDgEcWvWZJV8ySvB0b+xWBeVb11cfuUJElaEZmcLydVtQiY24M4lvqHgao6mu473yVJkrQEfCBUkiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmfZZJcn2T9SdYdSvLJdrxDkm2Xciz7JXntONdfmuQ97XiXJJsszfElSZJmmlWmOwBNn6qaD8xvpzsAtwPnL8X+j5zg+inAKe10F+BU4KqlNb4kSdJM48p5TyV5d5ID2vERSc5sx89L8rUkOye5IMmlSU5IsuZA83cl+VH7eXxrt1uSK5JcnuScVrZDklOTzAH2A96ZZEGS7ZNskOSkJBe3n+3GiHOltlq/7kDZfyd5RJKDkxzYyg5IclWShUmOa2V7J/l0W7F/KXB4G3/j0epLkiTNdq6c99c5wP8DPgkMAQ9OsirwLGAR8H5gp6q6I8k/Av8AfLC1vbWqtmpbSv4NeDFwEPC3VfWrwUQaoKquT3IkcHtVfQwgydeBI6rqvCQbAacBTxkZZFXdl+TbwMuBo5NsDVxfVb9JMlj1PcDjquruUcY/P8kpwKlVdWIbf8z6w5LsC+wLsNFGG403l5IkSTOCK+f9dQmwRZK1gLuBC+iS9O2BO4FNgHlJFgCvAx470PYbA7+3acfzgGOSvBFYeRLj7wR8uvV/CrB2i2U0xwO7t+M92vlIC4Fjk7wauGcS409Yv6qOqqqhqhraYIMNJtGlJElSv7ly3lNV9eck1wOvp9sHvhDYEdgYuA44o6r2HKv5yOOq2q+tar8IWJBk7gQhrARsU1V3TiLcC4DHJ9mAbu/4h0ep8yLg2XTbVz6Q5KkT9PmA+lU1maRekiRpxnLlvN/OAQ5sv8+l2xe+ALgQ2G5gP/nqSZ440G73gd8XtDobV9VFVXUQcBPwmBFj3QYMroyfDuw/fDJeMl9VBXwT+Djw46r63eD1JCsBj6mqs4B3A+sCa47o5i/jT7K+JEnSrGNy3m/nAhsCF1TVb4C7gHOr6kZgb+AbSRbSJetPHmj34CQXAW8H3tnKDk+yKMkVdMn+5SPG+g7w8uEHQoEDgKH2QOZVdB8MxnM88GpG39KyMvC1JIuAy+j2sv9hRJ3j6B5kvQx4wiTqS5IkzTrpFj2lmW1oaKjmz58/cUVJkqRpluSSqhoa7Zor55IkSVJP+ECoJi3J6+m2ygyaV1VvnY54JEmSZhuTc01aVR0NHD3dcUiSJM1WbmuRJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6gmTc0mSJKknTM4lSZKknjA5lyRJknrC5FySJEnqCZNzSZIkqSdMziVJkqSeMDmXJEmSesLkXJIkSeoJk3NJkiSpJ0zOJUmSpJ5IVU13DNISS3IbcM10xzHDrA/cNN1BzCDO19Q4X1PjfE2dczY1ztfULOv5emxVbTDahVWW4aDS8nRNVQ1NdxAzSZL5ztnkOV9T43xNjfM1dc7Z1DhfUzOd8+W2FkmSJKknTM4lSZKknjA512xx1HQHMAM5Z1PjfE2N8zU1ztfUOWdT43xNzbTNlw+ESpIkST3hyrkkSZLUEybnkiRJUk+YnKv3kvxdkmuS/HeS94xyPUk+2a4vTPKMybadjZZwvq5PsijJgiTzl2/k02MS8/XkJBckuTvJgVNpO1st4Zz5Hnvg9b3a/xYXJjk/ydMn23Y2WsL5WuHeXzCpOXtZm68FSeYnedZk285GSzhfy/49VlX++NPbH2Bl4GfA3wAPAi4HNhlR54XAfwEBnglcNNm2s+1nSearXbseWH+676Nn8/VwYEvgI8CBU2k7G3+WZM58j405X9sC67XjF/jfsMWbrxXx/TWFOVuT+58z3Ay42vfY1Odreb3HXDlX320F/HdVXVtVfwKOA142os7LgK9U50Jg3SQbTrLtbLMk87UimnC+quq3VXUx8Oeptp2llmTOVkSTma/zq+rmdnoh8OjJtp2FlmS+VlSTmbPbq2WWwBpATbbtLLQk87VcmJyr7x4F/GLg/JetbDJ1JtN2tlmS+YLuP0CnJ7kkyb7LLMr+WJL3yIr4/oIlv2/fY+PP1z50f9lanLazwZLMF6x47y+Y5JwleXmSq4HvAm+YSttZZknmC5bDe2yVZdGptBRllLKRn2DHqjOZtrPNkswXwHZV9eskDwfOSHJ1VZ2zVCPslyV5j6yI7y9Y8vv2PTbGfCXZkS7ZHN7fuiK+x5ZkvmDFe3/BJOesqr4JfDPJs4EPATtNtu0ssyTzBcvhPebKufrul8BjBs4fDfx6knUm03a2WZL5oqqGf/8W+Cbdn/9msyV5j6yI7y9Ywvv2PTb6fCXZDPgi8LKq+t1U2s4ySzJfK+L7C6b4PmmJ5MZJ1p9q21liSeZrubzHTM7VdxcDT0jyuCQPAvYAThlR5xTgte1bSJ4J3FJVN0yy7Wyz2POVZI0kawEkWQPYGbhieQY/DZbkPbIivr9gCe7b99jo85VkI+Bk4DVV9ZOptJ2FFnu+VtD3F0xuzh6fJO34GXQPQv5uMm1nocWer+X1HnNbi3qtqu5Jsj9wGt0T1l+qqiuT7NeuHwn8J903kPw38Efg9eO1nYbbWG6WZL6AR9D9CQ+6/zZ8vaq+t5xvYbmazHwl+T/AfGBt4L4k76B7sv/WFe39BUs2Z8D6+B4b7X+TBwEPAz7b5uaeqhryv2FTmy9WwP+GwaTn7BV0izJ/Bu4Edm8PPPoem8J8JVku77Hhr4mRJEmSNM3c1iJJkiT1hMm5JEmS1BMm55IkSVJPmJxLkiRJPWFyLkmSJPWEybkkaZlIcm+SBUmuSPKdJOtOUP/gJAdOUGeXJJsMnH8wyU7jtZlkrMck2XVJ+5nimO9IsvryHFNS/5mcS5KWlTuram5VbQr8HnjrUuhzF7rvTAegqg6qqu8vhX6XqyQrA+8ATM4l/RWTc0nS8nAB8CiAJBsn+V6SS5Kcm+TJIysneWOSi5NcnuSkJKsn2RZ4KXB4W5HfeHjFO8kLkvzHQPsdknynHe+c5IIklyY5Icma4wWa5Pok/9zazE/yjCSnJfnZ8D9U0vo/J8k3k1yV5MgkK7VreyZZ1P5icNhAv7e3lf6LgPcBjwTOSnJWu/65Nt6VSQ4ZEc8hLf5Fw/OVZM0kR7eyhUlesTj3K6lfTM4lSctUWyV+Hvf/E9lHAW+rqi2AA4HPjtLs5KrasqqeDvwY2Keqzm99vKutyP9soP4ZwDPbP6kNsDtwfJL1gfcDO1XVM+j+5dJ/mETYv6iqbYBzgWOAXYFnAh8cqLMV8P+ApwEbA3+f5JHAYcBzgbnAlkl2afXXAK6oqq2r6oPAr4Edq2rHdv197V+63Ax4TpLNBsa6qcX/uTZnAB8Abqmqp1XVZsCZS3C/knpilekOQJI0az0kyQJgDnAJcEZbxd0WOKH9E9gADx6l7aZJPgysC6xJ909tj6n9k9zfA16S5ETgRcC7gefQbYOZ18Z7EN0q/kSGP0gsAtasqtuA25LcNbB3/kdVdS1Akm8AzwL+DJxdVTe28mOBZwPfAu4FThpnzFcm2Zfu/5s3bHEvbNdObr8vAf6+He8E7DEwBzcnefFi3q+knjA5lyQtK3dW1dwk6wCn0u05Pwb4Q1XNnaDtMcAuVXV5kr2BHSYx3vFtjN8DF1fVbeky1DOqas8pxn53+33fwPHw+fD/d9aINgWEsd1VVfeOdiHJ4+hWxLdsSfYxwGqjxHPvwPgZJYbFvV9JPeG2FknSMlVVtwAH0CWfdwLXJdkNIJ2nj9JsLeCGJKsCew2U39aujeZs4BnAG+kSdYALge2SPL6Nt3qSJy7ZHf3FVkke1/aa7w6cB1xEtyVl/badZ0/gh2O0H7yXtYE7gFuSPAJ4wSTGPx3Yf/gkyXos2/uVtByYnEuSlrmqugy4nG4bxl7APkkuB64EXjZKkw/QJbpnAFcPlB8HvCvJZUk2HjHGvXQr9C9ov2nbS/YGvpFkIV3y+oAHUBfTBcChwBXAdcA3q+oG4L3AWXT3e2lVfXuM9kcB/5XkrKq6HLiMbj6+BMybxPgfBtZrD55eTrd/fVner6TlIFUj/yImSZLGk2QH4MCqevE0hyJplnHlXJIkSeoJV84lSZKknnDlXJIkSeoJk3NJkiSpJ0zOJUmSpJ4wOZckSZJ6wuRckiRJ6on/HxF6MGyPQZByAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the feature importance\n",
