ModelSelectionForLogisticRegression (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 5: ML Life Cycle: Evaluation and Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_curve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will continue practicing the evaluation phase of the machine learning life cycle. You will perform model selection for logistic regression to solve a classification problem. You will complete the following tasks:\n",
    "    \n",
    "\n",
    "1. Build your DataFrame and define your ML problem:\n",
    "    * Load the Airbnb \"listings\" data set\n",
    "    * Define the label - what are you predicting?\n",
    "    * Identify the features\n",
    "2. Create labeled examples from the data set\n",
    "3. Split the data into training and test data sets\n",
    "4. Train, test and evaluate a logistic regression (LR) model using the scikit-learn default value for hyperparameter $C$\n",
    "5. Perform a grid search to identify the optimal value of $C$ for a logistic regression model\n",
    "6. Train, test and evaluate a logisitic regression model using the optimal value of $C$\n",
    "7. Plot a precision-recall curve for both models\n",
    "8. Plot the ROC and compute the AUC for both models\n",
    "9. Perform feature selection\n",
    "10. Make your model persistent for future use\n",
    "\n",
    "\n",
    "**<font color='red'>Note: Some of the code cells in this notebook may take a while to run.</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1. Build Your DataFrame and Define Your ML Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load a Data Set and Save it as a Pandas DataFrame\n",
    "\n",
    "We will work with the data set ``airbnbData_train``. This data set already has all the necessary preprocessing steps implemented, including one-hot encoding of the categorical variables, scaling of all numerical variable values, and imputing missing values. It is ready for modeling.\n",
    "\n",
    "<b>Task</b>: In the code cell below, use the same method you have been using to load the data using `pd.read_csv()` and save it to DataFrame `df`.\n",
    "\n",
    "You will be working with the file named \"airbnbData_train.csv\" that is located in a folder named \"data_LR\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"data_LR/airbnbData_train.csv\"\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the Label\n",
    "\n",
    "Your goal is to train a machine learning model that predicts whether an Airbnb host is a 'super host'. This is an example of supervised learning and is a binary classification problem. In our dataset, our label will be the `host_is_superhost` column and the label will either contain the value `True` or `False`.\n",
    "\n",
    "#### Identify Features\n",
    "\n",
    "Our features will be all of the remaining columns in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2. Create Labeled Examples from the Data Set \n",
    "\n",
    "<b>Task</b>: In the code cell below, create labeled examples from DataFrame `df`. Assign the label to variable `y` and the features to variable `X`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: host_is_superhost, dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['host_is_superhost']\n",
    "X = df.drop(columns=['host_is_superhost'])\n",
    "X.head()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3. Create Training and Test Data Sets\n",
    "<b>Task</b>: In the code cell below, create training and test sets out of the labeled examples. Create a test set that is 10 percent of the size of the data set. Save the results to variables `X_train, X_test, y_train, y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4. Train, Test and Evaluate a Logistic Regression Model With Default Hyperparameter Values\n",
    "\n",
    "You will fit a logisitic regression model to the training data using scikit-learn's default value for hyperparameter $C$. You will then make predictions on the test data and evaluate the model's performance. The goal is to later find a value for hyperparameter $C$ that can improve this performance of the model on the test data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: In the code cell below:\n",
    "\n",
    "1. Using the scikit-learn `LogisticRegression` class, create a logistic regression model object with the following arguments: `max_iter=1000`. You will use the scikit-learn default value for hyperparameter $C$, which is 1.0. Assign the model object to the variable `model_default`.\n",
    "\n",
    "2. Fit the model to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_default = LogisticRegression(max_iter=1000)\n",
    "\n",
    "model_default.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Test your model on the test set (`X_test`). \n",
    "\n",
    "1. Use the ``predict_proba()`` method  to use the fitted model to predict class probabilities for the test set. Note that the `predict_proba()` method returns two columns, one column per class label. The first column contains the probability that an unlabeled example belongs to class `False` (`great_quality` is \"False\") and the second column contains the probability that an unlabeled example belongs to class `True` (`great_quality` is \"True\"). Save the values of the *second* column to a list called ``proba_predictions_default``.\n",
    "\n",
    "2. Use the ```predict()``` method to use the fitted model `model_default` to predict the class labels for the test set. Store the outcome in the variable ```class_label_predictions_default```. Note that the `predict()` method returns the class label (True or False) per unlabeled example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Make predictions on the test data using the predict_proba() method\n",
