diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 1970c46..59fd5fb 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Before-your-start:\" data-toc-modified-id=\"Before-your-start:-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Before your start:</a></span></li><li><span><a href=\"#Challenge-1---Explore-the-Scikit-Learn-Datasets\" data-toc-modified-id=\"Challenge-1---Explore-the-Scikit-Learn-Datasets-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Challenge 1 - Explore the Scikit-Learn Datasets</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#The-next-step-is-to-read-the-description-of-the-dataset.\" data-toc-modified-id=\"The-next-step-is-to-read-the-description-of-the-dataset.-2.0.0.1\"><span class=\"toc-item-num\">2.0.0.1&nbsp;&nbsp;</span>The next step is to read the description of the dataset.</a></span></li><li><span><a href=\"#Based-on-the-data-description,-answer-the-following-questions:\" data-toc-modified-id=\"Based-on-the-data-description,-answer-the-following-questions:-2.0.0.2\"><span class=\"toc-item-num\">2.0.0.2&nbsp;&nbsp;</span>Based on the data description, answer the following questions:</a></span></li><li><span><a href=\"#Now-explore-what-are-contained-in-the-data-portion-as-well-as-the-target-portion-of-diabetes.\" data-toc-modified-id=\"Now-explore-what-are-contained-in-the-data-portion-as-well-as-the-target-portion-of-diabetes.-2.0.0.3\"><span class=\"toc-item-num\">2.0.0.3&nbsp;&nbsp;</span>Now explore what are contained in the <em>data</em> portion as well as the <em>target</em> portion of <code>diabetes</code>.</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Challenge-2---Perform-Supervised-Learning-on-the-Dataset\" data-toc-modified-id=\"Challenge-2---Perform-Supervised-Learning-on-the-Dataset-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Challenge 2 - Perform Supervised Learning on the Dataset</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Let's-briefly-revisit-the-linear-regression-formula:\" data-toc-modified-id=\"Let's-briefly-revisit-the-linear-regression-formula:-3.0.0.1\"><span class=\"toc-item-num\">3.0.0.1&nbsp;&nbsp;</span>Let's briefly revisit the linear regression formula:</a></span></li><li><span><a href=\"#In-the-cell-below,-import-the-linear_model-class-from-sklearn.\" data-toc-modified-id=\"In-the-cell-below,-import-the-linear_model-class-from-sklearn.-3.0.0.2\"><span class=\"toc-item-num\">3.0.0.2&nbsp;&nbsp;</span>In the cell below, import the <code>linear_model</code> class from <code>sklearn</code>.</a></span></li><li><span><a href=\"#Create-a-new-instance-of-the-linear-regression-model-and-assign-the-new-instance-to-the-variable-diabetes_model.\" data-toc-modified-id=\"Create-a-new-instance-of-the-linear-regression-model-and-assign-the-new-instance-to-the-variable-diabetes_model.-3.0.0.3\"><span class=\"toc-item-num\">3.0.0.3&nbsp;&nbsp;</span>Create a new instance of the linear regression model and assign the new instance to the variable <code>diabetes_model</code>.</a></span></li><li><span><a href=\"#Next,-let's-split-the-training-and-test-data.\" data-toc-modified-id=\"Next,-let's-split-the-training-and-test-data.-3.0.0.4\"><span class=\"toc-item-num\">3.0.0.4&nbsp;&nbsp;</span>Next, let's split the training and test data.</a></span></li><li><span><a href=\"#Inspecting-the-results\" data-toc-modified-id=\"Inspecting-the-results-3.0.0.5\"><span class=\"toc-item-num\">3.0.0.5&nbsp;&nbsp;</span>Inspecting the results</a></span></li><li><span><a href=\"#Using-your-fitted-linear-regression-model,-predict-the-y-of-diabetes_data_test.\" data-toc-modified-id=\"Using-your-fitted-linear-regression-model,-predict-the-y-of-diabetes_data_test.-3.0.0.6\"><span class=\"toc-item-num\">3.0.0.6&nbsp;&nbsp;</span>Using your fitted linear regression model, predict the <em>y</em> of <code>diabetes_data_test</code>.</a></span></li><li><span><a href=\"#Print-your-diabetes_target_test-and-compare-with-the-prediction.\" data-toc-modified-id=\"Print-your-diabetes_target_test-and-compare-with-the-prediction.-3.0.0.7\"><span class=\"toc-item-num\">3.0.0.7&nbsp;&nbsp;</span>Print your <code>diabetes_target_test</code> and compare with the prediction.</a></span></li><li><span><a href=\"#Is-diabetes_target_test-exactly-the-same-as-the-model-prediction?-Explain.\" data-toc-modified-id=\"Is-diabetes_target_test-exactly-the-same-as-the-model-prediction?-Explain.-3.0.0.8\"><span class=\"toc-item-num\">3.0.0.8&nbsp;&nbsp;</span>Is <code>diabetes_target_test</code> exactly the same as the model prediction? Explain.</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Bonus-Challenge-1---Hypothesis-Testing-with-statsmodels\" data-toc-modified-id=\"Bonus-Challenge-1---Hypothesis-Testing-with-statsmodels-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Bonus Challenge 1 - Hypothesis Testing with <code>statsmodels</code></a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#In-the-next-cell,-analyze-diabetes_data_train-and-diabetes_target_train-with-the-linear-regression-model-of-statsmodels.-Print-the-fit-summary.\" data-toc-modified-id=\"In-the-next-cell,-analyze-diabetes_data_train-and-diabetes_target_train-with-the-linear-regression-model-of-statsmodels.-Print-the-fit-summary.-4.0.0.1\"><span class=\"toc-item-num\">4.0.0.1&nbsp;&nbsp;</span>In the next cell, analyze <code>diabetes_data_train</code> and <code>diabetes_target_train</code> with the linear regression model of <code>statsmodels</code>. Print the fit summary.</a></span></li><li><span><a href=\"#Interpreting-hypothesis-testing-results\" data-toc-modified-id=\"Interpreting-hypothesis-testing-results-4.0.0.2\"><span class=\"toc-item-num\">4.0.0.2&nbsp;&nbsp;</span>Interpreting hypothesis testing results</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Challenge-3---Peform-Supervised-Learning-on-a-Pandas-Dataframe\" data-toc-modified-id=\"Challenge-3---Peform-Supervised-Learning-on-a-Pandas-Dataframe-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Challenge 3 - Peform Supervised Learning on a Pandas Dataframe</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#In-the-next-cell,-create-an-instance-of-the-linear-regression-model-and-call-it-auto_model.-Fit-auto_model-with-your-training-data.\" data-toc-modified-id=\"In-the-next-cell,-create-an-instance-of-the-linear-regression-model-and-call-it-auto_model.-Fit-auto_model-with-your-training-data.-5.0.0.1\"><span class=\"toc-item-num\">5.0.0.1&nbsp;&nbsp;</span>In the next cell, create an instance of the linear regression model and call it <code>auto_model</code>. Fit <code>auto_model</code> with your training data.