From 0513d9396df35ee4e81686e07b7eb3e3057837cc Mon Sep 17 00:00:00 2001
From: 202310715145 SUMIH <202310715145@mhs.ubharajaya.ac.id>
Date: Thu, 20 Nov 2025 10:53:15 +0700
Subject: [PATCH] Upload files to "MODUL 2_REGRESSION"

---
 ...N-Reg-Mulitple-Linear-Regression-Co2.ipynb |  822 +++++++++++
 ...45_ML0101EN-Reg-NoneLinearRegression.ipynb |  888 ++++++++++++
 ...0101EN-Reg-Polynomial-Regression-Co2.ipynb |  937 +++++++++++++
 ...1EN-Reg-Simple-Linear-Regression-Co2.ipynb | 1235 +++++++++++++++++
 4 files changed, 3882 insertions(+)
 create mode 100644 MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Mulitple-Linear-Regression-Co2.ipynb
 create mode 100644 MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-NoneLinearRegression.ipynb
 create mode 100644 MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Polynomial-Regression-Co2.ipynb
 create mode 100644 MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb

diff --git a/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Mulitple-Linear-Regression-Co2.ipynb b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Mulitple-Linear-Regression-Co2.ipynb
new file mode 100644
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@@ -0,0 +1,822 @@
+{
+ "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",
+    "\n",
+    "\n",
+    "# Multiple Linear Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement Multiple Linear Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Table of contents</h1>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
+    "    <ol>\n",
+    "        <li><a href=\"#understanding-data\">Understanding the Data</a></li>\n",
+    "        <li><a href=\"#reading_data\">Reading the Data in</a></li>\n",
+    "        <li><a href=\"#multiple_regression_model\">Multiple Regression Model</a></li>\n",
+    "        <li><a href=\"#prediction\">Prediction</a></li>\n",
+    "        <li><a href=\"#practice\">Practice</a></li>\n",
+    "    </ol>\n",
+    "</div>\n",
+    "<br>\n",
+    "<hr>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Downloading Data\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-17 02:26:23--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.003s  \n",
+      "\n",
+      "2025-10-17 02:26:23 (22.0 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<h2 id=\"understanding_data\">Understanding the Data</h2>\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUELTYPE** e.g. z\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"reading_data\">Reading the data in</h2>\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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0         2.0          4                   9.9                  6.7   \n",
+       "1         2.4          4                  11.2                  7.7   \n",
+       "2         1.5          4                   6.0                  5.8   \n",
+       "3         3.5          6                  12.7                  9.1   \n",
+       "4         3.5          6                  12.1                  8.7   \n",
+       "5         3.5          6                  11.9                  7.7   \n",
+       "6         3.5          6                  11.8                  8.1   \n",
+       "7         3.7          6                  12.8                  9.0   \n",
+       "8         3.7          6                  13.4                  9.5   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  CO2EMISSIONS  \n",
+       "0                   8.5           196  \n",
+       "1                   9.6           221  \n",
+       "2                   5.9           136  \n",
+       "3                  11.1           255  \n",
+       "4                  10.6           244  \n",
+       "5                  10.0           230  \n",
+       "6                  10.1           232  \n",
+       "7                  11.1           255  \n",
+       "8                  11.6           267  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+    "\n",
+    "We know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+    "\n",
+    "Let's split our dataset into train and test sets. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the  __np.random.rand()__ function: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"multiple_regression_model\">Multiple Regression Model</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In reality, there are multiple variables that impact the co2emission. When more than one independent variable is present, the process is called multiple linear regression. An example of multiple linear regression is predicting co2emission using the features FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that multiple linear regression model is the extension of the simple linear regression model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[10.74801928  7.48168159  9.63200326]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  precompute=False, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, random_state=None,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit (x, y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__  are the parameters of the fitted line. \n",
+    "Given that it is a multiple linear regression model with 3 parameters and that the parameters are the intercept and coefficients of the hyperplane, sklearn can estimate them from our data. Scikit-learn uses plain Ordinary Least Squares method to solve this problem.\n",
+    "\n",
+    "#### Ordinary Least Squares (OLS)\n",
+    "OLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ($\\hat{y}$) over all samples in the dataset.\n",
+    "\n",
+    "OLS can find the best parameters using of the following methods:\n",
+    "* Solving the model parameters analytically using closed-form equations\n",
+    "* Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"prediction\">Prediction</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE) : 534.77\n",
+      "Variance score: 0.87\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Mean Squared Error (MSE) : %.2f\"\n",
