Supervised Learning with Scikit Learn.ipynb

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    "##### Exercise-1: Numerical EDA\n",
    "In this chapter, you'll be working with a dataset obtained from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records) consisting of votes made by US House of Representatives Congressmen. Your goal will be to predict their party affiliation ('Democrat' or 'Republican') based on how they voted on certain key issues. Here, it's worth noting that we have preprocessed this dataset to deal with missing values. This is so that your focus can be directed towards understanding how to train and evaluate supervised learning models. Once you have mastered these fundamentals, you will be introduced to preprocessing techniques in Chapter 4 and have the chance to apply them there yourself - including on this very same dataset!\n",
    "\n",
    "Before thinking about what supervised learning models you can apply to this, however, you need to perform Exploratory data analysis (EDA) in order to understand the structure of the data. For a refresher on the importance of EDA, check out the first two chapters of Statistical Thinking in Python (Part 1) (https://learn.datacamp.com/courses/statistical-thinking-in-python-part-1).\n",
    "\n",
    "Get started with your EDA now by exploring this voting records dataset numerically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>party</th>\n",
       "      <th>infants</th>\n",
       "      <th>water</th>\n",
       "      <th>budget</th>\n",
       "      <th>physician</th>\n",
       "      <th>salvador</th>\n",
       "      <th>religious</th>\n",
       "      <th>satellite</th>\n",
       "      <th>aid</th>\n",
       "      <th>missile</th>\n",
       "      <th>immigration</th>\n",
       "      <th>synfuels</th>\n",
       "      <th>education</th>\n",
       "      <th>superfund</th>\n",
       "      <th>crime</th>\n",
       "      <th>duty_free_exports</th>\n",
       "      <th>eaa_rsa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>democrat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "            party  infants  water  budget  physician  salvador  religious  \\\n",
       "Index                                                                       \n",
       "0      republican        0      1       0          1         1          1   \n",
       "1      republican        0      1       0          1         1          1   \n",
       "2        democrat        0      1       1          0         1          1   \n",
       "3        democrat        0      1       1          0         1          1   \n",
       "4        democrat        1      1       1          0         1          1   \n",
       "\n",
       "       satellite  aid  missile  immigration  synfuels  education  superfund  \\\n",
       "Index                                                                         \n",
       "0              0    0        0            1         0          1          1   \n",
       "1              0    0        0            0         0          1          1   \n",
       "2              0    0        0            0         1          0          1   \n",
       "3              0    0        0            0         1          0          1   \n",
       "4              0    0        0            0         1          0          1   \n",
       "\n",
       "       crime  duty_free_exports  eaa_rsa  \n",
       "Index                                     \n",
       "0          1                  0        1  \n",
       "1          1                  0        1  \n",
       "2          1                  0        0  \n",
       "3          0                  0        1  \n",
       "4          1                  1        1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('house-votes-84-2.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 435 entries, 0 to 434\n",
      "Data columns (total 17 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   party              435 non-null    object\n",
      " 1   infants            435 non-null    int64 \n",
      " 2   water              435 non-null    int64 \n",
      " 3   budget             435 non-null    int64 \n",
      " 4   physician          435 non-null    int64 \n",
      " 5   salvador           435 non-null    int64 \n",
      " 6   religious          435 non-null    int64 \n",
      " 7   satellite          435 non-null    int64 \n",
      " 8   aid                435 non-null    int64 \n",
      " 9   missile            435 non-null    int64 \n",
      " 10  immigration        435 non-null    int64 \n",
      " 11  synfuels           435 non-null    int64 \n",
      " 12  education          435 non-null    int64 \n",
      " 13  superfund          435 non-null    int64 \n",
      " 14  crime              435 non-null    int64 \n",
      " 15  duty_free_exports  435 non-null    int64 \n",
      " 16  eaa_rsa            435 non-null    int64 \n",
      "dtypes: int64(16), object(1)\n",
      "memory usage: 61.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>435.000000</td>\n",
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       "      <td>435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.429885</td>\n",
       "      <td>0.558621</td>\n",
       "      <td>0.606897</td>\n",
       "      <td>0.406897</td>\n",
       "      <td>0.521839</td>\n",
       "      <td>0.650575</td>\n",
       "      <td>0.581609</td>\n",
       "      <td>0.590805</td>\n",
       "      <td>0.526437</td>\n",
       "      <td>0.512644</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.393103</td>\n",
       "      <td>0.537931</td>\n",
       "      <td>0.609195</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.857471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.495630</td>\n",
       "      <td>0.497123</td>\n",
       "      <td>0.489002</td>\n",
       "      <td>0.491821</td>\n",
       "      <td>0.500098</td>\n",
       "      <td>0.477337</td>\n",
       "      <td>0.493863</td>\n",
       "      <td>0.492252</td>\n",
       "      <td>0.499876</td>\n",
       "      <td>0.500416</td>\n",
       "      <td>0.475859</td>\n",
       "      <td>0.489002</td>\n",
       "      <td>0.499133</td>\n",
       "      <td>0.488493</td>\n",
       "      <td>0.490462</td>\n",
       "      <td>0.349994</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "          infants       water      budget   physician    salvador   religious  \\\n",
       "count  435.000000  435.000000  435.000000  435.000000  435.000000  435.000000   \n",
       "mean     0.429885    0.558621    0.606897    0.406897    0.521839    0.650575   \n",
       "std      0.495630    0.497123    0.489002    0.491821    0.500098    0.477337   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    1.000000    1.000000    0.000000    1.000000    1.000000   \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        satellite         aid     missile  immigration    synfuels  \\\n",
       "count  435.000000  435.000000  435.000000   435.000000  435.000000   \n",
       "mean     0.581609    0.590805    0.526437     0.512644    0.344828   \n",
       "std      0.493863    0.492252    0.499876     0.500416    0.475859   \n",
       "min      0.000000    0.000000    0.000000     0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000     0.000000    0.000000   \n",
       "50%      1.000000    1.000000    1.000000     1.000000    0.000000   \n",
       "75%      1.000000    1.000000    1.000000     1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000     1.000000    1.000000   \n",
       "\n",
       "        education   superfund       crime  duty_free_exports     eaa_rsa  \n",
       "count  435.000000  435.000000  435.000000         435.000000  435.000000  \n",
       "mean     0.393103    0.537931    0.609195           0.400000    0.857471  \n",
       "std      0.489002    0.499133    0.488493           0.490462    0.349994  \n",
       "min      0.000000    0.000000    0.000000           0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000           0.000000    1.000000  \n",
       "50%      0.000000    1.000000    1.000000           0.000000    1.000000  \n",
       "75%      1.000000    1.000000    1.000000           1.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000           1.000000    1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-2: Visual EDA\n",
    "The Numerical EDA you did in the previous exercise gave you some very important information, such as the names and data types of the columns, and the dimensions of the DataFrame. Following this with some visual EDA will give you an even better understanding of the data. In the video, Hugo used the scatter_matrix() function on the Iris data for this purpose. However, you may have noticed in the previous exercise that all the features in this dataset are binary; that is, they are either 0 or 1. So a different type of plot would be more useful here, such as Seaborn's countplot.\n",
    "\n",
    "Given on the right is a countplot of the 'education' bill, generated from the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure()\n",
    "sns.countplot(x='education', hue='party', data=df, palette='RdBu')\n",
    "plt.xticks([0,1], ['No', 'Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In sns.countplot(), we specify the x-axis data to be 'education', and hue to be 'party'. Recall that 'party' is also our target variable. So the resulting plot shows the difference in voting behavior between the two parties for the 'education' bill, with each party colored differently. We manually specified the color to be 'RdBu', as the Republican party has been traditionally associated with red, and the Democratic party with blue.\n",
    "\n",
    "It seems like Democrats voted resoundingly against this bill, compared to Republicans. This is the kind of information that our machine learning model will seek to learn when we try to predict party affiliation solely based on voting behavior. An expert in U.S politics may be able to predict this without machine learning, but probably not instantaneously - and certainly not if we are dealing with hundreds of samples!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(x='satellite', hue='party', data=df, palette='RdBu')\n",
