diff --git a/machine learning in diabetus b/machine learning in diabetus
new file mode 100644
index 00000000..78f30f23
--- /dev/null
+++ b/machine learning in diabetus	
@@ -0,0 +1,1188 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
+      "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "diabetes = pd.read_csv('diabetes.csv')\n",
+    "print(diabetes.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pregnancies</th>\n",
+       "      <th>Glucose</th>\n",
+       "      <th>BloodPressure</th>\n",
+       "      <th>SkinThickness</th>\n",
+       "      <th>Insulin</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>DiabetesPedigreeFunction</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Outcome</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>148</td>\n",
+       "      <td>72</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.6</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>85</td>\n",
+       "      <td>66</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>31</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>183</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>0.672</td>\n",
+       "      <td>32</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>89</td>\n",
+       "      <td>66</td>\n",
+       "      <td>23</td>\n",
+       "      <td>94</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>137</td>\n",
+       "      <td>40</td>\n",
+       "      <td>35</td>\n",
+       "      <td>168</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2.288</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "0            6      148             72             35        0  33.6   \n",
+       "1            1       85             66             29        0  26.6   \n",
+       "2            8      183             64              0        0  23.3   \n",
+       "3            1       89             66             23       94  28.1   \n",
+       "4            0      137             40             35      168  43.1   \n",
+       "\n",
+       "   DiabetesPedigreeFunction  Age  Outcome  \n",
+       "0                     0.627   50        1  \n",
+       "1                     0.351   31        0  \n",
+       "2                     0.672   32        1  \n",
+       "3                     0.167   21        0  \n",
+       "4                     2.288   33        1  "
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The diabetes dataset consists of 768 data points, with 9 features each"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dimension of diabetes data: (768, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"dimension of diabetes data: {}\".format(diabetes.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outcome 0 means No diabetes, outcome 1 means diabetes\n",
+    "Of these 768 data points, 500 are labeled as 0 and 268 as 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outcome\n",
+      "0    500\n",
+      "1    268\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(diabetes.groupby('Outcome').size())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x225d1b17f28>"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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emOTaJJe0bWcm2Zbkm0k+3GrPSLKl1bckOabVL0pyQZKrk9yU5Ffacw9uTHLR2M97SZKv\nJPlGkn9I8uRF+68iYUBIzwW2jheq6gfAzTzIM9ur6lzgv6vquKp6TZLnAn8KnFxVLwDe0Ia+H7i4\nqp4PXAKcP7abwxiF05uATwPntV6el+S4JMuBPwNeXFXHA/PAOY/GLyxNqvs/gDRDQn/22ger95wM\nXFZVtwNU1cLzC34JeHlb/jDwN2Of+XRVVZLrgNuq6jqAJDcAqxlNmngs8KXRWTAOYTTlhLRoDAjN\nuhuA3xovJHkqoxlu7+L+R9lPeJB9TBom42N+0t5/Ora8sL4MuBe4qqpePcF+pUF4ikmzbgvwpCRn\nwv8/gvVdjB51eRNwXJKDkqxi9PS9Bf+b5HFj+3hlkiPaPg5v9S8zmh0X4DXAFx9CX9cAJyV5Vtvn\nk5I8+6H+ctIjYUBoptVotsrfBM5Ish34N+BuRncpfQn4d+A64J3AN8Y+ugnYluSSNvvt24DPJ/km\n8O425g+As5JsA17HfdcmJulrD/DbwEfa568BfuHh/p7Sw+FsrpKkLo8gJEldBoQkqcuAkCR1GRCS\npC4DQpLUZUBIkroMCElSlwEhSer6Pz8/pjgBxhB9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d1bc96a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.countplot(diabetes['Outcome'],label=\"Count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 768 entries, 0 to 767\n",
+      "Data columns (total 9 columns):\n",
+      "Pregnancies                 768 non-null int64\n",
+      "Glucose                     768 non-null int64\n",
+      "BloodPressure               768 non-null int64\n",
+      "SkinThickness               768 non-null int64\n",
+      "Insulin                     768 non-null int64\n",
+      "BMI                         768 non-null float64\n",
+      "DiabetesPedigreeFunction    768 non-null float64\n",
+      "Age                         768 non-null int64\n",
+      "Outcome                     768 non-null int64\n",
+      "dtypes: float64(2), int64(7)\n",
+      "memory usage: 54.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "diabetes.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### k-Nearest Neighbors\n",
+    "The k-NN algorithm is arguably the simplest machine learning algorithm. Building the model consists only of storing the training dataset. To make a prediction for a new data point, the algorithm finds the closest data points in the training dataset—its “nearest neighbors.”"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let’s investigate whether we can confirm the connection between model complexity and accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/+YVH3Q7FmGYXFhbGvn37LGG0YarKvn37CAsLa/IxfHrNQkQmA48DgcDzqvpIjfWnAXOA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\nXQbQXtYscC4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d1b1c7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "training_accuracy = []\n",
+    "test_accuracy = []\n",
+    "# try n_neighbors from 1 to 10\n",
+    "neighbors_settings = range(1, 11)\n",
