ml-model-training-n-predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Megaline Plan Recommendations to Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contents <a id='contents'></a>\n",
    "\n",
    "[1. Contents](#contents)   \n",
    "[2. Introduction](#introduction)  \n",
    "[3. Project Goal](#project_goal)     \n",
    "[4. Initialization](#initialization)  \n",
    "[5. Load data](#load-data)  \n",
    "[6. Data Preprocessing](#data-preprocessing)      \n",
    "[7. Develop a model with the highest possible accuracy](#develop-a-model)      \n",
    "- [7.1. Split the data](#split-the-data)  \n",
    "- [7.2. Train different models](#train-different-models)  \n",
    "- [7.3. Test the quality of the models using the test set](#test-the-quality)\n",
    "- [7.4. Find the best model](#the-best-model)  \n",
    "- [7.5. Sanity check the model](#sanity-check)  \n",
    "\n",
    "[8. Conclusion](#conclusion)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction <a id='introduction'></a> \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Megaline** is a telecom operator and it offers its clients two prepaid plans, **Surf** and **Ultimate**.  \n",
    "\n",
    "**Surf**\n",
    "- Monthly charge: &dollar;20\n",
    "- 500 monthly minutes, 50 texts, and 15 GB of data\n",
    "- After exceeding the package limits:\n",
    "    - 1 minute: 3 cents\n",
    "    - 1 text message: 3 cents\n",
    "    - 1 GB of data: &dollar;10  \n",
    "\n",
    "**Ultimate**\n",
    "- Monthly charge: &dollar;70\n",
    "- 3000 monthly minutes, 1000 text messages, and 30 GB of data\n",
    "- After exceeding the package limits:\n",
    "    - 1 minute: 1 cent\n",
    "    - 1 text message: 1 cent\n",
    "    - 1 GB of data: &dollar;7  \n",
    "    \n",
    "Megaline has found out that many of their subscribers use legacy plans. **They want to develop a model that would analyze subscribers' behavior and recommend one of Megaline's newer plans: Smart or Ultra**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Project Goal <a id='project_goal'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mobile carrier **Megaline** has found out that many of their subscribers use legacy plans. **They want to develop a model that would analyze subscribers' behavior and recommend one of Megaline's newer plans: Smart or Ultra**.  \n",
    "\n",
    "We need to develop a model with the highest possible accuracy. In this project, the threshold for accuracy is 0.75. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Initialization <a id='initialization'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to import the `sklearn` library in Python because **it provides a wide range of tools and functionalities for machine learning tasks**. It includes various algorithms and evaluation metrics that are commonly used in the field.  \n",
    "\n",
    "In this specific project, **we want to develop a model to analyze subscriber behavior and recommend one of Megaline's newer plans: Smart or Ultra. This is a binary classification problem since we have two groups of classification into categories**. We need to classify subscribers into one of the two categories based on their behavior.  \n",
    "\n",
    "We will train, test and predict using the following models for this task:  \n",
    "\n",
    "1. **[Decision Tree Classifier](https://scikit-learn.org/stable/modules/tree.html#decision-trees)**: Decision trees are versatile and widely used in classification problems. They are simple to understand and interpret, and they can handle both numerical and categorical data. Decision trees create a tree-like model of decisions and their possible consequences, making them suitable for analyzing subscriber behavior and predicting their plan preference. The `sklearn.tree` module provides us with `DecisionTreeClassifier` class which is capable of performing multi-class classification on a dataset.\n",
    "\n",
    "2. **[Random Forest Classifier](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)**: Random forests are an ensemble learning method that combines multiple decision trees to make predictions. They are known for their high accuracy and ability to handle complex datasets. The `sklearn.ensemble` module provides us with `RandomForestClassifier` class which is an averaging algorithm based on randomized decision trees.\n",
    "\n",
    "3. **[Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)**: Logistic regression is a popular algorithm for binary classification problems. It models the relationship between the dependent variable and independent variables using the logistic function, which produces probabilities of class membership. The `sklearn.linear_model` module provides us with `LogisticRegression` class to implement the model.  \n",
    "\n",
    "Apart from the models, we will also need `train_test_split` method from `sklearn.model_selection` module to split our dataset into random train and test subsets and `accuracy_score` method from `sklearn.metrics` module to check the accuracy of the trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from joblib import dump, load\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Load data <a id='load-data'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have been provided dataset in `./dataset/users_behavior.csv`. Let's load the dataset in `df_users_behavior` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset from the given csv file\n",
    "df_users_behavior = pd.read_csv('./dataset/users_behavior.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Data Preprocessing <a id='data-preprocessing'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some general information of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3214 entries, 0 to 3213\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   calls     3214 non-null   float64\n",
      " 1   minutes   3214 non-null   float64\n",
      " 2   messages  3214 non-null   float64\n",
      " 3   mb_used   3214 non-null   float64\n",
      " 4   is_ultra  3214 non-null   int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 125.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Get some general infromation\n",
    "df_users_behavior.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 3214 rows and 5 columns without any missing values. The columns description are as follows:  \n",
    "- `сalls` — number of calls,\n",
    "- `minutes` — total call duration in minutes,\n",
    "- `messages` — number of text messages,\n",
    "- `mb_used` — Internet traffic used in MB,\n",
    "- `is_ultra` — plan for the current month (Ultra - 1, Smart - 0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get first 10 rows of the dataframe to have an understanding of the data in it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calls</th>\n",
       "      <th>minutes</th>\n",
       "      <th>messages</th>\n",
       "      <th>mb_used</th>\n",
       "      <th>is_ultra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.0</td>\n",
       "      <td>311.90</td>\n",
       "      <td>83.0</td>\n",
       "      <td>19915.42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.0</td>\n",
       "      <td>516.75</td>\n",
       "      <td>56.0</td>\n",
       "      <td>22696.96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77.0</td>\n",
       "      <td>467.66</td>\n",
       "      <td>86.0</td>\n",
       "      <td>21060.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106.0</td>\n",
       "      <td>745.53</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8437.39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0</td>\n",
       "      <td>418.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14502.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58.0</td>\n",
       "      <td>344.56</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15823.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.0</td>\n",
       "      <td>431.64</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3738.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.0</td>\n",
       "      <td>132.40</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21911.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>43.39</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2538.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90.0</td>\n",
       "      <td>665.41</td>\n",
       "      <td>38.0</td>\n",
       "      <td>17358.61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calls  minutes  messages   mb_used  is_ultra\n",
       "0   40.0   311.90      83.0  19915.42         0\n",
       "1   85.0   516.75      56.0  22696.96         0\n",
       "2   77.0   467.66      86.0  21060.45         0\n",
       "3  106.0   745.53      81.0   8437.39         1\n",
       "4   66.0   418.74       1.0  14502.75         0\n",
       "5   58.0   344.56      21.0  15823.37         0\n",
       "6   57.0   431.64      20.0   3738.90         1\n",
       "7   15.0   132.40       6.0  21911.60         0\n",
       "8    7.0    43.39       3.0   2538.67         1\n",
       "9   90.0   665.41      38.0  17358.61         0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get first 10 rows of the dataframe\n",
    "df_users_behavior.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! Let's check if we have any dupluicate rows in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users_behavior.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We don't have any duplicate rows and missing values in the dataframe - `df_users_behavior`. We have a clean dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Develop a model with the highest possible accuracy <a id='develop-a-model'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data <a id='split-the-data'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our data in the Dataframe - `df_users_behavior`. But we need to split the entire Dataframe into **features** and **target**. **Features** are the input variables that describe the characteristics or properties of the data, while the **target** is the output variable that the model aims to predict or classify. In supervised learning, where the model is trained on labeled data, the target variable is typically provided in the dataset. The model learns from the relationship between the features and the target during training and uses that knowledge to make predictions or classifications on new data.  \n",