    "plt.xticks([0,1], ['No', 'Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(x='missile', hue='party', data=df, palette='RdBu')\n",
    "plt.xticks([0,1], ['No', 'Yes'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of these two bills, for which ones do Democrats vote resoundingly in favor of, compared to Republicans? Both!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-3: k-Nearest Neighbors: Fit\n",
    "Having explored the Congressional voting records dataset, it is time now to build your first classifier. In this exercise, you will fit a k-Nearest Neighbors classifier to the voting dataset, which has once again been pre-loaded for you into a DataFrame df.\n",
    "\n",
    "In the video, Hugo discussed the importance of ensuring your data adheres to the format required by the scikit-learn API. The features need to be in an array where each column is a feature and each row a different observation or data point - in this case, a Congressman's voting record. The target needs to be a single column with the same number of observations as the feature data. We have done this for you in this exercise. Notice we named the feature array X and response variable y: This is in accordance with the common scikit-learn practice.\n",
    "\n",
    "Your job is to create an instance of a k-NN classifier with 6 neighbors (by specifying the n_neighbors parameter) and then fit it to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import KNeighborsClassifier from sklearn.neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Create arrays for the features and the response variable\n",
    "y = df['party'].values\n",
    "X = df.drop('party', axis=1).values\n",
    "\n",
    "# Create a k-NN classifier with 6 neighbors\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "# Fit the classifier to the data\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-4: k-Nearest Neighbors: Predict\n",
    "Having fit a k-NN classifier, you can now use it to predict the label of a new data point. However, there is no unlabeled data available since all of it was used to fit the model! You can still use the .predict() method on the X that was used to fit the model, but it is not a good indicator of the model's ability to generalize to new, unseen data.\n",
    "\n",
    "In the next video, Hugo will discuss a solution to this problem. For now, a random unlabeled data point has been generated and is available to you as X_new. You will use your classifier to predict the label for this new data point, as well as on the training data X that the model has already seen. Using .predict() on X_new will generate 1 prediction, while using it on X will generate 435 predictions: 1 for each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: ['republican']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Predict the labels for the training data X\n",
    "y_pred = knn.predict(X)\n",
    "\n",
    "# Predict and print the label for the new data point X_new\n",
    "new_prediction = knn.predict(np.random.rand(1, 16))\n",
    "print(\"Prediction: {}\".format(new_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-5: The digits recognition dataset\n",
    "Up until now, you have been performing binary classification, since the target variable had two possible outcomes. Hugo, however, got to perform multi-class classification in the videos, where the target variable could take on three possible outcomes. Why does he get to have all the fun?! In the following exercises, you'll be working with the MNIST digits recognition dataset, which has 10 classes, the digits 0 through 9! A reduced version of the MNIST dataset is one of scikit-learn's included datasets, and that is the one we will use in this exercise.\n",
    "\n",
    "Each sample in this scikit-learn dataset is an 8x8 image representing a handwritten digit. Each pixel is represented by an integer in the range 0 to 16, indicating varying levels of black. Recall that scikit-learn's built-in datasets are of type Bunch, which are dictionary-like objects. Helpfully for the MNIST (http://yann.lecun.com/exdb/mnist/) dataset, scikit-learn provides an 'images' key in addition to the 'data' and 'target' keys that you have seen with the Iris data. Because it is a 2D array of the images corresponding to each sample, this 'images' key is useful for visualizing the images, as you'll see in this exercise (for more on plotting 2D arrays, see Chapter 2 (https://learn.datacamp.com/courses/introduction-to-data-visualization-with-python) of DataCamp's course on Data Visualization with Python). On the other hand, the 'data' key contains the feature array - that is, the images as a flattened array of 64 pixels.\n",
    "\n",
    "Notice that you can access the keys of these Bunch objects in two different ways: By using the . notation, as in digits.images, or the \\[\\] notation, as in digits\\['images'\\].\n",
    "\n",
    "For more on the MNIST data, check out this exercise (https://campus.datacamp.com/courses/introduction-to-importing-data-in-python/introduction-and-flat-files-1?ex=10) in Part 1 of DataCamp's Importing Data in Python course. There, the full version of the MNIST dataset is used, in which the images are 28x28. It is a famous dataset in machine learning and computer vision, and frequently used as a benchmark to evaluate the performance of a new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])\n",
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 5620\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "(1797, 8, 8)\n",
      "(1797, 64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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0PDyscDislpYWbdu2Ld3RRs3n88nn82nOnDmSpCVLllBqE7NmzdK9e/cUCoVUUFCgCxcuWPELEovF1NraqqKiIlVXV6c7TtLU1NSopqZGknTt2jWdPHnSiv9ekpSXlyefz6e7d++qsLBQnZ2d4/KDzbSX2uv1qq6uTrt371Y0GtWKFSs0ffr0dMcata6uLp07d04lJSXasWOHJGndunWqqKhIczL8m7q6OrW0tCgSiWjKlCnasmVLuiM5xmWigGXS/kEZgOSi1IBlKDVgGUoNWIZSA5ah1IBlKDVgmf8AfOH15UOs3K4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the digits dataset: digits\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Print the keys and DESCR of the dataset\n",
    "print(digits.keys())\n",
    "print(digits['DESCR'])\n",
    "\n",
    "# Print the shape of the images and data keys\n",
    "print(digits.images.shape)\n",
    "print(digits.data.shape)\n",
    "\n",
    "# Display digit 1010\n",
    "plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-6: Train/Test Split + Fit/Predict/Accuracy\n",
    "Now that you have learned about the importance of splitting your data into training and test sets, it's time to practice doing this on the digits dataset! After creating arrays for the features and target variable, you will split them into training and test sets, fit a k-NN classifier to the training data, and then compute its accuracy using the .score() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9833333333333333\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Create feature and target arrays\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "# Split into training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
    "\n",
    "# Create a k-NN classifier with 7 neighbors: knn\n",
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Print the accuracy\n",
    "print(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-7: Overfitting and underfitting\n",
    "Remember the model complexity curve that Hugo showed in the video? You will now construct such a curve for the digits dataset! In this exercise, you will compute and plot the training and testing accuracy scores for a variety of different neighbor values. By observing how the accuracy scores differ for the training and testing sets with different values of k, you will develop your intuition for overfitting and underfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF16gZcuW3Lp1i9dff13v12JpacnAgQPRaDQMGzZM7+UeRKFQsG7dOiwsLGjdujVdu3alW7dueHl5lWg6eRQLCwtmzJhRYv/5+fnx3XffsWzZMho1asSSJUuYPn36Y2e/q3bt2gwdOpSePXvy/PPPY2dnx5o1a7RflmFhYWzfvp3Dhw/TunVrAgICePvtt3FwcNBpKtOHlZUVf/zxB0qlktatWxMSEoK9vT1btmypkOtzOnXqRHh4uM5RN8DChQuJjIxk2rRp+Pr6EhYWxtKlSx94JALw448/4uvrS8eOHWnbti2enp6EhoaW+X0FaNmyJc2aNSMsLIzOnTsTEBDAwoULtc9HRkYyefJkPvzwQ/z8/Jg9ezazZs1i8ODBD12vSqVi3bp1tG/fHm9vb8aPH8/kyZMfuVx1Jcn6NLYKgoH17NkTWZb1ug6mPLKysnB3d2fmzJm8+uqrlbINoeoVFhbi7e1N7969+eSTTwwd54kkOtaFai09PZ3o6Gh+++23h16zUlbr1q3D3NycBg0akJyczJQpUzAxMXns5hnBsKKiokhPTycgIIDs7Gw+/fRTrly5YrS/8o2BKCJCtda4cWOysrKYMGECrVu3rrD13rp1i/Hjx3Px4kVsbGwICgrir7/+wtnZucK2IVS9wsJCpk6dyrlz5zA1NaVx48ZERUUZbX+DMRDNWYIgCEK5iY51QRAEodxEEREEQRDK7ansEynvaJlOTk4657JXd8aUV2StPMaU15iygnHlfdysNWvWLHW6OBIRBEEQyk0UEUEQBKHcRBERBEEQyu2p7BMRBEE/siyTl5eHRqOpkJGPHyU5ObnE8CfVmTHl1SerLMsoFAosLCz0fr9FEREE4YHy8vIwNTXFxKRqvipMTEzKPLaXIRlTXn2zFhYWkpeXh6WlpX7rfdxg+po3bx6HDx/G3t6+xG0oobgCLl68mCNHjmBubs5rr72mHWgtKiqKNWvWAMVjKN0dqTQxMZGvv/6a/Px8mjRpwtChQ6vk15IgPC00Gk2VFRChejAxMSnT0VWV9YmEhIQ8dIjkI0eOkJSUxBdffMErr7zCggULALh58yarV69m+vTpTJ8+ndWrV2vH+J8/fz6jRo3iiy++ICkpidjY2Cp5LYLwtBA/yp5OZXnfq6yI+Pn5PfRmOAcPHiQ4OBhJkvD29ubWrVtkZGQQGxtL48aNdW7xGRsbS0ZGBrm5uXh7eyNJEsHBwRw4cKDS8sdczmH98aRKW78gCIIxqjbHqenp6Tg5OWkfq9Vq0tPTSU9PR61Wa6erVKpSp9+dvzTbt29n+/btAMyYMUNnO/rasy+V/Rev89zLTXGxffhdy6oLExOTcr1WQxBZK8/j5E1OTq7y5qy720tPT9eOqpySkoJSqdT+n9+6dWuJW9w+yPLlywkLC8PFxQWAMWPGEBkZSb169Sok7+bNmxk5ciT79u3Tue99daTve2lubq73Z6baFJHSxoF80CGVvvecvissLIywsDDt4/JctTmwoT37LqQzd+dp3m5V+pWb1c3TdDVtVTKmrPB4ee/cuVOlHccmJiYUFhYCxfci37ZtGwBz5szB2tqa0aNHa+e9O9+jLF++HD8/P1QqlXZdZVn+UXnXrFlDs2bN+PXXX/n3v//92Ot8kMLCwscq6Pfu20e5c+dOic9Mtb9iXa1W64ROS0vD0dERlUpFWlqadnp6ejqOjo6o1Wqd6WlpadoPSWVwtTGjf1N3oi9kE59StrsMCoJQsVatWkVERATh4eGMHz8ejUZDYWEhkZGRtGvXjtDQUBYuXMj69euJi4vj1VdfJTw8nPz8fLp3786JEycoLCzE19eX6dOnExYWxgsvvKD9DkpMTKRLly5EREQwc+ZM7b3r75eTk8ORI0eYNWsW69ev13nuiy++oF27doSFhTFjxgwAzp07R+/evQkLC6NDhw5cvnyZ6OhonTt2vvvuu/z6669A8b3m586dS7du3diyZQtLliyhc+fOhIWF8corr2jvqJmSksLQoUO1P5gPHz7Mxx9/zA8//KBd77Rp03QeV5RqcyQSFBTE1q1badWqFQkJCVhZWeHo6EhgYCA///yztjP96NGj9O/fHxsbGywtLTlz5gz169cnOjqajh07VmrGgUHubDhxne8PJjOnY12UCtHpKDw9NCvmI18+X6HrlGp7oug3skzLnDp1iq1bt7J+/XpMTEx45513WL9+PR4eHmRkZLBjxw6g+G6V9vb2LF68mA8//JCGDRuWWFd2djYtWrRgwoQJTJkyhRUrVvDGG28wadIkRo0axQsvvMDixYsfmGXTpk20a9eOevXqYWVlRXx8PH5+fmzbto1du3axceNGLC0tycjIAOD1119n7NixtG/fnry8PGRZ5vz5h+9TKysrbYFKT0/n5ZdfBmD69OmsWrWKwYMH89577xEcHMzQoUMpLCwkNzcXlUrFa6+9xpAhQygqKuK3335jy5YtZdrX+qiyIvLZZ58RHx9PTk4Oo0ePpk+fPtpDq/bt29OkSRMOHz7Mm2++iZmZGa+99hoANjY29OrVi/HjxwPw4osvajvoR4wYwbx588jPzycwMJAmTZpU6muwNFUytIkLs/+6xh/nMulY37FStycIQkm7d+/m6NGjdOrUCSi+lqVGjRq0adOGc+fO8f777xMaGkqbNm0euS4LCwtCQ0OB4hugxcTEAMVniy5duhSA7t27M3PmzFKXX7t2rfa7qmvXrqxfvx4/Pz/27NlDv379tNdaODo6kpmZSXp6Ou3bt9duWx9du3bV/vvkyZPMnj2b7Oxsbt68qW2m37t3L/PmzQOKm61sbW2xtbXF2tqakydPcuXKFZo0aYKDg4Ne2yyLKisij2orlCSJESNGlPpcaGio9o2+l5eXV6nXnFSm1h62bEmwZNnRG7SqY4etuXFcaCQIj6usRwyVRZZl+vbtyzvvvFPiue3bt7Nz504WLlzI5s2bH/jlf9e9nfNKpZKioiK9c6SlpfH333+TkJCAJEkUFhZiamrKf//73wf22ZbWz2tiYoJGo9E+vv8aDSsrK+2/x4wZw7Jly2jQoAHLly/n8OHDD133Sy+9xKpVqyr1FsHVpk/EWEiSxMggV27lF/HzsVRDxxGEp87zzz/Phg0btGdjpqenc/XqVdLS0pBlmRdeeIFx48Zx/PhxoLg149atW2XaRmBgoLbp5/6+jrs2bNhA//792b9/PzExMRw6dAhXV1cOHTpEcHAwK1as0PZZZGRk4ODggEql0p4skJeXR25uLu7u7pw5c4b8/HwyMzP566+/HpgrNzcXFxcXCgoKWLt2rXZ6y5YttUdORUVF5OTkANC5c2f++OMP4uLiCA4OLtM+0JcoIuXg6WhBh3oObEnI5EJGnqHjCMJTxdfXl7Fjx9K3b1/CwsLo378/qampXLt2jZ49exIeHs5//vMf/vvf/wLQp08fxo0bp+1Y18cHH3zAvHnziIiIIC0tDVtb2xLzrFu3Ttukdlfnzp1Zt24d4eHhhISE0LlzZ8LDw5k/fz4AX375Jd9//z1hYWH06NGDtLQ06tSpQ4cOHWjXrh1jxoyhUaNGD8w1btw4IiIieOmll/D29tZO/+ijj4iKiqJdu3Z06tSJs2fPAsVNZi1atKBbt24oFJXzdf9U3mO9Im5KlX2niNd+O0ddRwumtatdLa/sNaZTUUXWyvM4eW/fvq3TnFLZynIaamW6ffs2lpaWSJLEr7/+ytatW7WF4F7VJe+DaDQa2rdvz8KFC/Hy8tI7a2nve7U/xdfY2Jkr6R/gzPHk2+y9nGPoOIIgVKDY2Fjat29PWFgYy5cvZ+LEiYaOVGYnT56kZcuWhISE4OHhUWnbqTan+BqjDvUc+D0hk8WHUgiqaYO5iajJgvAkaNmyJX/88YehYzwWX19f9u3bV+nbEd96j0GpKO5kT71dyJr4tEcvIAiC8IQRReQxNXS1orWHLWvi00m5WWDoOIIgCFVKFJEKMKRJ8cBui4+kGDiJIAhC1RJFpAI4W5vS21/N3ks5HEsq2/nogiAIxkwUkQrS3U+Fq40p8w8mU6R56s6aFoQKl56eTnh4OOHh4QQGBtK0aVPtY32v93jrrbe010w8yA8//KC9c2pFSE1NpU6dOvz8888Vts7qTFwnUgaPOt/+78s5zIi+ysggF7r4VN6IwvoypusZRNbK8yRcJ1LaUPBQPASKLMuVdiHdo5SWd+HChWzcuBEzMzNWrlxZadsu69DwZbmmRVwnYiAt3G0IcLNi+bEbZOVV3wuQBMGYnT9/ntDQUN599106dOhAcnIy77zzDp06daJt27bMnTtXO68+w75/8skn2gsJu3fvzvTp04mIiOD555/X3i319u3bjBw5krCwMF577TU6derEiRMnSs23bt06pk6dyqVLl0hJ+X8/6fbt2+nQoQNhYWG89NJLQPHtv8eMGaMdMn7r1q3arHetX7+ecePGARAZGcnUqVN58cUXmTFjBocOHeKFF16gffv2dOvWjcTERKC4wEyePJnQ0FDCwsL44Ycf2LVrF6+88op2vTt37mTUqFGP/X6I60QqkCRJjAhyZcym8/x09AavNXczdCRBqDALDiZzvoKH+fF0tGBEkGuZlztz5gyffvopn3zyCQDjx4/H0dGRwsJCevfuTUREhM6wIPDgYd/vJ8symzZtYtu2bXz22Wf89NNPLFq0CGdnZ+bPn09cXNwDbztx+fJlsrKyaNy4MREREWzYsIHhw4eTkpLC+PHjWbt2Le7u7tqh4efMmYNarWbHjh3IskxWVtYjX/vFixdZtWoVCoWC7Oxs1q1bh1KpZNeuXcycOZNvv/2WJUuWkJyczB9//IFSqSQjIwO1Ws2ECRNIT09HpVKxcuVK+vbtW9ZdX4I4EqlgdezNifBxZNvZTM6li3G1BKEyeHh4EBgYqH28fv16OnToQMeOHUlISODMmTMllrl/2PfLly+Xuu6742E1atRIO8/+/fvp1q0bAP7+/vj4+JS67Lp167RDt3fr1o1169YBcOjQIVq2bIm7uztQPDQ8FA9rP2TIEKD4R6g+Q7V36dJF23yXnZ3NiBEjCA0NZdq0adrXvXv3bgYNGqS9K6WjoyMKhYIePXqwbt06MjIyOH78uF7D5T+KOBKpBP0aORF9Ppv5B5P5OLxOtRxXSxDKqjxHDJXl3vb6xMREFixYwKZNm7C3tycyMrLEcOqg/7Dvd+e7dx59u47Xr19PRkYGv/zyC1B8j/pLly4hy/JDb/d9L4VCobO9vDzdH6P3vvZPPvmENm3aMGTIEM6fP8/AgQO1eUvbXt++fbVNWi+88EKF3PpYHIlUAhszJYMCnTmZmkv0hWxDxxGEJ9rNmzexsbHB1taW5ORkoqKiKnwbzZo1Y8OGDUDxmFSlHemcOXOGoqIiDh06RExMDDExMYwePZr169fz7LPP8tdff3HlyhUAbXNWcHCw9s6JsiyTmZmJQqHA3t6exMRENBoNW7dufWCu7OxsatSoARTfMviuNm3asGTJEm0RvLu9WrVqoVKp+Prrr+nTp8/j7hZAFJFK087LHi+VBT8cSSW3QPPoBQRBKJdGjRpRv359QkND+c9//sOzzz5b4dsYNmwYSUlJhIWF8d133+Hj44OdnZ3OPGvXri3RVxIREcG6detwdnbm448/1t4HPTIyEoCxY8eSmppKaGgo4eHh7N+/H4AJEyYwcOBA+vTpoy0SpXn99deZNm2atqntroEDB+Li4qK95/rdAgjFJw/Url0bLy+vx9ond4lTfMugrKdKnky9zX+3XeJFfzWDAp3Ltc3HYUynooqsledJOMXX0AoLCyksLMTCwoLExET69+/Pnj17sLCwqJZ5S3N337777rs0bdr0oUciZTnFV/SJVCJfZytCPO1YdzKdMC97atiaPXohQRCqnVu3btG3b19twfjkk0/KdI1GdREeHo69vT3Tpk2rsHUa314wMi8HOrPv8k0WHkphYoi7oeMIglAO9vb2D+2bMBaVMby96BOpZGorU/o2VHPg6k0OX7tp6DiCUCZPYWu3QNned1FEqsALDRypaWvKgkMpFBSJ/5SC8VAoFEbT5i9UjMLCwjINIyOas6qAqVLB8KauTIjSOZoAACAASURBVIu6wqYz6XT3VRs6kiDoxcLCgry8PO7cuVMl1zuZm5uXeo1HdWVMefXJenccMgsLC73XK4pIFQmqZUPTmtasOJZGm7r2OFqKXS9Uf5IkYWlpWWXbe5rOfKtqlZVVNGdVoeFNXSnQaFgSm2roKIIgCBVCFJEqVMvOjK4NVOxMzOL0jVxDxxEEQXhsoohUsd4N1ThaKJl/MBmNOPNFEAQjJ4pIFbMyVTK4iQsJaXnsSnz0sM+CIAjVmSgiBtDG0w4fJ0uWxKZyK7/0kUQFQRCMgSgiBqCQJEYGuZCVV8SqE2mGjiMIglBuoogYSH21Je287NlwKp0rWcZxnrkgCML9RBExoEGBzpibKFhwKEUMLyEIglESRcSAHCxMeKmxE0eu3+LAVTGuliAIxkcUEQPr7O2Iu50ZCw+lkF8kbl4lCIJxEUXEwEwUEiODXEm6WcBvJzMMHUcQBKFMRBGpBgJrWNOitg2rTtzgxu0CQ8cRBEHQW5WNAhgbG8vixYvRaDS0a9eO7t276zyfmprKN998Q3Z2NjY2NkRGRqJWF492u2zZMo4cOQJAr169aNmyJQBff/018fHx2ts4vv7669StW7eqXlKFGvYvF16/ep4fj6TydqvSb0MpCIJQ3VRJEdFoNCxcuJCJEyeiVqsZP348QUFBuLv//05/S5cuJTg4mJCQEE6cOMHy5cuJjIzk8OHDnD9/npkzZ1JQUMCUKVMIDAzUFo5BgwbRokWLqngZlcrVxowefipWnUijU30H/Fyq7r7WgiAI5VUlzVlnz57Fzc0NV1dXTExMaNmyJQcOHNCZ58qVKzRq1AgAf39/Dh48qJ3u5+eHUqnEwsICDw8PYmNjqyJ2levlr0ZtZcL3B5Mp0ohTfgVBqP6q5EgkPT1d2zQFoFarSUhI0JnHw8ODmJgYOnfuzP79+8nNzSUnJwcPDw9Wr15Nly5duHPnDnFxcTpHMD///DOrV6+mYcOGDBgwAFNT0xLb3759O9u3bwdgxowZODk5let1mJiYlHtZfb3ZRmLyltPsSy6iWyO3x1pXVeStKCJr5TGmvMaUFYwrb2VlrZIiUtqFdPffJW3QoEEsWrSIqKgofH19UalUKJVKAgICOHfuHBMnTsTOzg5vb2+USiUA/fv3x8HBgcLCQr777jvWr1/Piy++WGJbYWFhhIWFaR+X98YsVXEDmgBH8Hex5Ju/zhOgkrAxV5Z7XeKGOZXDmLKCceU1pqxgXHkfN2vNmqX31VZJEVGr1aSl/X+MqLS0NBwdHXXmUalUjBs3DoC8vDxiYmK0/R49e/akZ8+eAHz++ee4uRX/Qr+7DlNTU9q2bcuGDRsq/bVUNkkqPuV37JYLLD9+g1eCXA0dSRAE4YGqpE/Ey8uL69evk5KSQmFhIXv37iUoKEhnnuzsbDSa4ovt1q5dS9u2bYHiTvmcnBwALl68yKVLlwgICAAgI6P4ugpZljlw4AC1a9euipdT6TwdLehQz4EtZzK4kJFn6DiCIAgPVCVHIkqlkmHDhvHRRx+h0Who27YttWvXZuXKlXh5eREUFER8fDzLly9HkiR8fX0ZPnw4AIWFhbz//vsAWFlZERkZqW3O+uKLL8jOzgaK+1ReeeWVqng5VaJ/gDO7L2az4FAK09rVLtH8JwiCUB1I8lM48t+1a9fKtVxVt39uPpPBdweSeef5mrSqY1fm5Z+m9tqqZExZwbjyGlNWMK68ldUnIq5Yr8Y61HOgroM5iw+lcKdQjKslCEL1I4pINab8Z1yt1NuFrI1PN3QcQRCEEkQRqeYaulrR2sOWX+PTSLkpxtUSBKF6EUXECAxp4gLA4iMpBk4iCIKgq8oGYBTKz9nalBf91Sw/doNjSbdo7GZt6EgVQpZlTt/IY8uZDJJuX6GwsNDQkfRiZXGNZjUsaOdlj5Vp+S8GFYQngSgiRqK7r4rt57JYcDCFuZ3rolQY7ym/BUUadl/MYdPpDM6m52FlqqBxTXsKCozjRMGcgiIWHEph2dEbtHvGjs4+jrjbmRs6liAYhCgiRsLcRMGwpi7MiL7K1oRMInwcH71QNZN2u4CtCZn8npBJ1p0i3O3MGPWsK2097aldw8WoTpX8+/RlNp7O4PezWWw6k0lgDWte8HHkXzWtUYhreoSniCgiRqSFuw0Bblb8dCyV5z1ssbOo/m+fLMucSs1lw+kM9l3OQSNDUC0buvg4EuBmZbQXUdZXW/JWS0uGNink97OZbEnIZFrUFdxsTOns7Ug7L3tszERTl/Dkq/7fQoKWJEmMCHJlzKbzLDt6g9eaP94ov5Upv0hD9IVsNp3OIDHjDtZmCl5ooKJTfQfcbM0MHa/COFia0LeRE7381fx9KYeNpzNYdDiF5cdSaetpT2cfR+rYi6Yu4cklioiRqWNvToSPIxtPZdCxvgPPqCwMHUlH6q3iJqttZzPJvlNEHXszXm3mSoinPRYmT+7JgCYKiefr2vF8XTvOpeex8XQG289lsSUhkwA3KyJ8HAmqaWPUfVmCUBpRRIxQv0ZORJ/P5vuDyXwcXsfgTUKyLBOfksvGM8VNVgDP/tNk1cjVeJusystLZcGY52owpIkz285msuVMJtP/vIqrjSmdvR0Ie8bhsYb4F4TqRBQRI2RjpmRgoDNfxyQRfSGbNp72Bslxp/CfJqszGZzPuIONmYLuvio61nfA1ebJabIqL3sLE3o3dKKHn5qYy8VNXYsPp7L86A1CPO2J8HHEw0E0dQnGTRQRI9XuGXu2JmTy45FUmrnbYmladU1FKTcL2JKQwR9nM8nJ1+DhYM7rzd1oU9cO8ye4yaq8TBQSrTzsaOVhR2J6HpvOZLDrfBa/n82kkWtxU1ezWqKpSzBOoogYqeJxtVz477ZLrI5LY1Cgc6VuT5ZlTqTcZuPpDPZfuQlAc3dbuvg44u9i+dQ1WZXXMyoLIlvUYHATF/44m8nmMxnMiL6Ki7UJneo7El7PAVvR1CUYEVFEjJivsxUhnnasO5lOmJc9NSrhrKc7hRqizhefZXUx6w625kp6+Kro5O2Is3XJ+9kL+rEzV9LLX013XxX7r9xk45kMfoxN5efjN2hT144uPo7UdaxeJ00IQmlEETFyLwc6s+/yTRYdTuG9Nu4Vtt7km/lsPpPJ9nOZ3MzX4OloTmQLN573EE1WFUmpkHiuji3P1bHlQkZxU1fU+Wz+OJdFQxdLInwcae5uK5q6hGpLFBEjp7YypW9DNT/GpnL42k3+VdOm3OuSZZljybfZ9E+TlSTBc7WLm6x8nUWTVWWr62jB681r8HKgC3+cy2TLmQw+2X0NJysTOnk70t7L3iguMBWeLuIT+QR4oYEj285lsuBQCp+7WmOqLNuXfV6hhl2JWWw6k8HlrHzszZW86K+mo7cDTlaiyaqq2Zor6emnplsDFQev3mTj6QyWxqay4tgNgv9p6qpu1wcJTy9RRJ4ApkoFI5q6Mi3qCpvOpNPdV63Xctdz8tl8JoMd57K4VaDRXt/Q2sMWM6VosjI0pUKieW1bmte25VLmneKzuhKz2JGYhZ9zcVNXi9q2mIimLsGARBF5QgTVsqFpTWtWHEujTV17HC1Lf2tlWSY26TabTqdz8OotFBK0rGNLFx8VPk4WosmqmqrjYM6rzdwYFODMjsQsNp/JYNaea6gtTejo7UCHeg7Yi6YuwQDEp+4JMrypK29uSmRpbCpvPldD57nbBUXsSsxm85kMrmTnY2+hpE8jNR3qOaAWTVZGw8ZcSTdfFV18HDl07SabTmfw09EbrDyeRnBdWyK8VdRTi6YuoeqIIvIEqWVnRtcGKtbEp9OxvgNOTnAt+58mq8QsbhdoqK+24K2WNWhVxxZT0WRltJQKiWbutjRzt+Vy1h02nS6+gHFnYjY+TpZ08XGkZR3R1CVUPlFEnjC9G6rZlZjF1zFJrDmdzd8XMjBRQKs6dkT4OOLjZGnoiEIFq21vzuhmbgwKdGbnPydIzPnrGosOm9CxvgP9m9kZOqLwBNOriGzevJnWrVtjZyc+jNWdlamSwU1c+Ozv6+Tka3ipkRMd6js8sI9EeHJYmyl5oYGKCB9HDl+7xabTGfx87Aar49JoVduWCB9HvMWPCKGC6fXNcvz4cX7++Wf8/f0JDg7m2WefxdRUtKNXVyGedng4mNPEqyZZGemGjiNUMYUkEVTLhqBaNlzJvsOuS3lsiksm6kI23moLInwcaVXHrsynggtCaSRZlvW6sXVOTg5//fUXu3fv5tq1azRv3pzg4GD8/PwqO2OFu3btWrmWc3JyMppbuIJx5RVZK4+TkxOXricXN3WdzuRaTj4OFko61negQ31HVNXoKNUY962x5H3crDVr1ix1ut6fHltbWzp27EjHjh25ePEiX331Fbt27cLJyYl27drRuXNnLCzEWSGCUB1ZmSrp4qOis7cjsddvsfF0BiuOp7E6Lo2Wte3o0sARb7U4xVsouzL9BDl+/Di7d+/mwIEDeHl58cYbb+Dk5MTmzZuZPn06H3zwQWXlFAShAigkiX/VtOFfNW24npPPpn8uNo2+mE09VXFT1/Me4sw9QX96FZElS5awd+9erKysCA4OZs6cOahUKu3z9evXZ+jQoZUWUhCEilfD1owRTV0Z0NiZXeez2HQ6g8//vs4PR1LoUM+BjvXFNUTCo+lVRAoKChg3bhz16tUrfSUmJsyYMaNCgwmCUDUsTRV09nakU30HjiYV3zPmlxNp/BqXxnN1bOni7UgDMQCn8AB6FZEePXpgZqZ7r4qbN2+Sn5+vPSKpVatWxacTBKHKSJJEYA1rAmtYk5STz5aETP44m8meizk842hOFx9Hnq9rJ8ZVE3To9WmYNWsW6em6p4qmp6cze/bsSgklCIJhudmaMfRfLizqWY9Xm7lSoJH5Yl8Sw9eeY2lsKqm3CgwdUagm9DoSuXbtGnXq1NGZVqdOHa5evVopoQRBqB4sTBR0rO9Ih3oOHE8ubupaE5/Gmvg0Wvxzrxk/0dT1VNOriNjZ2ZGUlISbm5t2WlJSEra2tpUWTBCE6kOSJBq7WdPYzZrkm/lsOZPJH+cy2XspB09HcyK8HQmuK+56+TTSq4i0bduWOXPm0K9fP1xdXUlKSmLlypWEhoZWdj5BEKoZVxszhvzLhZcaO/HnhWw2ns7gq5gkfjySQng9Bzp7O+JsLc7qelroVUS6d++OiYkJS5cuJS0tDbVaTWhoKF26dNF7Q7GxsSxevBiNRkO7du3o3r27zvOpqal88803ZGdnY2NjQ2RkJGp18c2Vli1bxpEjRwDo1asXLVu2BCAlJYXPPvuMmzdv4unpSWRkJCYm1efqW0F4kpmbKGhfz4FwL3viUnLZeDqDdSfTWXcynebuNkT4ONLQxcrom7pkWSb7ThHXcwq4lpPPde2fAizMr+JsIVHD1uyfP6bUtDXD2kxp6NhVRq9vXIVCQdeuXenatWu5NqLRaFi4cCETJ05ErVYzfvx4goKCcHd3186zdOlSgoODCQkJ4cSJEyxfvpzIyEgOHz7M+fPnmTlzJgUFBUyZMoXAwECsrKxYtmwZERERtGrViu+//56dO3fSvn37cmUUBKF8JEmioasVDV2tSL1VwJYzGWw7l8Xfl2/i4VDc1BXiWb2bumRZJutOkbY4XM/J/6dgFJCUk8+tAo12XoUEztam1LAxRVIqOJZ8i13ns3XWZ2eu1Ckqd/9dw9YMmyeswOj9s72wsJBr166Rna27sxo2bPjIZc+ePYubmxuurq4AtGzZkgMHDugUkStXrjB48GAA/P39mTVrlna6n58fSqUSpVKJh4cHsbGxPPfcc8TFxTFmzBgAQkJC+OWXX0QREQQDcrY25eUmLvRt5MTui8VNXfP2J7EkNoUwLwc6ezvgamP26BVVAlmWycor0ikQ12/+/6ji9n2FwsW6+Evfx8nunkJghou1qXbwyrvjUd0p1JB08//FJ+mfo5YTybeJuq/A2JorqWlrSg0bM2rYmVHDxpSadmbUsDHDxtz4CoxeReTUqVN8+umnFBQUkJubi6WlJXl5eajVar766qtHLp+enq5tmgJQq9UkJCTozOPh4UFMTAydO3dm//795ObmkpOTg4eHB6tXr6ZLly7cuXOHuLg43N3dycnJwcrKCqWyeKerVKoSpyHftX37drZv3w7AjBkzcHJy0udll2BiYlLuZQ3BmPKKrJXHUHn7ubnQt5nMsWvZrD56nQ2nbvDbqXRaeap4MaAmTWvbl2jqetyssiyTfruAK5m5XMnKK/47M0/7+HZ+kXZepQRudha4O1gSWFuFu33xv90dLKlhZ67X0C/35n3QlXJ3Cou4mpX3/xz//H0yLY8/L2Rz7wi4dhYmxRnsLajtYEkth+K/3R0ssLN4vH6myvoc6FVEfvzxR7p27UqXLl0YOnQoixcvZvXq1SUuQHyQ0gYKvv/DM2jQIBYtWkRUVBS+vr6oVCqUSiUBAQGcO3eOiRMnYmdnh7e3t7Zw6CssLIywsDDt4/KOZGlMI3aCceUVWSuPofPWMocxzZwY0NCerWcy+f1sJrsT06ljb0Znb0faPmOPxT9NXfpklWWZjLwinb6Je48u8gp1jyhcbYp/9bf1tCv+1X/3iMLGtJQ7P2qg6BZZGbf0em367ls7wM8e/OwtwMMCcADgTqGG5FsFXM/O5/rNfK5lFx8dHbmcwR+nU3UKjK2ZAjdbs3/ym97TD2OGnR5HMAYdxffatWt07txZZ1r37t15/fXX9eonUavVpKWlaR+npaXh6OioM49KpWLcuHEA5OXlERMTg5WVFQA9e/akZ8+eAHz++ee4ublha2vL7du3KSoqQqlUkp6erjOelyAI1YuTlSkDA53p00jNnos5bDydzrcHklkam0qYlz2dvR25+0NZWyi0X675XP+nueh6Tj55hf//elXeLRS2Zvi7WOn0Qzhbl1Yoqg9zEwV17M2pY29e4rn8Ig3JNwt0mseu5+RzMvU20RcKdQqMjZmiROd+DdvipjJbc2WlntygVxGxsrIiNzcXa2trHBwcuHLlCjY2NuTl5em1ES8vL65fv05KSgoqlYq9e/fy5ptv6sxz96wshULB2rVradu2LVDcKX/r1i1sbW25ePEily5dIiAgAEmS8Pf3Z9++fbRq1YqoqCiCgoLK+PIFQahqZkoFoc/Y09bTjlM3ctl0OoONpzP47VQGjWveIDv3zgMKRfEXpL+Llc6v8epeKMrLTKmgtr05tUspMAVF/++DuXskdj0nn1Opuey5mI3mngpjbaagho0Znk5p9PG1w8WmYk+/1quING/enCNHjtC6dWtCQ0OZOnUqSqWS5557Tq+NKJVKhg0bxkcffYRGo6Ft27bUrl2blStX4uXlRVBQEPHx8SxfvhxJkvD19WX48OFAcYf++++/DxQXs8jISG1z1oABA/jss89YsWIFnp6e4roVQTAikiTh62yFr7MVabcL2JqQydGUPNSWJjR0sdL5Ve1sbYryCSwU5WX6iAKTfLNA95TkmwXEJWWj9K/4W5zrfWfDe506dYrc3FwCAgJQKKrvaXsPIu5sWP2IrJXHmPIaU1YwrryV1SfyyAqg0WiIjIykoOD/A641aNCAJk2aGGUBEQRBECrOI6uAQqFAoVDoFBFBEARBAD37RDp37szcuXPp0aMHKpVKp6f/7gWEgiAIwtNHryKyaNEiAI4dO1biuZUrV1ZsIkEQBMFo6FVERKEQBEEQSiN6xgVBEIRy0+tI5P3333/gFY9Tp06t0ECCIAiC8dCriNx/EV9mZia7du3i+eefr5RQgiAIgnHQq4iEhISUmNaiRQvmzZvHiy++WNGZBEEQBCNR7j4RlUrFxYsXKzKLIAiCYGT0OhLZuXOnzuP8/HxiYmLw9vaulFCCIAiCcdCriOzevVvnsbm5OT4+PkRERFRKKEEQBME46FVEJk+eXNk5BEEQBCOkV5/In3/+WaL/48KFC0RHR1dKKEEQBME46FVEVq5cqXOPdCgeVnjFihWVEkoQBEEwDnoVkdzcXO2tau+ysrLi1i397kMsCIIgPJn0KiLu7u7s27dPZ9r+/ftxd3evlFCCIAiCcdCrY33AgAF8/PHH7N27Fzc3N5KSkjh+/Djjx4+v7HyCIAhCNaZXEWnQoAFz5sxhz5493Lhxg3r16jFkyBCcnJwqO58gCIJQjelVRAoKCnBwcKB79+7aaYWFhRQUFGBqalpp4QRBEITqTa8+kQ8//JDExESdaYmJiXz00UeVEkoQBEEwDnoVkUuXLlG/fn2dafXq1RNjZwmCIDzl9CoiVlZWZGVl6UzLysrC3Ny8UkIJgiAIxkGvItK8eXM+//xzLl26xJ07d7h06RJfffUVLVq0qOx8giAIQjWmV8d6v379WLJkCRMmTKCgoAAzMzPatm1Lv379KjufIAiCUI3pVUTMzMwYMWIEw4cPJycnh4yMDP7880/GjBnDd999V9kZBUEQhGpKryICkJ2dzZ49e/jzzz+5cOECvr6+DBkypBKjCYIgCNXdQ4tIYWEhBw8eJCoqiqNHj+Lm5karVq1ISUnhrbfewt7evqpyCoIgCNXQQ4vIyJEjUSgUtGnThj59+vDMM88AsG3btioJV50UzZtOctwRQDZ0FL0lI2EsedM9vZG7D0Kq52voKIIglMFDi4iHhwenTp3i7Nmz1KhRAxcXF2xsbKoqW7UiNX4WSw8vcnNvGzqK3iwtrYwjr0ZD0aG/0HzyLtKzzyP1GoykdjF0KkEQ9PDQIjJlyhRSU1P5888/2bBhA4sXL6Zx48bcuXOHoqKiqspYLShah2Pr5MSdGzcMHUVvxpRXPexNUn+aj7xtDfKRfUjtuyN16oVkYfXohQVBMBjllClTpjxsBmtra/z8/OjUqRP+/v4kJydz5coVtm/fTk5ODo0bN66iqBUnJyenXMtZWVlx+7YR/LL/hzHltbazJ7eOF9JzbSErHTlqM/LeHWBlA+51kSS9LmmqEsa0X8G48hpTVjCuvI+b1dbWttTpep+dBcWj+TZo0IChQ4eyf/9+cXtcocJJKmekEW8jh3ZBs2oh8o9fIu/ciKLvCCSfRoaOJwjCfcpURO4yMzOjdevWtG7duqLzCAIA0jM+KN79BPnAbuRff0Qz+z1o0gLFi0ORXGoYOp4gCP+oPm0EgnAfSZJQNAtGMW0eUveBEB+L5v3X0fyyGPm2uDWzIFQHoogI1Z5kZo4iog+KD79FatEG+Y91aCaORhO1BfkpO8FDEKqbcjVnlUdsbCyLFy9Go9HQrl07nRtcAaSmpvLNN9+QnZ2NjY0NkZGRqNVqAJYtW8bhw4eRZZlGjRoxdOhQJEliypQpZGRkYGZmBsDEiRPFBZBPMMlBhTRkDHLbLmhWLUD+6RvkqM0o+gxD8mti6HiC8FSqkiKi0WhYuHAhEydORK1WM378eIKCgnB3d9fOs3TpUoKDgwkJCeHEiRMsX76cyMhITp8+zenTp5k9ezYAkyZNIj4+Hn9/fwDefPNNvLy8quJlCNWE5OGFYtx0OPI3ml8Wo5k7GRo/i6L3UCQ390evQBCEClMlzVlnz57Fzc0NV1dXTExMaNmyJQcOHNCZ58qVKzRqVHz2jb+/PwcPHgSK28Xz8/O1t+MtKioSRxsCkiQh/aslig/mIb04BM6cQDMlEs2K+ci3yncKtyAIZVclRyLp6enapikAtVpNQkKCzjweHh7ExMTQuXNn9u/fT25uLjk5OXh7e+Pv788rr7yCLMt07NhR5whm3rx5KBQKmjdvTq9evZAkqcT2t2/fzvbt2wGYMWMGTk5O5XodJiYm5V7WEIwp72NlHfAKmogXufnzAnK3/wYxUVj3G4Flhx5IJhX/ETem/QrGldeYsoJx5a2srFVSRGS55PhN93/ZDxo0iEWLFhEVFYWvry8qlQqlUklSUhJXr17l22+/BWDatGnEx8fj5+fHm2++iUqlIjc3lzlz5hAdHU2bNm1KbCssLIywsDDt4xvlvIrbycmp3MsagjHlrZCsvYeheC4UzaqF5CyYS87GX1D0HgqNgkr9cVFexrRfwbjyGlNWMK68j5u1Zs2apU6vkiKiVqtJS0vTPk5LS8PR0VFnHpVKxbhx4wDIy8sjJiYGKysrtm/fTv369bGwsACgSZMmJCQk4Ofnh0qlAsDS0pLWrVtz9uzZUouI8PSQ3OuieOsDOHYQzS+L0Hw5DfyaoOgzHKlWHUPHE4QnTpX0iXh5eXH9+nVSUlIoLCxk7969BAUF6cyTnZ2NRqMBYO3atbRt2xYorp4nT56kqKiIwsJC4uPjqVWrFkVFRWRnZwPFQ9YfOnSI2rVrV8XLEao5SZKQAp5FMeULpL7D4cIZNFPfRPPTN8g5WYaOJwhPlCo5ElEqlQwbNoyPPvoIjUZD27ZtqV27NitXrsTLy4ugoCDi4+NZvnw5kiTh6+vL8OHDAWjRogUnTpzQHqUEBgYSFBREXl4eH330EUVFRWg0Gho1aqTTZCUIkokpUlg35BZtkTesKB6PKyYaqUsfpNAuSCamho4oCEZPkkvrsHjCXbt2rVzLGVP7JxhX3qrIKl+/jOaXxXD8IDi7oeg9DAKbl7m/xJj2KxhXXmPKCsaVt7L6RMQV68JTQ6pRG+Wb76MYMwVMTNHMm45mzkTkS4mGjiYIRqvKrlgXhOpCavgvFL4ByNG/I//2E5oP30JqHY7UbQCSveOjVyAIgpYoIsJTSVIqkdp2Rm4ejLxxJfLOTcj7dyN1fhEpvBuSqZmhIwqCURDNWcJTTbKyQdFnOIqpX4FvAPLapWgmvYZ8cE+p1zcJgqBLFBFBACTXmihfn4Bi7DSwtEbz3Uw0M8cjX0h49MKC8BQTRUQQ7iH5BqCY9CnSy29A8lU0H72NZtFc5Iy0Ry8sCE8h0SciCPeRFEqk59sjB7VG3vIL8h+/IR/ai9SxF1L7XmfIAQAAGqVJREFUHoaOJwjViigigvAAkqUVUs/ByM93QP71R+TfliPv3kZu/5HIljaGjqe3IpMAQ0d4IsnpN9BYiBMwRBERhEeQnN2QRr+LfCYOzaqFZH/9saEjlckNcwukDj2R2vdAMjc3dByjJ9+6ibxxBfKuTaS71ET+z8dItnaGjmUwoogIgp4kb38UE2Zjn5FC1o1UQ8fRT1ERpvt2ceefoyip58tIzYKRFKI7tKzkwkLk6K3Iv/0Mt28iBbWmKDYGvv4QxdhpSGZPZ4EWRUQQykBSKDDzaYikNo6hLgAcgsNI3RuFZuVC5IWfIu/ciKLvCCSvBoaOZjTk44fQ/LIIrl8Gn0bF+6+2JzYJx8maNRHNwk9RjHoHSaE0dNQqJ36OCMJTQPJuiOK9OUhDx0D6DTQz3kHz/SzktBRDR6vW5GuXKPp8CpovpkJRIYrXJ6B4+0Ok2p4AWDzXFqnPMDj8N/KqRU/ltUXiSEQQnhKSQoHUsh3yv1oi/74G+fe1yLExSOHdkTr1QrKwNHTEakPOyUbesBz5z61gbonUZzhS286ljvysCOuGJi0VeftvoHJGat/dAIkNRxQRQXjKSBaWSN0GILduj7xmCfLmVch//YHUYxDSc6FPdX+JXFiAvGsz8oYVcCcXKbgjUtf+j+w4l3oPQ864gfzLImSVE1JQ6ypKbHiiiAjCU0pSOyONfBs5NALNygXIP3yBvHMTir7DkbwbGjpelZJlGY7GoPnlB0i5Bv7/3A2zpn53w5QUChTDx6LJykCzcC4KexVSfb/KDV1NPL0/OQRBAEDyaoBi/CykEW/DzSw0syZQ9M0M5NQkQ0erEvKV82g+nYTm6+mgVKJ4czLKf0/Vu4DcJZmaoXj9PXByQfPVh8jXr1RS4upFFBFBEJAkCUXzNig++Aap2wA4cQjN+6+hWb0Y+fYtQ8erFHJ2BpolX6H54C24fB7ppVdQvP85UqOm5V6nZGOH4s3JoFSi+XwKclZGBSb+X3v3HhdVnf9x/HXOjAg6PwkGXBTzAmJl5t3ES7Ze2vypXVcwTcvMVFR0t7yka2WZa5msSj8QV2QzSzN3y7S0djX9abWV15UkFdF1NWW5euU6c76/P+bXPCJAZQQOw36ef+HMnHPenIgP53utm6SICCHctIYN0YeNQF+YhHb3vajPPsSYNwnjfz9FGU6z41ULVVqCse0vGL+bhPpqB9rAYegLV6IPGIZmvfkWfi04BH3ai3D5Ikb8K6iiwmpIXXdJERFClKPdYkd/ajr6vD9ASCjqnUSMV36DSjtkdjSPKaVQ+7/EeHEK6oM1rvke8//HNeejcfUuY6O1jkCfOAvOnHINpXbWjwJcESkiQohKaa3aos9chD5pNhQXYSx9Eef/vIrK/MHsaFWiTp/AeGMORtLr0NAX/bevYJk6Dy0ktMauqXXsgTZ6EqTuQ727ot7OIZHRWUKIa9I0Dbr1Qe/YA7V9C2rr+xjzp6L1H4o27LFq/yu+OqkLuagP1qK+3gm2JmhjJru2Qq6lmeV6v8GuOSRbN7rmkAwbUSvXrU1SRIQQN0Rr4IP2379G9RmA2vQuasfHqL/vRHtwpGs+RTX0J1QXVVyM+tuHqG1/AcPpWnxySBRao8a1nkV7eDTk5aA+ehcjMBi994Baz1CT6s5/dSGEV9CaBKA9MRXVfyjG+6tR6/+I2rnVNa/iJkY2VQelFOrb3a4+j7wc6NobffhYtOAQ0zJpmgZPTkVdzEO9/SbqlkC09p1Ny1PdpE9ECOER7dY26M8ucM2NMAyM+JdxLnsJ9cO/TMmjMo5ivDYLlRwHtiboM36PJeZ5UwvIjzRrA/RJz0NIC4wVi1BnTpkdqdpIERFCeEzTNLTOPdFffhMt+mk4eRzjlWkY7yahLl+qlQwqNxtjVRzGa7MgNwtt7HT03/0B7ba6Netea9TYNYfEtxFG/MuoPC/ZTuA6pIgIIW6aZm2Aft9D6AtXot07GLX7U4zfTcT46yaUo7RGrqmKCjE2vYPxQgzq4N/RhkSjv5qE3mdgnV3/SwsMQp/+kmukW/wrqIIrZke6aXXzTgshvJL2X03QR01Cfykewm9DbUzBeGkq6tDX1TbEVRkGxpc7MObFoD55H61LJPqCFeiPjPaKlYi1Fq3RY+ZA5g8YiYtqrMjWFikiQohqpzVviWX6/P9fAsSKkfB7jLh5N90XoI4fwfj9DNRbyyEwCP35xejPzECzB1dT8tqh3dEJ7clYOJbqWvjSi+eQyOgsIUSN0e7qhn5HJ9Sez1AfrcNY8BvXPI2HH0drEnDD51HZmRh/eQv2fwUBQWhPP+v12/zqvfpj5GWjNr3jmkPy6BNmR/KIFBEhRI3SrFa0/kNRd9+L+ngDaufHqL17XPM2Bj2I1sCn0mNVYQHqk/dROzaDbnHt7fGrR9Aa1o/9zLUhUZCXjdr2Zwx7U/R7B5sdqcqkiAghaoXW2IY24mnUvYNdqwN/8Dbqfz9FHz4WuvVxzaf4f8pwor7Y7vor/fJFtF790R55Ai3Abt43UAM0TYNRk1D5uah3k1C32NE69TA7VpV477OgEMIraSGhWKbOQ392Afj6YaxcjLF4Duqf6QCo7/+BseC3qLUJ8Ivm6HPj0Mf9tt4VkB9pFgv6hJnQMgzjj4tRp9LNjlQlmvLmHh0PnTt3zqPjgoKCyMnJqeY0Nceb8krWmlOX87qeOP6G2vQuXL6INawdjpPHwd60wieUuqY67626mI+xaCaUFLs2CavmSZI3m7V58+YVvi5PIkII02i6Bb3fYPRXk9DufxRVWIj26BPoCxLRuvet0wWkumn+Aei/me+a/b/8ZdSV2pmsebOkiAghTKc1aow+fCxBiRvQ/3v4NTvb6zMtpAX61N9BbpZri92SYrMjXVetdawfOnSIP/3pTxiGwcCBA3n44YfLvJ+dnc2KFSu4dOkSNpuN2NhY7HZXG+g777zDgQMHUEpx11138dRTT6FpGidPniQhIYGSkhK6dOnifl0IIbyV1rY9+vhnXX1Fq/+APnFWrS1d74laeRIxDIPVq1czd+5cli5dypdffsnZs2U3sV+7di39+vVjyZIlDB8+nHXr1gFw7Ngxjh07xpIlS4iLiyMjI4O0tDQAVq1axcSJE4mPjyczM5NDh7x31zUhhPiR1q0PWtQ4OPB31PspZse5plopIidOnCAkJIRf/OIXWK1Wevfuzd69e8t85uzZs9x1110A3Hnnnezbtw9wDYErKSnB4XBQWlqK0+nE39+f/Px8CgsLadeuHZqm0a9fv3LnFEIIb6Xf9xDaoAdRO7Zg/O0js+NUqlaas/Ly8txNUwB2u5309LLD2Fq1asU333zDkCFD+PbbbyksLOTy5cu0a9eOO++8kwkTJqCUYvDgwbRo0YKMjIxy58zLy6vw+tu3b2f79u0AvPbaawQFBXn0fVitVo+PNYM35ZWsNceb8npTVqj5vCpmFhevXqJ4Ywq2Vm3wvYkNrWoqa60UkYpGEf+872LMmDGkpKSwa9cu7rjjDgIDA7FYLGRmZvLDDz+QlJQEwIIFC0hLS8PH58Y73gYNGsSgQYPc//Z0mFtdHipZEW/KK1lrjjfl9aasUDt51egpkJXJxaUvc1mzokW09+g8Xj3E1263k5ub6/53bm4uAQFl180JDAxkxowZLF68mJEjRwLQqFEjvv32WyIiIvD19cXX15cuXbqQnp5e4TkDAwNr49sRQohao/k0RJ86D+xNXSO2zp+9/kG1qFaKSHh4OOfPnycrKwuHw8FXX31F9+7dy3zm0qVLGIYBwIcffkj//v0BV/X8/vvvcTqdOBwO0tLSCA0NJSAgAD8/P44fP45Sit27d5c7pxBC1AearYlrHxKLBWP5fNTFfLMjudVKc5bFYmHcuHEsXLgQwzDo378/t956Kxs2bCA8PJzu3buTlpbGunXr0DSNO+64g6effhqAyMhIvvvuO2bMmAFA586d3cVi/PjxJCYmUlJSQufOnenSpUttfDtCCFHrtOAQ9GkvYrwxF+PNBegzFtaJ/VNk2ZMqkPbamiNZa4435fWmrGBOXvWPvRgJC6FDV/Qpv0Oz3NgcEq/uExFCCFE9tE490B6fBKn7UO+uMH1DK1kKXgghvIx+72DXhlZbN4K9KdrQaNOySBERQggvpD082rWh1aZ3MAKD0Xv1NyWHFBEhhPBCmqbBk7GoC3moNfEo/wC09p1rPYf0iQghhJfSrA3QY+ZASAuMFYtQZ0/VegYpIkII4cW0Ro3Rp70Ivo0wlr+Cyqvd0WJSRIQQwstpgcHo01+E4kKM+JdRBVdr7dpSRIQQoh7QWrRxNW1lnnU1bTlKa+W6UkSEEKKe0O7ohPZELBw9jFrzZq3MIZHRWUIIUY/ovQdg5OegNr0DgcFoj4yp0etJERFCiHpGGxIFuVmorRsx7MHo/QbX2LWkOUsIIeoZTdPQHo+Bu7qj3klC/aPmdn2VIiKEEPWQZrGgT5gJLcMw/riY0vS0GrmOFBEhhKinNF8/9NgX4L/8ubBwJio7s9qvIUVECCHqMc0/AH36fKxh7aBhw2o/vxQRIYSo57RmLQh4cSlak4Drf7iKpIgIIYTwmBQRIYQQHpMiIoQQwmNSRIQQQnhMiogQQgiPSRERQgjhMSkiQgghPCZFRAghhMc0VRsLzgshhKiX5EmkCp5//nmzI1SJN+WVrDXHm/J6U1bwrrw1lVWKiBBCCI9JERFCCOExy/z58+ebHcKbhIWFmR2hSrwpr2StOd6U15uygnflrYms0rEuhBDCY9KcJYQQwmNSRIQQQnjManYAb5CYmMiBAwfw9/cnLi7O7DjXlJOTQ0JCAhcuXEDTNAYNGsSQIUPMjlWpkpISXnrpJRwOB06nk8jISKKjo82OdU2GYfD8888TGBhY54d4TpkyBV9fX3Rdx2Kx8Nprr5kdqVJXr14lKSmJM2fOoGkaMTExtGvXzuxYFTp37hxLly51/zsrK4vo6GiGDh1qYqrKffzxx3z++edomsatt97K5MmT8fHxqZ6TK3FdR44cURkZGerZZ581O8p15eXlqYyMDKWUUgUFBWratGnqzJkzJqeqnGEYqrCwUCmlVGlpqZozZ446duyYyamubcuWLWrZsmVq0aJFZke5rsmTJ6uLFy+aHeOGvPnmm2r79u1KKdfPwpUrV0xOdGOcTqcaP368ysrKMjtKhXJzc9XkyZNVcXGxUkqpuLg4tXPnzmo7vzRn3YD27dtjs9nMjnFDAgIC3CMw/Pz8CA0NJS8vz+RUldM0DV9fXwCcTidOpxNN00xOVbnc3FwOHDjAwIEDzY5SrxQUFPD9998zYMAAAKxWK40bNzY51Y1JTU0lJCSE4OBgs6NUyjAMSkpKcDqdlJSUEBBQfdvkSnNWPZaVlcWpU6do27at2VGuyTAMZs+eTWZmJvfffz8RERFmR6rUW2+9xejRoyksLDQ7yg1buHAhAPfddx+DBg0yOU3FsrKyaNKkCYmJiZw+fZqwsDDGjh3r/gOjLvvyyy/p06eP2TEqFRgYyAMPPEBMTAw+Pj506tSJTp06Vdv55UmknioqKiIuLo6xY8fSqFEjs+Nck