</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Challenge-4---Evaluate-the-Model\" data-toc-modified-id=\"Challenge-4---Evaluate-the-Model-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Challenge 4 - Evaluate the Model</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#In-the-next-cell,-compute-the-predicted-y-based-on-X_train-and-call-it-y_pred.-Then-calcualte-the-r-squared-score-between-y_pred-and-y_train-which-indicates-how-well-the-estimated-regression-model-fits-the-training-data.\" data-toc-modified-id=\"In-the-next-cell,-compute-the-predicted-y-based-on-X_train-and-call-it-y_pred.-Then-calcualte-the-r-squared-score-between-y_pred-and-y_train-which-indicates-how-well-the-estimated-regression-model-fits-the-training-data.-6.0.0.1\"><span class=\"toc-item-num\">6.0.0.1&nbsp;&nbsp;</span>In the next cell, compute the predicted <em>y</em> based on <code>X_train</code> and call it <code>y_pred</code>. Then calcualte the r squared score between <code>y_pred</code> and <code>y_train</code> which indicates how well the estimated regression model fits the training data.</a></span></li><li><span><a href=\"#Our-next-step-is-to-evaluate-the-model-using-the-test-data.\" data-toc-modified-id=\"Our-next-step-is-to-evaluate-the-model-using-the-test-data.-6.0.0.2\"><span class=\"toc-item-num\">6.0.0.2&nbsp;&nbsp;</span>Our next step is to evaluate the model using the test data.</a></span></li><li><span><a href=\"#Explaining-the-results\" data-toc-modified-id=\"Explaining-the-results-6.0.0.3\"><span class=\"toc-item-num\">6.0.0.3&nbsp;&nbsp;</span>Explaining the results</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Challenge-5---Improve-the-Model-Fit\" data-toc-modified-id=\"Challenge-5---Improve-the-Model-Fit-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Challenge 5 - Improve the Model Fit</a></span></li><li><span><a href=\"#Tree-Based-models\" data-toc-modified-id=\"Tree-Based-models-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Tree-Based models</a></span><ul class=\"toc-item\"><li><span><a href=\"#VarianceThreshold\" data-toc-modified-id=\"VarianceThreshold-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>VarianceThreshold</a></span></li><li><span><a href=\"#Correlation-matrix\" data-toc-modified-id=\"Correlation-matrix-8.2\"><span class=\"toc-item-num\">8.2&nbsp;&nbsp;</span>Correlation matrix</a></span></li><li><span><a href=\"#RFE---Recursive-feature-elimination\" data-toc-modified-id=\"RFE---Recursive-feature-elimination-8.3\"><span class=\"toc-item-num\">8.3&nbsp;&nbsp;</span>RFE - Recursive feature elimination</a></span></li><li><span><a href=\"#SelectBestK\" data-toc-modified-id=\"SelectBestK-8.4\"><span class=\"toc-item-num\">8.4&nbsp;&nbsp;</span>SelectBestK</a></span></li></ul></li><li><span><a href=\"#Bonus-Challenge-2---Backward-Elimination\" data-toc-modified-id=\"Bonus-Challenge-2---Backward-Elimination-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Bonus Challenge 2 - Backward Elimination</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-9.0.0.1\"><span class=\"toc-item-num\">9.0.0.1&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></li></ul></li></ul></li></ul></div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -12,11 +22,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1382,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.442690Z",
+     "start_time": "2021-05-06T13:36:09.439828Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import numpy as np\n",
+    "import pandas as pd"
    ]
   },
   {
@@ -37,11 +53,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1383,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.469597Z",
+     "start_time": "2021-05-06T13:36:09.450291Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn import datasets\n",
+    "diabetes = datasets.load_diabetes()"
    ]
   },
   {
@@ -53,11 +75,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1384,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.480069Z",
+     "start_time": "2021-05-06T13:36:09.472463Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])"
+      ]
+     },
+     "execution_count": 1384,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes.keys()"
    ]
   },
   {
@@ -73,13 +111,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1385,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.498017Z",
+     "start_time": "2021-05-06T13:36:09.485009Z"
+    },
     "scrolled": false
    },
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _diabetes_dataset:\n",
+      "\n",
+      "Diabetes dataset\n",
+      "----------------\n",
+      "\n",
+      "Ten baseline variables, age, sex, body mass index, average blood\n",
+      "pressure, and six blood serum measurements were obtained for each of n =\n",
+      "442 diabetes patients, as well as the response of interest, a\n",
+      "quantitative measure of disease progression one year after baseline.\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "  :Number of Instances: 442\n",
+      "\n",
+      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
+      "\n",
+      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
+      "\n",
+      "  :Attribute Information:\n",
+      "      - age     age in years\n",
+      "      - sex\n",
+      "      - bmi     body mass index\n",
+      "      - bp      average blood pressure\n",
+      "      - s1      tc, T-Cells (a type of white blood cells)\n",
+      "      - s2      ldl, low-density lipoproteins\n",
+      "      - s3      hdl, high-density lipoproteins\n",
+      "      - s4      tch, thyroid stimulating hormone\n",
+      "      - s5      ltg, lamotrigine\n",
+      "      - s6      glu, blood sugar level\n",
+      "\n",
+      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
+      "\n",
+      "Source URL:\n",
+      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
+      "\n",
+      "For more information see:\n",
+      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
+      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes.DESCR)"
    ]
   },
   {
@@ -97,11 +184,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1386,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.523228Z",
+     "start_time": "2021-05-06T13:36:09.518869Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your answer here"
+    "#1: 10 attributes that refer to the quantitative measures of disease progression\n",
+    "#2: Regressor\n",
+    "#3: 442"
    ]
   },
   {
@@ -115,11 +209,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1387,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.542271Z",