    "proba_predictions_default = model_default.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# 2. Make predictions on the test data using the predict() method\n",
    "class_label_predictions_default = model_default.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Evaluate the accuracy of the model using a confusion matrix. In the cell below, create a confusion matrix out of `y_test` and `class_label_predictions_default`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix = confusion_matrix(y_test, class_label_predictions_default)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5. Perform Logistic Regression Model Selection Using `GridSearchSV()`\n",
    "\n",
    "Our goal is to find the optimal choice of hyperparameter $C$. We will then fit a logistic regression model to the training data using this value of $C$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Up a Parameter Grid \n",
    "\n",
    "<b>Task</b>: Create a dictionary called `param_grid` that contains 10 possible hyperparameter values for $C$. The dictionary should contain the following key/value pair:\n",
    "\n",
    "* a key called `C` \n",
    "* a value which is a list consisting of 10 values for the hyperparameter $C$. A smaller value for “C” (e.g. C=0.01) leads to stronger regularization and a simpler model, while a larger value (e.g. C=1.0) leads to weaker regularization and a more complex model. Use the following values for $C$: `cs=[10**i for i in range(-5,5)]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs = [10**i for i in range(-5, 5)]\n",
    "\n",
    "param_grid = {'C': cs}\n",
    "\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Grid Search Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Use `GridSearchCV` to search over the different values of hyperparameter $C$ to find the one that results in the best cross-validation (CV) score.\n",
    "\n",
    "Complete the code in the cell below. <b>Note</b>: This will take a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Grid Search...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "print('Running Grid Search...')\n",
    "\n",
    "# 1. Create a LogisticRegression model object with the argument max_iter=1000. \n",
    "#    Save the model object to the variable 'model'\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "\n",
    "\n",
    "# 2. Run a grid search with 5-fold cross-validation and assign the output to the \n",
    "# object 'grid'.\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "\n",
    "# 3. Fit the model on the training data and assign the fitted model to the \n",
    "#    variable 'grid_search'\n",
    "grid_search = grid.fit(X, y)\n",
    "\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Retrieve the value of the hyperparameter $C$ for which the best score was attained. Save the result to the variable `best_c`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_C = grid_search.best_params_['C']\n",
    "\n",
    "best_C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6. Train, Test and Evaluate the Optimal Logistic Regression Model \n",
    "\n",
    "Now that we have the optimal value for hyperparameter $C$, let's train a logistic regression model using that value, test the model on our test data, and evaluate the model's performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Initialize a `LogisticRegression` model object with the best value of hyperparameter `C` model and fit the model to the training data. The model object should be named `model_best`. Note: Supply `max_iter=1000` as an argument when creating the model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=100, max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=100, max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=100, max_iter=1000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_best = LogisticRegression(C=best_C, max_iter=1000)\n",
    "\n",
    "model_best.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Test your model on the test set (`X_test`).\n",
    "\n",
    "1. Use the ``predict_proba()`` method  to use the fitted model `model_best` to predict class probabilities for the test set. Save the values of the *second* column to a list called ``proba_predictions_best``.\n",
    "\n",
    "2. Use the ```predict()``` method to use the fitted model `model_best` to predict the class labels for the test set. Store the outcome in the variable ```class_label_predictions_best```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Make predictions on the test data using the predict_proba() method\n",
    "proba_predictions_best = model_best.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# 2. Make predictions on the test data using the predict() method\n",
    "class_label_predictions_best = model_best.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Evaluate the accuracy of the model using a confusion matrix. In the cell below, create a confusion matrix out of `y_test` and `class_label_predictions_best`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix_best = confusion_matrix(y_test, class_label_predictions_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7.  Plot Precision-Recall Curves for Both Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> In the code cell below, use `precision_recall_curve()` to compute precision-recall pairs for both models.\n",
    "\n",
    "For `model_default`:\n",