67rvPHGGyQlJZGRkcG//vUvsyNVaP/+/fj7+3vVvIAFCxbw+uuvM3fuXD777DPS0tLMjlQhp9PJqVOn+NWvfsXixYtp2LAhmzZtMjvWdTkcDvbv309kZKTZUSp15coV9u7dS0JCAitXrqSoqIjdu3dX2/mliNRDDoeDuLg47rnnHnr27Gl2nBvWuHFj2rdvz6FDh8yOUqFjx46xb98+pkyZwrJly/juu++Ij483O9Y1BQYGAuDv70+PHj04ceKEyYkqZrfbsdvt7qfQyMhITp06ZXKq6zt48CBt2rThlltuMTtKpVJTU2natClNmjTBarXSs2dPjh8/Xm3nl+asekYpRVJSEqGhoQwbNszsONd16dIlLBYLjRs3pqSkhNTUVB566CGzY1Vo1KhRjBo1CoAjR46wZcsWpk2bZnKqyhUVFaGUws/Pj6KiIg4fPszw4cPNjlWhW265Bbvdzrlz52jevDmpqam0aNHC7FjXVdebsgCCgoJIT0+nuLgYHx8fUlNTCQ8Pr7bzSxG5AcuWLSMtLY3Lly8zadIkoqOj3R2Adc2xY8fYvXs3LVu2ZObMmQCMHDmSrl27mpysYvn5+SQkJGAYBkopevXqRbdu3cyOVS9cvHiRJUuWAK7mor59+9K5c2eTU1Vu3LhxxMfH43A4aNq0KZMnTzY70jUVFxdz+PBhJkyYYHaUa4qIiCAyMpLZs2djsVho3bp1tfaNybInQgghPCZ9IkIIITwmRUQIIYTHpIgIIYTwmBQRIYQQHpMiIoQQwmNSRMR/tISEBN577z1Trq2UIjExkaeeeoo5c+ZU+/lzcnIYM2YMhmFc97M/rkLrdDorfP/999+v8xMrhTmkiIg6ZcqUKTzzzDMUFRW5X9uxYwf1cRfno0ePcvjwYVasWMGiRYvKvb9r1y6io6PZvHlzmdcnTZrEkSNHrnv+oKAg1q5di67L/+ai5shPl6hznE4nW7duNTtGld3IX/w/lZ2dTXBw8DUXGbTZbHz00UcUFBTcbLw6o6r3SdRtMmNd1DkPPvggH330Effff3+55cCzsrKYOnUq69evx2KxADB//nzuueceBg4cyK5du9ixYwfh4eHs2rULm81GbGws58+fZ8OGDZSWljJ69Gh++ctfus956dIlFixYQHp6Om3atGHq1KnuZb1/+OEHUlJSOHnyJE2aNGHEiBH07t0bcDWF+fj4kJOTQ1paGjNnzqRjx45l8ubl5bFq1SqOHj2KzWbjoYceYtCgQXz++eesXr0ah8PBmDFjeOCBByrcjCs0NJTGjRvzySefEBUVVe59wzDYvHkzO3bs4OrVq3To0IEJEyZgs9nK3ausrCwSEhI4deoUERERNGvWjIKCgjJLt+zZs4cNGzZQUlLC0KFDefTRR93vlZaWsnTpUg4ePEizZs2IiYmhdevWAJw9e5bk5GT++c9/EhgYyKhRo+jevXul98nhcLB27Vpyc3Px8/Nj6NChPPjggzf6IyLqEHkSEXVOWFgYd955J1u2bPHo+PT0dFq1akVKSgp9+/Zl2bJlnDhxgvj4eGJjY0lJSSnTXPbFF1/w61//mtWrV9O6dWt3239RURGvvvoqffv2JTk5menTp7N69WrOnDlT5thHHnmENWvWcPvtt5fLsnz5cux2OytXruS5555j/fr1pKamMmDAAJ555hnatWvH2rVrr7mb42OPPcYnn3zClStXyr23bds29u7dy/z581m5ciU2m43k5OQKz7N8+XLCw8NJSUkhKiqKPXv2lPvM0aNHWb58OS+88AJ//vOfOXv2rPu9ffv20atXL1JSUujTpw9vvPEGDocDh8PB66+/TseOHUlOTnYvX3Lu3LlK71NSUhITJkzg7bffJi4ujg4dOlT6/Yu6TYqIqJOio6PZtm0bly5dqvKxTZs2pX///ui6Tu/evcnNzWX48OE0aNCATp06YbVayczMdH++a9eutG/fngYNGjBy5EiOHz9OTk4OBw4cIDg4mP79+2OxWAgLC6Nnz558/fXX7mN79OjB7bffjq7r5bYbzcnJ4ejRozz++OP4+PjQunVrBg4cWOVluFu3bk3Hjh0rXBp9+/btPPbYY9jtdho0aEBUVBTffPNNuQ7ynJwcMjIyGDFiBFarldtvv73CNcqioqLcWVu1asXp06fd74WFhREZGYnVamXYsGGUlpaSnp5Oeno6RUVFPPzww1itVjp06EDXrl354osvKr1PFouFs2fPUlBQgM1m86rl9UVZ0pwl6qSWLVvSrVs3Nm3aRGhoaJWO9ff3d3/94y/2ny7V7ePjU+ZJxG63u7/29fXFZrORn59PdnY26enpjB071v2+0+mkX79+FR77c/n5+dhsNvz8/NyvBQUFkZGRUaXvB2DEiBHMnTu33MrM2dnZLFmypMxukLquc/HixTKfy8vLw2az0bBhwzJZcnJyynzup/epYcOGld4nXdex2+3k5+e7z/XTDvzg4OAyO2r+/D4999xzfPDBB6xbt46WLVvy+OOP19n91MW1SRERdVZ0dDSzZ88u84vzx07o4uJi92ZbFy5cuKnr5Obmur8uKiriypUrBAQEYLfbad++PS+88EKlx15rK9+AgACuXLlCYWGhu5Dk5OS49/ioitDQUO6++24++OCDMq/b7XZiYmIqbErLysoql6W4uNhdSH5eQK7np/fJMAxyc3Pd26zm5ORgGIa7kOTk5NCsWTP3539+n9q2bcusWbNwOBx8+umnLF26lBUrVlQpj6gbpDlL1FkhISH06tWLbdu2uV9r0qQJgYGB7NmzB8Mw+Pzzz/n3v/99U9c5ePAgR48exeFw8N577xEREUFQUBDdunXj/Pnz7N692932f+LEiTL9BNcSFBTEbbfdxrp16ygpKeH06dPs3LmTe+65x6OcUVFR7Nq1i6tXr7pfu++++3jvvffIzs4GXIME9u7dW+7Y4OBgwsPD2bhxIw6Hg+PHj7N///4qXf/kyZPuprKtW7fSoEEDIiIiiIiIwNfXl82bN+NwODhy5Aj79++vdJ8Nh8PBnj17KCgowGq10qhRIxmG7MXkSUTUacOHDy/XATxx4kSSk5NZv349AwYMuOlmkD59+rBx40aOHz9OWFiYe7SSn58f8+bNY82aNaxZswalFK1ateLJJ5+84XNPnz6dVatWMXHiRGw2G1FRUeVGcN2opk2b0q9fP/7617+6XxsyZAgAr776Kvn5+fj7+9OrVy969OhR7vjY2FgSExMZN24cbdu2pXfv3lUabtu9e3e++uorEhISCAkJ4bnnnsNqdf0KmTVrFsnJyXz44YcEBgYyderUazZD7t69m5SUFAzDoHnz5sTGxt5wDlG3yH4iQvyHWrp0KaGhodccGSbE9cgzpBD/IU6cOEFmZiaGYXDo0CH27dtX4ROLEFUhzVlC/Ie4cOECcXFxXL58Gbvdzvjx42nTpo3ZsYSXk+YsIYQQHpPmLCGEEB6TIiKEEMJjUkSEEEJ4TIqIEEIIj0kREUII4bH/A1x59O3tshqyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup arrays to store train and test accuracies\n",
    "neighbors = np.arange(1, 9)\n",
    "train_accuracy = np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "# Loop over different values of k\n",
    "for i, k in enumerate(neighbors):\n",
    "    # Setup a k-NN Classifier with k neighbors: knn\n",
    "    knn = KNeighborsClassifier(k)\n",
    "\n",
    "    # Fit the classifier to the training data\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    #Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "\n",
    "    #Compute accuracy on the testing set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)\n",
    "\n",
    "# Generate plot\n",
    "plt.title('k-NN: Varying Number of Neighbors')\n",
    "plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')\n",
    "plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-8: Importing data for supervised learning\n",
    "In this chapter, you will work with Gapminder (https://www.gapminder.org/data/) data that we have consolidated into one CSV file available in the workspace as 'gapminder.csv'. Specifically, your goal will be to use this data to predict the life expectancy in a given country based on features such as the country's GDP, fertility rate, and population. As in Chapter 1, the dataset has been preprocessed.\n",
    "\n",
    "Since the target variable here is quantitative, this is a regression problem. To begin, you will fit a linear regression with just one feature: 'fertility', which is the average number of children a woman in a given country gives birth to. In later exercises, you will use all the features to build regression models.\n",
    "\n",
    "Before that, however, you need to import the data and get it into the form needed by scikit-learn. This involves creating feature and target variable arrays. Furthermore, since you are going to use only one feature to begin with, you need to do some reshaping using NumPy's .reshape() method. Don't worry too much about this reshaping right now, but it is something you will have to do occasionally when working with scikit-learn so it is useful to practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of y before reshaping: (139,)\n",
      "Dimensions of X before reshaping: (139,)\n",
      "Dimensions of y after reshaping: (139, 1)\n",
      "Dimensions of X after reshaping: (139, 1)\n"
     ]
    }
   ],
   "source": [
    "# Import numpy and pandas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Read the CSV file into a DataFrame: df\n",
    "df = pd.read_csv('gapminder_all.csv', index_col=0)\n",
    "\n",
    "# Create arrays for features and target variable\n",
    "y = df['life'].values\n",
    "X = df['fertility'].values\n",
    "\n",
    "# Print the dimensions of X and y before reshaping\n",
    "print(\"Dimensions of y before reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X before reshaping: {}\".format(X.shape))\n",
    "\n",
    "# Reshape X and y\n",
    "y = y.reshape(-1, 1)\n",
    "X = X.reshape(-1, 1)\n",
    "\n",
    "# Print the dimensions of X and y after reshaping\n",
    "print(\"Dimensions of y after reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X after reshaping: {}\".format(X.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-9: Exploring the Gapminder data\n",
    "As always, it is important to explore your data before building models. On the right, we have constructed a heatmap showing the correlation between the different features of the Gapminder dataset, which has been pre-loaded into a DataFrame as df and is available for exploration in the IPython Shell. Cells that are in green show positive correlation, while cells that are in red show negative correlation. Take a moment to explore this: Which features are positively correlated with life, and which ones are negatively correlated? Does this match your intuition?\n",
    "\n",
    "Then, in the IPython Shell, explore the DataFrame using pandas methods such as .info(), .describe(), .head().\n",
    "\n",
    "In case you are curious, the heatmap was generated using Seaborn's heatmap function and the following line of code, where df.corr() computes the pairwise correlation between columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x204ad806e88>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), square=True, cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 139 entries, 0 to 138\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   population       139 non-null    float64\n",
      " 1   fertility        139 non-null    float64\n",
      " 2   HIV              139 non-null    float64\n",
      " 3   CO2              139 non-null    float64\n",
      " 4   BMI_male         139 non-null    float64\n",
      " 5   GDP              139 non-null    float64\n",
      " 6   BMI_female       139 non-null    float64\n",
      " 7   life             139 non-null    float64\n",
      " 8   child_mortality  139 non-null    float64\n",
      " 9   Region           139 non-null    object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 11.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.390000e+02</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.549977e+07</td>\n",
       "      <td>3.005108</td>\n",
       "      <td>1.915612</td>\n",
       "      <td>4.459874</td>\n",
       "      <td>24.623054</td>\n",
       "      <td>16638.784173</td>\n",
       "      <td>126.701914</td>\n",
       "      <td>69.602878</td>\n",
       "      <td>45.097122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.095121e+08</td>\n",
       "      <td>1.615354</td>\n",
       "      <td>4.408974</td>\n",
       "      <td>6.268349</td>\n",
       "      <td>2.209368</td>\n",
       "      <td>19207.299083</td>\n",
       "      <td>4.471997</td>\n",
       "      <td>9.122189</td>\n",
       "      <td>45.724667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.773150e+05</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.008618</td>\n",
       "      <td>20.397420</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>117.375500</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.752776e+06</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.496190</td>\n",
       "      <td>22.448135</td>\n",
       "      <td>2899.000000</td>\n",
       "      <td>123.232200</td>\n",
       "      <td>62.200000</td>\n",
       "      <td>8.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.705130e+06</td>\n",
       "      <td>2.410000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.223796</td>\n",
       "      <td>25.156990</td>\n",
       "      <td>9938.000000</td>\n",