    "importances = d_tree_tuned.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(figsize = (10, 10))\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "\n",
    "plt.barh(range(len(indices)), importances[indices], color = 'violet', align = 'center')\n",
    "\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "\n",
    "plt.xlabel('Relative Importance')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd76752",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- **Time spent on the website and first_interaction_website are the most important features** **followed by profile_completed, age, and last_activity**.\n",
    "- **The rest of the variables have no impact in this model, while deciding whether a lead will be converted or not**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018bcb7c",
   "metadata": {},
   "source": [
    "### **Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "65eef9e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', random_state=7)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the random forest tree classifier on the training data\n",
    "rf_estimator = RandomForestClassifier(random_state = 7, criterion = \"entropy\")\n",
    "\n",
    "rf_estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "31cdd7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2273\n",
      "           1       1.00      1.00      1.00       955\n",
      "\n",
      "    accuracy                           1.00      3228\n",
      "   macro avg       1.00      1.00      1.00      3228\n",
      "weighted avg       1.00      1.00      1.00      3228\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the training data\n",
    "y_pred_train3 = rf_estimator.predict(X_train)\n",
    "\n",
    "metrics_score(y_train, y_pred_train3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef61f6d",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- Looks like **Random Forest Classifier** gives us the same situation we had with the Decision Tree: **model is likely to be overfitting** (outperforms, its performance on training data is too good). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d025f7ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.90       962\n",
      "           1       0.81      0.70      0.75       422\n",
      "\n",
      "    accuracy                           0.86      1384\n",
      "   macro avg       0.84      0.81      0.83      1384\n",
      "weighted avg       0.86      0.86      0.86      1384\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the testing data\n",
    "y_pred_test3 = rf_estimator.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c48cad",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "We can see that model does perform better on training data, its perfomance is rather poor in terms of testing data.\n",
    "All the metrics scores drastically decreased for testing data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e0492b",
   "metadata": {},
   "source": [
    "**Let's see if we can get a better model by tuning the random forest classifier**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eab743b",
   "metadata": {},
   "source": [
    "### **Random Forest Classifier - Hyperparameter Tuning**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648b3bc8",
   "metadata": {},
   "source": [
    "Let's try **tuning some of the important hyperparameters of the Random Forest Classifier**. \n",
    "\n",
    "We will **not** tune the `criterion` hyperparameter as we know from hyperparameter tuning for decision trees that `entropy` is a better splitting criterion for this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15e12959",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the type of classifier\n",
    "rf_estimator_tuned = RandomForestClassifier(criterion = \"entropy\", random_state = 7)\n",
    "\n",
    "# grid of parameters to choose from\n",
    "parameters = {\"n_estimators\": [100, 110, 120],\n",
    "    \"max_depth\": [5, 6, 7],\n",
    "    \"max_features\": [0.8, 0.9, 1]\n",
    "             }\n",
    "\n",
