04 Metrics_for_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMBD0231ENSkillsNetwork866-2023-01-01\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics for Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **30** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='color: red'>The purpose of this lab is to show you how to evaluate a classification model using various metrics.</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Table of Contents__\n",
    "\n",
    "<ol>\n",
    "    <li><a href=\"#Objectives\">Objectives</a></li>\n",
    "    <li><a href=\"#Datasets\">Datasets</a></li>\n",
    "    <li>\n",
    "        <a href=\"#Setup\">Setup</a>\n",
    "        <ol>\n",
    "            <li><a href=\"#Installing-Required-Libraries\">Installing Required Libraries</a></li>\n",
    "            <li><a href=\"#Importing-Required-Libraries\">Importing Required Libraries</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"#Examples\">Examples</a>\n",
    "        <ol>\n",
    "            <li><a href=\"#Task-1---Load-the-data-in-a-csv-file-into-a-dataframe\">Task 1 - Load the data in a csv file into a dataframe</a></li>\n",
    "    <li><a href=\"#Task-2---Identify-the-target-column-and-the-data-columns\">Task 2 - Identify the target column and the data columns</a></li>\n",
    "    <li><a href=\"#Task-3---Split-the-data-set\">Task 3 - Split the data set</a></li>\n",
    "    <li><a href=\"#Task-4---Build-and-train-a-classifier\">Task 4 - Build and train a classifier</a></li>\n",
    "    <li><a href=\"#Task-5---Evaluate-the-model\">Task 5 - Evaluate the model</a></li>        </ol>\n",
    "    </li>\n",
    "\n",
    "<li><a href=\"#Exercises\">Exercises</a></li>\n",
    "<ol>\n",
    "    <li><a href=\"#Exercise-1---Load-a-dataset\">Exercise 1 - Load a dataset</a></li>\n",
    "    <li><a href=\"#Exercise-2---Identify-the-target-column-and-the-data-columns\">Exercise 2 - Identify the target column and the data columns</a></li>\n",
    "    <li><a href=\"#Exercise-3---Split-the-data\">Exercise 3 - Split the data</a></li>\n",
    "    <li><a href=\"#Exercise-4---Build-and-Train-a-new-classifier\">Exercise 4 - Build and Train a new classifier</a></li>\n",
    "    <li><a href=\"#Exercise-5---Evaluate-the-model\">Exercise 5 - Evaluate the model</a></li>\n",
    "    </ol>\n",
    "</ol>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    " - Use Pandas to load data sets.\n",
    " - Identify the target and features.\n",
    " - Use Logistic Regression to build a classifier.\n",
    " - Use metrics to evaluate the model.\n",
    " - Make predictions using a trained model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets\n",
    "\n",
    "In this lab you will be using dataset(s):\n",
    "\n",
    " - Pima Indians Diabetes Database. Available at https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this lab, we will be using the following libraries:\n",
    "\n",
    "*   [`pandas`](https://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for managing the data.\n",
    "*   [`sklearn`](https://scikit-learn.org/stable/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for machine learning and machine-learning-pipeline related functions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing Required Libraries\n",
    "\n",
    "The following required libraries are pre-installed in the Skills Network Labs environment. However, if you run this notebook commands in a different Jupyter environment (e.g. Watson Studio or Ananconda), you will need to install these libraries by removing the `#` sign before `!pip` in the code cell below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented.\n",
    "# !pip install pandas==1.3.4\n",
    "# !pip install scikit-learn==0.20.1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following required libraries are __not__ pre-installed in the Skills Network Labs environment. __You will need to run the following cell__ to install them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Required Libraries\n",
    "\n",
    "_We recommend you import all required libraries in one place (here):_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# You can also use this section to suppress warnings generated by your code:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#import functions for train test split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# functions for metrics\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1 - Load the data in a csv file into a dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# the data set is available at the url below.\n",
    "URL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-BD0231EN-SkillsNetwork/datasets/diabetes.csv\"\n",
    "\n",
    "# using the read_csv function in the pandas library, we load the data into a dataframe.\n",
    "\n",
    "df = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some sample rows from the dataset we loaded:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>33</td>\n",
       "      <td>146</td>\n",
       "      <td>36.6</td>\n",
       "      <td>0.254</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>2</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0.292</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>4</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.212</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4</td>\n",
       "      <td>146</td>\n",
       "      <td>85</td>\n",
       "      <td>27</td>\n",
       "      <td>100</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0.189</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>142</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.173</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "24            11      143             94             33      146  36.6   \n",
       "491            2       89             90             30        0  33.5   \n",
       "604            4      183              0              0        0  28.4   \n",
       "69             4      146             85             27      100  28.9   \n",
       "59             0      105             64             41      142  41.5   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "24                      0.254   51        1  \n",
       "491                     0.292   42        0  \n",
       "604                     0.212   36        1  \n",
       "69                      0.189   27        0  \n",