+    "      % np.mean((y_hat - y) ** 2))\n",
+    "\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % regr.score(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Explained variance regression score:__  \n",
+    "Let $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n",
+    "\n",
+    "$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var\\{ y - \\hat{y}\\}}{Var\\{y\\}}$  \n",
+    "The best possible score is 1.0, the lower values are worse.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[10.77414197  7.12392251  6.25127329  3.09723422]]\n",
+      "Residual sum of squares : 535.12\n",
+      "Variance score: 0.87\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "regr = linear_model.LinearRegression()\n",
+    "x = np.asanyarray(train[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit (x, y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "\n",
+    "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Residual sum of squares : %.2f\"% np.mean((y_ - y) ** 2))\n",
+    "\n",
+    "# Explained variance score: 1 is perfect prediction\n",
+    "print('Variance score: %.2f' % regr.score(x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python\n",
+    "regr = linear_model.LinearRegression()\n",
+    "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit (x, y)\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
+    "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
+    "print('Variance score: %.2f' % regr.score(x, y))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "    \n",
+    "<!--\n",
+    "\n",
+    "## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  | Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   |\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "--!>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "c1170d4cb1c9bbce7dbbef74b645fc6b265a5aaf4ce89c4ac861feed8769ed99"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-NoneLinearRegression.ipynb b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-NoneLinearRegression.ipynb
new file mode 100644
index 0000000..7e315c7
--- /dev/null
+++ b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-NoneLinearRegression.ipynb	
@@ -0,0 +1,888 @@
+{
+ "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",
+    "\n",
+    "\n",
+    "# Non Linear Regression Analysis\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **20** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Differentiate between linear and non-linear regression\n",
+    "* Use non-linear regression model in Python\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the data shows a curvy trend, then linear regression will not produce very accurate results when compared to a non-linear regression since linear regression presumes that the data is linear. \n",
+    "Let's learn about non linear regressions and apply an example in python. In this notebook, we fit a non-linear model to the datapoints corrensponding to China's GDP from 1960 to 2014. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"importing_libraries\">Importing required libraries</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although linear regression can do a great job at modeling some datasets, it cannot be used for all datasets. First recall how linear regression, models a dataset. It models the linear relationship between a dependent variable y and the independent variables x. It has a simple equation, of degree 1, for example y = $2x$ + 3.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "y = 2*(x) + 3\n",
+    "y_noise = 2 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "#plt.figure(figsize=(8,6))\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Non-linear regression is a method to model the non-linear relationship between the independent variables $x$ and the dependent variable $y$. Essentially any relationship that is not linear can be termed as non-linear, and is usually represented by the polynomial of $k$ degrees (maximum power of $x$).  For example:\n",
+    "\n",
+    "$$ \\ y = a x^3 + b x^2 + c x + d \\ $$\n",
+    "\n",
+    "Non-linear functions can have elements like exponentials, logarithms, fractions, and so on. For example: $$ y = \\log(x)$$\n",
+    "    \n",
+    "We can have a function that's even more complicated such as :\n",
+    "$$ y = \\log(a x^3 + b x^2 + c x + d)$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at a cubic function's graph.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "y = 1*(x**3) + 1*(x**2) + 1*x + 3\n",
+    "y_noise = 20 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, this function has $x^3$ and $x^2$ as independent variables. Also, the graphic of this function is not a straight line over the 2D plane. So this is a non-linear function.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some other types of non-linear functions are:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$ Y = X^2 $$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "\n",
+    "y = np.power(x,2)\n",
+    "y_noise = 2 * np.random.normal(size=x.size)\n",
+    "ydata = y + y_noise\n",
+    "plt.plot(x, ydata,  'bo')\n",
+    "plt.plot(x,y, 'r') \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exponential\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An exponential function with base c is defined by $$ Y = a + b c^X$$ where b ≠0, c > 0 , c ≠1, and x is any real number. The base, c, is constant and the exponent, x, is a variable. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "##You can adjust the slope and intercept to verify the changes in the graph\n",
+    "\n",
+    "Y= np.exp(X)\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logarithmic\n",
+    "\n",
+    "The response $y$ is a results of applying the logarithmic map from the input $x$ to the output $y$. It is one of the simplest form of __log()__: i.e. $$ y = \\log(x)$$\n",
+    "\n",
+    "Please consider that instead of $x$, we can use $X$, which can be a polynomial representation of the $x$ values. In general form it would be written as  \n",
