01 Building_and_training_a_model_using_Linear_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": [
    "## Predictions: Regression for Car Mileage and Diamond Price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-BD0231EN-SkillsNetwork/CarsnDiamonds.png\" width=\"50%\">\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 use a car data set to train a regression model that will predict the mileage of a car.</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---Build-and-Train-a-Linear-Regression-Model\">Task 3 - Build and Train a Linear Regression Model</a></li>\n",
    "    <li><a href=\"#Task-4---Evaluate-the-model-and-make-predictions\">Task 4 - Evaluate the model and make predictions</a></li>        </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---Build-and-Train-a-new-Linear-Regression-Model\">Exercise 3 - Build and Train a new Linear Regression Model</a></li>\n",
    "    <li><a href=\"#Exercise-4---Evaluate-the-model\">Exercise 4 - Evaluate the model</a></li>\n",
    "    <li><a href=\"#Exercise-5---Predict-the-price-of-a-diamond\">Exercise 5 - Predict the price of a diamond</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 Linear Regression to build a model to predict car mileage.\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",
    " - Modified version of car mileage dataset. Available at https://archive.ics.uci.edu/ml/datasets/auto+mpg \n",
    " - Modified version of diamonds 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": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas==1.3.4\n",
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      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas==1.3.4) (2.8.2)\n",
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      "Installing collected packages: pandas\n",
      "  Attempting uninstall: pandas\n",
      "    Found existing installation: pandas 1.3.5\n",
      "    Uninstalling pandas-1.3.5:\n",
      "      Successfully uninstalled pandas-1.3.5\n",
      "Successfully installed pandas-1.3.4\n",
      "Collecting scikit-learn==1.0.2\n",
      "  Downloading scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (24.8 MB)\n",
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      "\u001b[?25hCollecting threadpoolctl>=2.0.0 (from scikit-learn==1.0.2)\n",
      "  Downloading threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
      "Installing collected packages: threadpoolctl, joblib, scikit-learn\n",
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      "    Found existing installation: scikit-learn 0.20.1\n",
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     ]
    }
   ],
   "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==1.0.2\n",
    "!pip install numpy==1.21.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Required Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1 - Load the data in a csv file into a dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some sample rows from the dataset we loaded:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>37</th>\n",
       "      <td>13.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>170</td>\n",
       "      <td>4746</td>\n",
       "      <td>12.0</td>\n",
       "      <td>71</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>29.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>83</td>\n",
       "      <td>2219</td>\n",
       "      <td>16.5</td>\n",
       "      <td>74</td>\n",
       "      <td>European</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>19.2</td>\n",
       "      <td>6</td>\n",
       "      <td>231.0</td>\n",
       "      <td>105</td>\n",
       "      <td>3535</td>\n",
       "      <td>19.2</td>\n",
       "      <td>78</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6</td>\n",
       "      <td>232.0</td>\n",
       "      <td>100</td>\n",
       "      <td>2945</td>\n",
       "      <td>16.0</td>\n",
       "      <td>73</td>\n",
       "      <td>American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>European</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Engine Disp  Horsepower  Weight  Accelerate  Year  \\\n",
       "37   13.0          8        400.0         170    4746        12.0    71   \n",
       "138  29.0          4         98.0          83    2219        16.5    74   \n",
       "263  19.2          6        231.0         105    3535        19.2    78   \n",
       "86   18.0          6        232.0         100    2945        16.0    73   \n",
       "24   25.0          4        110.0          87    2672        17.5    70   \n",
       "\n",
       "       Origin  \n",
       "37   American  \n",
       "138  European  \n",
       "263  American  \n",
       "86   American  \n",
       "24   European  "
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(392, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a scatter plot of Horsepower versus mileage(MPG) . This will help us visualize the relationship between them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Horsepower', ylabel='MPG'>"
      ]
     },
     "execution_count": 7,
     "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 = \"Horsepower\", y = \"MPG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Engine Disp', ylabel='Horsepower'>"
      ]
     },
     "execution_count": 8,
     "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 = \"Engine Disp\", y = \"Horsepower\")"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "target = df[\"MPG\"]"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "features = df[[\"Horsepower\",\"Weight\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3 - Build and Train a Linear Regression Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a LR model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train/Fit the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(features,target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4 - Evaluate the model and make predictions\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": [
    {
     "data": {
      "text/plain": [
       "0.7063752737298348"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Higher the score, better the model.\n",
    "lr.score(features,target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions. Let us predict the mileage for a car with HorsePower = 100 and Weight = 2000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.3216098])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[100,2000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 29.3216098 miles per gallon is the mileage of a car with HorsePower = 100 and Weight = 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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 price column as target\n",
    " - use the columns carat and depth as features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "target = df2[\"price\"]\n",
    "features = df2[[\"carat\",\"depth\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "Refer to Task2\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "target = df2[\"price\"]\n",
    "features = df2[[\"carat\",\"depth\"]]\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3 - Build and Train a new Linear Regression Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new Linear Regression Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2 = LinearRegression()\n",
    "lr2.fit(features,target)"
   ]
  },
  {
   "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",
    "lr2 = LinearRegression()\n",
    "lr2.fit(features,target)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4 - Evaluate the model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the score of the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8506754571636563"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code goes here\n",
    "lr2.score(features,target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for a Hint</summary>\n",
    "    \n",
    "use the score() method\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "    <summary>Click here for Solution</summary>\n",
    "\n",
    "```python\n",
    "lr2.score(features,target)\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5 - Predict the price of a diamond\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the price of a diamond with carat = 0.3 and depth = 60\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([244.95605225])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code goes herelr2.predict([[0.3, 60]])\n",
    "lr2.predict([[0.3, 60]])"
   ]
  },
  {
   "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",
    "lr2.predict([[0.3, 60]])\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You have completed this lab.<br>\n",
    "You are encouraged to experiment creating models with more than 2 feature columns\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": [
    "## Contributors\n",
    "[Vicky Kuo](https://author.skills.network/instructors/vicky_kuo)\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",
    "|2023-04-14|0.1|Ramesh Sannareddy|Initial Version Created|\n",
    "|2023-06-20|0.3|Vicky Kuo|Proofreading|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2023 IBM Corporation. All rights reserved.\n"
   ]
  }
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