+     "start_time": "2021-05-06T13:36:09.526557Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442, 10)"
+      ]
+     },
+     "execution_count": 1387,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes[\"data\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1388,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.557639Z",
+     "start_time": "2021-05-06T13:36:09.550726Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(442,)"
+      ]
+     },
+     "execution_count": 1388,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes[\"target\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1389,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.564461Z",
+     "start_time": "2021-05-06T13:36:09.560106Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "## They sum up to 11 columns of same value of rows"
    ]
   },
   {
@@ -156,11 +305,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1390,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.580313Z",
+     "start_time": "2021-05-06T13:36:09.576816Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn import linear_model"
    ]
   },
   {
@@ -172,11 +326,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1391,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.586887Z",
+     "start_time": "2021-05-06T13:36:09.583991Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.preprocessing import PowerTransformer\n",
+    "diabetes_model = LinearRegression()\n",
+    "transformer = PowerTransformer()"
    ]
   },
   {
@@ -190,11 +352,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1392,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.620790Z",
+     "start_time": "2021-05-06T13:36:09.589719Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "diabetes_data_train = diabetes[\"data\"][:421]\n",
+    "diabetes_data_test = diabetes[\"data\"][422:]\n",
+    "diabetes_target_train = diabetes[\"target\"][:421]\n",
+    "diabetes_target_test = diabetes[\"target\"][422:]\n",
+    "\n",
+    "transformer.fit(diabetes_data_train)\n",
+    "diabetes_data_train = transformer.transform(diabetes_data_train)\n",
+    "diabetes_data_test  = transformer.transform(diabetes_data_test)"
    ]
   },
   {
@@ -206,11 +380,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1393,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.630362Z",
+     "start_time": "2021-05-06T13:36:09.624016Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 1393,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "diabetes_model.fit(diabetes_data_train, diabetes_target_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1394,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.637970Z",
+     "start_time": "2021-05-06T13:36:09.632879Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "153.22327790973873"
+      ]
+     },
+     "execution_count": 1394,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_model.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1395,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.645415Z",
+     "start_time": "2021-05-06T13:36:09.640323Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ -0.26563519, -11.99397953,  23.6293098 ,  15.49074079,\n",
+       "       -22.2750571 ,   8.35425795,  -1.45892875,   8.90297332,\n",
+       "        28.94729622,   4.04007069])"
+      ]
+     },
+     "execution_count": 1395,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_model.coef_"
    ]
   },
   {
@@ -231,11 +473,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1396,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.654134Z",
+     "start_time": "2021-05-06T13:36:09.648763Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[194.05442602 150.34026015 176.08358293 111.29281743 164.03684249\n",
+      " 138.14868545 249.99791523 103.42574867 115.05716004 125.45965381\n",
+      " 219.57219992  60.62475621 136.16202072 124.22398597  43.73729148\n",
+      " 194.75934329 102.90098965 128.45104006 212.97017533  49.25295532]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "y_pred = diabetes_model.predict(diabetes_data_test)\n",
+    "print(y_pred)"
    ]
   },
   {
@@ -247,11 +506,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1397,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.660800Z",
+     "start_time": "2021-05-06T13:36:09.656660Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[233.  91. 111. 152. 120.  67. 310.  94. 183.  66. 173.  72.  49.  64.\n",
+      "  48. 178. 104. 132. 220.  57.]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(diabetes_target_test)"
    ]
   },
   {
@@ -263,11 +536,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1398,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.667767Z",
+     "start_time": "2021-05-06T13:36:09.664634Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# No, predictions are different but the values are close. the predictions should never be exactly the same as y-test"
    ]
   },
   {
@@ -302,8 +580,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1399,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.675009Z",
+     "start_time": "2021-05-06T13:36:09.670914Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -326,8 +609,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1400,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.682627Z",
+     "start_time": "2021-05-06T13:36:09.678184Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your answer here"
@@ -351,11 +639,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1401,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.693226Z",
+     "start_time": "2021-05-06T13:36:09.685071Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = pd.read_csv(\"../data/auto-mpg.csv\")"
    ]
   },
   {
@@ -367,11 +660,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 1402,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.710482Z",
+     "start_time": "2021-05-06T13:36:09.697247Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 1402,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.head()"
    ]
   },
   {
@@ -383,11 +793,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 1403,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.725548Z",
+     "start_time": "2021-05-06T13:36:09.713729Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 398 entries, 0 to 397\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column        Non-Null Count  Dtype  \n",
+      "---  ------        --------------  -----  \n",
+      " 0   mpg           398 non-null    float64\n",
+      " 1   cylinders     398 non-null    int64  \n",
+      " 2   displacement  398 non-null    float64\n",
+      " 3   horse_power   392 non-null    float64\n",