+    "\\begin{equation}\n",
+    "y = \\log(X)\n",
+    "\\end{equation}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "Y = np.log(X)\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sigmoidal/Logistic\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$ Y = a + \\frac{b}{1+ c^{(X-d)}}$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "\n",
+    "\n",
+    "Y = 1-4/(1+np.power(3, X-2))\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"ref2\"></a>\n",
+    "# Non-Linear Regression example\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For an example, we're going to try and fit a non-linear model to the datapoints corresponding to China's GDP from 1960 to 2014. We download a dataset with two columns, the first, a year between 1960 and 2014, the second, China's corresponding annual gross domestic income in US dollars for that year. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2025-10-17 02:42:15 URL:https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/china_gdp.csv [1218/1218] -> \"china_gdp.csv\" [1]\n"
+     ]
+    },
+    {
+     "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>Year</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1960</td>\n",
+       "      <td>5.918412e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1961</td>\n",
+       "      <td>4.955705e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>4.668518e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1963</td>\n",
+       "      <td>5.009730e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1964</td>\n",
+       "      <td>5.906225e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1965</td>\n",
+       "      <td>6.970915e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1966</td>\n",
+       "      <td>7.587943e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>7.205703e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1968</td>\n",
+       "      <td>6.999350e+10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1969</td>\n",
+       "      <td>7.871882e+10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year         Value\n",
+       "0  1960  5.918412e+10\n",
+       "1  1961  4.955705e+10\n",
+       "2  1962  4.668518e+10\n",
+       "3  1963  5.009730e+10\n",
+       "4  1964  5.906225e+10\n",
+       "5  1965  6.970915e+10\n",
+       "6  1966  7.587943e+10\n",
+       "7  1967  7.205703e+10\n",
+       "8  1968  6.999350e+10\n",
+       "9  1969  7.871882e+10"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "#downloading dataset\n",
+    "!wget -nv -O china_gdp.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/china_gdp.csv\n",
+    "    \n",
+    "df = pd.read_csv(\"china_gdp.csv\")\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the Dataset ###\n",
+    "This is what the datapoints look like. It kind of looks like an either logistic or exponential function. The growth starts off slow, then from 2005 on forward, the growth is very significant. And finally, it decelerates slightly in the 2010s.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,5))\n",
+    "x_data, y_data = (df[\"Year\"].values, df[\"Value\"].values)\n",
+    "plt.plot(x_data, y_data, 'ro')\n",
+    "plt.ylabel('GDP')\n",
+    "plt.xlabel('Year')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Choosing a model ###\n",
+    "\n",
+    "From an initial look at the plot, we determine that the logistic function could be a good approximation,\n",
+    "since it has the property of starting with a slow growth, increasing growth in the middle, and then decreasing again at the end; as illustrated below:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = np.arange(-5.0, 5.0, 0.1)\n",
+    "Y = 1.0 / (1.0 + np.exp(-X))\n",
+    "\n",
+    "plt.plot(X,Y) \n",
+    "plt.ylabel('Dependent Variable')\n",
+    "plt.xlabel('Independent Variable')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "The formula for the logistic function is the following:\n",
+    "\n",
+    "$$ \\hat{Y} = \\frac1{1+e^{-\\beta_1(X-\\beta_2)}}$$\n",
+    "\n",
+    "$\\beta_1$: Controls the curve's steepness,\n",
+    "\n",
+    "$\\beta_2$: Slides the curve on the x-axis.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building The Model ###\n",
+    "Now, let's build our regression model and initialize its parameters. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def sigmoid(x, Beta_1, Beta_2):\n",
+    "     y = 1 / (1 + np.exp(-Beta_1*(x-Beta_2)))\n",
+    "     return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets look at a sample sigmoid line that might fit with the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ae77a346d90>]"
+      ]
+     },
+     "execution_count": 12,
+     "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": [
+    "beta_1 = 0.10\n",
+    "beta_2 = 1990.0\n",
+    "\n",
+    "#logistic function\n",
+    "Y_pred = sigmoid(x_data, beta_1 , beta_2)\n",
+    "\n",
+    "#plot initial prediction against datapoints\n",
+    "plt.plot(x_data, Y_pred*15000000000000.)\n",
+    "plt.plot(x_data, y_data, 'ro')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our task here is to find the best parameters for our model. Lets first normalize our x and y:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Lets normalize our data\n",
+    "xdata =x_data/max(x_data)\n",
+    "ydata =y_data/max(y_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### How we find the best parameters for our fit line?\n",
+    "we can use __curve_fit__ which uses non-linear least squares to fit our sigmoid function, to data. Optimize values for the parameters so that the sum of the squared residuals of sigmoid(xdata, *popt) - ydata is minimized.\n",
+    "\n",
+    "popt are our optimized parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " beta_1 = 690.451712, beta_2 = 0.997207\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import curve_fit\n",
+    "popt, pcov = curve_fit(sigmoid, xdata, ydata)\n",
+    "#print the final parameters\n",
