C2_W1_Lab_1_create_a_linear_model__M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Linear Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear model using scikit-learn\n",
    "\n",
    "Welcome to the first lab of this course!\n",
    "\n",
    "You'll practice using a scikit-learn model for linear regression. You will do something similar in this week's assignment (but with a logistic regression model).\n",
    "\n",
    "[sklearn.linear_model.LinearRegression()](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import `LinearRegression`, which is a Python 'class'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the module 'LinearRegression' from sklearn\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, use the class to create an object of type LinearRegression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an object of type LinearRegression\n",
    "model = LinearRegression()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some data by importing a module 'load_data', which is implemented for you.  The features in `X' are: \n",
    "\n",
    "- Age: (years)\n",
    "- Systolic_BP: Systolic blood pressure (mmHg)\n",
    "- Diastolic_BP: Diastolic blood pressure (mmHg)\n",
    "- Cholesterol: (mg/DL)\n",
    "\n",
    "The labels in `y` indicate whether the patient has a disease (diabetic retinopathy).\n",
    "- y = 1 : patient has retinopathy.\n",
    "- y = 0 : patient does not have retinopathy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the load_data function from the utils module\n",
    "from utils import load_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate features and labels using the imported function\n",
    "X, y = load_data(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the data by viewing the features and the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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>Systolic_BP</th>\n",
       "      <th>Diastolic_BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>77.196340</td>\n",
       "      <td>78.784208</td>\n",
       "      <td>87.026569</td>\n",
       "      <td>82.760275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.529850</td>\n",
       "      <td>105.171676</td>\n",
       "      <td>83.396113</td>\n",
       "      <td>80.923284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.003986</td>\n",
       "      <td>117.582259</td>\n",
       "      <td>91.161966</td>\n",
       "      <td>92.915422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82.638210</td>\n",
       "      <td>94.131208</td>\n",
       "      <td>69.470423</td>\n",
       "      <td>95.766098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78.346286</td>\n",
       "      <td>105.385186</td>\n",
       "      <td>87.250583</td>\n",
       "      <td>120.868124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age  Systolic_BP  Diastolic_BP  Cholesterol\n",
       "0  77.196340    78.784208     87.026569    82.760275\n",
       "1  63.529850   105.171676     83.396113    80.923284\n",
       "2  69.003986   117.582259     91.161966    92.915422\n",
       "3  82.638210    94.131208     69.470423    95.766098\n",
       "4  78.346286   105.385186     87.250583   120.868124"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the features\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot a histogram of the Age feature\n",
    "X['Age'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot a histogram of the systolic blood pressure feature\n",
    "X['Systolic_BP'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot a histogram of the diastolic blood pressure feature\n",
    "X['Diastolic_BP'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot a histogram of the cholesterol feature\n",
    "X['Cholesterol'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also take a look at the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    0.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    1.0\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View a few values of the labels\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KcDadD7l6ORvoR5CcaQHe5vb8vcDWZvk9wJeyeXegqK49R8SlwCfphHf1uVHo0nNmLmbmeZk5lZlTdOb9r8nM/eMpt29tfq7/js7ZNxFxHp0pladGWeSAten5X4ErASLiZ+kE+H+OtMrR2wv8anM1yhXAYmY+2/Ozjftd25O8S/stOu9gf7TZ9kd0/gJD50X+HPAk8FXg9eOueQQ9/yNwGHi4+bN33DUPu+dl+85T+CqUlq9x0Jk2egxYAG4Yd80j6Hkz8GU6V6g8DLx73DUPoOfPAs8CP6TzW9U24P3A+094nT/RfE8W+v259lZ6SSrqTJtCkSS1ZIBLUlEGuCQVZYBLUlEGuCQVZYBLUlEGuCQV9X/WD3PFKKiuVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of the labels\n",
    "y.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the LinearRegression using the features in `X` and the labels in `y`.  To \"fit\" the model is another way of saying that we are training the model on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the linear regression model\n",
    "model.fit(X, y)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- View the coefficients of the trained model.\n",
    "- The coefficients are the 'weights' or $\\beta$s associated with each feature\n",
    "- You'll use the coefficients for making predictions.\n",
    "$$\\hat{y} = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... \\beta_N x_N$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00975155, 0.00835816, 0.00836864, 0.00971064])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the coefficients of the model\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the assignment, you will do something similar, but using a logistic regression, so that the output of the prediction will be bounded between 0 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the end of this practice section.\n",
    "\n",
    "Please continue on with the lecture videos!\n",
    "\n",
    "---"
   ]
  }
 ],
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