Notebooks/02_Linear_Regression.ipynb

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   "source": [
    "# Linear Regression\n",
    "This is the practical session that is on predictive modelling using Linear Regression. You can find the slides [here](https://docs.google.com/presentation/d/16VhmU8YMTqHkUYXwOVapR88wFLRIIoHwqLQ6QhcD7LU/edit?usp=sharing).\n",
    "\n",
    "\"Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered an explanatory variable, and the other is considered a dependent variable. For example, a modeller might want to relate the weights of individuals to their heights using a linear regression model. \" [1](http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm)\n",
    "\n",
    "Linear Regression attempts to predict a continuous outcome feature (**Y**) from one or more explanatory features (**X**).  \n",
    "\n",
    "$$Y = \\beta_0 + \\beta_1 X$$\n",
    "\n",
    "$\\beta_0$ is called the intercept term and represents the expected mean value of Y when all explanatory features equal 0.  \n",
    "$\\beta_1$ is called a beta coefficient and represents the expected change in the value of Y that results from a one-unit change in X.  \n",
    "\n",
    "We will use a diabetes dataset that is part of the `sklearn.datasets` module\n",
    "\n",
    "**What impacts diabetes disease progression?**\n",
    "\n",
    "To ensure that our linear regressor is appropriate and interpretable, we will have to confirm the following assumptions are not violated:\n",
    "\n",
    "1. Linear relationship between x and y - We can draw a line or curve within the relationship\n",
    "2. Normality - A normal curve, a histogram of the values should look like a bell\n",
    "3. Minimal multicollinearity (if multiple variables) - Each variable should introduce **new** information\n",
    "4. No autocorrelation - No relationship **within** a variable depending on the time/magnitude scale\n",
    "5. Homoscedasticity - The variation in the error of the model should not be increasing as the variation in the data increases\n",
    "\n",
    "- Additional rule of thumb: at least 20 observations per independent variable in the analysis\n",
    "  \n",
    "If these assumptions are violated, then the predictive power of the linear regression is still valid but the information concerning the most important features is not. It is important to keep this in mind!\n",
    "\n",
    "## Import packages\n",
    "First, we need to import several packages. In Python, the most comprehensive machine learning library is SciKit-Learn. It provides many common machine learning algorithms as well as a large number of utility functions and tools.\n",
    "\n",
    "Let's import SciKit-Learn, which is packaged as `sklearn`, and some dependencies. It is common to use a combination of SciKit-Learn, NumPy, Pandas, and SciPy for machine learning tasks."
   ]
  },
  {
   "cell_type": "code",
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      "Note: you may need to restart the kernel to use updated packages.\n"
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    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
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   "cell_type": "code",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scipy"
   ]
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    "pip install scikit-learn"
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    "pip install matplotlib"
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      "\u001b[?25hDownloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n",
      "\u001b[2K   \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.9/233.9 kB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: patsy, statsmodels\n",
      "Successfully installed patsy-0.5.6 statsmodels-0.14.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the installed packages\n",
    "\n",
    "**pandas** - creating datasets in python(tool to wrangle dataframes)\n",
    "**scikit-learn** - contains most of the datasets in python\n",
    "**stats** - has models\n",
    "**matplotlib** - for plotting\n",
    "**seaborn**\n",
    "**statsmodels**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats as stats\n",
    "\n",
    "# Plotting package\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns # seaborn is a data visualization library\n",
    "#provides a high-level interface for drawing attractive and informative statistical graphics.\n",
    "plt.rcParams['figure.figsize'] = (12, 8)\n",
    "sns.set()\n",
    "sns.set(font_scale=1.5)\n",
    "\n",
    "# We'll be using statsmodels to fit linear regression\n",
    "import statsmodels.api as sm\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.model_selection import cross_val_predict, GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from sklearn import model_selection\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Our Data\n",
    "\n",
    "### Diabetes dataset\n",
    "In this example, we will analyse a diabetes dataset that is part of the `sklearn.datasets` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Loading the diabetes dataset\n",
    "diabetes = datasets.load_diabetes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at what we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
      "         0.01990749, -0.01764613],\n",
      "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
      "        -0.06833155, -0.09220405],\n",
      "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
      "         0.00286131, -0.02593034],\n",
      "       ...,\n",
      "       [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
      "        -0.04688253,  0.01549073],\n",
      "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
      "         0.04452873, -0.02593034],\n",
      "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
      "        -0.00422151,  0.00306441]]), 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
      "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
      "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
      "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
      "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
      "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
      "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
      "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
      "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
      "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
      "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
      "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
      "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
      "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
      "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
      "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
      "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
      "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
      "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
      "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
      "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
      "       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
      "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
      "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
      "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
      "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
      "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
      "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
      "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
      "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
      "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
      "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
      "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
      "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
      "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