+    "\n",
+    "for n_neighbors in neighbors_settings:\n",
+    "    # build the model\n",
+    "    knn = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
+    "    knn.fit(X_train, y_train)\n",
+    "    # record training set accuracy\n",
+    "    training_accuracy.append(knn.score(X_train, y_train))\n",
+    "    # record test set accuracy\n",
+    "    test_accuracy.append(knn.score(X_test, y_test))\n",
+    "\n",
+    "plt.plot(neighbors_settings, training_accuracy, label=\"training accuracy\")\n",
+    "plt.plot(neighbors_settings, test_accuracy, label=\"test accuracy\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.xlabel(\"n_neighbors\")\n",
+    "plt.legend()\n",
+    "plt.savefig('knn_compare_model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot shows the training and test set accuracy on the y-axis against the setting of n_neighbors on the x-axis. Considering if we choose one single nearest neighbor, the prediction on the training set is perfect. But when more neighbors are considered, the training accuracy drops, indicating that using the single nearest neighbor leads to a model that is too complex.\n",
+    "The best performance is somewhere around 9 neighbors.\n",
+    "\n",
+    "The above plot suggests that we should choose n_neighbors=9. Here we are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of K-NN classifier on training set: 0.79\n",
+      "Accuracy of K-NN classifier on test set: 0.78\n"
+     ]
+    }
+   ],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=9)\n",
+    "knn.fit(X_train, y_train)\n",
+    "\n",
+    "print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train)))\n",
+    "print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic Regression\n",
+    "Logistic regression is one of the most common classification algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy: 0.781\n",
+      "Test set accuracy: 0.771\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "logreg = LogisticRegression().fit(X_train, y_train)\n",
+    "print(\"Training set accuracy: {:.3f}\".format(logreg.score(X_train, y_train)))\n",
+    "print(\"Test set accuracy: {:.3f}\".format(logreg.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default value of C=1 provides with 78% accuracy on training and 77% accuracy on test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy: 0.700\n",
+      "Test set accuracy: 0.703\n"
+     ]
+    }
+   ],
+   "source": [
+    "logreg001 = LogisticRegression(C=0.01).fit(X_train, y_train)\n",
+    "print(\"Training set accuracy: {:.3f}\".format(logreg001.score(X_train, y_train)))\n",
+    "print(\"Test set accuracy: {:.3f}\".format(logreg001.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using C=0.01 results in lower accuracy on both the training and the test sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy: 0.785\n",
+      "Test set accuracy: 0.766\n"
+     ]
+    }
+   ],
+   "source": [
+    "logreg100 = LogisticRegression(C=100).fit(X_train, y_train)\n",
+    "print(\"Training set accuracy: {:.3f}\".format(logreg100.score(X_train, y_train)))\n",
+    "print(\"Test set accuracy: {:.3f}\".format(logreg100.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using C=100 results in a little bit higher accuracy on training set little bit lower accuracy on test set, confirming that less regularization and a more complex model may not generalize better than default setting. \n",
+    "\n",
+    "Therefore, we should choose default value C=1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let’s look at the coefficients learned by the models with the three different settings of the regularization parameter C.\n",
+    "\n",
+    "Stronger regularization (C=0.001) pushes coefficients more and more toward zero. Inspecting the plot more closely, we can also see that feature “DiabetesPedigreeFunction”, for C=100, C=1 and C=0.001, the coefficient is positive. This indicates that high “DiabetesPedigreeFunction” feature is related to a sample being “diabetes”, regardless which model we look at."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4BJiTmbdW8f6SJDXNqHa9cET8BnhpFw99Cvgk8PoWX+f9wPsBNt988wHLJ0lS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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d1a95da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diabetes_features = [x for i,x in enumerate(diabetes.columns) if i!=8]\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "plt.plot(logreg.coef_.T, 'o', label=\"C=1\")\n",
+    "plt.plot(logreg100.coef_.T, '^', label=\"C=100\")\n",
+    "plt.plot(logreg001.coef_.T, 'v', label=\"C=0.001\")\n",
+    "plt.xticks(range(diabetes.shape[1]), diabetes_features, rotation=90)\n",
+    "plt.hlines(0, 0, diabetes.shape[1])\n",
+    "plt.ylim(-5, 5)\n",
+    "plt.xlabel(\"Feature\")\n",
+    "plt.ylabel(\"Coefficient magnitude\")\n",
+    "plt.legend()\n",
+    "plt.savefig('log_coef')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 1.000\n",
+      "Accuracy on test set: 0.714\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "tree = DecisionTreeClassifier(random_state=0)\n",
+    "tree.fit(X_train, y_train)\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy on the training set is 100%, while the test set accuracy is much worse. This is an indicative that the tree is  overfitting and not generalizing well to new data. Therefore, we need to apply pre-pruning to the tree."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We set max_depth=3, limiting the depth of the tree decreases overfitting. This leads to a lower accuracy on the training set, but an improvement on the test set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.773\n",
+      "Accuracy on test set: 0.740\n"
