00 - Data Loading.ipynb

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   "cell_type": "markdown",
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   "source": [
    "Get some data to play with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "print(digits.images[0])"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "plt.matshow(digits.images[0], cmap=plt.cm.Greys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "digits.data.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "digits.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data is always a numpy array (or sparse matrix) of shape (n_samples, n_features)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data to get going"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data,\n",
    "                                                    digits.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "Load the iris dataset from the ``sklearn.datasets`` module using the ``load_iris`` function.\n",
    "The function returns a dictionary-like object that has the same attributes as ``digits``.\n",
    "\n",
    "What is the number of classes, features and data points in this dataset?\n",
    "Use a scatterplot to visualize the dataset.\n",
    "\n",
    "You can look at ``DESCR`` attribute to learn more about the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load solutions/load_iris.py"
   ]
  }
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