      "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
      "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
      "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
      "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
      "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
      "       220.,  57.]), 'frame': None, 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative 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, total serum cholesterol\\n    - s2      ldl, low-density lipoproteins\\n    - s3      hdl, high-density lipoproteins\\n    - s4      tch, total cholesterol / HDL\\n    - s5      ltg, possibly log of serum triglycerides level\\n    - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley 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', 'feature_names': ['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6'], 'data_filename': 'diabetes_data_raw.csv.gz', 'target_filename': 'diabetes_target.csv.gz', 'data_module': 'sklearn.datasets.data'}\n"
     ]
    }
   ],
   "source": [
    "#Viewing our diabetes dataset\n",
    "print(diabetes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking into the variables in the dataset\n",
    "diabetes.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
       "        87.,  65., 102., 265., 276., 252.,  90., 100.,  55.,  61.,  92.,\n",
       "       259.,  53., 190., 142.,  75., 142., 155., 225.,  59., 104., 182.,\n",
       "       128.,  52.,  37., 170., 170.,  61., 144.,  52., 128.,  71., 163.,\n",
       "       150.,  97., 160., 178.,  48., 270., 202., 111.,  85.,  42., 170.,\n",
       "       200., 252., 113., 143.,  51.,  52., 210.,  65., 141.,  55., 134.,\n",
       "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
       "       173., 180.,  84., 121., 161.,  99., 109., 115., 268., 274., 158.,\n",
       "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
       "        60., 174., 259., 178., 128.,  96., 126., 288.,  88., 292.,  71.,\n",
       "       197., 186.,  25.,  84.,  96., 195.,  53., 217., 172., 131., 214.,\n",
       "        59.,  70., 220., 268., 152.,  47.,  74., 295., 101., 151., 127.,\n",
       "       237., 225.,  81., 151., 107.,  64., 138., 185., 265., 101., 137.,\n",
       "       143., 141.,  79., 292., 178.,  91., 116.,  86., 122.,  72., 129.,\n",
       "       142.,  90., 158.,  39., 196., 222., 277.,  99., 196., 202., 155.,\n",
       "        77., 191.,  70.,  73.,  49.,  65., 263., 248., 296., 214., 185.,\n",
       "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
       "       154., 259.,  90., 246., 124.,  67.,  72., 257., 262., 275., 177.,\n",
       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
       "        60., 219.,  80.,  68., 332., 248.,  84., 200.,  55.,  85.,  89.,\n",
       "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "       220.,  57.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look into the measures\n",
    "diabetes.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Data is an a NumPy array. Let us create a Pandas DataFrame version of the dataset, as several tools can work with DataFrames and these are often easier to work with than NumPy data structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Converting the data into a dataframe\n",
    "diabetes = datasets.load_diabetes()\n",
    "diabetes_pd = pd.DataFrame(diabetes.data)\n",
    "feature_names = [\"age\", \"sex\", \"bmi\", \"map\", \"tc\", \"ldl\", \"hdl\", \"tch\", \"ltg\", \"glu\"] #these features will be used for prediction\n",
    "diabetes_pd.columns = feature_names\n",
    "diabetes_pd[\"y\"] = diabetes.target # adding an extra column named 'y'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine the top few rows of the data set: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>map</th>\n",
       "      <th>tc</th>\n",
       "      <th>ldl</th>\n",
       "      <th>hdl</th>\n",
       "      <th>tch</th>\n",
       "      <th>ltg</th>\n",
       "      <th>glu</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068332</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "        tch       ltg       glu      y  \n",
       "0 -0.002592  0.019907 -0.017646  151.0  \n",
       "1 -0.039493 -0.068332 -0.092204   75.0  \n",
       "2 -0.002592  0.002861 -0.025930  141.0  \n",
       "3  0.034309  0.022688 -0.009362  206.0  \n",
       "4 -0.002592 -0.031988 -0.046641  135.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the DataFrame does not contain any column names, so we currently have no indication as to what each colum represents. \n",
    "\n",
    "For more information, the data itself is described here:\n",
    "<http://www4.stat.ncsu.edu/%7Eboos/var.select/diabetes.html> and the paper regarding the data's analysis can be found here <http://web.stanford.edu/%7Ehastie/Papers/LARS/LeastAngle_2002.pdf>\n",
    "\n",
    "The paper itself describes a technique called Least Angle Regression (LARS), but in this case we will apply  linear regression with least squares error.\n",
    "\n",
    "Notice also that the data is the `sklearn.datasets` packages has already been normalised.\n",
    "\n",
    "From the links above we can find the column names (we will call them the *features*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.059744</td>\n",
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       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
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       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu      y  \n",
       "0   -0.002592  0.019907 -0.017646  151.0  \n",
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       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables are age, sex, body mass index, average blood pressure, and six blood serum values.\n",
    "\n",
    "To give each column a name, we can access the DataFrame's `columns` member variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#diabetes_pd.columns = feature_names\n",
    "#diabetes_pd[\"y\"] = diabetes.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
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       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
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      ],
      "text/plain": [
       "        age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "        tch       ltg       glu      y  \n",
       "0 -0.002592  0.019908 -0.017646  151.0  \n",
       "1 -0.039493 -0.068330 -0.092204   75.0  \n",
       "2 -0.002592  0.002864 -0.025930  141.0  \n",
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       "4 -0.002592 -0.031991 -0.046641  135.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "diabetes_pd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may notice that the `sex` feature contains strange data. We may first want to look at the column's unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05068012, -0.04464164])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd.sex.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will notice that the sex of the patient is either `0.0506` or `-0.0446`, in other words either slightly negative or slightly positive. Because it is a categorical value we will drop this column for simplicity. Also we will add a `y` target column for the disease progression. The disease progression is a score that gives a numerical value to the extent at which diabetes has affected the patient. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#diabetes_pd[\"y\"] = diabetes.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have added a new column called `y` that contains the disease progression, we can see how this looks by using `head` or `tail`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044529</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081413</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004222</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017293 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu      y  \n",
       "437 -0.002592  0.031193  0.007207  178.0  \n",