    "# type of scoring used to compare parameter combinations - recall score for class 1\n",
    "scorer = metrics.make_scorer(recall_score, pos_label = 1)\n",
    "\n",
    "# run the grid search\n",
    "grid_obj = GridSearchCV(rf_estimator_tuned, parameters, scoring = scorer, cv = 5)\n",
    "\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the classifier to the best combination of parameters\n",
    "rf_estimator_tuned = grid_obj.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "023dcd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=6, max_features=0.8,\n",
       "                       n_estimators=110, random_state=7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the best algorithm to the training data\n",
    "rf_estimator_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4c83efa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.92      0.91      2273\n",
      "           1       0.80      0.78      0.79       955\n",
      "\n",
      "    accuracy                           0.88      3228\n",
      "   macro avg       0.86      0.85      0.85      3228\n",
      "weighted avg       0.88      0.88      0.88      3228\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the training data\n",
    "y_pred_train4 = rf_estimator_tuned.predict(X_train)\n",
    "\n",
    "metrics_score(y_train, y_pred_train4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "462cbb0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.92      0.90       962\n",
      "           1       0.79      0.73      0.76       422\n",
      "\n",
      "    accuracy                           0.86      1384\n",
      "   macro avg       0.84      0.82      0.83      1384\n",
      "weighted avg       0.86      0.86      0.86      1384\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_test4 = rf_estimator_tuned.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33352f6d",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- We can see that after hyperparameter tuning, the model is performing poorly on the train data as well.\n",
    "- We can try adding some other hyperparameters and/or changing values of some hyperparameters to tune the model and see if we can get better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5965979b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=6, max_features=0.8, max_samples=0.9,\n",
       "                       min_samples_leaf=25, n_estimators=120, random_state=7)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose the type of classifier \n",
    "rf_estimator_tuned = RandomForestClassifier(criterion = \"entropy\", random_state = 7)\n",
    "\n",
    "# grid of parameters to choose from\n",
    "parameters = {\"n_estimators\": [110, 120],\n",
    "    \"max_depth\": [6, 7],\n",
    "    \"min_samples_leaf\": [20, 25],\n",
    "    \"max_features\": [0.8, 0.9],\n",
    "    \"max_samples\": [0.9, 1],\n",
    "    \"class_weight\": [\"balanced\",{0: 0.3, 1: 0.7}]\n",
    "             }\n",
    "\n",
    "# type of scoring used to compare parameter combinations - recall score for class 1\n",
    "scorer = metrics.make_scorer(recall_score, pos_label = 1)\n",
    "\n",
    "# run the grid search on the training data using scorer=scorer and cv=5\n",
    "grid_obj = GridSearchCV(rf_estimator_tuned, parameters, scoring = scorer, cv = 5)\n",
    "\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# save the best estimator to variable rf_estimator_tuned\n",
    "rf_estimator_tuned = grid_obj.best_estimator_\n",
    "\n",
    "#fit the best estimator to the training data\n",
    "rf_estimator_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "98f369bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.83      0.88      2273\n",
      "           1       0.68      0.87      0.76       955\n",
      "\n",
      "    accuracy                           0.84      3228\n",
      "   macro avg       0.81      0.85      0.82      3228\n",
      "weighted avg       0.86      0.84      0.84      3228\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the training data\n",
    "y_pred_train5 = rf_estimator_tuned.predict(X_train)\n",
    "\n",