       "59                      0.173   22        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show 5 random rows from the dataset\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find out the number of rows and columns in the dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the types and count of Outcome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Outcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Outcome.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 500 people without diabetes and 268 people with diabetes in this dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2 - Identify the target column and the data columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we identify the target. Target is the value that our machine learning model needs to classify\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = df[\"Outcome\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We identify the features next. Features are the input values our machine learning model learns from\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3 - Split the data set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data set in the ratio of 70:30. 70% training data, 30% testing data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4 - Build and train a classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Logistic Regression model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "classifier = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train/Fit the model on training data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5 - Evaluate the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your model is now trained. Time to evaluate the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation : 0.7402597402597403 (Higher the score, better the model)\n"
     ]
    }
   ],
   "source": [
    "#Higher the score, better the model.\n",
    "print(\"Model evaluation :\",classifier.score(X_test,y_test),\"(Higher the score, better the model)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To compute the detailed metrics we need two values, the original mileage and the predicted mileage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_values = y_test\n",
    "predicted_values = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7543859649122807"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(original_values, predicted_values) # Higher the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recall\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48314606741573035"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(original_values, predicted_values) # Higher the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### F1 Score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.589041095890411"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(original_values, predicted_values) # Higher the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion Matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[128,  14],\n",
       "       [ 46,  43]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(original_values, predicted_values) # can be used to manually calculate various met"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "URL2 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-BD0231EN-SkillsNetwork/datasets/diabetes.csv\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1 - Load a dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the cancer dataset available at URL2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(URL2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "Use the read_csv function\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "df2 = pd.read_csv(URL2)\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2 - Identify the target column and the data columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - use the Outcome column as target\n",
    " - use columns 'Pregnancies', 'Glucose', 'Insulin', 'DiabetesPedigreeFunction', 'Age' as features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = df2[\"Outcome\"]\n",
    "X = df2[['Pregnancies', 'Glucose', 'Insulin', 'DiabetesPedigreeFunction', 'Age']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "Refer to Task 2\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```\n",
    "y = df2[\"Outcome\"]\n",
    "X = df2[['Pregnancies', 'Glucose', 'Insulin', 'DiabetesPedigreeFunction', 'Age']]\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3 - Split the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset into training and testing sets. Make 33% of the data as testing set. Use 40 as random state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "use the train_test_split function\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=40)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4 - Build and Train a new classifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new Classifier and train using the training data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier2 = LogisticRegression()\n",
    "classifier2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "fit using the features and target\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "classifier2 = LogisticRegression()\n",
    "classifier2.fit(X_train,y_train)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5 - Evaluate the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_values = y_test\n",
    "predicted_values = classifier2.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the metrics :\n",
    "- Precision Score\n",
    "- Recall Score\n",
    "- F1 Score \t\t\t\t\t\t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision score : 0.7678571428571429\n",
      "Recall score : 0.43434343434343436\n",
      "F1 score : 0.5548387096774193\n"
     ]
    }
   ],
   "source": [
    "#your code goes here\n",
    "print(\"Precision score :\",precision_score(original_values, predicted_values))\n",
    "print(\"Recall score :\",recall_score(original_values, predicted_values))\n",
    "print(\"F1 score :\",f1_score(original_values, predicted_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "use the metrics functions\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```\n",
    "print(precision_score(original_values, predicted_values))\n",
    "print(recall_score(original_values, predicted_values))\n",
    "print(f1_score(original_values, predicted_values))\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations you have completed this lab.<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Ramesh Sannareddy](https://www.linkedin.com/in/rsannareddy/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMBD0231ENSkillsNetwork866-2023-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Contributors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Date (YYYY-MM-DD)|Version|Changed By|Change Description|\n",
    "|-|-|-|-|\n",
    "|2023-04-15|0.1|Ramesh Sannareddy|Initial Version Created|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2023 IBM Corporation. All rights reserved.\n"
   ]
  }
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