       "      <td>126.519600</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.791973e+07</td>\n",
       "      <td>4.095000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>6.589156</td>\n",
       "      <td>26.497575</td>\n",
       "      <td>23278.500000</td>\n",
       "      <td>130.275900</td>\n",
       "      <td>76.850000</td>\n",
       "      <td>74.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.197070e+09</td>\n",
       "      <td>7.590000</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>48.702062</td>\n",
       "      <td>28.456980</td>\n",
       "      <td>126076.000000</td>\n",
       "      <td>135.492000</td>\n",
       "      <td>82.600000</td>\n",
       "      <td>192.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population   fertility         HIV         CO2    BMI_male  \\\n",
       "count  1.390000e+02  139.000000  139.000000  139.000000  139.000000   \n",
       "mean   3.549977e+07    3.005108    1.915612    4.459874   24.623054   \n",
       "std    1.095121e+08    1.615354    4.408974    6.268349    2.209368   \n",
       "min    2.773150e+05    1.280000    0.060000    0.008618   20.397420   \n",
       "25%    3.752776e+06    1.810000    0.100000    0.496190   22.448135   \n",
       "50%    9.705130e+06    2.410000    0.400000    2.223796   25.156990   \n",
       "75%    2.791973e+07    4.095000    1.300000    6.589156   26.497575   \n",
       "max    1.197070e+09    7.590000   25.900000   48.702062   28.456980   \n",
       "\n",
       "                 GDP  BMI_female        life  child_mortality  \n",
       "count     139.000000  139.000000  139.000000       139.000000  \n",
       "mean    16638.784173  126.701914   69.602878        45.097122  \n",
       "std     19207.299083    4.471997    9.122189        45.724667  \n",
       "min       588.000000  117.375500   45.200000         2.700000  \n",
       "25%      2899.000000  123.232200   62.200000         8.100000  \n",
       "50%      9938.000000  126.519600   72.000000        24.000000  \n",
       "75%     23278.500000  130.275900   76.850000        74.200000  \n",
       "max    126076.000000  135.492000   82.600000       192.000000  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34811059.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.328945</td>\n",
       "      <td>24.59620</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>129.9049</td>\n",
       "      <td>75.3</td>\n",
       "      <td>29.5</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19842251.0</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.474353</td>\n",
       "      <td>22.25083</td>\n",
       "      <td>7103.0</td>\n",
       "      <td>130.1247</td>\n",
       "      <td>58.3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40381860.0</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.785170</td>\n",
       "      <td>27.50170</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>118.8915</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15.4</td>\n",
       "      <td>America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2975029.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.804106</td>\n",
       "      <td>25.35542</td>\n",
       "      <td>7383.0</td>\n",
       "      <td>132.8108</td>\n",
       "      <td>72.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21370348.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.1</td>\n",
       "      <td>18.016313</td>\n",
       "      <td>27.56373</td>\n",
       "      <td>41312.0</td>\n",
       "      <td>117.3755</td>\n",
       "      <td>81.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population  fertility  HIV        CO2  BMI_male      GDP  BMI_female  \\\n",
       "index                                                                         \n",
       "0      34811059.0       2.73  0.1   3.328945  24.59620  12314.0    129.9049   \n",
       "1      19842251.0       6.43  2.0   1.474353  22.25083   7103.0    130.1247   \n",
       "2      40381860.0       2.24  0.5   4.785170  27.50170  14646.0    118.8915   \n",
       "3       2975029.0       1.40  0.1   1.804106  25.35542   7383.0    132.8108   \n",
       "4      21370348.0       1.96  0.1  18.016313  27.56373  41312.0    117.3755   \n",
       "\n",
       "       life  child_mortality                      Region  \n",
       "index                                                     \n",
       "0      75.3             29.5  Middle East & North Africa  \n",
       "1      58.3            192.0          Sub-Saharan Africa  \n",
       "2      75.5             15.4                     America  \n",
       "3      72.5             20.0       Europe & Central Asia  \n",
       "4      81.5              5.2         East Asia & Pacific  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-10: Fit & predict for regression\n",
    "Now, you will fit a linear regression and predict life expectancy using just one feature. You saw Andy do this earlier using the 'RM' feature of the Boston housing dataset. In this exercise, you will use the 'fertility' feature of the Gapminder dataset. Since the goal is to predict life expectancy, the target variable here is 'life'. The array for the target variable has been pre-loaded as y and the array for 'fertility' has been pre-loaded as X_fertility.\n",
    "\n",
    "A scatter plot with 'fertility' on the x-axis and 'life' on the y-axis has been generated. As you can see, there is a strongly negative correlation, so a linear regression should be able to capture this trend. Your job is to fit a linear regression and then predict the life expectancy, overlaying these predicted values on the plot to generate a regression line. You will also compute and print the R2 score using sckit-learn's .score() method.\n",
    "\n",
    "**Difference b/w Mean-Square Error and R2-Score**\n",
    "Variance = the difference of observed values from the predicted value mean\n",
    "\n",
    "<code>err = y - y_pred\n",
    "variance = np.var(err) </code>\n",
    "\n",
    "R2-Score = (total variance explained by model) / total variance\n",
    "\n",
    "<code>y = y - np.mean(y_pred)\n",
    "g = (err)^2\n",
    "r2_score = 1 - (g/y)</code>\n",
    "\n",
    "MSE = average of the square of the errors\n",
    "\n",
    "<code>mse = sum(g) / len(g)</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6192442167740035\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import LinearRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create the regressor: reg\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Create the prediction space\n",
    "prediction_space = np.linspace(min(X), max(X)).reshape(-1,1)\n",
    "\n",
    "# Fit the model to the data\n",
    "reg.fit(X, y)\n",
    "\n",
    "# Compute predictions over the prediction space: y_pred\n",
    "y_pred = reg.predict(prediction_space)\n",
    "\n",
    "# Print R^2 \n",
    "print(reg.score(X, y))\n",
    "\n",
    "# Plot regression line\n",
    "plt.scatter(X, y)\n",
    "plt.plot(prediction_space, y_pred, color='black', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-11: Train/test split for regression\n",
    "As you learned in Chapter 1, train and test sets are vital to ensure that your supervised learning model is able to generalize well to new data. This was true for classification models, and is equally true for linear regression models.\n",
    "\n",
    "In this exercise, you will split the Gapminder dataset into training and testing sets, and then fit and predict a linear regression over all features. In addition to computing the R2 score, you will also compute the Root Mean Squared Error (RMSE), which is another commonly used metric to evaluate regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34811059.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.328945</td>\n",
       "      <td>24.59620</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>129.9049</td>\n",
       "      <td>75.3</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19842251.0</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.474353</td>\n",
       "      <td>22.25083</td>\n",
       "      <td>7103.0</td>\n",
       "      <td>130.1247</td>\n",
       "      <td>58.3</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40381860.0</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.785170</td>\n",
       "      <td>27.50170</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>118.8915</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2975029.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.804106</td>\n",
       "      <td>25.35542</td>\n",
       "      <td>7383.0</td>\n",
       "      <td>132.8108</td>\n",
       "      <td>72.5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21370348.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.1</td>\n",
       "      <td>18.016313</td>\n",
       "      <td>27.56373</td>\n",
       "      <td>41312.0</td>\n",
       "      <td>117.3755</td>\n",
       "      <td>81.5</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population  fertility  HIV        CO2  BMI_male      GDP  BMI_female  \\\n",
       "index                                                                         \n",
       "0      34811059.0       2.73  0.1   3.328945  24.59620  12314.0    129.9049   \n",
       "1      19842251.0       6.43  2.0   1.474353  22.25083   7103.0    130.1247   \n",
       "2      40381860.0       2.24  0.5   4.785170  27.50170  14646.0    118.8915   \n",
       "3       2975029.0       1.40  0.1   1.804106  25.35542   7383.0    132.8108   \n",
       "4      21370348.0       1.96  0.1  18.016313  27.56373  41312.0    117.3755   \n",
       "\n",
       "       life  child_mortality  \n",
       "index                         \n",
       "0      75.3             29.5  \n",
       "1      58.3            192.0  \n",
       "2      75.5             15.4  \n",
       "3      72.5             20.0  \n",
       "4      81.5              5.2  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('gapminder_drop_region.csv', index_col=0)\n",
    "X = df.drop('life', axis=1).values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8380468731430135\n",