+    "print(\" beta_1 = %f, beta_2 = %f\" % (popt[0], popt[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we plot our resulting regression model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(1960, 2015, 55)\n",
+    "x = x/max(x)\n",
+    "plt.figure(figsize=(8,5))\n",
+    "y = sigmoid(x, *popt)\n",
+    "plt.plot(xdata, ydata, 'ro', label='data')\n",
+    "plt.plot(x,y, linewidth=3.0, label='fit')\n",
+    "plt.legend(loc='best')\n",
+    "plt.ylabel('GDP')\n",
+    "plt.xlabel('Year')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "Can you calculate what is the accuracy of our model?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 0.03\n",
+      "Residual sum of squares (MSE): 0.00\n",
+      "R2-score: 0.95\n"
+     ]
+    }
+   ],
+   "source": [
+    "# memisahkan data train/test\n",
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train_x = xdata[msk]\n",
+    "test_x = xdata[~msk]\n",
+    "train_y = ydata[msk]\n",
+    "test_y = ydata[~msk]\n",
+    "\n",
+    "# membangun model, menggunakan training\n",
+    "popt, pcov = curve_fit(sigmoid, train_x, train_y)\n",
+    "\n",
+    "# prediksi menggunakan testing\n",
+    "y_hat = sigmoid(test_x, *popt)\n",
+    "\n",
+    "# evaluasi\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(y_hat - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_hat -test_y)**2))\n",
+    "from sklearn.metrics import r2_score\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y, y_hat))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "# split data into train/test\n",
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train_x = xdata[msk]\n",
+    "test_x = xdata[~msk]\n",
+    "train_y = ydata[msk]\n",
+    "test_y = ydata[~msk]\n",
+    "\n",
+    "# build the model using train set\n",
+    "popt, pcov = curve_fit(sigmoid, train_x, train_y)\n",
+    "\n",
+    "# predict using test set\n",
+    "y_hat = sigmoid(test_x, *popt)\n",
+    "\n",
+    "# evaluation\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(y_hat - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_hat - test_y) ** 2))\n",
+    "from sklearn.metrics import r2_score\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,y_hat) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Want to learn more?</h2>\n",
+    "\n",
+    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
+    "\n",
+    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03  | 2.1 | Lakshmi |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "f873d3177bf529d2d648c46bab1627042a257e5ec6ce42ca68028520459f817e"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Polynomial-Regression-Co2.ipynb b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Polynomial-Regression-Co2.ipynb
new file mode 100644
index 0000000..0ed3ba3
--- /dev/null
+++ b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Polynomial-Regression-Co2.ipynb	
@@ -0,0 +1,937 @@
+{
+ "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",
+    "\n",
+    "\n",
+    "# Polynomial Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement Polynomial Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>Table of contents</h1>\n",
+    "\n",
+    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
+    "    <ol>\n",
+    "        <li><a href=\"#download_data\">Downloading Data</a></li>\n",
+    "        <li><a href=\"#polynomial_regression\">Polynomial regression</a></li>\n",
+    "        <li><a href=\"#evaluation\">Evaluation</a></li>\n",
+    "        <li><a href=\"#practice\">Practice</a></li>\n",
+    "    </ol>\n",
+    "</div>\n",
+    "<br>\n",
+    "<hr>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"download_data\">Downloading Data</h2>\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-17 01:55:05--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.002s  \n",
+      "\n",
+      "2025-10-17 01:55:05 (28.3 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://www.ibm.com/us-en/cloud/object-storage?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features that we want to use for regression.\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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
+       "0         2.0          4                   8.5           196\n",
+       "1         2.4          4                   9.6           221\n",
+       "2         1.5          4                   5.9           136\n",
+       "3         3.5          6                  11.1           255\n",
+       "4         3.5          6                  10.6           244\n",
+       "5         3.5          6                  10.0           230\n",
+       "6         3.5          6                  10.1           232\n",
+       "7         3.7          6                  11.1           255\n",
+       "8         3.7          6                  11.6           267"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot Emission values with respect to Engine size:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"polynomial_regression\">Polynomial regression</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, the trend of data is not really linear, and looks curvy. In this case we can use Polynomial regression methods. In fact, many different regressions exist that can be used to fit whatever the dataset looks like, such as quadratic, cubic, and so on, and it can go on and on to infinite degrees.\n",
+    "\n",
+    "In essence, we can call all of these, polynomial regression, where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Lets say you want to have a polynomial regression (let's make 2 degree polynomial):\n",
+    "\n",
+    "\n",
+    "$$y = b + \\theta_1  x + \\theta_2 x^2$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "Now, the question is: how we can fit our data on this equation while we have only x values, such as __Engine Size__? \n",
