01-introduction.ipynb

{
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   "metadata": {
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   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "# Introduction\n",
    "### Why machine learning?\n",
    "### Problems that machine learning can solve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Knowing your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What this book will cover\n",
    "### What this book will not cover"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn\n",
    "### Installing Scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essential Libraries and Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy array:\n",
      "[[ 1.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.]\n",
      " [ 0.  0.  0.  1.]]\n",
      "\n",
      "Scipy sparse CSR matrix:\n",
      "  (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "# create a 2d numpy array with a diagonal of ones, and zeros everywhere else\n",
    "eye = np.eye(4)\n",
    "print(\"Numpy array:\\n%s\" % eye)\n",
    "\n",
    "# convert the numpy array to a scipy sparse matrix in CSR format\n",
    "# only the non-zero entries are stored\n",
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(\"\\nScipy sparse CSR matrix:\\n%s\" % sparse_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9c066c8a20>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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/7XgZP7bX+InFeNT/2mvAxInREX5AxN9mvIFJWRkwf74bAxMbc/wBEX8juOD7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\ngiAIEeT/AXJXhWFhAgVPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c1f841240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence of integers\n",
    "x = np.arange(20)\n",
    "# create a second array using sinus\n",
    "y = np.sin(x)\n",
    "# The plot function makes a line chart of one array against another\n",
    "plt.plot(x, y, marker=\"x\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Location</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>New York</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Anna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Peter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>London</td>\n",
       "      <td>Linda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Location   Name\n",
       "0   24  New York   John\n",
       "1   13     Paris   Anna\n",
       "2   53    Berlin  Peter\n",
       "3   33    London  Linda"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# create a simple dataset of people\n",
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "        'Age' : [24, 13, 53, 33]\n",
    "       }\n",
    "\n",
    "data_pandas = pd.DataFrame(data)\n",
    "data_pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python2 versus Python3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Versions Used in this Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas version: 0.17.1\n",
      "matplotlib version: 1.5.1\n",
      "numpy version: 1.10.4\n",
      "IPython version: 4.1.2\n",
      "scikit-learn version: 0.18.dev0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(\"pandas version: %s\" % pd.__version__)\n",
    "\n",
    "import matplotlib\n",
    "print(\"matplotlib version: %s\" % matplotlib.__version__)\n",
    "\n",
    "import numpy as np\n",
    "print(\"numpy version: %s\" % np.__version__)\n",
    "\n",
    "import IPython\n",
    "print(\"IPython version: %s\" % IPython.__version__)\n",
    "\n",
    "import sklearn\n",
    "print(\"scikit-learn version: %s\" % sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A First Application: Classifying iris species\n",
    "### Meet the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['DESCR', 'data', 'target_names', 'feature_names', 'target'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive att\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "print(iris['DESCR'][:193] + \"\\n...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target_names']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['data'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Success: Training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris['data'], iris['target'],\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112, 4)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First things first: Look at your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4qTTp6en069kTz+bNDExPJwrIAJYZw/IaNViycmXuLalJSUn06tKF2vHx9M/MxA+kA4vd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M4j76L8swvPcWf5XVo1a8XDRw+pXrk6/fv1F4v3/w+SJCHLcn4lpF4bYnzSpa93dnOma0gAzrVK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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c066d44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 3, figsize=(15, 15))\n",
    "plt.suptitle(\"iris_pairplot\")\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(3):\n",
    "        ax[i, j].scatter(X_train[:, j], X_train[:, i + 1], c=y_train, s=60)\n",
    "        ax[i, j].set_xticks(())\n",
    "        ax[i, j].set_yticks(())\n",
    "        if i == 2:\n",
    "            ax[i, j].set_xlabel(iris['feature_names'][j])\n",
    "        if j == 0:\n",
    "            ax[i, j].set_ylabel(iris['feature_names'][i + 1])\n",
    "        if j > i:\n",
    "            ax[i, j].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building your first model: k nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Making predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = knn.predict(X_new)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['target_names'][prediction]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "np.mean(y_pred == y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris['data'], iris['target'],\n",
    "                                                    random_state=0)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "knn.score(X_test, y_test)"
   ]
  }
 ],
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