      "Root Mean Squared Error: 3.2476010800369446\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=42)\n",
    "\n",
    "# Create the regressor: reg_all\n",
    "reg_all = LinearRegression()\n",
    "\n",
    "# Fit the regressor to the training data\n",
    "reg_all.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test data: y_pred\n",
    "y_pred = reg_all.predict(X_test)\n",
    "\n",
    "# Compute and print R^2 and RMSE\n",
    "print(\"R^2: {}\".format(reg_all.score(X_test, y_test)))\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-12: 5-fold cross-validation\n",
    "Cross-validation is a vital step in evaluating a model. It maximizes the amount of data that is used to train the model, as during the course of training, the model is not only trained, but also tested on all of the available data.\n",
    "\n",
    "In this exercise, you will practice 5-fold cross validation on the Gapminder data. By default, scikit-learn's cross_val_score() function uses R2 as the metric of choice for regression. Since you are performing 5-fold cross-validation, the function will return 5 scores. Your job is to compute these 5 scores and then take their average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81720569 0.82917058 0.90214134 0.80633989 0.94495637]\n",
      "Average 5-Fold CV Score: 0.8599627722793508\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary modules\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Create a linear regression object: reg\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Compute 5-fold cross-validation scores: cv_scores\n",
    "cv_scores = cross_val_score(reg, X, y, cv=5)\n",
    "\n",
    "# Print the 5-fold cross-validation scores\n",
    "print(cv_scores)\n",
    "\n",
    "print(\"Average 5-Fold CV Score: {}\".format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-13: K-Fold CV comparison\n",
    "Cross validation is essential but do not forget that the more folds you use, the more computationally expensive cross-validation becomes. In this exercise, you will explore this for yourself. Your job is to perform 3-fold cross-validation and then 10-fold cross-validation on the Gapminder dataset.\n",
    "\n",
    "In the IPython Shell, you can use %timeit to see how long each 3-fold CV takes compared to 10-fold CV by executing the following cv=3 and cv=10:\n",
    "\n",
    "<code>%timeit cross_val_score(reg, X, y, cv = ____)</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8718712782622265\n",
      "0.8436128620131361\n"
     ]
    }
   ],
   "source": [
    "# Create a linear regression object: reg\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Perform 3-fold CV\n",
    "cvscores_3 = cross_val_score(reg, X, y, cv=3)\n",
    "print(np.mean(cvscores_3))\n",
    "\n",
    "# Perform 10-fold CV\n",
    "cvscores_10 = cross_val_score(reg, X, y, cv=10)\n",
    "print(np.mean(cvscores_10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-14: Regularization I: Lasso\n",
    "In the video, you saw how Lasso selected out the 'RM' feature as being the most important for predicting Boston house prices, while shrinking the coefficients of certain other features to 0. Its ability to perform feature selection in this way becomes even more useful when you are dealing with data involving thousands of features.\n",
    "\n",
    "In this exercise, you will fit a lasso regression to the Gapminder data you have been working with and plot the coefficients. Just as with the Boston data, you will find that the coefficients of some features are shrunk to 0, with only the most important ones remaining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_columns = list(df.columns)\n",
    "df_columns.remove('life')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.         -0.         -0.          0.          0.          0.\n",
      " -0.         -0.07087587]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Lasso\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Instantiate a lasso regressor: lasso\n",
    "lasso = Lasso(alpha=0.4, normalize=True)\n",
    "\n",
    "# Fit the regressor to the data\n",
    "lasso.fit(X, y)\n",
    "\n",
    "# Compute and print the coefficients\n",
    "lasso_coef = lasso.coef_\n",
    "print(lasso_coef)\n",
    "\n",
    "# Plot the coefficients\n",
    "plt.plot(range(len(df_columns)), lasso_coef)\n",
    "plt.xticks(range(len(df_columns)), df_columns, rotation=60)\n",
    "plt.margins(0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-15: Regularization II: Ridge\n",
    "Lasso is great for feature selection, but when building regression models, Ridge regression should be your first choice.\n",
    "\n",
    "Recall that lasso performs regularization by adding to the loss function a penalty term of the absolute value of each coefficient multiplied by some alpha. This is also known as L1 regularization because the regularization term is the L1 norm of the coefficients. This is not the only way to regularize, however.\n",
    "\n",
    "If instead you took the sum of the squared values of the coefficients multiplied by some alpha - like in Ridge regression - you would be computing the L2 norm. In this exercise, you will practice fitting ridge regression models over a range of different alphas, and plot cross-validated R2 scores for each, using this function that we have defined for you, which plots the R2 score as well as standard error for each alpha:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_plot(cv_scores, cv_scores_std):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.plot(alpha_space, cv_scores)\n",
    "\n",
    "    std_error = cv_scores_std / np.sqrt(10)\n",
    "\n",
    "    ax.fill_between(alpha_space, cv_scores + std_error, cv_scores - std_error, alpha=0.2)\n",
    "    ax.set_ylabel('CV Score +/- Std Error')\n",
    "    ax.set_xlabel('Alpha')\n",
    "    ax.axhline(np.max(cv_scores), linestyle='--', color='.5')\n",
    "    ax.set_xlim([alpha_space[0], alpha_space[-1]])\n",
    "    ax.set_xscale('log')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't worry about the specifics of the above function works. The motivation behind this exercise is for you to see how the R2 score varies with different alphas, and to understand the importance of selecting the right value for alpha. You'll learn how to tune alpha in the next chapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Setup the array of alphas and lists to store scores\n",
    "alpha_space = np.logspace(-4, 0, 50)\n",
    "ridge_scores = []\n",
    "ridge_scores_std = []\n",
    "\n",
    "# Create a ridge regressor: ridge\n",
    "ridge = Ridge(normalize=True)\n",
    "\n",
    "# Compute scores over range of alphas\n",
    "for alpha in alpha_space:\n",
    "\n",
    "    # Specify the alpha value to use: ridge.alpha\n",
    "    ridge.alpha = alpha\n",
    "    \n",
    "    # Perform 10-fold CV: ridge_cv_scores\n",
    "    ridge_cv_scores = cross_val_score(ridge, X, y, cv=10)\n",
    "    \n",
    "    # Append the mean of ridge_cv_scores to ridge_scores\n",
    "    ridge_scores.append(np.mean(ridge_cv_scores))\n",
    "    \n",
    "    # Append the std of ridge_cv_scores to ridge_scores_std\n",
    "    ridge_scores_std.append(np.std(ridge_cv_scores))\n",
    "\n",
    "# Display the plot\n",
    "display_plot(ridge_scores, ridge_scores_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-16: Metrics for classification\n",
    "In Chapter 1, you evaluated the performance of your k-NN classifier based on its accuracy. However, as Andy discussed, accuracy is not always an informative metric. In this exercise, you will dive more deeply into evaluating the performance of binary classifiers by computing a confusion matrix and generating a classification report.\n",
    "\n",
    "You may have noticed in the video that the classification report consisted of three rows, and an additional support column. The support gives the number of samples of the true response that lie in that class - so in the video example, the support was the number of Republicans or Democrats in the test set on which the classification report was computed. The precision, recall, and f1-score columns, then, gave the respective metrics for that particular class.\n",
    "\n",
    "Here, you'll work with the PIMA Indians dataset obtained from the UCI Machine Learning Repository. The goal is to predict whether or not a given female patient will contract diabetes based on features such as BMI, age, and number of pregnancies. Therefore, it is a binary classification problem. A target value of 0 indicates that the patient does not have diabetes, while a value of 1 indicates that the patient does have diabetes. As in Chapters 1 and 2, the dataset has been preprocessed to deal with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\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>diastolic</th>\n",
       "      <th>triceps</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>dpf</th>\n",
       "      <th>age</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>35.00000</td>\n",
       "      <td>155.548223</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>29.00000</td>\n",
       "      <td>155.548223</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>29.15342</td>\n",
       "      <td>155.548223</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>23.00000</td>\n",
       "      <td>94.000000</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>35.00000</td>\n",
       "      <td>168.000000</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  diastolic   triceps     insulin   bmi    dpf  \\\n",
       "index                                                                       \n",
       "0                6      148         72  35.00000  155.548223  33.6  0.627   \n",
       "1                1       85         66  29.00000  155.548223  26.6  0.351   \n",
       "2                8      183         64  29.15342  155.548223  23.3  0.672   \n",
       "3                1       89         66  23.00000   94.000000  28.1  0.167   \n",
       "4                0      137         40  35.00000  168.000000  43.1  2.288   \n",