+    "Well, we can create a few additional features: 1, $x$, and $x^2$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "__PolynomialFeatures()__ function in Scikit-learn library, drives a new feature sets from the original feature set. That is, a matrix will be generated consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, lets say the original feature set has only one feature, _ENGINESIZE_. Now, if we select the degree of the polynomial to be 2, then it generates 3 features, degree=0, degree=1 and degree=2: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  precompute=False, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, random_state=None,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.  ,  2.  ,  4.  ],\n",
+       "       [ 1.  ,  2.4 ,  5.76],\n",
+       "       [ 1.  ,  1.5 ,  2.25],\n",
+       "       ...,\n",
+       "       [ 1.  ,  3.  ,  9.  ],\n",
+       "       [ 1.  ,  3.2 , 10.24],\n",
+       "       [ 1.  ,  3.2 , 10.24]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn import linear_model\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "\n",
+    "\n",
+    "poly = PolynomialFeatures(degree=2)\n",
+    "train_x_poly = poly.fit_transform(train_x)\n",
+    "train_x_poly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**fit_transform** takes our x values, and output a list of our data raised from power of 0 to power of 2 (since we set the degree of our polynomial to 2).   \n",
+    "\n",
+    "The equation and the sample example is displayed below.   \n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "    v_1\\\\\\\\\\\\\n",
+    "    v_2\\\\\\\\\n",
+    "    \\vdots\\\\\\\\\n",
+    "    v_n\n",
+    "\\end{bmatrix}\\longrightarrow \\begin{bmatrix}\n",
+    "    [ 1 & v_1 & v_1^2]\\\\\\\\\n",
+    "    [ 1 & v_2 & v_2^2]\\\\\\\\\n",
+    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
+    "    [ 1 & v_n & v_n^2]\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "$$\n",
+    "\\begin{bmatrix}\n",
+    "    2.\\\\\\\\\n",
+    "    2.4\\\\\\\\\n",
+    "    1.5\\\\\\\\\n",
+    "    \\vdots\n",
+    "\\end{bmatrix} \\longrightarrow \\begin{bmatrix}\n",
+    "    [ 1 & 2. & 4.]\\\\\\\\\n",
+    "    [ 1 & 2.4 & 5.76]\\\\\\\\\n",
+    "    [ 1 & 1.5 & 2.25]\\\\\\\\\n",
+    "    \\vdots & \\vdots & \\vdots\\\\\\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like feature sets for multiple linear regression analysis, right? Yes. It Does. \n",
+    "Indeed, Polynomial regression is a special case of linear regression, with the main idea of how do you select your features. Just consider replacing the  $x$ with $x_1$, $x_1^2$ with $x_2$, and so on. Then the 2nd degree equation would be turn into:\n",
+    "\n",
+    "$$y = b + \\theta_1  x_1 + \\theta_2 x_2$$\n",
+    "\n",
+    "Now, we can deal with it as a 'linear regression' problem. Therefore, this polynomial regression is considered to be a special case of traditional multiple linear regression. So, you can use the same mechanism as linear regression to solve such problems. \n",
+    "\n",
+    "\n",
+    "\n",
+    "so we can use __LinearRegression()__ function to solve it:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         51.34464277 -1.64112566]]\n",
+      "Intercept:  [105.83262962]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = linear_model.LinearRegression()\n",
+    "train_y_ = clf.fit(train_x_poly, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf.coef_)\n",
+    "print ('Intercept: ',clf.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__ , are the parameters of the fit curvy line. \n",
+    "Given that it is a typical multiple linear regression, with 3 parameters, and knowing that the parameters are the intercept and coefficients of hyperplane, sklearn has estimated them from our new set of feature sets. Lets plot it:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 9,
+     "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": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf.intercept_[0]+ clf.coef_[0][1]*XX+ clf.coef_[0][2]*np.power(XX, 2)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"evaluation\">Evaluation</h2>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 22.99\n",
+      "Residual sum of squares (MSE): 939.81\n",
+      "R2-score: 0.76\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x_poly = poly.transform(test_x)\n",
+    "test_y_ = clf.predict(test_x_poly)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y_ ) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2 id=\"practice\">Practice</h2>\n",
+    "Try to use a polynomial regression with the dataset but this time with degree three (cubic). Does it result in better accuracy?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[ 0.         27.21448026  4.98041569 -0.54670275]]\n",
+      "Intercept:  [105.83262962]\n",
+      "Mean absolute error: 22.97\n",
+      "Residual sum of squares (MSE): 942.88\n",
+      "R2-score: 0.76\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf3.coef_)\n",
+    "print ('Intercept: ' , clf.intercept_)\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2)+ clf3.coef_[0][3]*np.power(XX,3)\n",
+    "plt.plot(XX, yy, '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_= clf3.predict(test_x_poly3)\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "train_x_poly3 = poly3.fit_transform(train_x)\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "train_y3_ = clf3.fit(train_x_poly3, train_y)\n",
+    "\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', clf3.coef_)\n",
+    "print ('Intercept: ',clf3.intercept_)\n",
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "XX = np.arange(0.0, 10.0, 0.1)\n",
+    "yy = clf3.intercept_[0]+ clf3.coef_[0][1]*XX + clf3.coef_[0][2]*np.power(XX, 2) + clf3.coef_[0][3]*np.power(XX, 3)\n",
+    "plt.plot(XX, yy, '-r' )\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "test_x_poly3 = poly3.transform(test_x)\n",
+    "test_y3_ = clf3.predict(test_x_poly3)\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y3_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y3_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y,test_y3_ ) )\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Want to learn more?</h2>\n",