       "438  0.034309 -0.018114  0.044485  104.0  \n",
       "439 -0.011080 -0.046883  0.015491  132.0  \n",
       "440  0.026560  0.044529 -0.025930  220.0  \n",
       "441 -0.039493 -0.004222  0.003064   57.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we will look to see if there is a correlation between BMI and disease progression. Later we will analyse the entire dataset. Using fewer features makes it easier to visualise and plot results, etc.\n",
    "\n",
    "Here we take the `bmi` column and store it in a variable `X`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X = diabetes_pd.bmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then convert this to a NumPy array for use with SciKit-Learn later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0.05630715,  0.09834182,  0.05954058,  0.03367309,  0.05630715,\n",
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       "        0.01858372, -0.0902753 , -0.00512814, -0.05255187, -0.02237314,\n",
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       "        0.07678558,  0.01858372, -0.02237314,  0.09295276, -0.03099563,\n",
       "        0.03906215, -0.06117437, -0.00836158, -0.0374625 , -0.01375064,\n",
       "        0.07355214, -0.02452876,  0.03367309,  0.0347509 , -0.03854032,\n",
       "       -0.03961813, -0.00189471, -0.03099563, -0.046085  ,  0.00133873,\n",
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       "        0.03043966,  0.0519959 ,  0.06169621, -0.00728377,  0.00564998,\n",
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       "       -0.01375064,  0.05954058,  0.02181716,  0.01858372, -0.01159501,\n",
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       "        0.06061839, -0.04069594, -0.07195249, -0.05578531,  0.04552903,\n",
       "       -0.00943939, -0.03315126,  0.04984027, -0.08488624,  0.00564998,\n",
       "        0.02073935, -0.00728377,  0.10480869, -0.02452876, -0.00620595,\n",
       "       -0.03854032,  0.13714305,  0.17055523,  0.00241654,  0.03798434,\n",
       "       -0.05794093, -0.00943939, -0.02345095, -0.0105172 , -0.03422907,\n",
       "       -0.00297252,  0.06816308,  0.00996123,  0.00241654, -0.03854032,\n",
       "        0.02612841, -0.08919748,  0.06061839, -0.02884001, -0.02991782,\n",
       "       -0.0191397 , -0.04069594,  0.01535029, -0.02452876,  0.00133873,\n",
       "        0.06924089, -0.06979687, -0.02991782, -0.046085  ,  0.01858372,\n",
       "        0.00133873, -0.03099563, -0.00405033,  0.01535029,  0.02289497,\n",
       "        0.04552903, -0.04500719, -0.03315126,  0.097264  ,  0.05415152,\n",
       "        0.12313149, -0.08057499,  0.09295276, -0.05039625, -0.01159501,\n",
       "       -0.0277622 ,  0.05846277,  0.08540807, -0.00081689,  0.00672779,\n",
       "        0.00888341,  0.08001901,  0.07139652, -0.02452876, -0.0547075 ,\n",
       "       -0.03638469,  0.0164281 ,  0.07786339, -0.03961813,  0.01103904,\n",
       "       -0.04069594, -0.03422907,  0.00564998,  0.08864151, -0.03315126,\n",
       "       -0.05686312, -0.03099563,  0.05522933, -0.06009656,  0.00133873,\n",
       "       -0.02345095, -0.07410811,  0.01966154, -0.01590626, -0.01590626,\n",
       "        0.03906215, -0.0730303 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target, or value we want to predict, will be stored in a variable `y`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "y = diabetes_pd.y\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will create restructure the data slightly to make it compatible with SciKit-Learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Resize as they want in new versions of SciKit as per the warning\n",
    "X = X.reshape(-1, 1)\n",
    "y = y.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting Data into a Training Set and a Test Set\n",
    "\n",
    "Now we have our data in to varibles, where `X` contains the data regarding each patient's BMI and `y` contains the data regarding each patient's disease activity score, we will split the data into what is known as a training set and a test set. \n",
    "\n",
    "The training set will be used by our linear regression algorithm to learn a model that predict's the disease progression. Once the model has been trained using the training data, we will use the test set to see how well the trained model fits to **new** data. This is the test set. In this simple example we will select $80$ samples from our total of $n=442$ samples in our test set. The remaining $442-80=362$ samples will be used to train our linear regression model.\n",
    "\n",
    "### Train - Test split\n",
    "\n",
    "Prior to building our model, we first need to split our dataset into a **training set** and a **test set**.  We will use our training set to train our regressor, and we will use our test set for model validation.\n",
    "To achieve this, we will use call sklearn's [train_test_split](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html), and set the input parameter `test_size` to .2 so that 20% of the data will be assigned to the test set and 80% of the data will be assigned to the training set.\n",
    "\n",
    "Spliting data manually, however, will lead to inequality. \n",
    "\n",
    "**We set the test set aside and only look at this at the end to evaluate the models performance on unseen data.**\n",
    "\n",
    "*Why do we do this?* We do this in order to ensure that we do not test on what the model has already learned from. This way, we are not simply solving for the data we *know* about and we can check if our model is applicable when we do not know the output values. As a result, we can also check for **overfitting** the training data.\n",
    "\n",
    "We fix the random state so that each time we run the train_test_split code, we get the same distribution of data. This is important as keeping the data split constant allows us to compare results from different runs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
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       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu      y  \n",
       "0   -0.002592  0.019908 -0.017646  151.0  \n",
       "1   -0.039493 -0.068330 -0.092204   75.0  \n",
       "2   -0.002592  0.002864 -0.025930  141.0  \n",
       "3    0.034309  0.022692 -0.009362  206.0  \n",
       "4   -0.002592 -0.031991 -0.046641  135.0  \n",
       "..        ...       ...       ...    ...  \n",
       "437 -0.002592  0.031193  0.007207  178.0  \n",
       "438  0.034309 -0.018118  0.044485  104.0  \n",
       "439 -0.011080 -0.046879  0.015491  132.0  \n",
       "440  0.026560  0.044528 -0.025930  220.0  \n",
       "441 -0.039493 -0.004220  0.003064   57.0  \n",
       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "       [-0.02345095],\n",
       "       [-0.07410811],\n",
       "       [ 0.01966154],\n",
       "       [-0.01590626],\n",
       "       [-0.01590626],\n",
       "       [ 0.03906215],\n",
       "       [-0.0730303 ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "# Define our dependent variable\n",
    "y_column = 'y'\n",
    "y = diabetes_pd[y_column]\n",
    "\n",
    "# Define our independent variables\n",
    "x_columns = ['bmi']\n",
    "X = diabetes_pd[x_columns]\n",
    "# Add an intercept term to the independent variables. This is needed in order to include the constant term from\n",
    "# linear regression equation.\n",
    "X['intercept'] = 1\n",
    "# Split our data into training and test data sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "442"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the length\n",
    "len(diabetes_pd.bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train contains 353 samples and the y_train contains 353 samples\n",
      "X_test contains 89 samples and the y_test contains 89 samples\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train contains %d samples and the y_train contains %d samples\" % (len(X_train), len(y_train)))\n",
