ml-schoo-and-maybe-andrew-ng/work/.ipynb_checkpoints/C1_W2_Lab08_Sklearn-checkpoint.ipynb

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   "source": [
    "# Ungraded Lab: Linear Regression using Scikit-Learn"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've implemented linear regression from scratch, let's see you can train a linear regression model using scikit-learn.\n",
    "\n",
    "## Dataset \n",
    "Let's start with the same dataset as the first labs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# X is the input variable (size in square feet)\n",
    "# y in the output variable (price in 1000s of dollars)\n",
    "X = np.array([1000, 2000])\n",
    "y = np.array([200, 400])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the model\n",
    "\n",
    "The code below imports the [linear regression model](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) from scikit-learn. You can fit this model on the training data by calling `fit` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linear_model = LinearRegression()\n",
    "# We must reshape X using .reshape(-1, 1) because our data has a single feature\n",
    "# If X has multiple features, you don't need to reshape\n",
    "linear_model.fit(X.reshape(-1, 1), y) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions\n",
    "\n",
    "You can see the predictions made by this model by calling the `predict` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = linear_model.predict(X.reshape(-1,1))\n",
    "\n",
    "print(\"Prediction on training set:\", y_pred)\n",
    "\n",
    "X_test = np.array([[1200]])\n",
    "print(f\"Prediction for 1200 sqft house: {linear_model.predict(X_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate score\n",
    "\n",
    "You can calculate how well this model is doing by calling the `score` function. Specifically, it, returns the coefficient of determination $R^2$ of the prediction. 1 is the best score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Accuracy on training set:\", linear_model.score(X.reshape(-1,1), y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Parameters \n",
    "Our $\\mathbf{w}$ parameters from our earlier labs are referred to as 'intercept' and 'coefficients' in sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"w = {linear_model.intercept_},{linear_model.coef_}\")"
   ]
  }
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