+      " 4   weight        398 non-null    int64  \n",
+      " 5   acceleration  398 non-null    float64\n",
+      " 6   model_year    398 non-null    int64  \n",
+      " 7   car_name      398 non-null    object \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 25.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "auto.info()"
    ]
   },
   {
@@ -399,11 +836,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1404,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.734343Z",
+     "start_time": "2021-05-06T13:36:09.728468Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82])"
+      ]
+     },
+     "execution_count": 1404,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "auto['model_year'].unique()"
    ]
   },
   {
@@ -415,11 +868,175 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1405,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.752562Z",
+     "start_time": "2021-05-06T13:36:09.743442Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             0\n",
+       "cylinders       0\n",
+       "displacement    0\n",
+       "horse_power     6\n",
+       "weight          0\n",
+       "acceleration    0\n",
+       "model_year      0\n",
+       "car_name        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 1405,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1406,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.762930Z",
+     "start_time": "2021-05-06T13:36:09.757070Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto = auto.dropna().reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1407,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.778420Z",
+     "start_time": "2021-05-06T13:36:09.765056Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>car_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"chevrolet chevelle malibu\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"buick skylark 320\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"plymouth satellite\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"amc rebel sst\"</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>\\t\"ford torino\"</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "0  18.0          8         307.0        130.0    3504          12.0   \n",
+       "1  15.0          8         350.0        165.0    3693          11.5   \n",
+       "2  18.0          8         318.0        150.0    3436          11.0   \n",
+       "3  16.0          8         304.0        150.0    3433          12.0   \n",
+       "4  17.0          8         302.0        140.0    3449          10.5   \n",
+       "\n",
+       "   model_year                       car_name  \n",
+       "0          70  \\t\"chevrolet chevelle malibu\"  \n",
+       "1          70          \\t\"buick skylark 320\"  \n",
+       "2          70         \\t\"plymouth satellite\"  \n",
+       "3          70              \\t\"amc rebel sst\"  \n",
+       "4          70                \\t\"ford torino\"  "
+      ]
+     },
+     "execution_count": 1407,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.head()"
    ]
   },
   {
@@ -431,11 +1048,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1408,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.787937Z",
+     "start_time": "2021-05-06T13:36:09.780638Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    199\n",
+       "8    103\n",
+       "6     83\n",
+       "3      4\n",
+       "5      3\n",
+       "Name: cylinders, dtype: int64"
+      ]
+     },
+     "execution_count": 1408,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto['cylinders'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1409,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.797919Z",
+     "start_time": "2021-05-06T13:36:09.791057Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 1409,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "len(auto['cylinders'].value_counts())"
    ]
   },
   {
@@ -451,11 +1114,387 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1410,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.806842Z",
+     "start_time": "2021-05-06T13:36:09.802377Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "auto.drop(columns = ['car_name'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1411,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.824799Z",
+     "start_time": "2021-05-06T13:36:09.810898Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2790</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2130</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2295</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horse_power  weight  acceleration  \\\n",
+       "387  27.0          4         140.0         86.0    2790          15.6   \n",
+       "388  44.0          4          97.0         52.0    2130          24.6   \n",
+       "389  32.0          4         135.0         84.0    2295          11.6   \n",
+       "390  28.0          4         120.0         79.0    2625          18.6   \n",
+       "391  31.0          4         119.0         82.0    2720          19.4   \n",
+       "\n",
+       "     model_year  \n",
+       "387          82  \n",
+       "388          82  \n",
+       "389          82  \n",
+       "390          82  \n",
+       "391          82  "
+      ]
+     },
+     "execution_count": 1411,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1412,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:09.836615Z",
+     "start_time": "2021-05-06T13:36:09.828537Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "auto_model = LinearRegression()\n",
+    "\n",
+    "X = auto.drop(columns = ['mpg'])\n",
+    "y = auto['mpg']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, random_state = 55)\n",
+    "\n",
+    "## Transforming data using PowerTransformer\n",
+    "#trans = PowerTransformer()\n",
+    "#trans.fit(X_train)\n",
+    "#X_train = trans.transform(X_train)\n",
+    "#X_test  = trans.transform(X_test)\n",
+    "\n",
+    "## Transforming dara using np.log\n",
+    "X_train = np.log(X_train)\n",
+    "X_test  = np.log(X_test)\n",
+    "y_train = np.log(y_train)\n",
+    "y_test = np.log(y_test)\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T10:06:28.126977Z",
+     "start_time": "2021-05-06T10:06:28.076252Z"
+    }
+   },
+   "source": [
+    "# Transforming data using PowerTransformer\n",
+    "\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "auto_model = LinearRegression()\n",
+    "\n",
+    "X = auto.drop(columns = ['mpg'])\n",
+    "y = auto['mpg']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state = 32)\n",
+    "\n",
+    "trans_x = PowerTransformer()\n",