    "print(\"X_test contains %d samples and the y_test contains %d samples\" % (len(X_test), len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mstratify\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Split arrays or matrices into random train and test subsets.\n",
       "\n",
       "Quick utility that wraps input validation,\n",
       "``next(ShuffleSplit().split(X, y))``, and application to input data\n",
       "into a single call for splitting (and optionally subsampling) data into a\n",
       "one-liner.\n",
       "\n",
       "Read more in the :ref:`User Guide <cross_validation>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "*arrays : sequence of indexables with same length / shape[0]\n",
       "    Allowed inputs are lists, numpy arrays, scipy-sparse\n",
       "    matrices or pandas dataframes.\n",
       "\n",
       "test_size : float or int, default=None\n",
       "    If float, should be between 0.0 and 1.0 and represent the proportion\n",
       "    of the dataset to include in the test split. If int, represents the\n",
       "    absolute number of test samples. If None, the value is set to the\n",
       "    complement of the train size. If ``train_size`` is also None, it will\n",
       "    be set to 0.25.\n",
       "\n",
       "train_size : float or int, default=None\n",
       "    If float, should be between 0.0 and 1.0 and represent the\n",
       "    proportion of the dataset to include in the train split. If\n",
       "    int, represents the absolute number of train samples. If None,\n",
       "    the value is automatically set to the complement of the test size.\n",
       "\n",
       "random_state : int, RandomState instance or None, default=None\n",
       "    Controls the shuffling applied to the data before applying the split.\n",
       "    Pass an int for reproducible output across multiple function calls.\n",
       "    See :term:`Glossary <random_state>`.\n",
       "\n",
       "shuffle : bool, default=True\n",
       "    Whether or not to shuffle the data before splitting. If shuffle=False\n",
       "    then stratify must be None.\n",
       "\n",
       "stratify : array-like, default=None\n",
       "    If not None, data is split in a stratified fashion, using this as\n",
       "    the class labels.\n",
       "    Read more in the :ref:`User Guide <stratification>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "splitting : list, length=2 * len(arrays)\n",
       "    List containing train-test split of inputs.\n",
       "\n",
       "    .. versionadded:: 0.16\n",
       "        If the input is sparse, the output will be a\n",
       "        ``scipy.sparse.csr_matrix``. Else, output type is the same as the\n",
       "        input type.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> import numpy as np\n",
       ">>> from sklearn.model_selection import train_test_split\n",
       ">>> X, y = np.arange(10).reshape((5, 2)), range(5)\n",
       ">>> X\n",
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7],\n",
       "       [8, 9]])\n",
       ">>> list(y)\n",
       "[0, 1, 2, 3, 4]\n",
       "\n",
       ">>> X_train, X_test, y_train, y_test = train_test_split(\n",
       "...     X, y, test_size=0.33, random_state=42)\n",
       "...\n",
       ">>> X_train\n",
       "array([[4, 5],\n",
       "       [0, 1],\n",
       "       [6, 7]])\n",
       ">>> y_train\n",
       "[2, 0, 3]\n",
       ">>> X_test\n",
       "array([[2, 3],\n",
       "       [8, 9]])\n",
       ">>> y_test\n",
       "[1, 4]\n",
       "\n",
       ">>> train_test_split(y, shuffle=False)\n",
       "[[0, 1, 2], [3, 4]]\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/eneza/lib/python3.12/site-packages/sklearn/model_selection/_split.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's use the OLS (Ordinary Least Squares)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Ordinary Least Squares\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "endog : array_like\n",
       "    A 1-d endogenous response variable. The dependent variable.\n",
       "exog : array_like\n",
       "    A nobs x k array where `nobs` is the number of observations and `k`\n",
       "    is the number of regressors. An intercept is not included by default\n",
       "    and should be added by the user. See\n",
       "    :func:`statsmodels.tools.add_constant`.\n",
       "missing : str\n",
       "    Available options are 'none', 'drop', and 'raise'. If 'none', no nan\n",
       "    checking is done. If 'drop', any observations with nans are dropped.\n",
       "    If 'raise', an error is raised. Default is 'none'.\n",
       "hasconst : None or bool\n",
       "    Indicates whether the RHS includes a user-supplied constant. If True,\n",
       "    a constant is not checked for and k_constant is set to 1 and all\n",
       "    result statistics are calculated as if a constant is present. If\n",
       "    False, a constant is not checked for and k_constant is set to 0.\n",
       "**kwargs\n",
       "    Extra arguments that are used to set model properties when using the\n",
       "    formula interface.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "weights : scalar\n",
       "    Has an attribute weights = array(1.0) due to inheritance from WLS.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "WLS : Fit a linear model using Weighted Least Squares.\n",
       "GLS : Fit a linear model using Generalized Least Squares.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "No constant is added by the model unless you are using formulas.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> import statsmodels.api as sm\n",
       ">>> import numpy as np\n",
       ">>> duncan_prestige = sm.datasets.get_rdataset(\"Duncan\", \"carData\")\n",
       ">>> Y = duncan_prestige.data['income']\n",
       ">>> X = duncan_prestige.data['education']\n",
       ">>> X = sm.add_constant(X)\n",
       ">>> model = sm.OLS(Y,X)\n",
       ">>> results = model.fit()\n",
       ">>> results.params\n",
       "const        10.603498\n",
       "education     0.594859\n",
       "dtype: float64\n",
       "\n",
       ">>> results.tvalues\n",
       "const        2.039813\n",
       "education    6.892802\n",
       "dtype: float64\n",
       "\n",
       ">>> print(results.t_test([1, 0]))\n",
       "                             Test for Constraints\n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "c0            10.6035      5.198      2.040      0.048       0.120      21.087\n",
       "==============================================================================\n",
       "\n",
       ">>> print(results.f_test(np.identity(2)))\n",
       "<F test: F=array([[159.63031026]]), p=1.2607168903696672e-20,\n",
       " df_denom=43, df_num=2>\n",
       "\u001b[0;31mFile:\u001b[0m           ~/anaconda3/envs/eneza/lib/python3.12/site-packages/statsmodels/regression/linear_model.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?sm.OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.357\n",
      "Model:                            OLS   Adj. R-squared:                  0.355\n",
      "Method:                 Least Squares   F-statistic:                     194.5\n",
      "Date:                Tue, 09 Jul 2024   Prob (F-statistic):           1.78e-35\n",
      "Time:                        15:53:02   Log-Likelihood:                -1958.9\n",
      "No. Observations:                 353   AIC:                             3922.\n",
      "Df Residuals:                     351   BIC:                             3930.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "bmi          967.0123     69.344     13.945      0.000     830.630    1103.394\n",
      "intercept    152.1059      3.321     45.806      0.000     145.575     158.637\n",
      "==============================================================================\n",
      "Omnibus:                        7.640   Durbin-Watson:                   2.037\n",
      "Prob(Omnibus):                  0.022   Jarque-Bera (JB):                5.074\n",