    "\n",
    "In our case, the **features could be the number of calls, total call duration, number of text messages, and Internet traffic used. The target variable would be the plan preference (Ultra or Smart), indicating which plan the subscriber is using.**\n",
    "\n",
    "Let's divide our Dataframe - `df_users_behavior` into features and target:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3214 entries, 0 to 3213\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   calls     3214 non-null   float64\n",
      " 1   minutes   3214 non-null   float64\n",
      " 2   messages  3214 non-null   float64\n",
      " 3   mb_used   3214 non-null   float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 100.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Get the list of features which model will use to predict the target - all columns except is_ultra\n",
    "features = df_users_behavior.drop(['is_ultra'], axis=1)\n",
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "5    0\n",
       "6    1\n",
       "7    0\n",
       "8    1\n",
       "9    0\n",
       "Name: is_ultra, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the target - is_ultra\n",
    "target = df_users_behavior['is_ultra']\n",
    "target.head(10)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! In machine learning, it is common practice to split the available data into three separate sets: the **training set**, the **validation set**, and the **test set**. Each set serves a specific purpose in the model development and evaluation process. Here's a breakdown of each set:  \n",
    "\n",
    "1. **Training Set**:  \n",
    "The training set is the largest portion of the dataset used to train the machine learning model. It contains input features and their corresponding target values. The model learns patterns and relationships between the features and the target by optimizing its parameters or weights based on this training data. The training set is crucial for the model to capture the underlying patterns in the data and generalize well to unseen instances.\n",
    "\n",
    "2. **Validation Set**:\n",
    "The validation set is used during the model development process to tune hyperparameters and evaluate the model's performance. The validation set helps to assess the generalization ability of the model and avoid overfitting (when the model performs well on the training data but poorly on new data). It provides an unbiased estimate of the model's performance and helps in making decisions on model improvements or hyperparameter adjustments.\n",
    "\n",
    "3. **Test Set**:\n",
    "The test set is a separate portion of the dataset that is used to evaluate the final performance of the trained model. It serves as an unbiased evaluation of the model's ability to generalize to new, unseen data. The test set should be representative of the real-world data that the model is expected to perform on. It should not be used during model development or hyperparameter tuning to ensure an unbiased assessment.  \n",
    "\n",
    "**It is important to note that the data should be split randomly and in a stratified manner if the dataset is imbalanced, ensuring that the distribution of target classes is maintained across the different sets**.\n",
    "\n",
    "In short, the training set is used to train the model, the validation set is used to tune hyperparameters and evaluate model performance, and the test set is used for the final unbiased evaluation of the trained model's performance.  \n",
    "\n",
    "Now, we need to split the source data i.e. `features` and `target` into a training set (`features_train`, `target_train`), a validation set (`features_valid`, `target_valid`), and a test set (`features_test`, `target_test`). In order to split the data we will use `train_test_split` method from `sklearn.model_selection` module. And to make sure that the data split is random, we should use `random_state` paramter.  \n",
    "\n",
    "But the question is what should be the proportion of split for each category? The proportion of data split for each category (training set, validation set, and test set) is a matter of choice and can depend on various factors such as the size of the dataset, the complexity of the problem, and the availability of data. The common rule is to split data in `3:1:1` proportion:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decide on a value for random state\n",
    "rand_state = 5678989"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the source dataset - features and target into two parts - 60% and 40%\n",
    "# 60% split set will become the training dataset\n",
    "# 40% split set will be used to get the validation set and test set\n",
    "features_train, features_remaining, target_train, target_remaining = train_test_split(features, target, test_size=0.4, random_state=rand_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! After doing our first split, **let us confirm that we have our training set has got 60% of balanced data**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3214, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total number of observations in features and target\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3214,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's get the total number of observations in our training set and see if it is 60% of 3214 (total number of observations in features):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1928, 4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total number of observations in features_train\n",
    "features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1928,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total number of observations in target_train\n",
    "target_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1928.3999999999999"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate 60% of 3214\n",
    "(60/100) * 3214"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! So, we have successfully validated that the training set (`features_train` and `target_train`) is indeed having now 60% data from the source set (`features` and `target`). **Let's also see if our training set is balanced for both the categories that our model has to predict**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1326\n",
       "1     602\n",
       "Name: is_ultra, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if training set is balanced\n",
    "target_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2229\n",
       "1     985\n",
       "Name: is_ultra, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the source set is balanced\n",
    "target.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, it doesn't seem like our training set is balanced as the number of observations where customers have selected the Smart plan - `is_ultra` is `0` is almost the double than what we have for Ultra plan - `is_ultra` is `1`. But, then investigating the source set, we can say that our source set itself doesn't have observations in balance. So, if compared to source set, our training set is good to train the model with. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now take our **remaining dataset that accounts for 40% of the source set**, and split it into two sets - **validation set** and **test set**, with equal proportion of data (that should evenutally be 20% of the source set):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the remaining dataset - features_remaining and target_remaining into two equal parts\n",
    "# The first split set will become the validation dataset\n",
    "# The second split set will become the test dataset\n",
    "features_valid, features_test, target_valid, target_test = train_test_split(features_remaining, target_remaining, test_size=0.5, random_state=rand_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! After doing our second split, let us confirm that we have our validation set and test set has got 20% data of the source set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(643, 4)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total number of observations in features_valid\n",
    "features_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(643, 4)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the total number of observations in features_test\n",
    "features_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "642.8000000000001"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate 20% of 3214\n",
    "(20/100) * 3214"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! so, finally **we have split our source dataset into three parts: training set (60% of data), validation set (20% of data) and test set (20% of data)**. We are good to start training our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train different models <a id='train-different-models'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a binary classification problem since we have two groups of classification into categories**. We need to classify subscribers into one of the two categories based on their behavior.  \n",
    "\n",
    "We will train, test and predict using the following models for this task:  \n",
    "\n",
    "1. **[Decision Tree Classifier](https://scikit-learn.org/stable/modules/tree.html#decision-trees)**: Decision trees are versatile and widely used in classification problems. They are simple to understand and interpret, and they can handle both numerical and categorical data. Decision trees create a tree-like model of decisions and their possible consequences, making them suitable for analyzing subscriber behavior and predicting their plan preference. The `sklearn.tree` module provides us with `DecisionTreeClassifier` class which is capable of performing multi-class classification on a dataset.\n",
    "\n",
    "2. **[Random Forest Classifier](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)**: Random forests are an ensemble learning method that combines multiple decision trees to make predictions. They are known for their high accuracy and ability to handle complex datasets. The `sklearn.ensemble` module provides us with `RandomForestClassifier` class which is an averaging algorithm based on randomized decision trees.\n",