+    "trans_y = PowerTransformer()\n",
+    "\n",
+    "trans_x.fit(X_train)\n",
+    "trans_y.fit(np.array(y_train).reshape(-1, 1))\n",
+    "\n",
+    "X_train = trans_x.transform(X_train)\n",
+    "X_test  = trans_x.transform(X_test)\n",
+    "y_train = trans_y.transform(np.array(y_train).reshape(-1, 1))\n",
+    "y_test = trans_y.transform(np.array(y_test).reshape(-1, 1))\n",
+    "\n",
+    "\n",
+    "## REVERSEING the power transfor on the y to get real values\n",
+    "print(trans_y.inverse_transform(y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1413,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:11.397336Z",
+     "start_time": "2021-05-06T13:36:09.841231Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "X_train = pd.DataFrame(X_train, columns = X.columns)\n",
+    "\n",
+    "for c in X_train.columns:\n",
+    "    sns.displot(X_train[c])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1414,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:12.922563Z",
+     "start_time": "2021-05-06T13:36:11.399739Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Y6CUISWrEAJakRgxgSWrEAJakRgxgSWqkSQAn2ZjkD5N8NclVLWqQpNbGHsBJjgd+A/hHwLnA25KcO+46JKm1FmfArwC+WlVfr6rvAR8HLm5QhyQ1laoa7waTtwAbq+qfddPvAP5+Vb1rxnKbgc3d5DnAH87yds8FHuux3MVgjYvDGheHNS6eUet8rKo2zjajxZ1ws913+Nf+F6iqLcCWed8o2VFV04tVWB+scXFY4+KwxsWzGHW2uASxB5gamj4T2NugDklqqkUA3wWcneT5SU4E3grc3KAOSWpq7JcgqupQkncBvwccD1xTVQ8t8O3mvUQxIaxxcVjj4rDGxXPMdY79QzhJ0oB3wklSIwawJDUy0QGc5Jwk9w69nkjynhnLvDbJnw0t84Ex1XZNkgNJHhxqOz3JrUl2dcPT5lh3LLdiz1HjryZ5OMn9SW5M8pw51n0kyQPdPt0x5hp/Mck3h36nb5hj3Zb7cdtQfY8kuXeOdce1H6eS3JZkZ5KHkry7a5+YY3KeGifmmJynxn6OyapaEi8GH9j9CfC3ZrS/FvhMg3peA7wMeHCo7T8BV3XjVwG/MsfP8TXgBcCJwH3AuWOs8fXAqm78V2arsZv3CPDcRvvxF4GfG+F4aLYfZ8z/L8AHGu/HtcDLuvFTgT9icKv/xByT89Q4McfkPDX2ckxO9BnwDBcAX6uqb7QuBKCqvgh8a0bzxcDWbnwrcMksq47tVuzZaqyqz1XVoW7yywy+h93MHPtxFE3342EZPNDsMuD6PrY9qqraV1X3dONPAjuBdUzQMTlXjZN0TM6zH0dx1PtxKQXwW5n7IH9VkvuS/G6SvzPOomZYU1X7YPCLBM6YZZl1wO6h6T2M/gtebD8J/O4c8wr4XJK7u9vCx+1d3Z+k18zxZ/Ok7Md/AOyvql1zzB/7fkxyFvBS4E4m9JicUeOwiTkmZ6lx0Y/JJRHA3Q0bbwI+OcvsexhclngJ8OvAb4+xtIUY6Vbs3otI3g8cAq6bY5Hzq+plDHqte2eS14ytOPgw8ELgPGAfgz/xZ5qI/Qi8jfnPfse6H5OcAnwaeE9VPTHqarO09bYv56pxko7JWWrs5ZhcEgHMYIffU1X7Z86oqieq6s+78c8CJyR57rgL7OxPshagGx6YZZnmt2In2QS8EXh7dRevZqqqvd3wAHAjgz+vxqKq9lfVU1X1NPDf5tj2JOzHVcCPAtvmWmac+zHJCQxC47qquqFrnqhjco4aJ+qYnK3Gvo7JpRLAc55lJPmb3XU4kryCwc/0+BhrG3YzsKkb3wTcNMsyTW/FTrIR+HngTVX1nTmWeWaSUw+PM/iQ5MHZlu2pxrVDk2+eY9uTcEv7hcDDVbVntpnj3I/dv4GPATur6teGZk3MMTlXjZN0TM5TYz/HZJ+fKC7Sp5LPYBCozx5q+2ngp7vxdwEPMfjE8cvAhjHVdT2DP0X+isH/fFcAfwPYDuzqhqd3yz4P+OzQum9g8Onq14D3j7nGrzK4TnVv9/rIzBoZfIp7X/d6qEGNvwU8ANzfHcBrJ20/du3XHj4Oh5ZttR9fzeDP3fuHfrdvmKRjcp4aJ+aYnKfGXo5Jb0WWpEaWyiUISVp2DGBJasQAlqRGDGBJasQAlqRGDGCtCEluT7IoD3pMckmSc4emfynJhYvx3lpZDGBpFkmOn2f2JQx6yAKgqj5QVb/fe1FadgxgTZQkv911tvLQ4Q5Xuj5W7+k6XNretZ2S5De7/mHvT/JjXfvrk3ypW/6T3T39M7cx6zJdf7MfSPIHwKVJfirJXd12P53kGUk2MOiX5Fe7fmFfmOTaJG/p3uOCJF/p6romyUlD7/0fum0+kOTFY9mhmmgGsCbNT1bV3wOmgZ9NsobBvfc/VoMOly7tlvsF4M+q6oer6u8Cn+/6APl3wIU16LRlB/De4TcfYZm/rKpXV9XHgRuq6uXddncyuAPuDgZ3Qr2vqs6rqq8NvffJDO6Ou7yqfpjBQ2//+dB7P9Zt88PAzx3rjtLSN/anIktH8LNJ3tyNTwGbgS9W1R8DVNXhfnkvZHCvPV37t5O8kcGlgf/ddQ9yIvClGe//yiMsM9yxzg8l+WXgOcApDJ7kPZ9zgD+uqj/qprcC7wQ+1E0f7nzmbgad+GiFM4A1MZK8lkGwvqqqvpPkdgb3/p8z2+L89a7+AtxaVW+bbzNHWOYvhsavBS6pqvuS/DiDp6/MZ7buCId9txs+hf/2hJcgNFmeDXy7C98XMzhbPQn4h0meD4NnnHXLfo5BR0x07acx6Izp/CQv6tqekeRvz9jGKMscdiqwr+ue8O1D7U9282Z6GDjr8HsD7wC+MMLPrRXKANYkuQVYleR+4D8yCMuDDC5D3JDkPr5/ieCXgdOSPNi1v66qDgI/DlzfvceXgf/vw65RlhnyCwyehnArg3A97OPA+7oP21449N5/CfwE8MkkDwBPAx9ZyI7QymBvaJLUiGfAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktTI/wOC5AHhrJPnOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "for c in X.columns:\n",
+    "    sns.displot(X[c])"
    ]
   },
   {
@@ -469,11 +1508,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1415,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:12.933846Z",
+     "start_time": "2021-05-06T13:36:12.925119Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 1415,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "auto_model.fit(X_train,y_train)\n",
+    "\n"
    ]
   },
   {
@@ -503,11 +1559,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 1416,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:12.959217Z",
+     "start_time": "2021-05-06T13:36:12.945053Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.888356227618743"
+      ]
+     },
+     "execution_count": 1416,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = auto_model.predict(X_train)\n",
+    "\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "r2_score(y_train, y_pred)"
    ]
   },
   {
@@ -523,11 +1599,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
+   "execution_count": 1417,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:12.976274Z",
+     "start_time": "2021-05-06T13:36:12.963925Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8861907828909094"
+      ]
+     },
+     "execution_count": 1417,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test_pred = auto_model.predict(X_test)\n",
+    "\n",
+    "r2_score(y_test, y_test_pred)"
    ]
   },
   {
@@ -541,11 +1635,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1418,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.000186Z",
+     "start_time": "2021-05-06T13:36:12.979953Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your answer here"
+    "## The r2 score is relatively good as it's a high value (close to one)\n",
+    "## The model performs a bit better on the train set so might be slightly overfitted "
    ]
   },
   {
@@ -561,11 +1661,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 1419,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.082889Z",
+     "start_time": "2021-05-06T13:36:13.020988Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "\n",
+    "\n",
+    "X = auto.drop(columns = ['mpg'])\n",
+    "y = auto['mpg']\n",
+    "\n",
+    "X_train09, X_test09, y_train09, y_test09 = train_test_split(X, y, train_size=0.90, random_state = 55)\n",
+    "\n",
+    "## Transforming data using PowerTransformer\n",
+    "#trans = PowerTransformer()\n",
+    "#trans.fit(X_train)\n",
+    "#X_train = trans.transform(X_train)\n",
+    "#X_test  = trans.transform(X_test)\n",
+    "\n",
+    "## Transforming dara using np.log\n",
+    "X_train09 = np.log(X_train09)\n",
+    "X_test09  = np.log(X_test09)\n",
+    "y_train09 = np.log(y_train09)\n",
+    "y_test09 = np.log(y_test09)"
    ]
   },
   {
@@ -577,11 +1699,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1420,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.202423Z",