+     ]
+    }
+   ],
+   "source": [
+    "tree = DecisionTreeClassifier(max_depth=3, random_state=0)\n",
+    "tree.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(tree.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(tree.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature importance in Decision trees\n",
+    "Feature importance rates how important each feature is for the decision a tree makes. It is a number between 0 and 1 for each feature, where 0 means “not used at all” and 1 means “perfectly predicts the target.” The feature importances always sum to 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature importances:\n",
+      "[ 0.04554275  0.6830362   0.          0.          0.          0.27142106\n",
+      "  0.          0.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Feature importances:\\n{}\".format(tree.feature_importances_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then visualize the feature importances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3JIHeqtlPmoCLqupVE/o9a5pzP9C3vQZYmORg4FDggKq6N8mlwOatz0NVVf39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\noEmSJHWMAU2SJKlj/j+8fBhkWsduFgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d39d4a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_feature_importances_diabetes(model):\n",
+    "    plt.figure(figsize=(8,6))\n",
+    "    n_features = 8\n",
+    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
+    "    plt.yticks(np.arange(n_features), diabetes_features)\n",
+    "    plt.xlabel(\"Feature importance\")\n",
+    "    plt.ylabel(\"Feature\")\n",
+    "    plt.ylim(-1, n_features)\n",
+    "\n",
+    "plot_feature_importances_diabetes(tree)\n",
+    "plt.savefig('feature_importance')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feature \"Glucose\" is by far the most important feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "let’s apply a random forest consisting of 100 trees on the diabetes dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 1.000\n",
+      "Accuracy on test set: 0.786\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
+    "rf.fit(X_train, y_train)\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(rf.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(rf.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The random forest gives us an accuracy of 78.6%, better than the logistic regression model or a single decision tree, without tuning any parameters. However, we can adjust the max_features setting, to see whether the result can be improved."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.800\n",
+      "Accuracy on test set: 0.755\n"
+     ]
+    }
+   ],
+   "source": [
+    "rf1 = RandomForestClassifier(max_depth=3, n_estimators=100, random_state=0)\n",
+    "rf1.fit(X_train, y_train)\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(rf1.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(rf1.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It did not, this indicates that the default parameters of the random forest work well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature importance in Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1EPAV4HWt/RxgSWs/GFg9pvajgQXtOAuBQ5I8D/g14PtVdVCbIbxs40dIkqTZ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\n5hI28tmO/asAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d3998a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_feature_importances_diabetes(rf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Similarly to the single decision tree, the random forest also gives a lot of importance to the “Glucose” feature, but it also chooses “BMI” to be the 2nd most informative feature overall. The randomness in building the random forest forces the algorithm to consider many possible explanations, the result being that the random forest captures a much broader picture of the data than a single tree."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.917\n",
+      "Accuracy on test set: 0.792\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "gb = GradientBoostingClassifier(random_state=0)\n",
+    "gb.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(gb.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(gb.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are likely to be overfitting. To reduce overfitting, we could either apply stronger pre-pruning by limiting the maximum depth or lower the learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.804\n",
+      "Accuracy on test set: 0.781\n"
+     ]
+    }
+   ],
+   "source": [
+    "gb1 = GradientBoostingClassifier(random_state=0, max_depth=1)\n",
+    "gb1.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(gb1.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(gb1.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.802\n",
+      "Accuracy on test set: 0.776\n"
+     ]
+    }
+   ],
+   "source": [
+    "gb2 = GradientBoostingClassifier(random_state=0, learning_rate=0.01)\n",
+    "gb2.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(gb2.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(gb2.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both methods of decreasing the model complexity reduced the training set accuracy, as expected. In this case, none of these methods increased the generalization performance of the test set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can visualize the feature importances to get more insight into our model even though we are not really happy with the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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aH/gq8KbWfg6wuLUfAKwaVvsRwPx2nAXAgUleDPwa8L2q2r/NEF624SMkSdLM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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d2f5fe80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_feature_importances_diabetes(gb1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the feature importances of the gradient boosted trees are somewhat similar to the feature importances of the random forests, it gives weight to all of the features in this case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Support Vector Machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 1.00\n",