    "\n",
    "3. **[Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)**: Logistic regression is a popular algorithm for binary classification problems. It models the relationship between the dependent variable and independent variables using the logistic function, which produces probabilities of class membership. The `sklearn.linear_model` module provides us with `LogisticRegression` class to implement the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate a Decision Tree Classifier model, train it using training set and tune the hyperparameters using validation set and check the accuracy of the model on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best Decision Tree Classifer model is of depth 3 with an accuracy of 0.7884914463452566 on validation set.\n"
     ]
    }
   ],
   "source": [
    "best_decision_tree_classifier_model = None\n",
    "best_accuracy = 0\n",
    "max_depth = 0\n",
    "\n",
    "# Change max_depth hyperparameter from 1 to 10 & find at what depth the model is the most accurate\n",
    "for depth in range(1, 11):\n",
    "    # Instantiate a model with max_depth = depth\n",
    "    current_decision_tree_classifier_model = DecisionTreeClassifier(max_depth=depth, random_state=rand_state)\n",
    "\n",
    "    # Train the model using training set\n",
    "    current_decision_tree_classifier_model.fit(features_train, target_train)\n",
    "\n",
    "    # Predict the target using the validation set features\n",
    "    current_predictions = current_decision_tree_classifier_model.predict(features_valid)\n",
    "    \n",
    "    # Check the accuracy of the predictions\n",
    "    current_accuracy = accuracy_score(target_valid, current_predictions)\n",
    "    \n",
    "    # Check for the best accurate model until the current iteration\n",
    "    if(best_accuracy < current_accuracy):\n",
    "        best_accuracy = current_accuracy\n",
    "        max_depth = depth\n",
    "        best_decision_tree_classifier_model = current_decision_tree_classifier_model\n",
    "\n",
    "# Save the best Decision Tree Classifer model\n",
    "dump(best_decision_tree_classifier_model, './models/best_decision_tree_classifier_model.joblib')\n",
    "\n",
    "print(f'The best Decision Tree Classifer model is of depth {max_depth} with an accuracy of {best_accuracy} on validation set.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the result above, we can conclude the following:\n",
    "\n",
    "1. **Best Decision Tree Classifier Model**: The best Decision Tree Classifier model has a depth of 3.\n",
    "\n",
    "2. **Accuracy on Validation Set**: The accuracy of the best Decision Tree Classifier model on the validation set is 0.7884914463452566. Accuracy is a commonly used metric that measures the proportion of correctly classified instances out of the total number of instances in the validation set. In this case, the model achieved an accuracy of approximately 78.85% on the validation set.\n",
    "\n",
    "3. **Evaluation of Model Performance**: The accuracy of 0.7884914463452566 indicates that the model is able to correctly predict the target variable (plan preference) for around 78.85% of the instances in the validation set. This suggests that the model has a reasonably good performance, but it is not perfect and may still make incorrect predictions for some instances.\n",
    "\n",
    "4. **Model Complexity**: The fact that the best model has a depth of 3 suggests that a relatively simpler decision tree structure is sufficient to achieve a good level of accuracy.\n",
    "\n",
    "In summary, we can conclude that **the best Decision Tree Classifier model has a depth of 3 and achieves an accuracy of 0.7884914463452566 on the validation set**. The model shows a reasonably good performance, but further evaluation on a separate test set would be beneficial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate a Random Forest Classifier model, train it using training set and tune the hyperparameters using validation set and check the accuracy of the model on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best Random Tree Classifer model has 40 trees in the ensemble, with a maximum depth of 8 and with an accuracy of 0.8149300155520995 on validation set.\n"
     ]
    }
   ],
   "source": [
    "best_random_forest_classifier_model = None\n",
    "best_accuracy = 0\n",
    "best_no_of_estimators = 0\n",
    "max_depth = 0\n",
    "\n",
    "# Change n_estimators hyperparameter from 10 to 80 (increment by 10 steps)\n",
    "# And find how many number of trees the ensemble should have for the model to be the most accurate\n",
    "for no_of_estimators in range(10, 90, 10):\n",
    "    for depth in range(1, 11):\n",
    "        # Instantiate a model with n_estimators = no_of_estimators (used to set number of trees in the ensemble)\n",
    "        current_random_forest_classifier_model = RandomForestClassifier(n_estimators=no_of_estimators, max_depth=depth, random_state=3478981)\n",
    "\n",
    "        # Train the model using training set\n",
    "        current_random_forest_classifier_model.fit(features_train, target_train)\n",
    "\n",
    "        # Predict the target using the validation set features\n",
    "        current_predictions = current_random_forest_classifier_model.predict(features_valid)\n",
    "\n",
    "        # Check the accuracy of the predictions\n",
    "        current_accuracy = accuracy_score(target_valid, current_predictions)\n",
    "\n",
    "        # Check for the best accurate model until the current iteration\n",
    "        if(best_accuracy < current_accuracy):\n",
    "            best_accuracy = current_accuracy\n",
    "            best_no_of_estimators = no_of_estimators\n",
    "            max_depth = depth\n",
    "            best_random_forest_classifier_model = current_random_forest_classifier_model\n",
    "\n",
    "# Save the best Random Tree Classifer model\n",
    "dump(best_random_forest_classifier_model, './models/best_random_forest_classifier_model.joblib')\n",
    "\n",
    "print(f'The best Random Tree Classifer model has {best_no_of_estimators} trees in the ensemble, with a maximum depth of {max_depth} and with an accuracy of {best_accuracy} on validation set.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the result above, we can conclude the following:\n",
    "\n",
    "1. **Best Random Forest Classifier Model**: Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. In this case, the best-performing model is composed of 40 decision trees.\n",
    "\n",
    "2. **Maximum Depth**: Each tree in the Random Forest has a maximum depth of 8.\n",
    "\n",
    "3. **Accuracy on Validation Set**: The accuracy of the best Random Forest Classifier model on the validation set is 0.8149300155520995.\n",
    "\n",
    "4. **Evaluation of Model Performance**: The accuracy of 0.8149300155520995 on the validation set suggests that the Random Forest Classifier model performs well, correctly predicting the target variable (plan preference) for approximately 81.49% of the instances in the validation set. This indicates a good level of performance on the given task.\n",
    "\n",
    "In summary, we can conclude that the best Random Forest Classifier model consists of 40 trees, each with a maximum depth of 8. The model achieves an accuracy of 0.8149300155520995 on the validation set, indicating a good level of performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate a Logistic Regression model, train it using training set and tune the hyperparameters using validation set and check the accuracy of the model on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression model by liblinear solver is 0.7153965785381027 on validation set\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a model with liblinear as solver\n",
    "logistic_regression_model = LogisticRegression(solver='liblinear', random_state=rand_state)\n",
    "\n",
    "# Train the model using training set\n",
    "logistic_regression_model.fit(features_train, target_train)\n",
    "\n",
    "# Predict the target using the validation set features\n",
    "predictions = logistic_regression_model.predict(features_valid)\n",
    "\n",
    "# Check the accuracy of the predictions\n",
    "accuracy = accuracy_score(target_valid, predictions)\n",
    "\n",
    "# Save the Logistic Regression model\n",
    "dump(logistic_regression_model, './models/logistic_regression_model.joblib')\n",
    "\n",
    "print(f'The accuracy of the Logistic Regression model by liblinear solver is {accuracy} on validation set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the result above, we can conclude the following:\n",
    "\n",
    "1. **Accuracy on Validation Set**: The accuracy of the Logistic Regression model on the validation set is 0.7153965785381027. The accuracy is less than our threshold too.\n",
    "\n",
    "2. **Evaluation of Model Performance**: The accuracy of 0.7153965785381027 on the validation set suggests that the Logistic Regression model performs reasonably well, correctly predicting the target variable (plan preference) for around 71.54% of the instances in the validation set. However, it is worth noting that the accuracy is relatively lower compared to the Random Forest Classifier model (81.49%).\n",
    "\n",
    "In summary, we can conclude that the Logistic Regression model, trained using the liblinear solver, achieves an accuracy of 0.7153965785381027 on the validation set. The model performs reasonably well but may not achieve the same level of accuracy as the Random Forest Classifier model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the quality of the models using the test set <a id='test-the-quality'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's proceed to test the quality of all the three trained models above using the test set. Testing the quality of models using a separate test set is important for several reasons:\n",
    "\n",
    "1. **Unbiased Evaluation**: The test set provides an unbiased evaluation of the model's performance on unseen data. By evaluating the model on data it has not seen during training or model development, we get a more accurate representation of its real-world performance.\n",