+     "start_time": "2021-05-06T13:36:13.192826Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 1420,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "auto_model09 = LinearRegression()\n",
+    "auto_model09.fit(X_train09,y_train09)\n"
    ]
   },
   {
@@ -593,29 +1732,1069 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1421,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.215662Z",
+     "start_time": "2021-05-06T13:36:13.206602Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8918992326752958"
+      ]
+     },
+     "execution_count": 1421,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train09_pred = auto_model09.predict(X_train09)\n",
+    "r2_score(y_train09, y_train09_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1422,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.230033Z",
+     "start_time": "2021-05-06T13:36:13.220189Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8630238096719487"
+      ]
+     },
+     "execution_count": 1422,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test09_pred = auto_model09.predict(X_test09)\n",
+    "r2_score(y_test09, y_test09_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1423,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T13:36:13.236267Z",
+     "start_time": "2021-05-06T13:36:13.233090Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "# your code here"
+    "## The r2 score for the 0.9 test-split is worse meaning that theres a slight overfitting occuring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T10:59:20.119967Z",
+     "start_time": "2021-05-06T10:59:20.117250Z"
+    }
+   },
+   "source": [
+    "# Tree-Based models\n",
+    " **Implement tree models & fine-tune the parameters to see if you can improve the results in these two labs. You should also visualize the trees.**\n",
+    "\n",
+    "Add feature selection strategies (VarianceThreshold, SelectBestK, RFE etc.)  and check if this improves the models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1494,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:55.092073Z",
+     "start_time": "2021-05-06T15:11:55.073427Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "dt_model = DecisionTreeRegressor(max_depth=3)\n",
+    "\n",
+    "X = auto.drop(columns = ['mpg'])\n",
+    "y = auto['mpg']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=55)\n",
+    "\n",
+    "\n",
+    "dt_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_train = dt_model.predict(X_train)\n",
+    "y_pred_test  = dt_model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1495,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:55.110999Z",
+     "start_time": "2021-05-06T15:11:55.099579Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "def performance_model(y_train, y_test, y_pred_train, y_pred_test):\n",
+    "\n",
+    "    # Model validation y_train - y_pred_train\n",
+    "    ME_train = np.mean(y_train-y_pred_train)\n",
+    "    ME_test  = np.mean(y_test-y_pred_test)\n",
+    "\n",
+    "    MAE_train = mean_absolute_error(y_train,y_pred_train)\n",
+    "    MAE_test  = mean_absolute_error(y_test,y_pred_test)\n",
+    "\n",
+    "    MSE_train = mean_squared_error(y_train,y_pred_train)\n",
+    "    MSE_test  = mean_squared_error(y_test,y_pred_test)\n",
+    "\n",
+    "    RMSE_train = np.sqrt(MSE_train)\n",
+    "    RMSE_test  = np.sqrt(MSE_test)\n",
+    "\n",
+    "    MAPE_train = np.mean((np.abs(y_train-y_pred_train) / y_train)* 100.)\n",
+    "    MAPE_test  = np.mean((np.abs(y_test-y_pred_test) / y_test)* 100.)\n",
+    "\n",
+    "    R2_train = r2_score(y_train,y_pred_train)\n",
+    "    R2_test  = r2_score(y_test,y_pred_test)\n",
+    "\n",
+    "    performance = pd.DataFrame({'Error_metric': ['Mean error','Mean absolute error','Mean squared error',\n",
+    "                                             'Root mean squared error','Mean absolute percentual error',\n",
+    "                                             'R2'],\n",
+    "                            'Train': [ME_train, MAE_train, MSE_train, RMSE_train, MAPE_train, R2_train],\n",
+    "                            'Test' : [ME_test, MAE_test , MSE_test, RMSE_test, MAPE_test, R2_test]})\n",
+    "\n",
+    "    pd.options.display.float_format = '{:.2f}'.format\n",
+    "\n",
+    "\n",
+    "    df_train = pd.DataFrame({'Real_value': y_train, 'Predicted_value': y_pred_train})\n",
+    "    df_test  = pd.DataFrame({'Real_value': y_test,  'PRedicted_value': y_pred_test})\n",
+    "\n",
+    "    return performance, df_train, df_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1496,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:55.136561Z",
+     "start_time": "2021-05-06T15:11:55.115179Z"
+    }
+   },
+   "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>Error_metric</th>\n",
+       "      <th>Train</th>\n",
+       "      <th>Test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mean error</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mean absolute error</td>\n",
+       "      <td>2.19</td>\n",
+       "      <td>2.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mean squared error</td>\n",
+       "      <td>8.66</td>\n",
+       "      <td>11.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Root mean squared error</td>\n",
+       "      <td>2.94</td>\n",
+       "      <td>3.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Mean absolute percentual error</td>\n",
+       "      <td>9.46</td>\n",
+       "      <td>11.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>R2</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.84</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Error_metric  Train  Test\n",
+       "0                      Mean error   0.00  0.61\n",
+       "1             Mean absolute error   2.19  2.62\n",
+       "2              Mean squared error   8.66 11.62\n",
+       "3         Root mean squared error   2.94  3.41\n",
+       "4  Mean absolute percentual error   9.46 11.62\n",
+       "5                              R2   0.85  0.84"
+      ]
+     },
+     "execution_count": 1496,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance, _ ,_ = performance_model(y_train, y_test, y_pred_train, y_pred_test)\n",
+    "performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1497,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:55.947897Z",
+     "start_time": "2021-05-06T15:11:55.141804Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import plot_tree\n",
+    "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n",
+    "\n",
+    "plot_tree(dt_model,filled = True, rounded=True)\n",
+    "plt.show()  "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?"