      "Skew:                           0.140   Prob(JB):                       0.0791\n",
      "Kurtosis:                       2.484   Cond. No.                         20.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(endog=y_train, exog=X_train)\n",
    "model_fit = model.fit()\n",
    "print(model_fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For an explanantion of the results in the above, see [this awesome resource](http://connor-johnson.com/2014/02/18/linear-regression-with-python/). \n",
    "\n",
    "The R-squared is not that great! Let's see what we can do by using all the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = model_fit.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7))\n",
    "plt.scatter(y_pred_test, y_test, alpha=0.5, c='r')\n",
    "plt.title('predicted vs true for test data')\n",
    "plt.xlabel('predicted disease progression')\n",
    "plt.ylabel('True Disease progression')\n",
    "axes = plt.gca()\n",
    "#axes.set_xlim([0,11000])\n",
    "#axes.set_ylim([0,10000])\n",
    "plt.show();\n",
    "plt.savefig('scatter.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Linear Regression Model using scikit-learn\n",
    "\n",
    "We begin by using the `LinearRegression` class of SciKit-Learn and create an instance of this class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our dependent variable\n",
    "y_column = 'y'\n",
    "y = diabetes_pd[y_column]\n",
    "\n",
    "# Define our independent variables\n",
    "x_columns = ['ltg']\n",
    "X = diabetes_pd[x_columns]\n",
    "# Add an intercept term to the independent variables. This is needed in order to include the constant term from\n",
    "# linear regression equation.\n",
    "#X['intercept'] = 1\n",
    "# Split our data into training and test data sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "linear_reg = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fit the model according to the data in the training set. This is a **supervised** algorithm, so the linear model is fitting according to the data contained in `X` and minimising the loss according to `y`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! The model has been fit, and we should now evaluate it on the test set. This will check how well the model fits to new, unseen data. Remember, the model was fit on the training set alone. It has never seen the data contained in `X_test` or the target data contained in `y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38269153958537394"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3270.593898919842)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean((linear_reg.predict(X_test) - y_test) ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same here when using SciKit-Learn, the score is low, as confirmed also by the plat below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Results\n",
    "\n",
    "We can plot the data in the test set, that is plot the BMI against the disease progression, and then plot the line that the Linear Regression model calculated as being the optimal line through the test data (based on the model learned on the training data): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test, label=\"Test Data (X_test)\")\n",
    "plt.plot(X_test, linear_reg.predict(X_test), color=\"red\")\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.ylabel(\"Disease Progression\")\n",
    "plt.text(0.06, 30, \"Score \" + str(linear_reg.score(X_test, y_test)))\n",
    "plt.grid(color=\"gray\", linestyle='--')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Higher-Dimensional Data\n",
    "\n",
    "You will remember that in the analysis above, we extracted only one feature from the dataset (BMI) and tried to fit a model that would predict the disease progression based on the value of the BMI. We extracted one feature as this makes it easier to plot results. However, generally you would not want to throw away most of your features when working on a dataset. \n",
    "\n",
    "In this example we will use all the features of the dataset, and see how to plot the results even if we have higher dimensional data.\n",
    "\n",
    "First, let's prepare the data for input into SciKit-Learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>map</th>\n",
       "      <th>tc</th>\n",
       "      <th>ldl</th>\n",
       "      <th>hdl</th>\n",
       "      <th>tch</th>\n",
       "      <th>ltg</th>\n",
       "      <th>glu</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu      y  \n",
       "0   -0.002592  0.019908 -0.017646  151.0  \n",
       "1   -0.039493 -0.068330 -0.092204   75.0  \n",
       "2   -0.002592  0.002864 -0.025930  141.0  \n",
       "3    0.034309  0.022692 -0.009362  206.0  \n",
       "4   -0.002592 -0.031991 -0.046641  135.0  \n",
       "..        ...       ...       ...    ...  \n",
       "437 -0.002592  0.031193  0.007207  178.0  \n",
       "438  0.034309 -0.018118  0.044485  104.0  \n",
       "439 -0.011080 -0.046879  0.015491  132.0  \n",
       "440  0.026560  0.044528 -0.025930  220.0  \n",
       "441 -0.039493 -0.004220  0.003064   57.0  \n",
       "\n",
       "[442 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "diabetes_pd['y'] = diabetes.target\n",
    "X = diabetes_pd\n",
    "y = diabetes_pd.y\n",
    "# Remove the \"target variable from the training data.\n",
    "del X[\"y\"]\n",
    "#del X[\"sex\"]\n",
    "#X = np.array(X)\n",
    "#y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
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       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "          tch       ltg       glu  \n",
       "0   -0.002592  0.019907 -0.017646  \n",
       "1   -0.039493 -0.068332 -0.092204  \n",
       "2   -0.002592  0.002861 -0.025930  \n",
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       "4   -0.002592 -0.031988 -0.046641  \n",
       "..        ...       ...       ...  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018114  0.044485  \n",
       "439 -0.011080 -0.046883  0.015491  \n",
       "440  0.026560  0.044529 -0.025930  \n",
       "441 -0.039493 -0.004222  0.003064  \n",
       "\n",
       "[442 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Where the exercise begins**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a training set and a test set. We will see later why you would shuffle this data and not simply slice a percentage of the data to create train/test splits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Split our data into training and test data sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.136\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.110\n",
      "Method:                 Least Squares   F-statistic:                              5.378\n",
      "Date:                Tue, 09 Jul 2024   Prob (F-statistic):                    2.13e-07\n",
      "Time:                        16:55:04   Log-Likelihood:                         -2293.0\n",
      "No. Observations:                 353   AIC:                                      4606.\n",
      "Df Residuals:                     343   BIC:                                      4645.\n",
      "Df Model:                          10                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "age          125.2103    206.271      0.607      0.544    -280.505     530.925\n",
      "sex         -258.2010    204.877     -1.260      0.208    -661.175     144.773\n",
      "bmi          627.4872    229.885      2.730      0.007     175.325    1079.649\n",
      "map          346.3131    213.207      1.624      0.105     -73.045     765.671\n",
      "tc          -803.3488   1347.937     -0.596      0.552   -3454.612    1847.914\n",
      "ldl          312.9159   1087.873      0.288      0.774   -1826.826    2452.657\n",
      "hdl           45.1232    696.190      0.065      0.948   -1324.216    1414.463\n",
      "tch          211.9889    553.944      0.383      0.702    -877.566    1301.544\n",
      "ltg          700.8818    574.980      1.219      0.224    -430.049    1831.813\n",