+    "\n",
+    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">SPSS Modeler</a>\n",
+    "\n",
+    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork\">Watson Studio</a>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
+    "\n",
+    "\n",
+    "<!--## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2021-01-11  | 2.3  | Lakshmi  |  Changed R2-score calculation in polynomial regression |\n",
+    "| 2020-11-04  | 2.2  | Lakshmi  |  Made changes in markdown of equations |\n",
+    "| 2020-11-03  | 2.1  | Lakshmi  |  Made changes in URL |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   | --!>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "4dc110debac287dfd374a575573c16e62a80a935b3bbe2b2f6d5a0598e6e33f6"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb
new file mode 100644
index 0000000..271ffc4
--- /dev/null
+++ b/MODUL 2_REGRESSION/SUMIH_202310715145_ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb	
@@ -0,0 +1,1235 @@
+{
+ "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",
+    "\n",
+    "\n",
+    "# Simple Linear Regression\n",
+    "\n",
+    "\n",
+    "Estimated time needed: **15** minutes\n",
+    "    \n",
+    "\n",
+    "## Objectives\n",
+    "\n",
+    "After completing this lab you will be able to:\n",
+    "\n",
+    "* Use scikit-learn to implement simple Linear Regression\n",
+    "* Create a model, train it, test it and use the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Needed packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Downloading Data\n",
+    "To download the data, we will use !wget to download it from IBM Object Storage.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2025-10-17 01:32:43--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\n",
+      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104, 169.63.118.104\n",
+      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 72629 (71K) [text/csv]\n",
+      "Saving to: ‘FuelConsumption.csv’\n",
+      "\n",
+      "FuelConsumption.csv 100%[===================>]  70.93K  --.-KB/s    in 0.003s  \n",
+      "\n",
+      "2025-10-17 01:32:43 (26.0 MB/s) - ‘FuelConsumption.csv’ saved [72629/72629]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O FuelConsumption.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you're working **locally** uncomment the below line. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!curl https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv -o FuelConsumptionCo2.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Understanding the Data\n",
+    "\n",
+    "### `FuelConsumption.csv`:\n",
+    "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64)\n",
+    "\n",
+    "- **MODELYEAR** e.g. 2014\n",
+    "- **MAKE** e.g. Acura\n",
+    "- **MODEL** e.g. ILX\n",
+    "- **VEHICLE CLASS** e.g. SUV\n",
+    "- **ENGINE SIZE** e.g. 4.7\n",
+    "- **CYLINDERS** e.g 6\n",
+    "- **TRANSMISSION** e.g. A6\n",
+    "- **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
+    "- **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
+    "- **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
+    "- **CO2 EMISSIONS (g/km)** e.g. 182   --> low --> 0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading the data in\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>MODELYEAR</th>\n",
+       "      <th>MAKE</th>\n",
+       "      <th>MODEL</th>\n",
+       "      <th>VEHICLECLASS</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>TRANSMISSION</th>\n",
+       "      <th>FUELTYPE</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AS5</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>M6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>29</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>ILX HYBRID</td>\n",
+       "      <td>COMPACT</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AV7</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>48</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>MDX 4WD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>ACURA</td>\n",
+       "      <td>RDX AWD</td>\n",
+       "      <td>SUV - SMALL</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS6</td>\n",
+       "      <td>Z</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
+       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
+       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
+       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
+       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
+       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
+       "\n",
+       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
+       "0          AS5        Z                   9.9                  6.7   \n",
+       "1           M6        Z                  11.2                  7.7   \n",
+       "2          AV7        Z                   6.0                  5.8   \n",
+       "3          AS6        Z                  12.7                  9.1   \n",
+       "4          AS6        Z                  12.1                  8.7   \n",
+       "\n",
+       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
+       "0                   8.5                        33           196  \n",
+       "1                   9.6                        29           221  \n",
+       "2                   5.9                        48           136  \n",
+       "3                  11.1                        25           255  \n",
+       "4                  10.6                        27           244  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
+    "\n",
+    "# take a look at the dataset\n",
+    "df.head()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Exploration\n",
+    "Let's first have a descriptive exploration on our data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>MODELYEAR</th>\n",
+       "      <th>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_CITY</th>\n",