    "metrics_score(y_train, y_pred_train5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6afd8c5",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- The **model performs a bit better on training data but in general it seems to be rather balanced** now (precision, recall and f1-score are balanced). Recall is a bit higher for the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6013b005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.83      0.87       962\n",
      "           1       0.68      0.85      0.76       422\n",
      "\n",
      "    accuracy                           0.83      1384\n",
      "   macro avg       0.81      0.84      0.82      1384\n",
      "weighted avg       0.85      0.83      0.84      1384\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking performance on the test data\n",
    "y_pred_test5 = rf_estimator_tuned.predict(X_test)\n",
    "\n",
    "metrics_score(y_test, y_pred_test5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6257affc",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- The latest Random Forest model's values for recision, recall and f1-score are 0.68, 0.85 and 0.76 respectively for class 1 (converted). This combination of the values makes **this model the most reliable compared to any other models used before** (either Decision Tree, Decision Tree Tuned or Random Forest model with default parameters). \n",
    "- Precision has decreased vs. Random Forest with default parameters.\n",
    "- **Recall has increased significantly** and can only be comparable to Decision Tree with Hyperparameter tuned (0.86) but all the other scores have proven to become more reliable now.\n",
    "- And **since maximized Recall is of top priority for the company (in order to minimize False Negative values) we may assume Random Forest classifier using GridSearchCV is the model which performs the best for our goals**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "09bc0d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = rf_estimator_tuned.feature_importances_\n",
    "\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "feature_names = list(X.columns)\n",
    "\n",
    "plt.figure(figsize = (12, 12))\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "\n",
    "plt.barh(range(len(indices)), importances[indices], color = 'violet', align = 'center')\n",
    "\n",
    "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
    "\n",
    "plt.xlabel('Relative Importance')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2383d5bb",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "- Similar to the decision tree model, **time spent on website, first_interaction_website, profile_completed, and age are the top four features** that help distinguish between not converted and converted leads.\n",
    "- Unlike the decision tree, **the random forest gives some importance to other variables like occupation, page_views_per_visit, as well.** This implies that the random forest is giving importance to more factors in comparison to the decision tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b0a13f",
   "metadata": {},
   "source": [
    "## **Conclusion and Recommendations**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b726f64",
   "metadata": {},
   "source": [
    "### **Conclusions:**\n",
    "\n",
    "- We've compiled a few **tree-based ML classification models ExtraaLearn may use to predict which of company's leads are more likely to convert to paid customers or not in order to distribute the business efforts and resources the best possible way**. This prediction is able to help the company to tailor the most applicable marketing and business decisions in order not to loose those and engage them more efficiently (better ad targeting, more appropriate sales funnel, etc.).\n",
    "\n",
    "\n",
    "- **The Tuned Random Forest model has shown the best performance: the highest recall which was claimed to be of the first priority (0.85) and f1-score (0.76)**. The Decision Tree model could be another alternative to Random Forest since it has shown slightly better recall (0.86) but lower f1-score (0.72) and macro average (0.78) after hyperparameters tuned (good balancing and generalization of the results provided by both training and testing data). \n",