      "glu          144.6871    219.349      0.660      0.510    -286.751     576.126\n",
      "==============================================================================\n",
      "Omnibus:                        0.382   Durbin-Watson:                   0.209\n",
      "Prob(Omnibus):                  0.826   Jarque-Bera (JB):                0.510\n",
      "Skew:                           0.033   Prob(JB):                        0.775\n",
      "Kurtosis:                       2.826   Cond. No.                         20.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(endog=y_train, exog=X_train)\n",
    "model_fit = model.fit()\n",
    "print(model_fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a linear regression model on this data, using all features (10 in total):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the model(Linear regression)\n",
    "#linear_reg_all is a variable storing the model considering all the features\n",
    "linear_reg_all = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
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       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
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       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
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       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "\n",
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       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model\n",
    "linear_reg_all.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45260276297191937"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking how good the model is - getting the loss function\n",
    "# the closer the loss function is to zero the better\n",
    "linear_reg_all.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have trained a model using the training data in `X_train`, we can test it on some unseen data, in this case stored in `X_test`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2900.193628493482)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean((linear_reg_all.predict(X_test) - y_test) ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Results\n",
    "\n",
    "When you wish to plot the results of a higher dimensional dataset's results, we can plot the measure outcome, stored in `y` against the predicted outcome, i.e. the output of `linear_reg_all.predict(y)` for each $y_i$ in y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, linear_reg_all.predict(X_test))\n",
    "plt.plot([linear_reg_all.predict(X_test).min(), linear_reg_all.predict(X_test).max()], [linear_reg_all.predict(X_test).min(), linear_reg_all.predict(X_test).max()], c='red')\n",
    "plt.xlabel(\"Measured\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.grid(color=\"gray\", linestyle='--')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above plot, and from the score we obtained, we can see that we have a better predictor when we use all  the features. \n",
    "\n",
    "But, we need to check if the data meet the requirement for using Linear Regression. \n",
    "\n",
    "## Check Assumptions\n",
    "\n",
    "1. Linear relationship between x and y - We can draw a line or curve within the relationship\n",
    "2. Normality - A normal curve, a histogram of the values should look like a bell\n",
    "3. Minimal multicollinearity (if multiple variables) - Each variable should introduce **new** information\n",
    "4. No autocorrelation - No relationship **within** a variable depending on the time/magnitude scale\n",
    "5. Homoscedasticity - The variation in the error of the model should not be increasing as the variation in the data increases\n",
    "\n",
    "- Additional rule of thumb: at least 20 observations per independent variable in the analysis\n",
    "\n",
    "### Linearity\n",
    "First, as the name implies, linear regression requires the underlying relationship between the independent (x) and dependent (y) variables to be linear. This means that a line drawn through the points can capture the relationship, with some degree of error. \n",
    "\n",
    "We must also be aware of outliers, which can throw off a linear relationship. \n",
    "\n",
    "The most straightforward check of linearity is to make scatter plots of the variables chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.136\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.110\n",
      "Method:                 Least Squares   F-statistic:                              5.378\n",
      "Date:                Tue, 09 Jul 2024   Prob (F-statistic):                    2.13e-07\n",
      "Time:                        16:57:54   Log-Likelihood:                         -2293.0\n",
      "No. Observations:                 353   AIC:                                      4606.\n",
      "Df Residuals:                     343   BIC:                                      4645.\n",
      "Df Model:                          10                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "age          125.2103    206.271      0.607      0.544    -280.505     530.925\n",
      "sex         -258.2010    204.877     -1.260      0.208    -661.175     144.773\n",
      "bmi          627.4872    229.885      2.730      0.007     175.325    1079.649\n",
      "map          346.3131    213.207      1.624      0.105     -73.045     765.671\n",
      "tc          -803.3488   1347.937     -0.596      0.552   -3454.612    1847.914\n",
      "ldl          312.9159   1087.873      0.288      0.774   -1826.826    2452.657\n",
      "hdl           45.1232    696.190      0.065      0.948   -1324.216    1414.463\n",
      "tch          211.9889    553.944      0.383      0.702    -877.566    1301.544\n",
      "ltg          700.8818    574.980      1.219      0.224    -430.049    1831.813\n",
      "glu          144.6871    219.349      0.660      0.510    -286.751     576.126\n",
      "==============================================================================\n",
      "Omnibus:                        0.382   Durbin-Watson:                   0.209\n",
      "Prob(Omnibus):                  0.826   Jarque-Bera (JB):                0.510\n",
      "Skew:                           0.033   Prob(JB):                        0.775\n",
      "Kurtosis:                       2.826   Cond. No.                         20.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(endog=y_train, exog=X_train).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes_pd[\"y\"] = diabetes.target\n",
    "ax = sns.regplot(x=x_columns[0], y=y_column, data=diabetes_pd, fit_reg=False);\n",
    "ax.set_title(f'Scatter plot of {x_columns[0].replace(\"_\", \" \").title()} vs requested {y_column.replace(\"_\", \" \").title()}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observer some level of linearity, and there are no outliers. \n",
    "\n",
    "-----\n",
    "Normality\n",
    "-----\n",
    "\n",
    "Normality means the distribution of values falls into a normal distribution. \n",
    "\n",
    "The results summary printed above gives us an indication of whether or not our data is normally distributed. If the **Prob(JB)** number is less than 0.05, this means that we can reject the Null hypothesis that the data distribution is normal. If this is the case, we need to look into our model more closely see where this is coming from and how/if we can rectify the issue.\n",
    "\n",
    "In our case, we can see that the that score is normal, as also shown in the skewness score, within -0.5 and 0.5. \n",
    "\n",
    "We can visualize the normality of the data by making histograms or Q-Q (quantile-quantile) plots.\n",
    "\n",
    "First we will look at a histogram for the dependent variable, disease progression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/caleb/opt/miniconda3/envs/malaria/lib/python3.9/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(diabetes_pd[y_column], kde=False, norm_hist=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4391\n"