+    "## VarianceThreshold"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1498,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:56.102136Z",
+     "start_time": "2021-05-06T15:11:55.950974Z"
+    }
+   },
+   "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>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>313.00</td>\n",
+       "      <td>313.00</td>\n",
+       "      <td>313.00</td>\n",
+       "      <td>313.00</td>\n",
+       "      <td>313.00</td>\n",
+       "      <td>313.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.47</td>\n",
+       "      <td>193.82</td>\n",
+       "      <td>103.79</td>\n",
+       "      <td>2968.21</td>\n",
+       "      <td>15.52</td>\n",
+       "      <td>76.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.69</td>\n",
+       "      <td>103.31</td>\n",
+       "      <td>36.97</td>\n",
+       "      <td>835.31</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>3.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.00</td>\n",
+       "      <td>68.00</td>\n",
+       "      <td>46.00</td>\n",
+       "      <td>1760.00</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>70.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.00</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>75.00</td>\n",
+       "      <td>2228.00</td>\n",
+       "      <td>13.90</td>\n",
+       "      <td>73.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.00</td>\n",
+       "      <td>151.00</td>\n",
+       "      <td>95.00</td>\n",
+       "      <td>2795.00</td>\n",
+       "      <td>15.50</td>\n",
+       "      <td>76.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>8.00</td>\n",
+       "      <td>262.00</td>\n",
+       "      <td>122.00</td>\n",
+       "      <td>3574.00</td>\n",
+       "      <td>17.00</td>\n",
+       "      <td>79.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8.00</td>\n",
+       "      <td>455.00</td>\n",
+       "      <td>230.00</td>\n",
+       "      <td>5140.00</td>\n",
+       "      <td>24.60</td>\n",
+       "      <td>82.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cylinders  displacement  horse_power  weight  acceleration  model_year\n",
+       "count     313.00        313.00       313.00  313.00        313.00      313.00\n",
+       "mean        5.47        193.82       103.79 2968.21         15.52       76.06\n",
+       "std         1.69        103.31        36.97  835.31          2.69        3.66\n",
+       "min         3.00         68.00        46.00 1760.00          8.00       70.00\n",
+       "25%         4.00        105.00        75.00 2228.00         13.90       73.00\n",
+       "50%         4.00        151.00        95.00 2795.00         15.50       76.00\n",
+       "75%         8.00        262.00       122.00 3574.00         17.00       79.00\n",
+       "max         8.00        455.00       230.00 5140.00         24.60       82.00"
+      ]
+     },
+     "execution_count": 1498,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1499,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:56.116962Z",
+     "start_time": "2021-05-06T15:11:56.111517Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.87146581e+00, 1.06733673e+04, 1.36646179e+03, 6.97740280e+05,\n",
+       "       7.21701360e+00, 1.34197596e+01])"
+      ]
+     },
+     "execution_count": 1499,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lis = [1.694540, 103.311990, 36.965684, 835.308494, 2.686450, 3.663299]\n",
+    "np.power(lis,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1500,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:56.150842Z",
+     "start_time": "2021-05-06T15:11:56.122335Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial number of numerical columns:  (313, 6)\n",
+      "\n",
+      "Final number of numerical columns:  (313, 4)\n",
+      "\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>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>85.00</td>\n",
+       "      <td>70.00</td>\n",
+       "      <td>2070.00</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>156.00</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>2745.00</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>122.00</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>80.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>122.00</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>2395.00</td>\n",
+       "      <td>72.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>156.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>2585.00</td>\n",
+       "      <td>82.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>151.00</td>\n",
+       "      <td>90.00</td>\n",
+       "      <td>2678.00</td>\n",
+       "      <td>80.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>97.00</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>2130.00</td>\n",
+       "      <td>71.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>262.00</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>3015.00</td>\n",
+       "      <td>82.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>183.00</td>\n",
+       "      <td>77.00</td>\n",
+       "      <td>3530.00</td>\n",
+       "      <td>79.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>400.00</td>\n",
+       "      <td>170.00</td>\n",
+       "      <td>4668.00</td>\n",
+       "      <td>75.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>313 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     displacement  horse_power  weight  model_year\n",
+       "0           85.00        70.00 2070.00       78.00\n",
+       "1          156.00       105.00 2745.00       78.00\n",
+       "2          122.00        88.00 2500.00       80.00\n",
+       "3          122.00        86.00 2395.00       72.00\n",
+       "4          156.00        92.00 2585.00       82.00\n",
+       "..            ...          ...     ...         ...\n",
+       "308        151.00        90.00 2678.00       80.00\n",
+       "309         97.00        88.00 2130.00       71.00\n",
+       "310        262.00        85.00 3015.00       82.00\n",
+       "311        183.00        77.00 3530.00       79.00\n",
+       "312        400.00       170.00 4668.00       75.00\n",
+       "\n",
+       "[313 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 1500,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.feature_selection import VarianceThreshold # It only works with numerical features\n",
+    "\n",
+    "\n",
+    "X_train = X_train.select_dtypes(include=np.number)\n",
+    "X_test  = X_test.select_dtypes(include=np.number)\n",
+    "\n",
+    "#display(X_train)\n",
+    "print(\"Initial number of numerical columns: \",X_train.shape)\n",
+    "print()\n",
+    "\n",
+    "selector = VarianceThreshold(threshold=10) # Default threshold value is 0\n",
+    "# Features with a training-set variance lower than this threshold will be removed.\n",
+    "selector.fit(X_train)\n",
+    "\n",
+    "kept_features_indexes = selector.get_support(indices = True) #returns an array of integers corresponding to nonremoved features\n",
+    "kept_features = list(X_train.iloc[:,kept_features_indexes].columns)\n",
+    "\n",
+    "X_train = selector.transform(X_train)\n",
+    "X_test  = selector.transform(X_test)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=kept_features)\n",
+    "X_test  = pd.DataFrame(X_test, columns=kept_features)\n",
+    "\n",
+    "print(\"Final number of numerical columns: \",X_train.shape)\n",
+    "print()\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# your code here"
+    "## Correlation matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1501,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.106370Z",
+     "start_time": "2021-05-06T15:11:56.155331Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x1008 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "corr = abs(auto.corr())\n",
+    "fig, ax = plt.subplots(figsize=(14,14))\n",
+    "sns.heatmap(corr, annot=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1502,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.146388Z",
+     "start_time": "2021-05-06T15:11:57.121133Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['weight', 'displacement', 'horse_power', 'cylinders', 'mpg']\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>weight</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>mpg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3504</td>\n",
+       "      <td>307.00</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>18.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3693</td>\n",