+       "      <th>FUELCONSUMPTION_HWY</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1067.0</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "      <td>1067.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.346298</td>\n",
+       "      <td>5.794752</td>\n",
+       "      <td>13.296532</td>\n",
+       "      <td>9.474602</td>\n",
+       "      <td>11.580881</td>\n",
+       "      <td>26.441425</td>\n",
+       "      <td>256.228679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.415895</td>\n",
+       "      <td>1.797447</td>\n",
+       "      <td>4.101253</td>\n",
+       "      <td>2.794510</td>\n",
+       "      <td>3.485595</td>\n",
+       "      <td>7.468702</td>\n",
+       "      <td>63.372304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>4.900000</td>\n",
+       "      <td>4.700000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>108.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>10.250000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>207.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>8.800000</td>\n",
+       "      <td>10.900000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>251.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>15.550000</td>\n",
+       "      <td>10.850000</td>\n",
+       "      <td>13.350000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>294.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>30.200000</td>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>25.800000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>488.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       MODELYEAR   ENGINESIZE    CYLINDERS  FUELCONSUMPTION_CITY  \\\n",
+       "count     1067.0  1067.000000  1067.000000           1067.000000   \n",
+       "mean      2014.0     3.346298     5.794752             13.296532   \n",
+       "std          0.0     1.415895     1.797447              4.101253   \n",
+       "min       2014.0     1.000000     3.000000              4.600000   \n",
+       "25%       2014.0     2.000000     4.000000             10.250000   \n",
+       "50%       2014.0     3.400000     6.000000             12.600000   \n",
+       "75%       2014.0     4.300000     8.000000             15.550000   \n",
+       "max       2014.0     8.400000    12.000000             30.200000   \n",
+       "\n",
+       "       FUELCONSUMPTION_HWY  FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  \\\n",
+       "count          1067.000000           1067.000000               1067.000000   \n",
+       "mean              9.474602             11.580881                 26.441425   \n",
+       "std               2.794510              3.485595                  7.468702   \n",
+       "min               4.900000              4.700000                 11.000000   \n",
+       "25%               7.500000              9.000000                 21.000000   \n",
+       "50%               8.800000             10.900000                 26.000000   \n",
+       "75%              10.850000             13.350000                 31.000000   \n",
+       "max              20.500000             25.800000                 60.000000   \n",
+       "\n",
+       "       CO2EMISSIONS  \n",
+       "count   1067.000000  \n",
+       "mean     256.228679  \n",
+       "std       63.372304  \n",
+       "min      108.000000  \n",
+       "25%      207.000000  \n",
+       "50%      251.000000  \n",
+       "75%      294.000000  \n",
+       "max      488.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarize the data\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's select some features to explore more.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>ENGINESIZE</th>\n",
+       "      <th>CYLINDERS</th>\n",
+       "      <th>FUELCONSUMPTION_COMB</th>\n",
+       "      <th>CO2EMISSIONS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>267</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
+       "0         2.0          4                   8.5           196\n",
+       "1         2.4          4                   9.6           221\n",
+       "2         1.5          4                   5.9           136\n",
+       "3         3.5          6                  11.1           255\n",
+       "4         3.5          6                  10.6           244\n",
+       "5         3.5          6                  10.0           230\n",
+       "6         3.5          6                  10.1           232\n",
+       "7         3.7          6                  11.1           255\n",
+       "8         3.7          6                  11.6           267"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\n",
+    "cdf.head(9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot each of these features:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\n",
+    "viz.hist()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"FUELCONSUMPTION_COMB\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Practice\n",
+    "Plot __CYLINDER__ vs the Emission, to see how linear is their relationship is:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS,  color='Green')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "plt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\n",
+    "plt.xlabel(\"Cylinders\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating train and test dataset\n",
+    "Train/Test Split involves splitting the dataset into training and testing sets that are mutually exclusive. After which, you train with the training set and test with the testing set. \n",
+    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
+    "\n",
+    "This means that we know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
+    "\n",
+    "Let's split our dataset into train and test sets. 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using __np.random.rand()__ function: \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "msk = np.random.rand(len(df)) < 0.8\n",
+    "train = cdf[msk]\n",
+    "test = cdf[~msk]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Regression Model\n",
