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  {
   "source": [
    "## Linear Regression for North American Pumpkins - Lesson 1"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Import needed libraries"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model, model_selection\n"
   ]
  },
  {
   "source": [
    "Load the diabetes dataset, divided into `X` data and `y` features"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(442, 10)\n[ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076\n -0.04340085 -0.00259226  0.01990842 -0.01764613]\n"
     ]
    }
   ],
   "source": [
    "X, y = datasets.load_diabetes(return_X_y=True)\n",
    "print(X.shape)\n",
    "print(X[0])"
   ]
  },
  {
   "source": [
    "Select just one feature to target for this exercise"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[:, np.newaxis, 2]\n"
   ]
  },
  {
   "source": [
    "Split the training and test data for both `X` and `y`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
   ]
  },
  {
   "source": [
    "Select the model and fit it with the training data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "source": [
    "Use test data to predict a line"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "source": [
    "Display the results in a plot"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}