    "* call `precision_recall_curve()` with `y_test` and `proba_predictions_default`\n",
    "* save the output to the variables `precision_default`, `recall_default` and `thresholds_default`, respectively\n",
    "\n",
    "For `model_best`:\n",
    "* call `precision_recall_curve()` with `y_test` and `proba_predictions_best`\n",
    "* save the output to the variables `precision_best`, `recall_best` and `thresholds_best`, respectively\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision_default, recall_default, thresholds_default = precision_recall_curve(y_test, proba_predictions_default)\n",
    "precision_best, recall_best, thresholds_best = precision_recall_curve(y_test, proba_predictions_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code cell below, create two `seaborn` lineplots to visualize the precision-recall curve for both models. \"Recall\" will be on the $x$-axis and \"Precision\" will be on the $y$-axis. \n",
    "\n",
    "The plot for \"default\" should be green. The plot for the \"best\" should be red.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Precision-Recall Curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "\n",
    "sns.lineplot(x=recall_default, y=precision_default, color='green', label='default')\n",
    "\n",
    "sns.lineplot(x=recall_best, y=precision_best, color='red', label='best')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8. Plot ROC Curves and Compute the AUC for Both Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will next use scikit-learn's `roc_curve()` function to plot the receiver operating characteristic (ROC) curve and the `auc()` function to compute the area under the curve (AUC) for both models.\n",
    "\n",
    "* An ROC curve plots the performance of a binary classifier for varying classification thresholds. It plots the fraction of true positives out of the positives vs. the fraction of false positives out of the negatives. For more information on how to use the `roc_curve()` function, consult the [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html).\n",
    "\n",
    "* The AUC measures the trade-off between the true positive rate and false positive rate. It provides a broad view of the performance of a classifier since it evaluates the performance for all the possible threshold values; it essentially provides a value that summarizes the the ROC curve. For more information on how to use the `auc()` function, consult the [scikit-learn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html).\n",
    "\n",
    "Let's first import the functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Using the `roc_curve()` function, record the true positive and false positive rates for both models. \n",
    "\n",
    "1. Call `roc_curve()` with arguments `y_test` and `proba_predictions_default`. The `roc_curve` function produces three outputs. Save the three items to the following variables, respectively: `fpr_default` (standing for 'false positive rate'),  `tpr_default` (standing for 'true positive rate'), and `thresholds_default`.\n",
    "\n",
    "2. Call `roc_curve()` with arguments `y_test` and `proba_predictions_best`. The `roc_curve` function produces three outputs. Save the three items to the following variables, respectively: `fpr_best` (standing for 'false positive rate'),  `tpr_best` (standing for 'true positive rate'), and `thresholds_best`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_default, tpr_default, thresholds_default = roc_curve(y_test, proba_predictions_default)\n",
    "fpr_best, tpr_best, thresholds_best = roc_curve(y_test, proba_predictions_best)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Create <b>two</b> `seaborn` lineplots to visualize the ROC curve for both models. \n",
    "\n",
    "The plot for the default hyperparameter should be green. The plot for the best hyperparameter should be red.\n",
    "\n",
    "* In each plot, the `fpr` values should be on the $x$-axis.\n",
    "* In each plot, the`tpr` values should be on the $y$-axis. \n",
    "* In each plot, label the $x$-axis \"False positive rate\".\n",
    "* In each plot, label the $y$-axis \"True positive rate\".\n",
    "* Give each plot the title \"Receiver operating characteristic (ROC) curve\".\n",
    "* Create a legend on each plot indicating that the plot represents either the default hyperparameter value or the best hyperparameter value.\n",
    "\n",
    "<b>Note:</b> It may take a few minutes to produce each plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot ROC Curve for Default Hyperparameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr_default, tpr_default, color='green', lw=2, label='ROC curve (AUC = %0.2f)')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # Plot diagonal line representing random classifier\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) curve for Default Model')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot ROC Curve for Best Hyperparameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(fpr_best, tpr_best, color='red', lw=2, label='ROC curve (AUC = %0.2f)')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # Plot diagonal line representing random classifier\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) curve for Best Model')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Use the `auc()` function to compute the area under the receiver operating characteristic (ROC) curve for both models.\n",
    "\n",
    "For each model, call the function with the `fpr` argument first and the `tpr` argument second. \n",
    "\n",