    "\n",
    "2. **Performance Estimation**: The test set allows us to estimate the model's performance metrics. These metrics provide a quantitative measure of how well the model performs in practice and can guide decision-making regarding model deployment or further improvements.\n",
    "\n",
    "3. **Overfitting Detection**: Testing the model on a separate test set helps to identify if the model has overfit the training data. Overfitting occurs when the model memorizes the training data too well and fails to generalize to new data. By evaluating the model's performance on unseen instances, we can detect whether the model suffers from overfitting and make necessary adjustments. \n",
    "\n",
    "4. **Model Selection**: When comparing multiple models or different configurations of the same model, the test set provides an objective basis for selecting the best-performing model. By evaluating their performance on the test set, we can choose the model that demonstrates the highest performance and is most suitable for the given problem.\n",
    "\n",
    "So, let's go ahead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the quality of **Decision Tree Classifier** model using the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree Classifer model on the test set is 0.80248833592535.\n"
     ]
    }
   ],
   "source": [
    "# Load the best Decision Tree Classifier model\n",
    "decision_tree_classifier_model = load('./models/best_decision_tree_classifier_model.joblib')\n",
    "\n",
    "# Predict the target using the test set features\n",
    "predictions = decision_tree_classifier_model.predict(features_test)\n",
    "\n",
    "# Check the accuracy of the predictions\n",
    "accuracy = accuracy_score(target_test, predictions)\n",
    "\n",
    "print(f'The accuracy of the Decision Tree Classifer model on the test set is {accuracy}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[419,  29],\n",
       "       [ 98,  97]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get confusion matrix for the performance of Decision Tree Classifier model\n",
    "decision_tree_classifier_model_conf_matrix = confusion_matrix(target_test, predictions)\n",
    "decision_tree_classifier_model_conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix above in a heat map\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(decision_tree_classifier_model_conf_matrix, annot=True, fmt='.3f', linewidths=.5, square=True, cmap='Reds_r')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plot_title = f'Accuracy Score: {accuracy}'\n",
    "plt.title(plot_title, size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above results, **we can conclude the following about the Decision Tree Classifier model's performance using the test set**:\n",
    "\n",
    "1. **Accuracy on Test Set**: The accuracy of the Decision Tree Classifier model on the test set is 0.80248833592535, indicating that **the model correctly predicts the target variable (plan preference) for approximately 80.25% of the instances in the test set**. This accuracy score is slightly higher than the accuracy achieved on the validation set (0.7884914463452566).\n",
    "\n",
    "2. Confusion Matrix: The confusion matrix for the performance of the Decision Tree Classifier model shows the distribution of predicted labels compared to the actual labels in the test set.  \n",
    "- **True Positives (TP)**: The model correctly predicted 97 instances of the Ultra plan.\n",
    "- **False Positives (FP)**: The model incorrectly predicted 29 instances as Ultra when they were actually Smart.\n",
    "- **False Negatives (FN)**: The model incorrectly predicted 98 instances as Smart when they were actually Ultra.\n",
    "- **True Negatives (TN)**: The model correctly predicted 419 instances of the Smart plan.  \n",
    "\n",
    "3. **Evaluation of Model Performance**: The accuracy of 0.80248833592535 on the test set suggests that **the Decision Tree Classifier model performs reasonably well**, with a similar level of accuracy compared to the validation set. However, the confusion matrix reveals that **the model struggles more with correctly predicting instances of the Ultra plan**, as indicated by the higher number of false negatives (98) compared to false positives (29). This might be possible because the training data had less observations for customers having Ultra plan.\n",
    "\n",
    "4. **Model Strengths and Weaknesses**: The Decision Tree Classifier model with a depth of 3 demonstrates good accuracy overall, but it may have limitations in correctly identifying instances of the Ultra plan.\n",
    "\n",
    "In summary, **we can conclude that the Decision Tree Classifier model achieves an accuracy of 0.80248833592535 on the test set. It shows reasonably good performance, but there is room for improvement, particularly in correctly predicting instances of the Ultra plan**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the quality of **Random Forest Classifier** model using the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random Forest Classifier model on the test set is 0.8133748055987559.\n"
     ]
    }
   ],
   "source": [
    "# Load the best Random Forest Classifier model\n",
    "random_forest_classifier_model = load('./models/best_random_forest_classifier_model.joblib')\n",
    "\n",
    "# Predict the target using the test set features\n",
    "predictions = random_forest_classifier_model.predict(features_test)\n",
    "\n",
    "# Check the accuracy of the predictions\n",
    "accuracy = accuracy_score(target_test, predictions)\n",
    "\n",
    "print(f'The accuracy of the Random Forest Classifier model on the test set is {accuracy}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[424,  24],\n",
       "       [ 96,  99]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get confusion matrix for the performance of Random Forest Classifier model\n",
    "random_forest_classifier_model_conf_matrix = confusion_matrix(target_test, predictions)\n",
    "random_forest_classifier_model_conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix above in a heat map\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(random_forest_classifier_model_conf_matrix, annot=True, fmt='.3f', linewidths=.5, square=True, cmap='Greens_r')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plot_title = f'Accuracy Score: {accuracy}'\n",
    "plt.title(plot_title, size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above results, **we can conclude the following about the Random Forest Classifier model's performance using the test set**:\n",
    "\n",
    "1. **Accuracy on Test Set**: The accuracy of the Random Forest Classifier model on the test set is 0.8133748055987559, indicating that **the model correctly predicts the target variable (plan preference) for approximately 81.34% of the instances in the test set**. This accuracy score is similar to the accuracy achieved on the validation set (0.8149300155520995).\n",
    "\n",
    "2. **Confusion Matrix**: The confusion matrix for the performance of the Random Forest Classifier model shows the distribution of predicted labels compared to the actual labels in the test set.\n",
    "- **True Positives (TP)**: The model correctly predicted 99 instances of the Ultra plan.\n",
    "- **False Positives (FP)**: The model incorrectly predicted 24 instances as Ultra when they were actually Smart.\n",
    "- **False Negatives (FN)**: The model incorrectly predicted 96 instances as Smart when they were actually Ultra.\n",
    "- **True Negatives (TN)**: The model correctly predicted 424 instances of the Smart plan.  \n",
    "\n",
    "3. **Evaluation of Model Performance**: The accuracy of 0.8133748055987559 on the test set suggests that **the Random Forest Classifier model performs well, with a similar level of accuracy compared to the validation set**. The model demonstrates good overall accuracy in predicting the plan preference.\n",
    "\n",
    "4. **Model Strengths and Weaknesses**: **The Random Forest Classifier model with 40 trees in the ensemble and a maximum depth of 8 shows strong performance with a high accuracy score. The confusion matrix indicates a good balance between correctly predicting instances of the Ultra and Smart plans**. However, there are still some instances where the model incorrectly predicts the plan preference.\n",
    "\n",
    "In summary, we can conclude that **the Random Forest Classifier model achieves an accuracy of 0.8133748055987559 on the test set. It demonstrates strong performance, with a high accuracy score and a balanced confusion matrix**. The model shows good overall accuracy in predicting the plan preference, but there is still room for improvement to reduce the number of false positives and false negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test the quality of **Logistic Regression** model using the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression model on the test set is 0.713841368584759.\n"
     ]
    }
   ],
   "source": [
    "# Load the trained Logistic Regression model\n",
    "logistic_regression_model = load('./models/logistic_regression_model.joblib')\n",
    "\n",
    "# Predict the target using the test set features\n",
    "predictions = logistic_regression_model.predict(features_test)\n",
    "\n",
    "# Check the accuracy of the predictions\n",
    "accuracy = accuracy_score(target_test, predictions)\n",
    "\n",
    "print(f'The accuracy of the Logistic Regression model on the test set is {accuracy}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[443,   5],\n",
       "       [179,  16]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get confusion matrix for the performance of Logistic Regression model\n",
    "logistic_regression_model_conf_matrix = confusion_matrix(target_test, predictions)\n",