+      "Accuracy on test set: 0.65\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "svc = SVC()\n",
+    "svc.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model overfits quite substantially, with a perfect score on the training set and only 65% accuracy on the test set.\n",
+    "\n",
+    "SVM requires all the features to vary on a similar scale. We will need to rescale our data that all the features are approximately on the same scale:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.77\n",
+      "Accuracy on test set: 0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.fit_transform(X_test)\n",
+    "\n",
+    "svc = SVC()\n",
+    "svc.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train_scaled, y_train)))\n",
+    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test_scaled, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scaling the data made a huge difference! Now we are actually in an underfitting regime, where training and test set performance are quite similar but less close to 100% accuracy. From here, we can try increasing either C or gamma to fit a more complex model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.790\n",
+      "Accuracy on test set: 0.797\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(C=1000)\n",
+    "svc.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(\n",
+    "    svc.score(X_train_scaled, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(svc.score(X_test_scaled, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, increasing C allows us to improve the model, resulting in 79.7% test set accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Neural Networks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.71\n",
+      "Accuracy on test set: 0.67\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "\n",
+    "mlp = MLPClassifier(random_state=42)\n",
+    "mlp.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.2f}\".format(mlp.score(X_train, y_train)))\n",
+    "print(\"Accuracy on test set: {:.2f}\".format(mlp.score(X_test, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy of the MLP is not as good as the other models at all, this is likely due to scaling of the data. Neural networks also expect all input features to vary in a similar way, and ideally to have a mean of 0, and a variance of 1. We must rescale our data so that it fulfills these requirements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.823\n",
+      "Accuracy on test set: 0.802\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.fit_transform(X_test)\n",
+    "\n",
+    "mlp = MLPClassifier(random_state=0)\n",
+    "mlp.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(\n",
+    "    mlp.score(X_train_scaled, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test_scaled, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are much better after scaling. As at matter of fact, we have obtained the highest test accuracy so far."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.877\n",
+      "Accuracy on test set: 0.755\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp = MLPClassifier(max_iter=1000, random_state=0)\n",
+    "mlp.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(\n",
+    "    mlp.score(X_train_scaled, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test_scaled, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on training set: 0.795\n",
+      "Accuracy on test set: 0.792\n"
+     ]
+    }
+   ],
+   "source": [
+    "mlp = MLPClassifier(max_iter=1000, alpha=1, random_state=0)\n",
+    "mlp.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(\"Accuracy on training set: {:.3f}\".format(\n",
+    "    mlp.score(X_train_scaled, y_train)))\n",
+    "print(\"Accuracy on test set: {:.3f}\".format(mlp.score(X_test_scaled, y_test)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x225d3a5f080>"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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TQ0TsWym9iDi6SPcgSWdHxPvK8lrTe66TdF3NhQcAAAAAALWp4wqfWlXTguhz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\nVNLjticXy76kUl/g39s+VaUvecd2Uvm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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x225d3955630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "plt.imshow(mlp.coefs_[0], interpolation='none', cmap='viridis')\n",
+    "plt.yticks(range(8), diabetes_features)\n",
+    "plt.xlabel(\"Columns in weight matrix\")\n",
+    "plt.ylabel(\"Input feature\")\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the heat map, it is not easy to point out quickly that which feature (features) have relatively low weights compared to the other features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary\n",
+    "We practiced a wide array of machine learning models for classification and regression, what their advantages and disadvantages are, and how to control model complexity for each of them. We saw that for many of the algorithms, setting the right parameters is important for good performance.\n",
+    "\n",
+    "We should be able to know how to apply, tune, and analyze the models we practiced above."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}