    "Save the result of the `auc()` function for `model_default` to the variable `auc_default`.\n",
    "Save the result of the `auc()` function for `model_best` to the variable `auc_best`. \n",
    "Compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8409129877214984\n",
      "0.842436995628485\n"
     ]
    }
   ],
   "source": [
    "auc_default = auc(fpr_default, tpr_default)\n",
    "\n",
    "auc_best = auc(fpr_best, tpr_best)\n",
    "\n",
    "print(auc_default)\n",
    "print(auc_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Dive: Feature Selection Using SelectKBest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code cell below, you will see how to use scikit-learn's `SelectKBest` class to obtain the best features in a given data set using a specified scoring function. For more information on how to use `SelectKBest`, consult the online [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html).\n",
    "\n",
    "We will extract the best 5 features from the Airbnb \"listings\" data set to create new training data, then fit our model with the optimal hyperparameter $C$ to the data and compute the AUC. Walk through the code to see how it works and complete the steps where prompted. Analyze the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best 5 features:\n",
      "Index(['host_response_rate', 'number_of_reviews', 'number_of_reviews_ltm',\n",
      "       'number_of_reviews_l30d', 'review_scores_cleanliness'],\n",
      "      dtype='object')\n",
      "0.8130305864348417\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "# Note that k=5 is specifying that we want the top 5 features\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(X, y)\n",
    "filter = selector.get_support()\n",
    "top_5_features = X.columns[filter]\n",
    "\n",
    "print(\"Best 5 features:\")\n",
    "print(top_5_features)\n",
    "\n",
    "# Create new training and test data for features\n",
    "new_X_train = X_train[top_5_features]\n",
    "new_X_test = X_test[top_5_features]\n",
    "\n",
    "\n",
    "# Initialize a LogisticRegression model object with the best value of hyperparameter C \n",
    "# The model object should be named 'model'\n",
    "# Note: Supply max_iter=1000 as an argument when creating the model object\n",
    "model = LogisticRegression(C=best_C, max_iter=1000)\n",
    "\n",
    "\n",
    "# Fit the model to the new training data\n",
    "model.fit(new_X_train, y_train)\n",
    "\n",
    "\n",
    "# Use the predict_proba() method to use your model to make predictions on the new test data \n",
    "# Save the values of the second column to a list called 'proba_predictions'\n",
    "proba_predictions = model.predict_proba(new_X_test)[:, 1]\n",
    "\n",
    "\n",
    "# Compute the auc-roc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, proba_predictions)\n",
    "auc_result = auc(fpr, tpr)\n",
    "print(auc_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task</b>: Consider the results. Change the specified number of features and re-run your code. Does this change the AUC value? What number of features results in the best AUC value? Record your findings in the cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of Features - AUC Result: 1 - 0.7356909884223259, 2 - 0.7597546224295835, 3 - 0.7533141812107598, 4 - 0.772851146247336, 5 - 0.7802880738436726, 6 - 0.7946528857784689, 7 - 0.7940700420482691, 8 - 0.793677999539197, 9 - 0.793871680778757, 10 - 0.7899775358562295\n",
    "\n",
    "It looks like the highest AUC Result (0.7946528857784689) was reached with 6 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 9. Make Your Model Persistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will next practice what you learned in the \"Making Your Model Persistent\" activity, and use the `pickle` module to save `model_best`.\n",
    "\n",
    "First we will import the pickle module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Use `pickle` to save your model to a `pkl` file in the current working directory. Choose the name of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'logistic_regression_model.pkl'\n",
    "\n",
    "with open(filename, 'wb') as file:\n",
    "    pickle.dump(model_best, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Test that your model is packaged and ready for future use by:\n",
    "\n",
    "1. Loading your model back from the file \n",
    "2. Using your model to make predictions on `X_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [False False False ... False False False]\n",
      "Probabilities: [[0.86870577 0.13129423]\n",
      " [0.7072175  0.2927825 ]\n",
      " [0.72706    0.27294   ]\n",
      " ...\n",
      " [0.57887469 0.42112531]\n",
      " [0.91157398 0.08842602]\n",
      " [0.89386285 0.10613715]]\n"
     ]
    }
   ],
   "source": [
    "filename = 'logistic_regression_model.pkl'\n",
    "with open(filename, 'rb') as file:\n",
    "    loaded_model = pickle.load(file)\n",
    "    \n",
    "predictions = loaded_model.predict(X_test)\n",
    "\n",
    "probabilities = loaded_model.predict_proba(X_test)\n",
    "\n",
    "print(\"Predictions:\", predictions)\n",
    "print(\"Probabilities:\", probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Task:</b> Download your `pkl` file and your `airbnbData_train` data set, and push these files to your GitHub repository. You can download these files by going to `File -> Open`. A new tab will open in your browser that will allow you to select your files and download them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}