    "logistic_regression_model_conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+vendu7d59NFHzd13323c3d2Ni4uL+c9//pPl/Oeff95IMp6enqZt27bmwQcfNNWrVzdubm6mT58+WbZTHD161Pj4+JhWrVrZx25kK8jixYtN7969Tc+ePU2zZs2Mi4uL8fDwMLNmzco0N2Mdx5QpU+ytLJcfLVu2zPLv8ddff23vpW7atKl56KGHTNOmTY3NZjPNmjUzf/75Z6bHZPzcXnm4ubmZcePGZflajh8/bu9JLl++vOnevbvp2LGj8fPzM0FBQebzzz/P8nEZ/cqzZ8/O9Pw5bQU5evSocXFxMYUKFcq2vSejFeTKw8vLy3z88ceZ5n/44YdGkmncuHGW1xsyZIiRZIoXL56jGFEwxcQn5ZujICKxRo5l/MP/1Vdf2cdOnjxp3NzcTOHChc3Zs2cd5g8cONBIMgMGDLjmtf/++2/j6upqPDw8zKFDh64539nEetOmTdd8jitlLIRbvHjxdcd96NAh4+LiYmrUqJHpXOPGjbPtB87O3Llz7X2elx+BgYHmxRdfzPQ9mT9/vj1RzskCpLJlyxpJZsWKFZnOZfTbV65c2WE8473Pqvc+OTnZlChRwnh7ezsk6hkuXLhggoKCTLFixUxqaqp9/OjRo6Zq1aqmatWq14w5w1NPPXXVHvR9+/YZSaZKlSo5vuaVNm7caO9JvfJDSnZyk1h/9tlnDt9XV1dX85///MfhA+mV3n77bVOoUCGHx1WuXDnTh9sMXbt2Ne7u7g49ujcysc7oEc84PD09zYcffujw/b7yuoUKFTJ33XWX2bp1q4mPjzc//vijqVChgpFkHnjggSyf56effrIvtM44fHx8zLhx47L8Xo0ePdp89tln5sCBA+bChQsmKirKTJgwwd7D/Pbbb2f5PKdOnTJt2rTJ9HewW7duZs+ePZnmHzlyxBQtWtS0bNnSYTy3iXXGgtSOHTtmO2fIkCHmm2++MX/99Ze5cOGC2blzpxk+fLhxdXU1NpstU6/03r17jSTj4uJidu3a5XAuISHBBAYGGknG3d09RzGiYMrrZJrEGv8Kf/31l7HZbMbPzy/Tf9S7dOliJJkZM2Y4jFepUsVIMr///vs1rz9nzpxM1fCrcSaxDg4Ovuq1U1JSzIoVK8zYsWNN//79Te/evU3v3r3tC3feeuut647bGGM6dOhgJDns5LF9+3YjyTRs2DDH18lw/vx58/nnn5snnnjC1KpVy9hsNoeE6vJqVkaymVXF/Ep//fWXvRqelbS0NOPn55dp8VbGe59VMp6RiN57773ZPm/Hjh2zXYyVGzcjsc6o4OXkw2OG61m8mJiYaKKioszLL79s3N3dTePGjTPt9nLp0iXTo0cP4+rqasaMGWMOHjxo4uPjzU8//WRq1aplJJlJkyY5PCZjZ5krd664GYsXL168aHbs2GGefvppI8ncd999mRJeNzc3+8/g+fPnHc7t2LHD/rMeFRXlcO7NN980Li4uplu3bmb79u3m/PnzZvv27aZr167XTEavtGLFCiPJ+Pn5Zar4b9u2zZQuXdqUK1fOfPvtt+bMmTPm6NGj5u233zaenp7G398/089x586djbu7e6akO7eJdUalfN68eTl+LRkyKtNZfVDN2OmkUqVKZtWqVSY+Pt5ERkaali1b2j+wFS5cONfPiYIjOi4p3xwFEYk1cmTixIlGkunXr1+mc1999ZWRMm9dVrhwYSPJxMfHX/P6r732mpFkhg8fnqN4nEmss/s1pzHp1aQ6depk+evTjOOVV1657riN+afS26dPH/tYRoL2wQcf5Pg62YmOjjbh4eH2Stvl37N27dplqrpnJyIi4prJfsZ7dfnOGhnvfVbVuoyKeU6OX375JZev3NGNbgVJTk42AQEBmT4kXYuzu4JkbBU4ePBgh/GMbemeeeaZTI/566+/jLe3t/Hy8jInTpwwxqTvKFGqVClTpUqVTB+Wb/auIBk//2+++abDeLFixYyUvp1dVu64445MH+p//vln+/f1yip4SkqKqVu3rpFkli1bluP4GjZsaCSZn3/+2T6WlJRkqlSpYlxcXMyWLVsyPSbj+9SjRw/72IIFC4wkM3r06Ezzc5NYb9u2zV6Bz+luKZdLTU21/+xe+XN4+vRp07x580x/H4sWLWr++9//5qg4gYItr5Ppgp5Yc4MY5EjGArzVq1dnWsGecWe/tWvX6q+//lK5cuVuenxXym5BnSQVLlw423P9+vXTtm3b1L17d73wwguqWrWqihYtKhcXF3344Yf6v//7v2xv4pBTHTp0UJkyZfTll1/qnXfekbu7uz7//HMVKVJEjzzyiFPXlqTAwEC9+OKL8vLy0jPPPKPvvvvO6Wtm52qLCbN6nzO+L5UrV1bTpk2vem1nd20oW7aspPS7yGUlY/x6f15/+OEHxcbGqmLFirrzzjuvL8jr8Nhjj2nEiBH69ttvNXXqVPt4xt/RjN1OLle2bFk1btxYP/30k7Zs2aK2bdtq69atOnbsmMqXL6+2bds6zI+OjpYkfffdd2rZsqWCgoI0b968G/qapk6dqm+//VYjRoywj5crV05nzpxR+fLls3xc+fLl9dtvvyk2NtY+lvE+3H///XJxcVyf7+rqqm7duikyMlJr165V+/btcxRflSpVtHnzZocdczZs2KB9+/apUqVKWS6QffDBBzVixAitXbvWPpaxA83KlSsdxiXZ74w5ZMgQ+fr6qk+fPurTp0+W8WQsWuzevXuOd0u5nIuLiypVqqTY2FgdP37c4f0tVqyY/SZEq1evtu+s06tXL+3Zs0eSVLNmzVw/J/BvQWKNa9qyZYv9H9T9+/dr//79Wc4zxmjOnDl66aWXJKXvWrFv3z4dOHBAdevWvepzZOxwceDAgRzFlLEtV3a7YRw5ciRH17lcQkKCVq5cqcDAQM2fP1+urq4O5//8889Mj8lt3FL6f9yfeuopjRkzRnPmzJGPj4/OnDmjfv36qWjRormOOzt33323JOnkyZPXFW+pUqUkSX/99Ve2czLOZXVXuqyULl1aklStWjXNmjUrR4+5Xhl3h9u6dWuW5zPGa9eufV3Xz+mODFbz9/eXi4tLpp0dMj4oZHeXv4zxM2fOOIwfOnQo29urR0dHKzo6+oZ/WC5RooQkZXpN9erVU2RkZKaYM5w+fVqSHLaGu9734Woy5np7e1vyPBs2bMj2uTIS7JYtW2Z5Pi0tTV988YWk9A8k1yur15TBZrOpQ4cOmXae+fjjj68aG24NzpWOwHZ7uKaMBOK5556TSW8fynSsXr3aYa4ktW7dWpL04YcfXvM5WrZsKVdXV61YsSJHSXFwcLAk6Y8//sh07o8//rjqFlnZiYuLU1pamoKDgzMl1cnJyVq4cKHTcWfo16+fChUqpBkzZmjGjBmSpKeeeipX8V6rcp7xAejypDfje/Lxxx9ftaovpVc5y5YtqxMnTmjVqlWZzn/33Xc6c+aMKleunONbpjdq1Ei+vr5as2aNPSm6UZo2bSpfX18dOHDAnqxcbsGCBZKkTp065fra58+ft+/le7MT63Xr1iktLU2VKlVyGM/4HmzevDnTY1JTU/X7779Lkr062bJly2z/Pmfso963b18ZY7JNvK2SsS3ela+pc+fODucvd/78efuHo8v3Kb/a+yBJmzZtkqRsq+BXOnHihNatWyfJcevGjOeJiorSuXPncvQ8s2bNyvY9b9GihaT0768xJts9+levXq2jR4+qTJky153g7tq1S1FRUfLy8lK1atVy9JgLFy7o448/lru7e7a3SAcgJ5ri8K+QkpJiXwmeVR9hhtTUVHPbbbc59NtGRUXZt9u7coFNWlqa+eGHHxx6O5944gkjybRq1SrTrY9jYmIctq07d+6c8fLyMoUKFXLo7z1x4oS566677H2BWfVYZ3cb6+TkZOPr62sKFSrk0N+bkpJi79dVFn3duYn7ct26dbNfs3bt2lnOuZonn3zSjB8/PstbX+/du9dUrFjRSDLPPfecw2vM2B5uxIgRJinJsYdt586dDrd6fuONN+zxXb6V4PHjx0316tWNlP12e9n1EE+YMMH+fbjyttLGpO8A8umnn2Yay+2uIMb8s5PLnXfe6bD47Wq3NJ86daqpWrWqefHFF7O97uzZs40k06RJk1zFY0zOeqwnTZqU5a3of/vtN1O5cuUs+5GHDh1qJJnSpUs7LOZLSUkxL7zwgpFkypUr53Cb6uxY3WMdGxtrPvzwQ5OQkJDp3A8//GBKlixpJJkFCxY4nEtJSbH/nE2bNs1hPGNxaq1atRy2l8y446Grq2um274vWrTIuLi4GBcXF4dFhb/++qtZuHBhpp1yDh48aJo2bWokmc6dOzucu3jxor1P+fHHH3f4t+zvv/+2Ly7M6Z1Rc9pjnfHvzdV+Po0x5rvvvjOrVq3KNL5t2zb7ezp06NBM56OiokxcXJzD2KlTp8x9992XaY0Jbk3H45LyzVEQkVjjqpYtW2Ykmdtvv/2ac4cPH55p8dQXX3xhX9lfo0YN8/DDD5v27dubMmXKGEkOe9DGx8fbd97w9vY27du3Nw899JBp0qSJcXd3z7Qoa8yYMfYV6m3btjXt2rUzxYoVM3feeacJDQ3NdWJtzD9Jn6urq7n33nvNQw89ZMqXL288PT3NoEGDskyscxt3hh9++MGeWL/77rvXfH+vlLEbi4uLiwkJCTHdu3c3PXr0MI0bN7bvb33HHXdk+o/kjh077PsDBwcHm27dupn777/fvnPEwoUL7XNTUlJM+/btjSTj6+tr7r//ftO1a1dTtGhRI8l07do10wKxayXWqamp5rHHHjOS7DtcPPzww6Zbt26mZs2axmazmTp16jg85nr2sTYmPfnJ2MYwODjY/v5I6TtNZJXYZywC7N27d7bXvffeezMle9nZsmWLady4sf3IWNRbt25d+9iVO+pc/t489NBD5v7773dYVNujR49MCfLJkyftSbuHh4dp1aqV6d69u/0DlqenZ5aJVlaulVgvXbrU4TVl7M5x+djSpUvt8zO+f15eXuauu+4yjzzyiOncubOpVq2a/TVlt8j0999/Nz4+PkaSqVOnjsNrKl68uNm+fbvD/LS0NPPggw/ar9uwYUPz4IMP2hcgSjITJkzI8vUGBQWZDh06mJ49e5qmTZvav1c1a9bMcq/ohQsX2nfKuO2220yXLl1MmzZt7H8/6tevn+WtwbOSk8T64sWL9vfiyu3wrpTxc1yuXDnTuXNn8/DDD5s77rjDHm/Lli2zXPg4duxYU7hwYXPXXXfZ/70uUqSIkdIXXGe1LSJuLcfPJuWboyAiscZVPfLII1kmk1nZtGmTkWQCAgIc/qO/bds28+ijj5rbbrvNuLm5mYCAANO0aVPz1ltvZUoOEhMTzTvvvGPuuOMOU6RIEePp6WkqVapknnjiiUwV87S0NPPGG2+YypUrGzc3N1O6dGkzYsQIk5CQcNVdQa6WWBuTXo2sV6+e8fLyMsWLFzddunQx27Ztu+pOJLmJO8PFixeNm5ub8fT0zPImF9dy9OhRM2PGDPPAAw+YGjVqGD8/P1OoUCFTsmRJc88995jp06dnqkhniImJMc8995y5/fbbTeHChY2vr68JCQkxI0eOdNg6z5j0Kvc777xjf0+8vLxMw4YNzbRp07LcC/taiXWGb7/91nTs2NEEBATYfy4aNGhgXnjhhUzv2fUm1sak7409evRoU6lSJePu7m6CgoJMnz59zJEjR7Kcf63E+tixY8bV1dW4ubll+g1FVjJ2qbjaceXP1NSpU023bt1MxYoVjbe3t3F3d7cnbpd/8LlSfHy8GTt2rKldu7bx9vY2bm5upmzZsqZ3795m9+7d14w1w7US64zzVztmzpxpn5+QkGAmTZpkOnToYMqVK2c8PT2Nh4eHKV++vHn44YcddtvIyp9//mkef/xxExQUZP+73q9fv2z3jk9LSzMff/yxad68uf3vRYkSJUyHDh3M8uXLM83fvXu3GThwoKlfv74pWbKkKVSokPH19TVNmjQxb7311lV33ti6davp2bOnKV26tHFzczPe3t6mbt26ZuLEibnasSMniXXGrjr16tW75vXWr19vnnzySRMSEmKKFy9uChUqZPz9/U3Lli3NjBkzst3Hfs2aNaZLly6mdOnSxt3d3fj7+5s2bdrk+AZIKPjyOpku6Im1zRgntzgAcF2++OIL9ezZU717977hC/kAAMiJ6LjkvA7BLsjXLa9DyDV2BQHyQHJysl5//XVJ0qBBg/I4GgAA0hn2BXEKiTVwEy1evFiLFi3Sb7/9pl27dqlr165q1KhRXocFAAAsQGIN3ERbt27VzJkzVaxYMfXs2dPhBh8AAOQ1GoSdQ481AAAAJEnHzibldQh2pfzc8zqEXOMGMQAAALhlvPbaa7LZbHr22WftY5cuXdKgQYNUvHhxFSlSRN27d1dMTIzD4w4fPqyOHTvKy8tLAQEBev7555WSkpKr5yaxBgAAgKRr7KN5k4/rsWnTJn3wwQeqXbu2w/iwYcO0ZMkSffXVV1qzZo2OHTumbt262c+npqaqY8eOSkpK0vr16zV79mzNmjVLY8aMydXz0woCAAAASdLf+agV5LZctoKcP39e9evX13vvvaf//Oc/qlu3rt5++23FxcWpZMmSmjt3rh544AFJ0t69e1W9enVFRESoSZMmWr58ue677z4dO3ZMgYGBkqTp06dr5MiROnHihNzdcxYLFWsAAADkO4mJiYqPj3c4EhMTs50/aNAgdezYUa1bt3YY37Jli5KTkx3Gq1WrprJlyyoiIkKSFBERoZCQEHtSLUlt27ZVfHy8du3aleOYSawBAAAgKX1XkPxyhIeHy9fX1+EIDw/PMu558+Zp69atWZ6Pjo6Wu7u7/Pz8HMYDAwMVHR1tn3N5Up1xPuNcTt2y2+1dyl2vOQBcU+FCkme9wXkdBoBbzMXf383rEPKlsLAwDR8+3GHMw8Mj07wjR47omWee0cqVK1W4cOGbFV6WqFgDAADgf/J6yeI/h4eHh3x8fByOrBLrLVu2KDY2VvXr11ehQoVUqFAhrVmzRlOmTFGhQoUUGBiopKQknT171uFxMTExCgoKkiQFBQVl2iUk4+uMOTlBYg0AAIAC65577tGOHTsUGRlpPxo2bKhevXrZ/+zm5qZVq1bZHxMVFaXDhw8rNDRUkhQaGqodO3YoNjbWPmflypXy8fFRjRo1chzLLdsKAgAAgFtf0aJFVatWLYcxb29vFS9e3D7et29fDR8+XP7+/vLx8dGQIUMUGhqqJk2aSJLatGmjGjVq6LHHHtOkSZMUHR2tUaNGadCgQVlWybNDYg0AAABJt+4tzSdPniwXFxd1795diYmJatu2rd577z37eVdXVy1dulQDBw5UaGiovL291bt3b40bNy5Xz3PL7mPN4kUAVmPxIoAbIT8tXjxyOvvt7G62Mv45rxTnF/RYAwAAABagFQQAAACSrv9W4khHxRoAAACwAIk1AAAAYAFaQQAAACDp1t0V5GahYg0AAABYgIo1AAAAJEmG5YtOoWINAAAAWIDEGgAAALAArSAAAABIRyeIU6hYAwAAABYgsQYAAAAsQCsIAAAAJNEJ4iwq1gAAAIAFqFgDAABAEndedBYVawAAAMACJNYAAACABWgFAQAAgCRuae4sKtYAAACABUisAQAAAAvQCgIAAIB0dII4hYo1AAAAYAEq1gAAAJBEwdpZVKwBAAAAC5BYAwAAABagFQQAAACSuKW5s6hYAwAAABYgsQYAAAAsQCsIAAAAJHFLc2dRsQYAAAAsQGINAAAAWIBWEAAAAKSjE8QpVKwBAAAAC1CxBgAAgCQK1s6iYg0AAABYgMQaAAAAsACtIAAAAJDELc2dRcUaAAAAsACJNQAAAGABWkEAAAAgiVuaO4uKNQAAAGABKtYAAABIR8HaKVSsAQAAAAuQWAMAAAAWoBUEAAAAkugEcRYVawAAAMACJNYAAACABWgFAQAAgCRuae4sKtYAAACABUisAQAAAAvQCgIAAABJ3NLcWVSsAQAAAAtQsQYAAEA6CtZOoWINAAAAWIDEGgAAALAArSAAAACQRCeIs6hYAwAAABYgsQYAAAAsQCsIAAAAJHFLc2dRsQYAAAAsQMUaAAAAkrjzorOoWAMAAAAWILEGAAAALEArCAAAANLRCeIUKtYAAACABUisAQAAAAvQCgIAAABJdII4i4o1AAAAYAESawAAAEhKv/Nifjly4/3331ft2rXl4+MjHx8fhYaGavny5fbzLVu2lM1mczgGDBjgcI3Dhw+rY8eO8vLyUkBAgJ5//nmlpKTkKg5aQQAAAFCglS5dWq+99pqqVKkiY4xmz56tLl266Pfff1fNmjUlSU899ZTGjRtnf4yXl5f9z6mpqerYsaOCgoK0fv16HT9+XI8//rjc3Nw0ceLEHMdBYg0AAIACrVOnTg5fT5gwQe+//742bNhgT6y9vLwUFBSU5eN/+OEH7d69Wz/++KMCAwNVt25djR8/XiNHjtQrr7wid3f3HMVBKwgAAAAkpd/SPL/8LzExUfHx8Q5HYmLiNV9Damqq5s2bp4SEBIWGhtrH58yZoxIlSqhWrVoKCwvThQsX7OciIiIUEhKiwMBA+1jbtm0VHx+vXbt25fj9I7EGAABAvhMeHi5fX1+HIzw8PNv5O3bsUJEiReTh4aEBAwZo4cKFqlGjhiSpZ8+e+vzzz/Xzzz8rLCxMn332mR599FH7Y6Ojox2Sakn2r6Ojo3McM60gAAAAyHfCwsI0fPhwhzEPD49s51etWlWRkZGKi4vTggUL1Lt3b61Zs0Y1atRQ//797fNCQkIUHByse+65RwcOHFClSpUsi5nEGgAAAOny0UbWHh4eV02kr+Tu7q7KlStLkho0aKBNmzbpnXfe0QcffJBpbuPGjSVJ+/fvV6VKlRQUFKTffvvNYU5MTIwkZduXnRVaQQAAAHDLSUtLy7YnOzIyUpIUHBwsSQoNDdWOHTsUGxtrn7Ny5Ur5+PjY20lygoo1AAAACrSwsDC1b99eZcuW1blz5zR37lytXr1aK1as0IEDBzR37lx16NBBxYsX1/bt2zVs2DA1b95ctWvXliS1adNGNWrU0GOPPaZJkyYpOjpao0aN0qBBg3JVNSexBgAAgKR81QmSK7GxsXr88cd1/Phx+fr6qnbt2lqxYoXuvfdeHTlyRD/++KPefvttJSQkqEyZMurevbtGjRplf7yrq6uWLl2qgQMHKjQ0VN7e3urdu7fDvtc5YTMmt/e2KRgu5e5GOQBwTYULSZ71Bud1GABuMRd/fzevQ7Dbcig+r0Owa1DeJ69DyDUq1gAAAJCU+1uJwxGLFwEAAAALkFgDAAAAFqAVBAAAAJLSb2mO60fFGgAAALAAiTUAAABgAVpBAAAAkI5OEKdQsQYAAAAsQMUaAAAAkihYO4uKNQAAAGABEmsAAADAArSCAAAAQBK3NHcWFWsAAADAAiTWAAAAgAVoBQEAAIAkbmnuLCrWAAAAgAVIrAEAAAAL0AoCAACAdHSCOIWKNQAAAGABKtYAAACQRMHaWVSsAQAAAAuQWAMAAAAWoBUEAAAAkqQ07mnuFCrWAAAAgAVIrAEAAAAL0AoCAAAASewK4iwq1gAAAIAFqFgDAABAksTaRedQsQYAAAAsQGINAAAAWIBWEAAAAEiSDMsXnULFGgAAALAAiTUAAABgAVpBAAAAIElKoxPEKVSsAQAAAAtQsQYAAIAkFi86i4o1AAAAYAESawAAAMACtIIAAABAErc0dxYVawAAAMACJNYAAACABWgFAQAAgCR2BXEWFWsAAADAAiTWAAAAgAVoBQEAAIAkbmnuLCrWAAAAgAWoWAMAAEASixedRcUa+d7HMz5UnZpVNSl8QqZzxhg9/X/9VKdmVf206kf7+NmzZzSwf1+1btlMDevWUpt7Wmjif8bp/PnzV32uuLNnFfbCCN15R301a9JQY0e/pAsJCQ5z/ojaqz6P9VSjeiFqc08Lzfx4Rqbr/LBiubrc106N6oWoe9dOWrd2zXW+egA308v/10EXf3/X4Yj8ZtRVH9OtdT1FfjNKZzZM1qYvX1LbZjUyzRk9sKP+/GGCTkf8V99NH6xKZUs6nC/m46WZE3orZt0bOr52kt4f21Penu6WvjYANx6JNfK1nTu2a8FX83T77VWzPP/5p7Nls9kyjbvYXNTq7nv0zrvva/GyFRo/4TVt3LBe/3l17FWfL2zkczqwf7+mfzRTU6ZN19bNmzXulTH28+fPn9eAp/oquFQpffHVNxo24gVNf+9dLfhyvn1O5O9b9eLzI3R/twc0f8Eitbr7Hj07ZJD27fvjOt8FADfTrv3HVL51mP2458nJ2c5tUqeCZof30exFEWryyGtasnqbvvxvf9WoFGyfM6JPaz39SAsNnThPzR9/UwkXk7Rk2iB5uP/zS+OZE3ureqVg3TfwXXUfOl3N6lfWtNE9b+jrBGA9EmvkWxcSEhQ28nmNffU/8vH1zXR+7549+nT2J3p1/MRM53x8fdXj4Z6qWStEpUrdpsZNQtXj4Z7aunVzts/354ED