+       "      <td>350.00</td>\n",
+       "      <td>165.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>15.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3436</td>\n",
+       "      <td>318.00</td>\n",
+       "      <td>150.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>18.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3433</td>\n",
+       "      <td>304.00</td>\n",
+       "      <td>150.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3449</td>\n",
+       "      <td>302.00</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>2790</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>27.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>2130</td>\n",
+       "      <td>97.00</td>\n",
+       "      <td>52.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>2295</td>\n",
+       "      <td>135.00</td>\n",
+       "      <td>84.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>2625</td>\n",
+       "      <td>120.00</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>28.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>2720</td>\n",
+       "      <td>119.00</td>\n",
+       "      <td>82.00</td>\n",
+       "      <td>4</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>392 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     weight  displacement  horse_power  cylinders   mpg\n",
+       "0      3504        307.00       130.00          8 18.00\n",
+       "1      3693        350.00       165.00          8 15.00\n",
+       "2      3436        318.00       150.00          8 18.00\n",
+       "3      3433        304.00       150.00          8 16.00\n",
+       "4      3449        302.00       140.00          8 17.00\n",
+       "..      ...           ...          ...        ...   ...\n",
+       "387    2790        140.00        86.00          4 27.00\n",
+       "388    2130         97.00        52.00          4 44.00\n",
+       "389    2295        135.00        84.00          4 32.00\n",
+       "390    2625        120.00        79.00          4 28.00\n",
+       "391    2720        119.00        82.00          4 31.00\n",
+       "\n",
+       "[392 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 1502,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c_last = corr['mpg'].sort_values(ascending=False)\n",
+    "c_thr = .7\n",
+    "\n",
+    "cols_to_keep = list(c_last[c_last > c_thr].index)[1:] + [list(c_last[c_last > c_thr].index)[0]]\n",
+    "print(cols_to_keep)\n",
+    "\n",
+    "auto[cols_to_keep]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RFE - Recursive feature elimination"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1504,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:12:05.605015Z",
+     "start_time": "2021-05-06T15:12:05.570453Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting estimator with 6 features.\n",
+      "Fitting estimator with 5 features.\n",
+      "Final selected features: \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>displacement</th>\n",
+       "      <th>horse_power</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>model_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>85.00</td>\n",
+       "      <td>70.00</td>\n",
+       "      <td>2070.00</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>156.00</td>\n",
+       "      <td>105.00</td>\n",
+       "      <td>2745.00</td>\n",
+       "      <td>78.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>122.00</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>2500.00</td>\n",
+       "      <td>80.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>122.00</td>\n",
+       "      <td>86.00</td>\n",
+       "      <td>2395.00</td>\n",
+       "      <td>72.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>156.00</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>2585.00</td>\n",
+       "      <td>82.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>151.00</td>\n",
+       "      <td>90.00</td>\n",
+       "      <td>2678.00</td>\n",
+       "      <td>80.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>97.00</td>\n",
+       "      <td>88.00</td>\n",
+       "      <td>2130.00</td>\n",
+       "      <td>71.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310</th>\n",
+       "      <td>262.00</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>3015.00</td>\n",
+       "      <td>82.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311</th>\n",
+       "      <td>183.00</td>\n",
+       "      <td>77.00</td>\n",
+       "      <td>3530.00</td>\n",
+       "      <td>79.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>400.00</td>\n",
+       "      <td>170.00</td>\n",
+       "      <td>4668.00</td>\n",
+       "      <td>75.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>313 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     displacement  horse_power  weight  model_year\n",
+       "0           85.00        70.00 2070.00       78.00\n",
+       "1          156.00       105.00 2745.00       78.00\n",
+       "2          122.00        88.00 2500.00       80.00\n",
+       "3          122.00        86.00 2395.00       72.00\n",
+       "4          156.00        92.00 2585.00       82.00\n",
+       "..            ...          ...     ...         ...\n",
+       "308        151.00        90.00 2678.00       80.00\n",
+       "309         97.00        88.00 2130.00       71.00\n",
+       "310        262.00        85.00 3015.00       82.00\n",
+       "311        183.00        77.00 3530.00       79.00\n",
+       "312        400.00       170.00 4668.00       75.00\n",
+       "\n",
+       "[313 rows x 4 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.feature_selection import RFE\n",
+    "\n",
+    "X = auto.drop(columns = ['mpg'])\n",
+    "y = auto['mpg']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=55)\n",
+    "\n",
+    "X_train = X_train.select_dtypes(include=np.number)\n",
+    "X_test  = X_test.select_dtypes(include=np.number)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=X.columns)\n",
+    "X_test  = pd.DataFrame(X_test, columns=X.columns)\n",
+    "\n",
+    "#display(X_train)\n",
+    "\n",
+    "dt = DecisionTreeRegressor(max_depth=3)\n",
+    "\n",
+    "selector = RFE(dt, n_features_to_select= 4, step = 1, verbose = 1) # Step is how many features to add or drop everytime\n",
+    "selector.fit(X_train, y_train)\n",
+    "\n",
+    "kept_features = selector.get_support(indices = True) #returns an array of integers corresponding to nonremoved features\n",
+    "kept_features = list(X_train.iloc[:,kept_features].columns)\n",
+    "\n",
+    "X_train = selector.transform(X_train)\n",
+    "X_test  = selector.transform(X_test)\n",
+    "\n",
+    "X_train = pd.DataFrame(X_train, columns=kept_features)\n",
+    "X_test  = pd.DataFrame(X_test, columns=kept_features)\n",
+    "\n",
+    "print(\"Final selected features: \")\n",
+    "display(X_train)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.189161Z",
+     "start_time": "2021-05-06T15:11:57.175232Z"
+    }
+   },
+   "source": [
+    "## SelectBestK"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -630,7 +2809,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.208617Z",
+     "start_time": "2021-05-06T15:11:55.151Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Libraries\n",
@@ -647,7 +2831,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.212044Z",
+     "start_time": "2021-05-06T15:11:55.157Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -663,7 +2852,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.216643Z",
+     "start_time": "2021-05-06T15:11:55.164Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -681,7 +2875,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.222274Z",
+     "start_time": "2021-05-06T15:11:55.170Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -697,7 +2896,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-05-06T15:11:57.225430Z",
+     "start_time": "2021-05-06T15:11:55.179Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# your code here"
@@ -719,6 +2923,13 @@
     "\n",
     "* Data integration specialists who are business or content experts but also understand data and programming. This cross-disciplinary track brings together data, technology, and business and will be in high demands in the next decade."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -737,7 +2948,49 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
   }
  },
  "nbformat": 4,