03 Metrics_for_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\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 Regression\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 regression 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-Linear-Regression-Model\">Task 4 - Build and Train a Linear Regression Model</a></li>\n",
    "        <li><a href=\"#Task-5---Evaluate-the-model\">Task 5 - Evaluate the model</a></li>    \n",
    "        </ol>\n",
    "    </li>\n",
    "    \n",
    "\n",
    "\n",
    "<li><a href=\"#Exercises\">Exercises</a></li>\n",
    "<ol>\n",
    "    <li><a href=\"#Exercise-1---Loading-a-dataset-(diamond-dataset)\">Exercise 1 - Loading a dataset (diamond 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---Predict-values\">Exercise 4 - Predict values</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",
    " - Split the dataset into training and testing sets.\n",
    " - Use Linear Regression to build a prediction model.\n",
    " - Use metrics to evaluate the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets\n",
    "\n",
    "In this lab you will be using dataset(s):\n",
    "\n",
    " - Modified version of car mileage dataset. Original dataset available at https://archive.ics.uci.edu/ml/datasets/auto+mpg \n",
    " - Modified version of diamonds dataset. Original dataset available at https://www.openml.org/search?type=data&sort=runs&id=42225&status=active\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\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",
    "# !pip install numpy==1.21.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# You can 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"
   ]
  },
  {
   "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": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#import functions for train test split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import functions for metrics\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1 - Load the data in a csv file into a dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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/mpg.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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some sample rows from the dataset we loaded:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Engine Disp</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Accelerate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>85</td>\n",
       "      <td>2587</td>\n",
       "      <td>16.0</td>\n",
       "      <td>70</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>33.5</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>83</td>\n",
       "      <td>2075</td>\n",
       "      <td>15.9</td>\n",
       "      <td>77</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>225.0</td>\n",
       "      <td>95</td>\n",
       "      <td>3785</td>\n",
       "      <td>19.0</td>\n",
       "      <td>75</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>18.2</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>135</td>\n",
       "      <td>3830</td>\n",
       "      <td>15.2</td>\n",
       "      <td>79</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>61</td>\n",
       "      <td>2003</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74</td>\n",
       "      <td>Japanese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Engine Disp  Horsepower  Weight  Accelerate  Year  \\\n",
       "14   21.0          6        200.0          85    2587        16.0    70   \n",
       "221  33.5          4         98.0          83    2075        15.9    77   \n",
       "165  18.0          6        225.0          95    3785        19.0    75   \n",
       "289  18.2          8        318.0         135    3830        15.2    79   \n",
       "144  32.0          4         83.0          61    2003        19.0    74   \n",
       "\n",
       "       Origin  \n",
       "14   American  \n",
       "221  American  \n",
       "165  American  \n",
       "289  American  \n",
       "144  Japanese  "
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(392, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a scatter plot of Weight versus mileage(MPG) . This will help us visualize the relationship between them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight', ylabel='MPG'>"
      ]
     },
     "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.plot.scatter(x = \"Weight\", y = \"MPG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are encouraged to create more plots to visualize relationships amongst other columns\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 predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = df[\"MPG\"] # y is the target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We identify the features next. Features are the values our machine learning model learns from\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df[[\"Horsepower\",\"Weight\"]] # X is the set of features"
   ]
  },
  {
   "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=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random_state variable controls the shuffling applied to the data before applying the split. Pass the same integer for reproducible output across multiple function calls\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4 - Build and Train a Linear Regression Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a LR model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train/Fit the model using the training data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.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. We use the testing data to evaluate the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7691968626070727"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "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": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_values = y_test\n",
    "predicted_values = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### R Squared\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7691968626070727"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(original_values, predicted_values) # Higher the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mean Squared Error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.212748938323992"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(original_values, predicted_values) # Lower the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Root Mean Squared Error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4946743679953918"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(mean_squared_error(original_values, predicted_values)) # Lower the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mean Absolute Error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.853067881509254"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(original_values, predicted_values) # Lower the value the better the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "URL2 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-BD0231EN-SkillsNetwork/datasets/diamonds.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1 - Loading a dataset (diamond dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": "code",
   "execution_count": 20,
   "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>s</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24062</th>\n",
       "      <td>24063</td>\n",
       "      <td>1.37</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>62.3</td>\n",
       "      <td>55.5</td>\n",
       "      <td>12207</td>\n",
       "      <td>7.05</td>\n",
       "      <td>7.14</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20093</th>\n",
       "      <td>20094</td>\n",
       "      <td>1.57</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>8572</td>\n",
       "      <td>7.46</td>\n",
       "      <td>7.43</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24427</th>\n",
       "      <td>24428</td>\n",
       "      <td>2.02</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.7</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12680</td>\n",
       "      <td>8.14</td>\n",
       "      <td>8.08</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4078</th>\n",
       "      <td>4079</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>571</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.38</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23748</th>\n",
       "      <td>23749</td>\n",
       "      <td>1.54</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11795</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.54</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           s  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "24062  24063   1.37    Ideal     G    VVS2   62.3   55.5  12207  7.05  7.14   \n",
       "20093  20094   1.57  Premium     G     SI2   61.1   61.0   8572  7.46  7.43   \n",
       "24427  24428   2.02  Premium     H     SI2   61.7   61.0  12680  8.14  8.08   \n",
       "4078    4079   0.31    Ideal     D     SI1   61.4   55.0    571  4.35  4.38   \n",
       "23748  23749   1.54  Premium     E     SI1   59.3   60.0  11795  7.58  7.54   \n",
       "\n",
       "          z  \n",
       "24062  4.43  \n",
       "20093  4.55  \n",
       "24427  5.01  \n",
       "4078   2.68  \n",
       "23748  4.48  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2 - Identify the target column and the data columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Use the price column as target\n",
    " - Use the columns carat, depth and table as features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = df2[\"price\"]\n",
    "X = df2[[\"carat\",\"depth\",\"table\"]]"
   ]
  },
  {
   "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",
    "```python\n",
    "y = df2[\"price\"]\n",
    "X = df2[[\"carat\",\"depth\",\"table\"]]\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 25% of the data as testing set. Use 42 as random state.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "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=0.25, random_state=42)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4 - Predict values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2 = LinearRegression()\n",
    "lr2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the values using the test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_values = y_test\n",
    "predicted_values = lr2.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "Use the predict() method\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "original_values = y_test\n",
    "predicted_values = lr2.predict(X_test)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5 - Evaluate the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the metrics :\n",
    "- R squared\n",
    "- mean absolute error\n",
    "- mean squared error\n",
    "- root mean squared error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score:  0.8529493985221452\n",
      "mean abs. error:  991.8625215830598\n",
      "mean sq. error:  2310119.6354749133\n",
      "Root mean sq. error:  1519.9077720292482\n"
     ]
    }
   ],
   "source": [
    "#your code goes here\n",
    "print(\"r2 score: \",r2_score(original_values, predicted_values))\n",
    "print(\"mean abs. error: \",mean_absolute_error(original_values, predicted_values))\n",
    "print(\"mean sq. error: \",mean_squared_error(original_values, predicted_values))\n",
    "print(\"Root mean sq. error: \",sqrt(mean_squared_error(original_values, predicted_values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "Use the metrics functions </details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "print(r2_score(original_values, predicted_values))\n",
    "print(mean_absolute_error(original_values, predicted_values))\n",
    "print(mean_squared_error(original_values, predicted_values))\n",
    "print(sqrt(mean_squared_error(original_values, predicted_values)))\n",
    "\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/)\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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