+vWXdRo77j+qXbuO6jdoqBdfGqXvl3+n2NgYSdKypYuVnJysceMnqnLlKmrfoaMe6fWYPvt0pv06cz7/VHc2u0t9nuynipUqafDQZ1W9Rg3Nm/u5Be8KgBstJTVNMafO2Y9TZxOynTvokZb6Yf0eTf50laIOxmjce98pcs8RDXi4xT9zerbS6zNWaOnqHdq575j6jf5UwSV91blVHUlS1QqBatu0pp4eN1ebdv6l9ZF/avjrX+nBtvUVXDLzv33AjWRM/jkKojxNrE+ePKlJkybp/vvvV2hoqEJDQ3X//ffrjTfe0IkTJ/IyNOQDE/8zTs2bt1CT0Dsznbt48aLCXhihl0aNUYmSJbN4tKPY2Bj99ONKNWjYKNs527b9rqI+PqpZK8Q+1jj0Trm4uGjH9u3/mxOpBg0bys39n1/R3tm0mQ4dPKj4uDhJ0vbISDVpEupw7TubNtP2yMhrxgkg71UuW1J//jBBu5e8opkTeqtMULFs5zauXUE/b9zrMLYyYo8a1y4vSSp/W3EFl/TVT5fNiT9/SZt2HrLPaVy7gs7EX9DW3Yftc37aGKW0NKNGtcpZ98IA3HB5llhv2rRJt99+u6ZMmSJfX181b95czZs3l6+vr6ZMmaJq1app8+bsq4u4tS1f9p327NmtocNGZHn+jdfDVadePbW6u/VVrzPyueFq3KCO7m3VXN7e3nplXOY+7QynTp6Uv7+/w1ihQoXk4+urUyfTP+idPHlS/sVLOMwp/r+vT548af//4pnmFNfJUyevGiuAvLdp5yH1H/O5Og+apqET56v8bcX14yfDVMTLI8v5gSV8FHv6nMNY7KlzCizuI0kKKpH+/1ebE1jcRyeuOJ+amqbT8RcU+L/HAygY8mxXkCFDhujBBx/U9OnTM/XIGmM0YMAADRkyRBEREVe9TmJiohITEx3GPDw8JNes/xFE/hd9/LgmvTZBH8z4JP17eYXVP63Spo0bNH/Bwmte6/mRYRrw9CD9deiQ3nn7v3rz9XC9POaVGxA1gFvBD7/utv95575j2rTjkKKWjVP3NvU1e9HV/3sE3AoKagtGfpFnFett27Zp2LBhWS48s9lsGjZsmCJz8Kvz8PBw+fr6Ohzh4eE3IGLcLLt379LpU6f08IPdVL92DdWvXUObN/2muXM+U/3aNRQRsV5HjhxWs9BG9vOSNOLZIerb5zGHa5UoWVIVKlZSy7vv0eixr+rL+V/oxInYLJ+3eIkSOn36tMNYSkqK4uPiVLxEertJiRIldPqKyvOp/31dokQJ+/+fyjTnlEpcUcUGkP/Fnb+o/YdjValM1i1nMSfjFeBf1GEsoHhRxZyKlyRFn0z//6vNiTkVr5JXnHd1dZG/j5di/vd4AAVDnlWsg4KC9Ntvv6latWpZnv/tt98UGBh4zeuEhYVp+PDhDmMeHh7swliANW7SRAsWLXEYG/tymMpXrKgn+j6lYn7F9ECPhxzOP9C1k54bGaYWLVtle13zv4/hSUlJWZ6vU6eezsXHa/eunapRs5Yk6beNG5SWlqaQ2rX/N6eupr7ztpKTk+Xm5iZJ2hCxXuUrVLAvsKxdt642btigRx/vY7/2hoj1ql23bs7fBAD5grenuyqULqHo737L8vzG7QfV8o6qenfuavvYPU2qaeP2Q5KkQ3+f0vETcWrVuKq2//G3JKmod2E1qlVeM776xX6NYj5eqle9jH7fc0SS1LLR7XJxsWnTzr9u3IsDspBGBuWUPEusn3vuOfXv319btmzRPffcY0+iY2JitGrVKs2YMUNvvvnmNa/j4eGRZbvApRTLQ8ZN4u1dRFWq3O4w5unlJT9fP/t4VgsWg4NLqXTpMpKkdWvX6NSpk6pZK0ReXl46sH+/Jr85SXXr1ddtt5WWJO3Yvl2jXnpBH348W4GBgapYqZKaNrtLr44drVFjXlVKSrLCJ4xXu/YdFRCQ/vPZvmMnTX9vml4Z87Ke6PuU9u/bpzmff6rnXwizx9Hr0cfVt89jmj3rEzVv3kLfL1+mXTt3avQr427I+wXAOuHD7td3a3fo8LHTKhXgq1EDOio1LU1ffr9FkvTR+Md0LDZOY6YuliRN+2K1fpjxrJ557G4tX7dLD7ZtoPo1ymrQ+C/s15w292eN7NdO+w+f0KG/T2ns0x11/EScFv+8TZIUdTBGK37dpWmje2rohHlyK+SqyS/20Fcrtur4ibib/yYAuG55llgPGjRIJUqU0OTJk/Xee+8pNTVVkuTq6qoGDRpo1qxZ6tGjR16FhwLOw8ND3yz4Sm++Hq6kpCQFBgXrntb36sl+/e1zLl26qEMHDyolJdk+Fv76mwqfMF79+/aWi4uL7rm3jV4M++fmEEWLFtX0GR9r4n/G6ZEHu8mvWDH934CnHSrodevVV/ikN/XulLc19e3/qmy58np76rRMHxYA5D+3Bfrp0/An5O/rpZNnzmt95J9q8fhbOnkm/eZSZYL8lZb2T0Vvw7aD6vPSLI0ddJ9eHdxJ+w+fUI/hH2r3geP2OW/N+lFenh56d9Qj8ivqqfWRB9R50HtKTPqnAvTES7M1+cUeWvbBEKWlGS1aFakRk766eS8cgCVsxuR9m3pycrJ9R4USJUrYf8XuDCrWAKxWuJDkWW9wXocB4BZz8fd38zoEu+92Zr0OKS90rBWQ1yHkWp5VrC/n5uam4ODga08EAAAA8inuvAgAAABYIF9UrAEAAJD3DLuCOIWKNQAAAGABEmsAAADAArSCAAAAQBK3NHcWFWsAAADAAlSsAQAAIIlbmjuLijUAAABgARJrAAAAwAIk1gAAAJCUvngxvxy58f7776t27dry8fGRj4+PQkNDtXz5cvv5S5cuadCgQSpevLiKFCmi7t27KyYmxuEahw8fVseOHeXl5aWAgAA9//zzSklJyVUcJNYAAAAo0EqXLq3XXntNW7Zs0ebNm3X33XerS5cu2rVrlyRp2LBhWrJkib766iutWbNGx44dU7du3eyPT01NVceOHZWUlKT169dr9uzZmjVrlsaMGZOrOGzG3Jobq1zK3QcMALimwoUkz3qD8zoMALeYi7+/m9ch2C3aHp3XIdh1rR3k1OP9/f31xhtv6IEHHlDJkiU1d+5cPfDAA5KkvXv3qnr16oqIiFCTJk20fPly3XfffTp27JgCAwMlSdOnT9fIkSN14sQJubu75+g5qVgDAABAkmTy0ZGYmKj4+HiHIzEx8ZqvITU1VfPmzVNCQoJCQ0O1ZcsWJScnq3Xr1vY51apVU9myZRURESFJioiIUEhIiD2plqS2bdsqPj7eXvXOCRJrAAAA5Dvh4eHy9fV1OMLDw7Odv2PHDhUpUkQeHh4aMGCAFi5cqBo1aig6Olru7u7y8/NzmB8YGKjo6PQKfXR0tENSnXE+41xOsY81AAAAJEn5qUM4LCxMw4cPdxjz8PDIdn7VqlUVGRmpuLg4LViwQL1799aaNWtudJgOSKwBAACQ73h4eFw1kb6Su7u7KleuLElq0KCBNm3apHfeeUcPPfSQkpKSdPbsWYeqdUxMjIKC0vu4g4KC9NtvvzlcL2PXkIw5OUErCAAAAG45aWlpSkxMVIMGDeTm5qZVq1bZz0VFRenw4cMKDQ2VJIWGhmrHjh2KjY21z1m5cqV8fHxUo0aNHD8nFWsAAABIktLyOoDrFBYWpvbt26ts2bI6d+6c5s6dq9WrV2vFihXy9fVV3759NXz4cPn7+8vHx0dDhgxRaGiomjRpIklq06aNatSooccee0yTJk1SdHS0Ro0apUGDBuWqak5iDQAAgAItNjZWjz/+uI4fPy5fX1/Vrl1bK1as0L333itJmjx5slxcXNS9e3clJiaqbdu2eu+99+yPd3V11dKlSzVw4ECFhobK29tbvXv31rhx43IVB/tYA0AOsY81gBshP+1jvWDb8bwOwe6BOsF5HUKuUbEGAACApPy1K0hBxOJFAAAAwAIk1gAAAIAFaAUBAACApPRbieP6UbEGAAAALEDFGgAAAJJYvOgsKtYAAACABUisAQAAAAvQCgIAAABJBfeW5vkFFWsAAADAAiTWAAAAgAVoBQEAAIAkdgVxFhVrAAAAwAJUrAEAACBJomDtHCrWAAAAgAVIrAEAAAAL0AoCAAAASRKdIM6hYg0AAABYgMQaAAAAsACtIAAAAJAkpbEtiFOoWAMAAAAWoGINAAAASSxedBYVawAAAMACJNYAAACABWgFAQAAgCTJsHjRKVSsAQAAAAuQWAMAAAAWoBUEAAAAkqS0vA6ggKNiDQAAAFiAxBoAAACwAK0gAAAAkCSxKYhzqFgDAAAAFqBiDQAAAElSGiVrp1CxBgAAACxAYg0AAABYgFYQAAAASGLxorOoWAMAAAAWILEGAAAALEArCAAAACSxK4izqFgDAAAAFqBiDQAAAElSGgVrp1CxBgAAACxAYg0AAABYgFYQAAAASGIfa2dRsQYAAAAsQGINAAAAWIBWEAAAAEiS0kQviDOoWAMAAAAWILEGAAAALEArCAAAACSxK4izqFgDAAAAFqBiDQAAAEnc0txZVKwBAAAAC5BYAwAAABagFQQAAACSpDRWLzqFijUAAABgARJrAAAAwAK0ggAAAEAS+1g7i4o1AAAAYAEq1gAAAJDEPtbOomINAAAAWIDEGgAAALAArSAAAACQJBlWLzqFijUAAABgARJrAAAAwAK0ggAAAEASu4I4i4o1AAAAYAEq1gAAAJBExdpZVKwBAABQoIWHh6tRo0YqWrSoAgIC1LVrV0VFRTnMadmypWw2m8MxYMAAhzmHDx9Wx44d5eXlpYCAAD3//PNKSUnJcRxUrAEAAFCgrVmzRoMGDVKjRo2UkpKil156SW3atNHu3bvl7e1tn/fUU09p3Lhx9q+9vLzsf05NTVXHjh0VFBSk9evX6/jx43r88cfl5uamiRMn5igOEmsAAABIkowKZi/I999/7/D1rFmzFBAQoC1btqh58+b2cS8vLwUFBWV5jR9++EG7d+/Wjz/+qMDAQNWtW1fjx4/XyJEj9corr8jd3f2acdAKAgAAgHwnMTFR8fHxDkdiYmKOHhsXFydJ8vf3dxifM2eOSpQooVq1aiksLEwXLlywn4uIiFBISIgCAwPtY23btlV8fLx27dqVo+clsQYAAEC+Ex4eLl9fX4cjPDz8mo9LS0vTs88+q6ZNm6pWrVr28Z49e+rzzz/Xzz//rLCwMH322Wd69NFH7eejo6MdkmpJ9q+jo6NzFDOtIAAAAJCUv3YFCQsL0/Dhwx3GPDw8rvm4QYMGaefOnfrll18cxvv372//c0hIiIKDg3XPPffowIEDqlSpkiUx5yixnjJlSo4vOHTo0OsOBgAAAJDSk+icJNKXGzx4sJYuXaq1a9eqdOnSV53buHFjSdL+/ftVqVIlBQUF6bfffnOYExMTI0nZ9mVfKUeJ9eTJk3N0MZvNRmINAACAm8oYoyFDhmjhwoVavXq1KlSocM3HREZGSpKCg4MlSaGhoZowYYJiY2MVEBAgSVq5cqV8fHxUo0aNHMWRo8T64MGDOboYAAAACi6Tj1pBcmPQoEGaO3euvv32WxUtWtTeE+3r6ytPT08dOHBAc+fOVYcOHVS8eHFt375dw4YNU/PmzVW7dm1JUps2bVSjRg099thjmjRpkqKjozVq1CgNGjQox5Xz6168mJSUpKioqFxtmg0AAABY7f3331dcXJxatmyp4OBg+zF//nxJkru7u3788Ue1adNG1apV04gRI9S9e3ctWbLEfg1XV1ctXbpUrq6uCg0N1aOPPqrHH3/cYd/ra8n14sULFy5oyJAhmj17tiTpjz/+UMWKFTVkyBDddtttevHFF3N7SQAAAOQDaQW0ZG2uEXeZMmW0Zs2aa16nXLlyWrZs2XXHkeuKdVhYmLZt26bVq1ercOHC9vHWrVvbPxUAAAAA/za5rlgvWrRI8+fPV5MmTWSz2ezjNWvW1IEDBywNDgAAACgocp1Ynzhxwr5S8nIJCQkOiTYAAAAKlvy0j3VBlOtWkIYNG+q7776zf52RTH/00UcKDQ21LjIAAACgAMl1xXrixIlq3769du/erZSUFL3zzjvavXu31q9fn6OmcAAAAOBWlOuKdbNmzRQZGamUlBSFhITohx9+UEBAgCIiItSgQYMbESMAAABuAmPyz1EQ5bpiLUmVKlXSjBkzrI4FAAAAKLCuK7FOTU3VwoULtWfPHklSjRo11KVLFxUqdF2XAwAAQD5QUPexzi9ynQnv2rVLnTt3VnR0tKpWrSpJev3111WyZEktWbJEtWrVsjxIAAAAIL/LdY91v379VLNmTR09elRbt27V1q1bdeTIEdWuXVv9+/e/ETECAAAA+V6uK9aRkZHavHmzihUrZh8rVqyYJkyYoEaNGlkaHAAAAG4eOkGck+uK9e23366YmJhM47GxsapcubIlQQEAAAAFTY4S6/j4ePsRHh6uoUOHasGCBTp69KiOHj2qBQsW6Nlnn9Xrr79+o+MFAAAA8qUctYL4+fk53K7cGKMePXrYx8z/fm/QqVMnpaam3oAwAQAAcKOl5XUABVyOEuuff/75RscBAAAAFGg5SqxbtGhxo+MAAAAACrTrvqPLhQsXdPjwYSUlJTmM165d2+mgAAAAcPNxgxjn5DqxPnHihJ544gktX748y/P0WAMAAODfKNfb7T377LM6e/asNm7cKE9PT33//feaPXu2qlSposWLF9+IGAEAAHATGJN/joIo1xXrn376Sd9++60aNmwoFxcXlStXTvfee698fHwUHh6ujh073og4AQAAgHwt1xXrhIQEBQQESEq/4+KJEyckSSEhIdq6dau10QEAAAAFRK4T66pVqyoqKkqSVKdOHX3wwQf6+++/NX36dAUHB1seIAAAAG6ONJN/joIo160gzzzzjI4fPy5JGjt2rNq1a6c5c+bI3d1ds2bNsjo+AAAAoEDIdWL96KOP2v/coEED/fXXX9q7d6/Kli2rEiVKWBocAAAAUFBc9z7WGby8vFS/fn0rYgEAAEAeMgV1O458IkeJ9fDhw3N8wf/+97/XHQwAAABQUOUosf79999zdDGbzeZUMAAAAMg7BXXRYH6Ro8T6559/vtFxAAAAAAVarrfbAwAAAJCZ04sXAQAAcGugFcQ5VKwBAAAAC5BYAwAAABagFQQAAACS2MfaWTlKrBcvXpzjC3bu3Pm6gwEAAAAKqhwl1l27ds3RxWw2m1JTU52JBwAAACiQcpRYp6Wl3eg4LFeYJhcAN8DF39/N6xAA4IYpeBlf/nLLpp8dpv+W1yEAuMUsG3CHPBs/n9dhALjFXNz4Rl6HAItcV2KdkJCgNWvW6PDhw0pKSnI4N3ToUEsCAwAAwM3F4kXn5Dqx/v3339WhQwdduHBBCQkJ8vf318mTJ+Xl5aWAgAASawAAAPwr5Xof62HDhqlTp046c+aMPD09tWHDBv31119q0KCB3nzzzRsRIwAAAJDv5TqxjoyM1IgRI+Ti4iJXV1clJiaqTJkymjRpkl566aUbESMAAABuAmPyz1EQ5TqxdnNzk4tL+sMCAgJ0+PBhSZKvr6+OHDlibXQAAABAAZHrHut69epp06ZNqlKlilq0aKExY8bo5MmT+uyzz1SrVq0bESMAAACQ7+W6Yj1x4kQFBwdLkiZMmKBixYpp4MCBOnHihD788EPLAwQAAMDNkWZMvjkKolxXrBs2bGj/c0BAgL7//ntLAwIAAAAKolv2BjEAAADInQJaKM43cp1YV6hQQTabLdvzf/75p1MBAQAAAAVRrhPrZ5991uHr5ORk/f777/r+++/1/PPc6hcAAAD/TrlOrJ955pksx6dNm6bNmzc7HRAAAADyBrc0d06udwXJTvv27fX1119bdTkAAACgQLEssV6wYIH8/f2tuhwAAABQoFzXDWIuX7xojFF0dLROnDih9957z9LgAAAAcPPQCeKcXCfWXbp0cUisXVxcVLJkSbVs2VLVqlWzNDgAAACgoMh1Yv3KK6/cgDAAAACQ1wrqHQ/zi1z3WLu6uio2NjbT+KlTp+Tq6mpJUAAAAEBBk+vEOrttWBITE+Xu7u50QAAAAEBBlONWkClTpkiSbDabPvroIxUpUsR+LjU1VWvXrqXHGgAAoACjEcQ5OU6sJ0+eLCm9Yj19+nSHtg93d3eVL19e06dPtz5CAAAAoADIcWJ98OBBSVKrVq30zTffqFixYjcsKAAAAKCgyfWuID///PONiAMAAAB5jFuaOyfXixe7d++u119/PdP4pEmT9OCDD1oSFAAAAFDQ5DqxXrt2rTp06JBpvH379lq7dq0lQQEAAAAFTa5bQc6fP5/ltnpubm6Kj4+3JCgAAADcfGl0gjgl1xXrkJAQzZ8/P9P4vHnzVKNGDUuCAgAAAAqaXFesR48erW7duunAgQO6++67JUmrVq3SF198oa+++sryAAEAAHBzsHjROblOrDt16qRFixZp4sSJWrBggTw9PVW7dm39+OOPatGixY2IEQAAAMj3cp1YS1LHjh3VsWPHTOM7d+5UrVq1nA4KAAAAKGhy3WN9pXPnzunDDz/UHXfcoTp16lgREwAAAPKAMfnnKIiuO7Feu3atHn/8cQUHB+vNN9/U3XffrQ0bNlgZGwAAAHBN4eHhatSokYoWLaqAgAB17dpVUVFRDnMuXbqkQYMGqXjx4ipSpIi6d++umJgYhzmHDx9Wx44d5eXlpYCAAD3//PNKSUnJcRy5Sqyjo6P12muvqUqVKnrwwQfl6+urxMRELVq0SK+99poaNWqUm8sBAAAATluzZo0GDRqkDRs2aOXKlUpOTlabNm2UkJBgnzNs2DAtWbJEX331ldasWaNjx46pW7du9vOpqanq2LGjkpKStH79es2ePVuzZs3SmDFjchyHzeRw+WenTp20du1adezYUb169VK7du3k6uoqNzc3bdu2Ld9ttddh+m95HQKAW8yyAXfIs/HzeR0GgFvMxY1v5HUIdr0+i8zrEOzmPFb3uh974sQJBQQEaM2aNWrevLni4uJUsmRJzZ07Vw888IAkae/evapevboiIiLUpEkTLV++XPfdd5+OHTumwMBASdL06dM1cuRInThxIsv7uFwpxxXr5cuXq2/fvnr11VfVsWNHubq6XudLBQAAAK4uMTFR8fHxDkdiYmKOHhsXFydJ8vf3lyRt2bJFycnJat26tX1OtWrVVLZsWUVEREiSIiIiFBISYk+qJalt27aKj4/Xrl27cvS8OU6sf/nlF507d04NGjRQ48aN9e677+rkyZM5fTgAAADyuTSTf47w8HD5+vo6HOHh4dd+DWlpevbZZ9W0aVP7bnXR0dFyd3eXn5+fw9zAwEBFR0fb51yeVGeczziXEzlOrJs0aaIZM2bo+PHj+r//+z/NmzdPpUqVUlpamlauXKlz587l9FIAAADAVYWFhSkuLs7hCAsLu+bjBg0apJ07d2revHk3IUpHud4VxNvbW08++aR++eUX7dixQyNGjNBrr72mgIAAde7c+UbECAAAgH8ZDw8P+fj4OBweHh5XfczgwYO1dOlS/fzzzypdurR9PCgoSElJSTp79qzD/JiYGAUFBdnnXLlLSMbXGXOuxal9rKtWrapJkybp6NGj+uKLL5y5FAAAAPKYMSbfHLmNe/DgwVq4cKF++uknVahQweF8gwYN5ObmplWrVtnHoqKidPjwYYWGhkqSQkNDtWPHDsXGxtrnrFy5Uj4+PjnepOO67rx4JVdXV3Xt2lVdu3a14nIAAABAjg0aNEhz587Vt99+q6JFi9p7on19feXp6SlfX1/17dtXw4cPl7+/v3x8fDRkyBCFhoaqSZMmkqQ2bdqoRo0aeuyxxzRp0iRFR0dr1KhRGjRo0DUr5RksSawBAACAvPL+++9Lklq2bOkwPnPmTPXp00eSNHnyZLm4uKh79+5KTExU27Zt9d5779nnurq6aunSpRo4cKBCQ0Pl7e2t3r17a9y4cTmOg8QaAAAAkqQCeifxHLWOFC5cWNOmTdO0adOynVOuXDktW7bsuuNwqscaAAAAQDoSawAAAMACtIIAAABAkpSWy9044IiKNQAAAGABKtYAAACQJFGwdg4VawAAAMACJNYAAACABWgFAQAAgKSc7QeN7FGxBgAAACxAYg0AAABYgFYQAAAASGJXEGdRsQYAAAAsQMUaAAAAkrjzorOoWAMAAAAWILEGAAAALEArCAAAACSxeNFZVKwBAAAAC5BYAwAAABagFQQAAACSuKW5s6hYAwAAABagYg0AAABJUhoFa6dQsQYAAAAsQGINAAAAWIBWEAAAAEiSjOgFcQYVawAAAMACJNYAAACABWgFAQAAgCRuae4sKtYAAACABUisAQAAAAvQCgIAAABJ3NLcWVSsAQAAAAtQsQYAAIAkbmnuLCrWAAAAgAVIrAEAAAAL0AoCAAAASSxedBYVawAAAMACJNYAAACABWgFAQAAgCRuae4sKtYAAACABahYAwAAQJKURsnaKVSsAQAAAAuQWAMAAAAWoBUEAAAAkli86Cwq1gAAAIAFSKwBAAAAC9AKAgAAAEnc0txZVKwBAAAAC5BYAwAAABagFQQAAACS2BXEWVSsAQAAAAtQsQYAAIAkFi86i4o1AAAAYAESawAAAMACtIIAAABAEosXnUXFGgAAALAAiTUAAABgAVpBAAAAIIldQZxFxRoAAACwABVrAAAASKJi7Swq1gAAAIAFSKwBAAAAC9AKAgAAAEnsY+0sKtYAAACABUisAQAAAAvQCgIAAABJ7AriLCrWAAAAgAWoWAMAAEASixedRcUaAAAAsACJNQAAAAq0tWvXqlOnTipVqpRsNpsWLVrkcL5Pnz6y2WwOR7t27RzmnD59Wr169ZKPj4/8/PzUt29fnT9/PldxkFgDAABAUvrixfxy5EZCQoLq1KmjadOmZTunXbt2On78uP344osvHM736tVLu3bt0sqVK7V06VKtXbtW/fv3z1Uc9FgDAACgQGvfvr3at29/1TkeHh4KCgrK8tyePXv0/fffa9OmTWrYsKEkaerUqerQoYPefPNNlSpVKkdxULEGAADALW/16tUKCAhQ1apVNXDgQJ06dcp+LiIiQn5+fvakWpJat24tFxcXbdy4McfPQcUaAAAAkvLXriCJiYlKTEx0GPPw8JCHh0eur9WuXTt169ZNFSpU0IEDB/TSSy+pffv2ioiIkKurq6KjoxUQEODwmEKFCsnf31/R0dE5fh4q1gAAAMh3wsPD5evr63CEh4df17Uefvhhde7cWSEhIeratauWLl2qTZs2afXq1ZbGTGINAACAfCcsLExxcXEOR1hYmCXXrlixokqUKKH9+/dLkoKCghQbG+swJyUlRadPn862LzsrtIIAAABAUv66pfn1tn3kxNGjR3Xq1CkFBwdLkkJDQ3X27Flt2bJFDRo0kCT99NNPSktLU+PGjXN8XRJrAAAAFGjnz5+3V58l6eDBg4qMjJS/v7/8/f316quvqnv37goKCtKBAwf0wgsvqHLlymrbtq0kqXr16mrXrp2eeuopTZ8+XcnJyRo8eLAefvjhHO8IIpFYAwAA4H/yUcE6VzZv3qxWrVrZvx4+fLgkqXfv3nr//fe1fft2zZ49W2fPnlWpUqXUpk0bjR8/3qEiPmfOHA0ePFj33HOPXFxc1L17d02ZMiVXcZBYAwAAoEBr2bLlVdtYVqxYcc1r+Pv7a+7cuU7FweJFAAAAwAJUrAEAACApfy1eLIioWAMAAAAWILEGAAAALEArCAAAACQV3F1B8gsq1gAAAIAFqFgDAABAEosXnUXFGgAAALAAiTUAAABgAVpBAAAAIInFi84isUa+VCu4qLrXCVLlkt4q7u2u8d//oYhDZ+3nlw24I8vHfRxxWF9vi5YkVSrhpSeblFGVkt5KM9Kvf57WjPWHdSkl7arP/WjD29Suekl5exTS7uhzmrbukI7FJdrPF/Fw1cBm5dS4XDGlGaNf/zyjD379y+G65f099fRd5XV7SW/FXUrWkp0xWhAZ7cQ7AuBGaFq3goY92lL1q92m4JK+6vH8LC1Zu8thTtXyAfrPoA66q35FFXJ11d6DMXrkxU91JOZsttftdndtjfm/tioXXEz7j5zUqGnLtGL9Xoc5o/u30RNdGsuviKcith/S0Enf6MCRk/bzxXw89d8RXdXhrhpKSzNa9PMOPfffb5VwMcnS9wCAdWgFQb5UuJCLDp66oPfW/ZXl+V6zf3c4Jv/8pz3JlSR/LzdNvK+ajsVd0rBvdmv0d1Eq5++p4a0qXvV5H6gbrM4hgXp33SEN+2aXLiWnaXzHqnJztdnnvHBPJZUt5qmXl+7VK8v/UK1SRTW0RQX7eU83F/3nvqqKPZeooV/v0scRR9SzQXqyDiB/8fZ01459x/TsG4uyPF/htuJa9eHT+uOvE2o7cLoa9fqvwj/5UZeSkrO9ZpOQcpo9vqdmL/lNTR5/W0vW7tKXk3qrRsVA+5wRj7XU0z2aaejr36h536lKuJSkJe/0k4f7P/Wuma/2VPWKQbpvyIfqPuITNatXQdPCHrDstQOwHok18qXNR+L06aa/FXHoTJbnz1xMdjialC+m7X/HK/pcemX5jnJ+Skkzem/dX/o77pL2nUjQu2sPqVklfwX7eGT7vF1DAjVv6zFtOHRWh05f1Fs//6niXu4KLV9MklTGr7AalvXTlDWHFBWboN3R5zX9l7/UvLK//L3cJEmtqpSQm4uL3l59UIfPXNTaA6e1eGeM7q8dZPG7BMBZP0RE6dUPVmjxmp1Znn91YDutWL9XL7/7nbb9cUwH/z6l79bt1okzCdlec9BDzfTDhihN/nyNog7FatwHKxQZ9bcGPNj0nzkP36XXZ67S0rW7tHP/cfV7ZZ6CS/ioc4uaktKr5G3vrKanJ3ylTbuOaP22Qxr+5rd68N46Ci7hY+2bAFzGGJNvjoKIxBoFnp9nITUq66sf9v7zK1Q3VxelpKXp8r+Wif9r1agZXDTL6wQV9ZC/t7sij8bbxy4kpSoq9ryqBxWRJFULLKJziSnad+Kf/6j+fjROxkhVA9LnVA8sop3Hzykl7Z9n33okTmWKeaqIu6vTrxfAzWGz2dTuzmrad/ikFr/TT38tH6u1Hw9Rp+Y1r/q4xiHl9POmfQ5jKzf8ocYh5SRJ5Uv5K7iEj3767Z858QmXtGnXYfucxiHldCb+grbuPWqf89OmfUpLM2pUs6xVLxGAxfJ1Yn3kyBE9+eSTeR0G8rnWVUvoYnKafj142j627e94FfN0U/c6QSrkYlMRd1c90biMJNkry1cq9r/xMxcdf8V79mKyinm62efEXXE+zUjnElPsjy/m5ZbpGhlfF8vmuQHkPwHFiqiod2E993grrYyIUqehM7R4zU7Ne/1xNauXfVtZYPGiij193mEs9vQ5BRZP/1Af9L//jz197oo55xXon34u0L+oTpxxvEZqappOx1+0XwdA/pOvFy+ePn1as2fP1ieffJLtnMTERCUmJjqMeXhk/6t+3HrurVpSP+87peTUfyrEh89c1H9/Pqh+d5ZRn8ZllGaMvt0Ro9MXkpRWMH+7BOAmc3FJX1uxdO0uTZ23TpK0fd8xNQ4pp6e6NdEvv/+Zl+EBN0QB7cDIN/I0sV68ePFVz//557X/0QoPD9err77qMDZ27FgpqINTsaFgqBlURGWKeeq1H/dnOrd6/ymt3n9Kfp6FdCk5vS3k/tpBio6/lOW1zlz4X1XZ083+Z0ny83TTn6cu2Of4ejpWnV1sUlGPQvbHnLnwT4U7Q8bXl18XQP528myCklNStedgjMN41KFY3VmnQjaPkmJOnVOAfxGHsQD/ooo5lV6hjv7f/wf4F7X/Of3rItq+71j6NU6fU8lijtdwdXWRv4+n/ToA8p88Tay7du0qm8121QZ1m82W7TlJCgsL0/Dhwx3GPDw8dP/MbZbEiPytTfWS2heboIOnLmY75+zFFEnSvVVLKDk1Tb9f1kN9uehziTqdkKQ6t/nYE2lPNxdVDSii73bFSpL2xpxXUY9CqlzCS/tPps+pc5uPbDYpKjb917Z7Ys6r9x2l5epiU+r/yuP1SvvqyJmLOp+Uas0LB3DDJaekasvuI7q9nOOOPlXKltTh6KwXVkvSxh1/qWXDKnp33i/2sXvuqKKNO9J3OTp07LSOn4xXq0aV7Yl0UW8PNapZVjO+ibBfo5iPl+pVu02/7/1bktSyYWW5uNi0addhS18ncLmCumgwv8jTHuvg4GB98803SktLy/LYunXrNa/h4eEhHx8fh4NWkIKvcCEXVSzupYrFvSRJgT4eqljcSyWLuNvneLq56K6K/lqxNzbLa9xXM0CVSnjpNt/Cuq9mgAY2K6dZG48q4bLk9oOHQuw7fkjSoh0xerhBKTUu56fy/p567u5KOnUhyb47yZGzl7T58FkNbVFBtwd4q0ZQET3drLzW7j+t0/+rRq/ef0rJaWl6tkUFlS3mqeaV/NUlJFALt7OPNZDfeHu6q3aVUqpdpZSk9IWFtauUUplAP0nS5M/X6IHWdfRElztUsXRxDXjgTnVoVl0ffr3efo2Pxj6scU+3t389bf4vahNaVc/0bK7by5XUy/3uVf3qpTX9q1//mTNvnUY+cY863lVDNSsF6eOxD+v4yXgtXpO+h3bUoVitWL9X08IeUMMaZRRau7wmP9dVX63cpuMnsy4OAMh7eVqxbtCggbZs2aIuXbpkef5a1WzcuqoEeOv1ztXtX/e/M32l/MqoE5r880FJUovKxSVJq/efznwBpe/S8Wij0vJ0c9GRM5f07tpD+mnfKYc5ZYp5ytvjn506FkQeV+FCLhrSoryKuBfSruhzGvPdHw7925NWHdDTzcpr4n3VZIzRrwfPaPov/+y3fSEpVaOWRunpu8prSveair+Uorlbjun7PSecfFcAWK1+9dL64f2B9q8nDessSfps6Wb1Hz9fi9fs1JDXv9HzvVvpreFd9cfhE3ok7DOt33bI/pgygX5Ku2zxxoYdf6nP6LkaO6CtXh3YXvuPnFSPF2Zr95//tJS89dlqeXm6692wB+RXpLDWbzukzs98pMSkFPucJ8bO1eTn7teyd/srzaTfIGbEW9/euDcDgNNsJg8z13Xr1ikhIUHt2rXL8nxCQoI2b96sFi1a5PraHab/5mx4AOBg2YA75Nn4+bwOA8At5uLGN/I6BLuQ0SvzOgS7HePvzesQci1PK9Z33XXXVc97e3tfV1INAAAA3Gz5eh9rAAAAoKDI1/tYAwAA4OZhaZtzqFgDAAAAFqBiDQAAAEnsY+0sKtYAAACABUisAQAAAAvQCgIAAABJLF50FhVrAAAAwAIk1gAAAIAFaAUBAACAJCktjV4QZ1CxBgAAACxAYg0AAABYgFYQAAAASGJXEGdRsQYAAAAsQMUaAAAAkrilubOoWAMAAAAWILEGAAAALEArCAAAACSxeNFZVKwBAAAAC5BYAwAAABagFQQAAACS2BXEWVSsAQAAAAtQsQYAAIAkFi86i4o1AAAAYAESawAAAMACtIIAAABAEosXnUXFGgAAALAAiTUAAABgAVpBAAAAIIlWEGdRsQYAAAAsQMUaAAAA6ShYO4WKNQAAAGABEmsAAADAArSCAAAAQBKLF51FxRoAAACwAIk1AAAAYAFaQQAAACCJVhBnUbEGAAAALEBiDQAAAFiAVhAAAABIohXEWVSsAQAAAAtQsQYAAIAkKtbOomINAAAAWIDEGgAAALAArSAAAABIRyeIU6hYAwAAABYgsQYAAECBtnbtWnXq1EmlSpWSzWbTokWLHM4bYzRmzBgFBwfL09NTrVu31r59+xzmnD59Wr169ZKPj4/8/PzUt29fnT9/PldxkFgDAABAUnoCml+O3EhISFCdOnU0bdq0LM9PmjRJU6ZM0fTp07Vx40Z5e3urbdu2unTpkn1Or169tGvXLq1cuVJLly7V2rVr1b9//1zFQY81AAAACrT27durffv2WZ4zxujtt9/WqFGj1KVLF0nSp59+qsDAQC1atEgPP/yw9uzZo++//16bNm1Sw4YNJUlTp05Vhw4d9Oabb6pUqVI5ioOKNQAAACQV3Ir11Rw8eFDR0dFq3bq1fczX11eNGzdWRESEJCkiIkJ+fn72pFqSWrduLRcXF23cuDHHz0XFGgAAAPlOYmKiEhMTHcY8PDzk4eGRq+tER0dLkgIDAx3GAwMD7eeio6MVEBDgcL5QoULy9/e3z8kJKtYAAADId8LDw+Xr6+twhIeH53VYV0XFGgAAAJLy1y3Nw8LCNHz4cIex3FarJSkoKEiSFBMTo+DgYPt4TEyM6tata58TGxvr8LiUlBSdPn3a/vicoGINAACAfMfDw0M+Pj4Ox/Uk1hUqVFBQUJBWrVplH4uPj9fGjRsVGhoqSQoNDdXZs2e1ZcsW+5yffvpJaWlpaty4cY6fi4o1AAAACrTz589r//799q8PHjyoyMhI+fv7q2zZsnr22Wf1n//8R1WqVFGFChU0evRolSpVSl27dpUkVa9eXe3atdNTTz2l6dOnKzk5WYMHD9bDDz+c4x1BJBJrAAAAZMg/nSC5snnzZrVq1cr+dUYLSe/evTVr1iy98MILSkhIUP/+/XX27Fk1a9ZM33//vQoXLmx/zJw5czR48GDdc889cnFxUffu3TVlypRcxWEz+amZxkIdpv+W1yEAuMUsG3CHPBs/n9dhALjFXNz4Rl6HYFfq/77J6xDsjn3QLa9DyDV6rAEAAAAL0AoCAAAASflrV5CCiIo1AAAAYAEq1gAAAJBExdpZVKwBAAAAC5BYAwAAABagFQQAAACSaAVxFhVrAAAAwAIk1gAAAIAFaAUBAACAJFpBnEXFGgAAALAAFWsAAACko2DtFCrWAAAAgAVIrAEAAAAL0AoCAAAASSxedBYVawAAAMACJNYAAACABWgFAQAAgCRaQZxFxRoAAACwABVrAAAASKJi7Swq1gAAAIAFSKwBAAAAC9AKAgAAgHR0gjiFijUAAABgARJrAAAAwAK0ggAAAEASu4I4i4o1AAAAYAESawAAAMACtIIAAABAEq0gzqJiDQAAAFiAijUAAAAkUbF2FhVrAAAAwAIk1gAAAIAFaAUBAACAJFpBnEXFGgAAALAAiTUAAABgAVpBAAAAkI5OEKdQsQYAAAAsQMUaAAAAkli86Cwq1gAAAIAFSKwBAAAAC9AKAgAAAEm0gjiLijUAAABgARJrAAAAwAK0ggAAAEASrSDOomINAAAAWIDEGgAAALAArSAAAACQRCuIs6hYAwAAABagYg0AAIB0FKydQsUaAAAAsACJNQAAAGABWkEAAAAgicWLzqJiDQAAAFiAxBoAAACwAK0gAAAAkEQriLOoWAMAAAAWoGINAACAdFSsnULFGgAAALAAiTUAAABgAVpBAAAAkM6k5XUEBRoVawAAAMACJNYAAACABWgFAQAAQDp2BXEKFWsAAADAAlSsAQAAkI7Fi06hYg0AAABYgMQaAAAAsACJNQAAANIZk3+OXHjllVdks9kcjmrVqtnPX7p0SYMGDVLx4sVVpEgRde/eXTExMVa/eyTWAAAAKPhq1qyp48eP249ffvnFfm7YsGFasmSJvvrqK61Zs0bHjh1Tt27dLI+BxYsAAAAo8AoVKqSgoKBM43Fxcfr44481d+5c3X333ZKkmTNnqnr16tqwYYOaNGliWQxUrAEAAJDOpOWfI5f27dunUqVKqWLFiurVq5cOHz4sSdqyZYuSk5PVunVr+9xq1aqpbNmyioiIsOytk6hYAwAAIB9KTExUYmKiw5iHh4c8PDwyzW3cuLFmzZqlqlWr6vjx43r11Vd11113aefOnYqOjpa7u7v8/PwcHhMYGKjo6GhLY6ZiDQAAgHwnPDxcvr6+Dkd4eHiWc9u3b68HH3xQtWvXVtu2bbVs2TKdPXtWX3755U2NmYo1AAAA0uWjG8SEhYVp+PDhDmNZVauz4ufnp9tvv1379+/Xvffeq6SkJJ09e9ahah0TE5NlT7YzqFgDAAAg3/Hw8JCPj4/DkdPE+vz58zpw4ICCg4PVoEEDubm5adWqVfbzUVFROnz4sEJDQy2NmYo1AAAA0uVy/+j84rnnnlOnTp1Urlw5HTt2TGPHjpWrq6seeeQR+fr6qm/fvho+fLj8/f3l4+OjIUOGKDQ01NIdQaRbOLFeNuCOvA4BBUBiYqLCw8MVFhaW40/B+He7uPGNvA4BBQD/tgA319GjR/XII4/o1KlTKlmypJo1a6YNGzaoZMmSkqTJkyfLxcVF3bt3V2Jiotq2bav33nvP8jhsxhTQjyaABeLj4+Xr66u4uDj5+PjkdTgAbhH824KCyrPDO3kdgt3FZc/kdQi5dstWrAEAAJBL+WjxYkHE4kUAAADAAiTWAAAAgAVoBcG/moeHh8aOHcviIgCW4t8WFFgsvXMKixcBAAAgSfJs99+8DsHu4vfDrz0pn6FiDQAAgHQsXnQKPdYAAACABUisAQAAAAuQWONfa9q0aSpfvrwKFy6sxo0b67fffsvrkAAUcGvXrlWnTp1UqlQp2Ww2LVq0KK9DAnLHmPxzFEAk1vhXmj9/voYPH66xY8dq69atqlOnjtq2bavY2Ni8Dg1AAZaQkKA6depo2rRpeR0KgDzAriD4V2rcuLEaNWqkd999V5KUlpamMmXKaMiQIXrxxRfzODoAtwKbzaaFCxeqa9eueR0KkGOebd7I6xDsLv7wfF6HkGtUrPGvk5SUpC1btqh169b2MRcXF7Vu3VoRERF5GBkAAHnMpOWfowAisca/zsmTJ5WamqrAwECH8cDAQEVHR+dRVAAAoKAjsQYAAAAswA1i8K9TokQJubq6KiYmxmE8JiZGQUFBeRQVAAD5AEvvnELFGv867u7uatCggVatWmUfS0tL06pVqxQaGpqHkQEAgIKMijX+lYYPH67evXurYcOGuuOOO/T2228rISFBTzzxRF6HBqAAO3/+vPbv32//+uDBg4qMjJS/v7/Kli2bh5EBOVRAFw3mFyTW+Fd66KGHdOLECY0ZM0bR0dGqW7euvv/++0wLGgEgNzZv3qxWrVrZvx4+fLgkqXfv3po1a1YeRQXgZmEfawAAAEiSPO+ZmNch2F1c9VJeh5BrVKwBAACQjnqrU1i8CAAAAFiAxBoAAACwAK0gAAAASMeuIE6hYg0AAABYgIo1AAAA0qWxeNEZVKwBAAAAC5BYAwAAABYgsQZwS+nTp4+6du1q/7ply5Z69tlnb3ocq1evls1m09mzZ7OdY7PZtGjRohxf85VXXlHdunWdiuvQoUOy2WyKjIx06joAblEmLf8cBRCJNYAbrk+fPrLZbLLZbHJ3d1flypU1btw4paSk3PDn/uabbzR+/Pgczc1JMgwAQHZYvAjgpmjXrp1mzpypxMRELVu2TIMGDZKbm5vCwsIyzU1KSpK7u7slz+vv72/JdQAAuBYq1gBuCg8PDwUFBalcuXIaOHCgWrdurcWLF0v6p31jwoQJKlWqlKpWrSpJOnLkiHr06CE/Pz/5+/urS5cuOnTokP2aqampGj58uPz8/FS8eHG98MILMlfcjvfKVpDExESNHDlSZcqUkYeHhypXrqyPP/5Yhw4dUqtWrSRJxYoVk81mU58+fSRJaWlpCg8PV4UKFeTp6ak6depowYIFDs+zbNky3X777fL09FSrVq0c4sypkSNH6vbbb5eXl5cqVqyo0aNHKzk5OdO8Dz74QGXKlJGXl5d69OihuLg4h/MfffSRqlevrsKFC6tatWp67733ch0LgH+pvG7/oBUEAHLP09NTSUlJ9q9XrVqlqKgorVy5UkuXLlVycrLatm2rokWLat26dfr1119VpEgRtWvXzv64t956S7NmzdInn3yiX375RadPn9bChQuv+ryPP/64vvjiC02ZMkV79uzRBx98oCJFiqhMmTL6+uuvJUlRUVE6fvy43nnnHUlSeHi4Pv30U02fPl27du3SsGHD9Oijj2rNmjWS0j8AdOvWTZ06dVJkZKT69eunF198MdfvSdGiRTVr1izt3r1b77zzjmbMmKHJkyc7zNm/f7++/PJLLVmyRN9//71+//13Pf300/bzc+bM0ZgxYzRhwgTt2bNHEydO1OjRozV79uxcxwMAyB1aQQDcVMYYrVq1SitWrNCQIUPs497e3vroo4/sLSCff/650tLS9NFHH8lms0mSZs6cKT8/P61evVpt2rTR22+/rbCwMHXr1k2SNH36dK1YsSLb5/7jjz/05ZdfauXKlWrdurUkqWLFivbzGW0jAQEB8vPzk5Re4Z44caJ+/PFHhYaG2h/zyy+/6IMPPlCLFi30/vvvq1KlSnrrrbckSVWrVtWOHTv0+uuv5+q9GTVqlP3P5cuX13PPPad58+bphRdesI9funRJn376qW677TZJ0tSpU9WxY0e99dZbCgoK0tixY/XWW2/Z35MKFSpo9+7d+uCDD9S7d+9cxQMAyB0SawA3xdKlS1WkSBElJycrLS1NPXv21CuvvGI/HxIS4tBXvW3bNu3fv19FixZ1uM6lS5d04MABxcXF6fjx42rcuLH9XKFChdSwYcNM7SAZIiMj5erqqhYtWuQ47v379+vChQu69957HcaTkpJUr149SdKePXsc4pBkT8JzY/78