    "\n",
    "\n",
    "- Random Forest Classifier has also given us spreader understanding of which parameters might be crucial when predicting if a lead is going to convert to a paid customer or not. Thus, we could find out that the **most important features** determining that are: **time spent on website, first interaction - via website, level of profile completion (Medium), age,  last activity - phone, occupation, pages views**, etc. This set of features is wider than provided by pure Decision Tree classifier and gives the company more complex understanding of what may drive the conversion and how to treat this properly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00a36de3",
   "metadata": {},
   "source": [
    "### **Business Recommendations**\n",
    "\n",
    "1. **Time spent on website is a crucial factor** identifying whether a lead is going to convert to a paid customer or not. People spending solid time (over 7 minutes) on website are more likely to become customers. \n",
    "\n",
    "\n",
    "2. **ExtraaLearn is recommended to pay the most attention to the users who meet the following criteria**: \n",
    "\n",
    "     - used **website as the first interaction platform**\n",
    "     - spent **solid time on website**\n",
    "     - **completed their profiles to the point of at least 50.. 75% (Medium)**\n",
    "     - **age 25+ years**\n",
    "\n",
    "  **The company is advised to allocate primary resourses and build a separate approach (overall communication funnel, more sophisticated sales funnel, one-to-one communication and follow-ups, etc.) for this  audience** as it is more likely to be converted to paid customers. \n",
    "\n",
    "  Yet, additional research about what this audience is mostly driven by, what communication/customer support tools they prefer etc. may be required to increase the current conversion rate in the future. \n",
    "  \n",
    "  This information is also recommended to be used when tailoring the most proper communication and ad targeting strategies.\n",
    "\n",
    "3. **Younger audience (below 25 y.o., students) has been estimated as one the least likely to convert** no matter how much time was spent on website. Still, the company may be interested in **starting building long-term relationships with this audience either - they are getting older and growing financially, and ExtraaLearn should target at becoming their top-of-mind online education platform**. \n",
    "\n",
    "   For instance, it might make sense to offer some short-term affordable courses, keen and up-to-date articles/lectures/reports to this particular audience in order to build some trust and loyalty already. Something little but highly appreciated and easily memorized when it comes to the future purchase decisions.\n",
    "\n",
    "\n",
    "4. Among the **other things discovered** which can lead to higher conversion if treated properly:\n",
    "\n",
    "\n",
    "  - **Application**. 45% of the leads use application for their first interaction with ExtraaLearn and that's a lot, still the conversion is comparatively low. It is **recommended to researched how this interaction could become smoother and more efficient**: probably, it just needs to be simplified - less information required to complete the profile, improved UI/UX, etc.\n",
    " \n",
    " \n",
    "  - **Website**. It was observed that many users spent **too much time on website (up to 40 minutes), had numerous visits with huge depth of views (high nu. of pages per view) - this might signalize the website flow is not good enough, people find it difficult to find the information needed, interface is not intuitive enough, etc**. **Website health check-up** may help to find out if there are any issues with the website - if fixed the conversion might increase significantly.\n",
    "\n",
    "\n",
    "  - **Media types**. Print (magazines, in particular) as ad medium has proven to show little efficiency. **Rellocating resourses towards more applicable media channels such as digital and educational channels** is recommened in order to reach better conversion."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}