+    "Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train data distribution\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modeling\n",
+    "Using sklearn package to model data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/utils/validation.py:37: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
+      "  LARGE_SPARSE_SUPPORTED = LooseVersion(scipy_version) >= '0.14.0'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients:  [[39.25604145]]\n",
+      "Intercept:  [125.23211126]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  precompute=False, eps=np.finfo(np.float).eps,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=np.finfo(np.float).eps, random_state=None,\n",
+      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import linear_model\n",
+    "regr = linear_model.LinearRegression()\n",
+    "train_x = np.asanyarray(train[['ENGINESIZE']])\n",
+    "train_y = np.asanyarray(train[['CO2EMISSIONS']])\n",
+    "regr.fit(train_x, train_y)\n",
+    "# The coefficients\n",
+    "print ('Coefficients: ', regr.coef_)\n",
+    "print ('Intercept: ',regr.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned before, __Coefficient__ and __Intercept__ in the simple linear regression, are the parameters of the fit line. \n",
+    "Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data. \n",
+    "Notice that all of the data must be available to traverse and calculate the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot outputs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the fit line over the data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Emission')"
+      ]
+     },
+     "execution_count": 18,
+     "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": [
+    "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS,  color='blue')\n",
+    "plt.plot(train_x, regr.coef_[0][0]*train_x + regr.intercept_[0], '-r')\n",
+    "plt.xlabel(\"Engine size\")\n",
+    "plt.ylabel(\"Emission\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Evaluation\n",
+    "We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.\n",
+    "\n",
+    "There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set: \n",
+    "* Mean Absolute Error: It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.\n",
+    "\n",
+    "* Mean Squared Error (MSE): Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.\n",
+    "\n",
+    "* Root Mean Squared Error (RMSE). \n",
+    "\n",
+    "* R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error: 22.99\n",
+      "Residual sum of squares (MSE): 911.17\n",
+      "R2-score: 0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "test_x = np.asanyarray(test[['ENGINESIZE']])\n",
+    "test_y = np.asanyarray(test[['CO2EMISSIONS']])\n",
+    "test_y_ = regr.predict(test_x)\n",
+    "\n",
+    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(test_y_ - test_y)))\n",
+    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((test_y_ - test_y) ** 2))\n",
+    "print(\"R2-score: %.2f\" % r2_score(test_y , test_y_) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets see what the evaluation metrics are if we trained a regression model using the `FUELCONSUMPTION_COMB` feature.\n",
+    "\n",
+    "Start by selecting `FUELCONSUMPTION_COMB` as the train_x data from the `train` dataframe, then select `FUELCONSUMPTION_COMB` as the test_x data from the `test` dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "train_x = train[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "test_x = test[[\"FUELCONSUMPTION_COMB\"]]\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now train a Linear Regression Model using the `train_x` you created and the `train_y` created previously\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "regr.fit(train_x,train_y)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "regr = linear_model.LinearRegression()\n",
+    "\n",
+    "regr.fit(train_x, train_y)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Find the predictions using the model's `predict` function and the `test_x` data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "predictions =regr.predict(test_x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "predictions = regr.predict(test_x)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally use the `predictions` and the `test_y` data and find the Mean Absolute Error value using the `np.absolute` and `np.mean` function like done previously\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Absolute Error: 20.42\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details><summary>Click here for the solution</summary>\n",
+    "\n",
+    "```python    \n",
+    "print(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "</details>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the MAE is much worse when we train using `ENGINESIZE` than `FUELCONSUMPTION_COMB`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thank you for completing this lab!\n",
+    "\n",
+    "\n",
+    "## Author\n",
+    "\n",
+    "Saeed Aghabozorgi\n",
+    "\n",
+    "\n",
+    "### Other Contributors\n",
+    "\n",
+    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
+    "\n",
+    "Azim Hirjani\n",
+    "\n",
+    "## <h3 align=\"center\"> © IBM Corporation. All rights reserved. <h3/>\n",
+    "\n",
+    "<!--\n",
+    "## Change Log\n",
+    "\n",
+    "\n",
+    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
+    "|---|---|---|---|\n",
+    "| 2020-11-03 | 2.1  | Lakshmi Holla  |  Changed URL of the csv |\n",
+    "| 2020-08-27  | 2.0  | Lavanya  |  Moved lab to course repo in GitLab |\n",
+    "|   |   |   |   |\n",
+    "|   |   |   |   |\n",
+    "\n",
+    "--!>\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python",
+   "language": "python",
+   "name": "conda-env-python-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "prev_pub_hash": "20d6dc1d9e74df451be22381c972d7921c93657bea402a00c749dca52bb85996"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}