+ZoyZYoOHDig8+fPKyUlRT4+Pg5zypYta0+qM54nLS1NUVFRKlq0qA4cOKC+ffvqqaeess9JSUmRr69vruMB8C+Uzb+fyBkSawA3RatWrfT+++/L3d1dpUqVUqFCjv/8eHt7O3x9/vx5NWjQQHPmzMl0rZIlS15XDJ6enrl+zPnz5yVJ3333nUNCK6X3jVslIiJCvXr10quvvqq2bdvK19dX8+bNs1fBcxPrjBkzMiX6rq6ulsUKAMgaiTWAm8Lb21uVK1fO8fz69etr/vz5CggIyFS1zRAcHKyNGzeqefPmktIrs1u2bFH9+vWznB8SEqK0tDStWbPG3gpyuYyKeWpqqn2sRo0a8vDw0OHDh7OtdFevXt2+EDPDhg0brv0iL7N+/XqVK1dOL7/8sn3sr7/+yjTv8OHDOnbsmEqVKmV/HhcXF1WtWlWBgYEqVaqU/vzzT/Xq1StXzw8AkgrsosH8gsWLAPKlXr16qUSJEurSpYvWrVungwcPavXq1Ro6dKiOHj0qSXrmmWf02muvadGiRdq7d6+efvrpq+5BXb58efXu3VtPPvmkFi1aZL/ml19+KUkqV66cbDabli5dqhMnTuj8+fMqWrSonnvuOQ0bNkyzZ8/WgQMHtHXrVk2dOtW+IHDAgAHat2+fnn/+eUVFRWnu3LmaNWtWrl5vlSpVdPjwYc2bN08HDhzQlClTslyIWbhwYfXu3Vvbtm3TunXrNHToUPXo0UNBQUGSpFdffVXh4eGaMmWK/vjjD+3YsUMzZ87Uf//731zFAwDIPRJrAPmSl5eX1q5dq7Jly6pbt26qXr26+vbtq0uXLtkr2CNGjNBjjz2m3r17KzQ0VEWLFtX9999/1eu+//77euCBB/T000+rWrVqeuqpp5SQkCBJuu222/Tqq6/qxRdfVGBgoAYPHixJGj9+vEaPHq3w8HBVr15d7dq103fffacKFSpISu97/vrrr7Vo0SLVqVNH06dP18SJE3P1ejt37qxhw4Zp8ODBqlu3rtavX6/Ro0dnmle5cmV169ZNHTp0UJs2bVS7dm2H7fT69eunjz76SDNnzlRISIhatGihWbNm2WMFANw4NpPdKh8AAAD8q3je+VJeh2B3cX3uChT5ARVrAAAAwAIk1gAAAIAF2BUEAAAA6dgVxClUrAEAAAALULEGAABAOva0cAoVawAAAMACJNYAAACABWgFAQAAQDoWLzqFijUAAABgARJrAAAAwAK0ggAAACAdu4I4hYo1AAAAYAEq1gAAAEjH4kWnULEGAAAALEBiDQAAAFiAVhAAAACkY/GiU6hYAwAAABYgsQYAAAAsQCsIAAAA0rEriFOoWAMAAAAWILEGAAAALEArCAAAANKxK4hTqFgDAAAAFqBiDQAAgHQsXnQKFWsAAADAAiTWAAAAgAVsxtClDgAAADiLijUAAABgARJrAAAAwAIk1gAAAIAFSKwBAAAAC5BYAwAAABYgsQYAAAAsQGINAAAAWIDEGgAAALAAiTUAAABggf8HflHWugBgQI8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix above in a heat map\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(logistic_regression_model_conf_matrix, annot=True, fmt='.3f', linewidths=.5, square=True, cmap='Blues_r')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plot_title = f'Accuracy Score: {accuracy}'\n",
    "plt.title(plot_title, size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the above results, **we can conclude the following about the Logistic Regression model's performance using the test set**:\n",
    "\n",
    "1. **Accuracy on Test Set**: The accuracy of the Logistic Regression model on the test set is 0.713841368584759, indicating that **the model correctly predicts the target variable (plan preference) for approximately 71.38% of the instances in the test set**. This accuracy score is slightly lower than the accuracy achieved on the validation set (0.7153965785381027).\n",
    "\n",
    "2. **Confusion Matrix**: The confusion matrix for the performance of the Logistic Regression model shows the distribution of predicted labels compared to the actual labels in the test set.  \n",
    "- **True Positives (TP)**: The model correctly predicted 16 instances of the Ultra plan.\n",
    "- **False Positives (FP)**: The model incorrectly predicted 5 instances as Ultra when they were actually Smart.\n",
    "- **False Negatives (FN)**: The model incorrectly predicted 179 instances as Smart when they were actually Ultra.\n",
    "- **True Negatives (TN)**: The model correctly predicted 443 instances of the Smart plan.  \n",
    "\n",
    "3. **Evaluation of Model Performance**: The accuracy of 0.713841368584759 on the test set suggests that **the Logistic Regression model performs reasonably well but falls short of the desired threshold of 0.75**. **The model's accuracy is relatively lower compared to the Random Forest Classifier and Decision Tree Classifier models previously evaluated**.\n",
    "\n",
    "4. **Model Strengths and Weaknesses**: Tthe model struggles to accurately predict the plan preference, as indicated by the higher number of false positives and false negatives in the confusion matrix.\n",
    "\n",
    "5. **Meeting Accuracy Threshold**: The Logistic Regression model's accuracy of 0.713841368584759 falls below the defined threshold of 0.75. This suggests that **the model may not meet the desired level of accuracy for this project**.\n",
    "\n",
    "In summary, we can conclude that the Logistic Regression model achieves an accuracy of 0.713841368584759 on the test set. **While the model performs reasonably well, it falls short of the desired threshold of 0.75. The model's performance is relatively lower compared to the Random Forest Classifier and Decision Tree Classifier models previously evaluated**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the best model <a id='the-best-model'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine the best model for the given problem in the project, we need to consider the performance metrics. Let's compare the three models:\n",
    "\n",
    "1. **Decision Tree Classifier** model:\n",
    "\n",
    "- Accuracy on Validation Set: 0.7884914463452566\n",
    "- Accuracy on Test Set: 0.80248833592535\n",
    "\n",
    "2. **Random Forest Classifier** model:\n",
    "\n",
    "- Accuracy on Validation Set: 0.8149300155520995\n",
    "- Accuracy on Test Set: 0.8133748055987559\n",
    "\n",
    "3. **Logistic Regression** model:\n",
    "\n",
    "- Accuracy on Validation Set: 0.7153965785381027\n",
    "- Accuracy on Test Set: 0.713841368584759  \n",
    "\n",
    "Based on the accuracy scores alone, the **Random Forest Classifier model performs the best on both the validation set and the test set, with an accuracy of 0.8149300155520995 on the validation set and 0.8133748055987559 on the test set**. It achieves the highest accuracy among the three models.\n",
    "\n",
    "Additionally, **the Random Forest Classifier model has the advantage of being an ensemble method, combining multiple decision trees to improve performance and reduce overfitting**. \n",
    "\n",
    "Therefore, **the Random Forest Classifier is the best model for this problem.** It achieves the highest accuracy and offers the benefits of ensemble learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Check the model <a id='sanity-check'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's perform a sanity check on **Random Forest Classifier** model by observing the precision and recall level. Precision and recall are performance metrics commonly used in binary classification problems. **Precision** attempts to answer the following question: **What proportion of positive identifications was actually correct?** **Recall** attempts to answer the following question: **What proportion of actual positives was identified correctly?**  \n",
    "\n",
    "- **Precision**: It measures the proportion of positive predictions that are actually correct. A high precision value indicates that the model has a low rate of false positives and is precise in its positive predictions. \n",
    "\n",
    "- **Recall** : It measures the proportion of positive instances that are correctly identified by the model. A high recall value indicates that the model has a low rate of false negatives and is effective in capturing positive instances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.95      0.88       455\n",
      "           1       0.79      0.50      0.61       188\n",
      "\n",
      "    accuracy                           0.81       643\n",
      "   macro avg       0.81      0.72      0.75       643\n",
      "weighted avg       0.81      0.81      0.80       643\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get a classification_report for validation set\n",
    "predictions_valid = random_forest_classifier_model.predict(features_valid)\n",
    "print(classification_report(target_valid, predictions_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.95      0.88       448\n",
      "           1       0.80      0.51      0.62       195\n",
      "\n",
      "    accuracy                           0.81       643\n",
      "   macro avg       0.81      0.73      0.75       643\n",
      "weighted avg       0.81      0.81      0.80       643\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get a classification_report for test set\n",
    "predictions_test = random_forest_classifier_model.predict(features_test)\n",
    "print(classification_report(target_test, predictions_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, we can conclude that **the Random Forest Classifier model demonstrates good precision, suggesting accurate positive predictions, and high recall for the negative class**. However, the recall for the positive class is relatively lower, indicating some difficulty in capturing all positive instances. The high overall accuracy further support the model's strong performance. Based on these observations, **we can conclude that the Random Forest Classifier model performs well on the given problem, but there is room for improvement**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Conclusion <a id='conclusion'></a>  \n",
    "[Back to Contents](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we analyzed and developed machine learning models to recommend plans (Smart or Ultra) to subscribers of Megaline, based on their behavior. The dataset consisted of 3214 rows and 5 columns without any missing values. The columns included features such as the number of calls, total call duration, number of text messages, internet traffic used, and the plan for the current month (Ultra or Smart).  \n",
    "\n",
    "We trained three different models using the `sklearn` library: **Decision Tree Classifier**, **Random Forest Classifier**, and **Logistic Regression**. These models were evaluated based on their accuracy on the validation set.  \n",
    "\n",
    "**The best-performing model was the Random Forest Classifier with 40 trees in the ensemble and a maximum depth of 8**. **It achieved an accuracy of approximately 81.49% on the validation set and 81.34% on the test set**. The Random Forest model demonstrated strong performance in accurately predicting the plan preference of subscribers, with a good balance between precision and recall.  \n",
    "\n",
    "The Decision Tree Classifier had a lower accuracy of 78.85% on the validation set. The Logistic Regression model achieved an accuracy of 71.54% on the validation set, which was lower compared to the other two models.\n",
    "\n",
    "In conclusion, the Random Forest Classifier model is recommended as the best model for this project."
   ]
  }
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