     ]
    }
   ],
   "source": [
    "print(f\"{stats.skew(diabetes_pd[y_column], axis=None):.4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity\n",
    "\n",
    "Multicollinearity is only applicable to multivariate regression, and means that two or more explanatory features are highly correlated. Think of these features as contributing the same information to the model, we aren't learning anything new having both of them versus only having one of them!\n",
    "\n",
    "This makes it difficult for the model to try to differentiate between each feature's influence on the explanatory feature.\n",
    "\n",
    "Again, the summary table from the linear regression can help us again. There is a statistic in the bottom right hand corner known as the **condition number**. This indicates the severity and impact of multicolinearity in the regression and as a rule fo thumb, values over 30 are an issue. \n",
    "\n",
    "One way to check this is by examining the correlation matrix and making sure none of the variables correlate to 1. Numpy's Corrcoef function returns a matrix of Pearson correlation coefficients. \n",
    "\n",
    "Read more about Numpy's Corrcoef [here](https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.corrcoef.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>map</th>\n",
       "      <th>tc</th>\n",
       "      <th>ldl</th>\n",
       "      <th>hdl</th>\n",
       "      <th>tch</th>\n",
       "      <th>ltg</th>\n",
       "      <th>glu</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173737</td>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.335427</td>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.219243</td>\n",
       "      <td>-0.075181</td>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.270777</td>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.187889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.173737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>0.241013</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.149918</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.043062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.395415</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.446159</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.586450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>map</th>\n",
       "      <td>0.335427</td>\n",
       "      <td>0.241013</td>\n",
       "      <td>0.395415</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242470</td>\n",
       "      <td>0.185558</td>\n",
       "      <td>-0.178761</td>\n",
       "      <td>0.257653</td>\n",
       "      <td>0.393478</td>\n",
       "      <td>0.390429</td>\n",
       "      <td>0.441484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tc</th>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.242470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.515501</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.212022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ldl</th>\n",
       "      <td>0.219243</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>0.185558</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>0.318353</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>0.174054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdl</th>\n",
       "      <td>-0.075181</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>-0.178761</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>-0.394789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tch</th>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.257653</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617857</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.430453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltg</th>\n",
       "      <td>0.270777</td>\n",
       "      <td>0.149918</td>\n",
       "      <td>0.446159</td>\n",
       "      <td>0.393478</td>\n",
       "      <td>0.515501</td>\n",
       "      <td>0.318353</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>0.617857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464670</td>\n",
       "      <td>0.565883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glu</th>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.390429</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.464670</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.382483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.187889</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.586450</td>\n",
       "      <td>0.441484</td>\n",
       "      <td>0.212022</td>\n",
       "      <td>0.174054</td>\n",
       "      <td>-0.394789</td>\n",
       "      <td>0.430453</td>\n",
       "      <td>0.565883</td>\n",
       "      <td>0.382483</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age       sex       bmi       map        tc       ldl       hdl  \\\n",
       "age  1.000000  0.173737  0.185085  0.335427  0.260061  0.219243 -0.075181   \n",
       "sex  0.173737  1.000000  0.088161  0.241013  0.035277  0.142637 -0.379090   \n",
       "bmi  0.185085  0.088161  1.000000  0.395415  0.249777  0.261170 -0.366811   \n",
       "map  0.335427  0.241013  0.395415  1.000000  0.242470  0.185558 -0.178761   \n",
       "tc   0.260061  0.035277  0.249777  0.242470  1.000000  0.896663  0.051519   \n",
       "ldl  0.219243  0.142637  0.261170  0.185558  0.896663  1.000000 -0.196455   \n",
       "hdl -0.075181 -0.379090 -0.366811 -0.178761  0.051519 -0.196455  1.000000   \n",
       "tch  0.203841  0.332115  0.413807  0.257653  0.542207  0.659817 -0.738493   \n",
       "ltg  0.270777  0.149918  0.446159  0.393478  0.515501  0.318353 -0.398577   \n",
       "glu  0.301731  0.208133  0.388680  0.390429  0.325717  0.290600 -0.273697   \n",
       "y    0.187889  0.043062  0.586450  0.441484  0.212022  0.174054 -0.394789   \n",
       "\n",
       "          tch       ltg       glu         y  \n",
       "age  0.203841  0.270777  0.301731  0.187889  \n",
       "sex  0.332115  0.149918  0.208133  0.043062  \n",
       "bmi  0.413807  0.446159  0.388680  0.586450  \n",
       "map  0.257653  0.393478  0.390429  0.441484  \n",
       "tc   0.542207  0.515501  0.325717  0.212022  \n",
       "ldl  0.659817  0.318353  0.290600  0.174054  \n",
       "hdl -0.738493 -0.398577 -0.273697 -0.394789  \n",
       "tch  1.000000  0.617857  0.417212  0.430453  \n",
       "ltg  0.617857  1.000000  0.464670  0.565883  \n",
       "glu  0.417212  0.464670  1.000000  0.382483  \n",
       "y    0.430453  0.565883  0.382483  1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_tbl = diabetes_pd[diabetes_pd.columns]\n",
    "df_corr_tbl.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1716a5fa0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_corr_tbl.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_corr_tbl.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No autocorrelation\n",
    "\n",
    "A variable has autocorrelation when values in the series correlate with others in the same series, separated by a given interval.\n",
    "\n",
    "In terms of the residuals, this means checking that residuals are independent.\n",
    "\n",
    "A check for this is the **Durbin-Watson test**. The durbin-watson statistic ranges from 0 to 4 with values around 2 suggesting no autocorrelation. Values < 2 suggest positive correlation and values > 2 suggest negative correlation.\n",
    "\n",
    "In the summary table above, the Durbin-Watson test has a value of approximately 0.220, which means we have a positive correlation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Homoscedasticity\n",
    "\n",
    "Homoscedasticity is a statistical term that means that the spread of the residuals are equal across the regression line. This assumption means that the variance around the regression line is the same for all values of the predictor variable (X). \n",
    "\n",
    "Again, the summary table from the linear regression allows us to evaluate whether the model has homoscedasticity. If **Prob(Omnibus)** number is less than 0.05 we can reject the Null Hypothesis that the residuals are distributed uniformly and normally around zero.             0.373\n",
    "\n",
    "In the case above, the Prob(Omnibus) is 0.373 which indicates that the model has homoscedasticity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In summary:\n",
    "\n",
    "1. Linear relationship between x and y\n",
    "      - We can observer some level of linearity, and there are no outliers. \n",
    "1. Minimal multicollinearity \n",
    "      - There is multicollinearity between funded_amount and some other predictors, so we should keep that in mind when we     perform a multivariate regression.\n",
    "1. No autocorrelation\n",
    "      - Durbin-Watson test indicates some autocorrelation\n",
    "1. Homoscedasticity \n",
    "      -  Model is homoscedastic\n",
    "  \n",
    "From all these, and from the score we got, we can state that Linear Regression may not be the best fit for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Credits\n",
    "\n",
    "1. [A Tutorial on Machine Learningand Data Science Tools with Python](https://github.com/mdbloice/MLDS/blob/master/LinearRegression.ipynb)\n",
    "2. [Machine learning for Good](https://github.com/DeltaAnalytics/machine_learning_for_good)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Regularization\n",
    "\n",
    "In the context of regression, regularization refers to techniques to constrain/shrink the coefficient estimates towards zero.  \n",
    "Shrinking the coefficients can 1) improve the fit of the model and 2) reduce the variance of the coefficients.\n",
    "\n",
    "Two common types of regularization are ridge and lasso.  \n",
    "\n",
    "Least squares linear regression minimizes the residual sum of squares (RSS).  In other words, it minimizes\n",
    "\n",
    "$ RSS = \\displaystyle \\sum^{n}_{i=1} (y_i - \\beta_0 - \\sum^{p}_{j=1} \\beta_j x_{ij})^2 $\n",
    "\n",
    "In ridge and lasso, we add a term to the value we are trying to minimize.  \n",
    "\n",
    "In ridge, we minimize \n",
    "\n",
    "$ RSS + \\lambda \\displaystyle \\sum^{p}_{j=1} \\beta_j^2 $\n",
    "\n",
    "\n",
    "**Intuition:** We want to minmize our coefficients so we are not multiplying by huge values, hence the **square** term. **Ridge dislikes ridiculously large values** but it is okay with medium values.\n",
    "\n",
    "In lasso, we minimize\n",
    "\n",
    "$ RSS + \\lambda \\displaystyle \\sum^{p}_{j=1} |\\beta_j| $\n",
    "\n",
    "**Intuition:** We want to remove and minimize the coefficients for features/variables that are not informative to our predictions. This form of regularization performs \"automatic\" feature selection. **LASSO rings in important features** and drives other coefficients to zero.\n",
    "\n",
    "The $\\lambda$ (pronounced \"lambda\") in the above values is a hyper-parameter which determines how 'strong' the regularization effect is.  Note: sometimes $\\alpha$ (pronounced \"alpha\") is used instead of $\\lambda$.\n",
    "\n",
    "A useful way to use ridge or lasso regression is to run the regression over a range of alphas and see which features maintain a large beta coefficient for the longest. It is these features which have the most predictive power!\n",
    "\n",
    "More in depth information can be found here: [Regularization Regression](https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some useful Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_train_data(train_df, pred_var):\n",
    "    \"\"\"\n",
    "    Given a dataframe and the predictive variable, this\n",
    "    function returns train and test arrays generated using  the  \n",
    "    scikit--learn split function\n",
    "    \"\"\"\n",
    "    rf_trainRes = train_df[pred_var]\n",
    "    train_df = train_df.drop(pred_var, axis=1)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(train_df, rf_trainRes, test_size=0.2, random_state=42)\n",
    "    \n",
    "    train_df[pred_var] = rf_trainRes\n",
    "\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "def train_linear(X, y):\n",
    "    linear_reg_all = linear_model.LinearRegression()\n",
    "    linear_reg_all.fit(X_train, y_train)\n",
    "    print(linear_reg_all.score(X_test, y_test))\n",
    "    return linear_reg_all\n",
    "\n",
    "def print_scores(regressor, X_train, y_train, X_test, y_test, oob_score=True):\n",
    "    print(\"Train score: \" + str(regressor.score(X_train, y_train)))\n",
    "    print(\"Test score: \" + str(regressor.score(X_test, y_test)))\n",
    "    if oob_score:\n",
    "        print(\"OOB score: \" + str(regressor.oob_score_))\n",
    "        \n",
    "def train_linear_r(X_train, y_train, X_test, y_test):\n",
    "    # Create linear regression object\n",
    "    regr = linear_model.LinearRegression()\n",
    "    \n",
    "    # Train the model using the training sets\n",
    "    model1 = regr.fit(X_train, y_train)\n",
    "    \n",
    "    print_scores(model1, X_train, y_train, X_test, y_test, oob_score=False)\n",
    "    plot_test(model1, X_train, y_train, 'train')\n",
    "    plot_test(model1, X_test, y_test, 'test')\n",
    "    \n",
    "def plot_test(regressor, X_test, y_test, data):\n",
    "    rf_y_predicted_test = regressor.predict(X_test)\n",
    "    print(\"Mean squared error: %d \" % round(mean_squared_error(y_test, rf_y_predicted_test)))\n",
    "    fig = plt.figure(figsize=(7, 7))\n",
    "    plt.scatter(y_test, rf_y_predicted_test, alpha=0.5)\n",
    "    plt.xlabel('True value')\n",
    "    plt.ylabel('Predicted value')\n",
    "    plt.title(\"Relationship between True Y and predicted Y* (%s dataset)\" % data)\n",
    "    plt.show()\n",
    "    \n",
    "def prepare_train_data(train_df, pred_var):\n",
    "    \"\"\"\n",
    "    Given a dataframe and the predictive variable, this\n",
    "    function returns train and test arrays generated using  the  \n",
    "    scikit--learn split function\n",
    "    \"\"\"\n",
    "    rf_trainRes = train_df[pred_var]\n",
    "    train_df = train_df.drop(pred_var, axis=1)\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(train_df, rf_trainRes, test_size=0.2, random_state=42)\n",
    "    \n",
    "    train_df[pred_var] = rf_trainRes\n",
    "\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install lofo-importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = prepare_train_data(diabetes_pd, 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.arange(0.001, 0.500, 0.002)\n",
    "lasso_coefs = []\n",
    "X_train_lasso= X_train[X_train.columns.tolist()] # Select columns / features for model\n",
    "\n",
    "for a in alphas:\n",
    "    lassoreg = Lasso(alpha=a, copy_X=True, normalize=True)\n",
    "    lassoreg.fit(X_train_lasso, y_train)\n",
    "    lasso_coefs.append(lassoreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_coefs = np.asarray(lasso_coefs).T\n",
    "\n",
    "plt.figure(figsize=(14,10))\n",
    "for coefs, feature in zip(lasso_coefs, X_train_lasso.columns):\n",
    "    plt.plot(alphas, coefs, label = feature);\n",
    "plt.ylabel('Feature Coefficient');\n",
    "plt.xlabel('Alpha Value');\n",
    "plt.legend(loc='best');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features that tend towards Zero are not that useful. Let us remove them and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cols = diabetes_pd[['sex', 'bmi', 'tc', 'ldl', 'hdl', 'tch', 'ltg', 'glu']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(use_cols, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.4925248212238953\n",
      "Test score: 0.46347602518166886\n",
      "Mean squared error: 3084 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 2843 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_linear_r(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assignment: Can you figure out a way to choose the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "## A Crude approach of Understanding Feature Importance"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}