       "\n",
       "       age  diabetes  \n",
       "index                 \n",
       "0       50         1  \n",
       "1       31         0  \n",
       "2       32         1  \n",
       "3       21         0  \n",
       "4       33         1  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('PIMA_Indians.csv', index_col=0)\n",
    "X = df.drop('diabetes', axis=1).values\n",
    "y = df['diabetes'].values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[176  30]\n",
      " [ 52  50]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.85      0.81       206\n",
      "           1       0.62      0.49      0.55       102\n",
      "\n",
      "    accuracy                           0.73       308\n",
      "   macro avg       0.70      0.67      0.68       308\n",
      "weighted avg       0.72      0.73      0.72       308\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# Create training and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Instantiate a k-NN classifier: knn\n",
    "knn = KNeighborsClassifier(n_neighbors=6)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels of the test data: y_pred\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# Generate the confusion matrix and classification report\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-17: Building a logistic regression model\n",
    "Time to build your first logistic regression model! As Hugo showed in the video, scikit-learn makes it very easy to try different models, since the Train-Test-Split/Instantiate/Fit/Predict paradigm applies to all classifiers and regressors - which are known in scikit-learn as 'estimators'. You'll see this now for yourself as you train a logistic regression model on exactly the same data as in the previous exercise. Will it outperform k-NN? There's only one way to find out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[168  38]\n",
      " [ 36  66]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82       206\n",
      "           1       0.63      0.65      0.64       102\n",
      "\n",
      "    accuracy                           0.76       308\n",
      "   macro avg       0.73      0.73      0.73       308\n",
      "weighted avg       0.76      0.76      0.76       308\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary modules\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)\n",
    "\n",
    "# Create the classifier: logreg\n",
    "logreg = LogisticRegression(max_iter=200)\n",
    "\n",
    "# Fit the classifier to the training data\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels of the test set: y_pred\n",
    "y_pred = logreg.predict(X_test)\n",
    "\n",
    "# Compute and print the confusion matrix and classification report\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-18: Plotting an ROC curve\n",
    "Great job in the previous exercise - you now have a new addition to your toolbox of classifiers!\n",
    "\n",
    "Classification reports and confusion matrices are great methods to quantitatively evaluate model performance, while ROC curves provide a way to visually evaluate models. As Hugo demonstrated in the video, most classifiers in scikit-learn have a .predict_proba() method which returns the probability of a given sample being in a particular class. Having built a logistic regression model, you'll now evaluate its performance by plotting an ROC curve. In doing so, you'll make use of the .predict_proba() method and become familiar with its functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "# Compute predicted probabilities: y_pred_prob\n",
    "y_pred_prob = logreg.predict_proba(X_test)[:,1]\n",
    "\n",
    "# Generate ROC curve values: fpr, tpr, thresholds\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-19: Precision-recall Curve\n",
    "When looking at your ROC curve, you may have noticed that the y-axis (True positive rate) is also known as recall. Indeed, in addition to the ROC curve, there are other ways to visually evaluate model performance. One such way is the precision-recall curve, which is generated by plotting the precision and recall for different thresholds. As a reminder, precision and recall are defined as:\n",
    "\n",
    "\\begin{align}\n",
    "Precision & = \\frac{TP}{(TP+FP)} \\\\\n",
    "Recall & = \\frac{TP}{(TP+FN)}\n",
    "\\end{align}\n",
    "\n",
    "On the right, a precision-recall curve has been generated for the diabetes dataset. The classification report and confusion matrix are displayed in the IPython Shell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_prob)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.plot([1, 0], [0, 1], 'k--')\n",
    "plt.plot(recall, precision)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-20: AUC computation\n",
    "Say you have a binary classifier that in fact is just randomly making guesses. It would be correct approximately 50% of the time, and the resulting ROC curve would be a diagonal line in which the True Positive Rate and False Positive Rate are always equal. The Area under this ROC curve would be 0.5. This is one way in which the AUC, which Hugo discussed in the video, is an informative metric to evaluate a model. If the AUC is greater than 0.5, the model is better than random guessing. Always a good sign!\n",
    "\n",
    "In this exercise, you'll calculate AUC scores using the roc_auc_score() function from sklearn.metrics as well as by performing cross-validation on the diabetes dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8261469636398249\n",
      "AUC scores computed using 5-fold cross-validation: [0.81888889 0.80537037 0.825      0.87       0.8454717 ]\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Compute predicted probabilities: y_pred_prob\n",
    "y_pred_prob = logreg.predict_proba(X_test)[:,1]\n",
    "\n",
    "# Compute and print AUC score\n",
    "print(\"AUC: {}\".format(roc_auc_score(y_test, y_pred_prob)))\n",
    "\n",
    "# Compute cross-validated AUC scores: cv_auc\n",
    "cv_auc = cross_val_score(logreg, X, y, cv=5, scoring='roc_auc')\n",
    "\n",
    "# Print list of AUC scores\n",
    "print(\"AUC scores computed using 5-fold cross-validation: {}\".format(cv_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-21: Hyperparameter tuning with GridSearchCV\n",
    "Hugo demonstrated how to tune the n_neighbors parameter of the KNeighborsClassifier() using GridSearchCV on the voting dataset. You will now practice this yourself, but by using logistic regression on the diabetes dataset instead!\n",
    "\n",
    "Like the alpha parameter of lasso and ridge regularization that you saw earlier, logistic regression also has a regularization parameter: C. C controls the inverse of the regularization strength, and this is what you will tune in this exercise. A large C can lead to an overfit model, while a small C can lead to an underfit model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Logistic Regression Parameters: {'C': 0.006105402296585327}\n",
      "Best score is 0.7734827264239028\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Setup the hyperparameter grid\n",
    "c_space = np.logspace(-5, 8, 15)\n",
    "param_grid = {'C': c_space}\n",
    "\n",
    "# Instantiate the GridSearchCV object: logreg_cv\n",
    "logreg_cv = GridSearchCV(logreg, param_grid, cv=5)\n",
    "\n",
    "# Fit it to the data\n",
    "logreg_cv.fit(X, y)\n",
    "\n",
    "# Print the tuned parameters and score\n",
    "print(\"Tuned Logistic Regression Parameters: {}\".format(logreg_cv.best_params_)) \n",
    "print(\"Best score is {}\".format(logreg_cv.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-22: Hyperparameter tuning with RandomizedSearchCV\n",
    "GridSearchCV can be computationally expensive, especially if you are searching over a large hyperparameter space and dealing with multiple hyperparameters. A solution to this is to use RandomizedSearchCV, in which not all hyperparameter values are tried out. Instead, a fixed number of hyperparameter settings is sampled from specified probability distributions. You'll practice using RandomizedSearchCV in this exercise and see how this works.\n",
    "\n",
    "Here, you'll also be introduced to a new model: the Decision Tree. Don't worry about the specifics of how this model works. Just like k-NN, linear regression, and logistic regression, decision trees in scikit-learn have .fit() and .predict() methods that you can use in exactly the same way as before. Decision trees have many parameters that can be tuned, such as max_features, max_depth, and min_samples_leaf: This makes it an ideal use case for RandomizedSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Decision Tree Parameters: {'criterion': 'entropy', 'max_depth': 3, 'max_features': 5, 'min_samples_leaf': 7}\n",
      "Best score is 0.7435362023597317\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from scipy.stats import randint\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Setup the parameters and distributions to sample from: param_dist\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": randint(1, 9),\n",
    "              \"min_samples_leaf\": randint(1, 9),\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# Instantiate a Decision Tree classifier: tree\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# Instantiate the RandomizedSearchCV object: tree_cv\n",
    "tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)\n",
    "\n",
    "# Fit it to the data\n",
    "tree_cv.fit(X, y)\n",
    "\n",
    "# Print the tuned parameters and score\n",
    "print(\"Tuned Decision Tree Parameters: {}\".format(tree_cv.best_params_))\n",
    "print(\"Best score is {}\".format(tree_cv.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-23: Hold-out set in practice I: Classification\n",
    "You will now practice evaluating a model with tuned hyperparameters on a hold-out set. The feature array and target variable array from the diabetes dataset have been pre-loaded as X and y.\n",
    "\n",
    "In addition to C, logistic regression has a 'penalty' hyperparameter which specifies whether to use 'l1' or 'l2' regularization. Your job in this exercise is to create a hold-out set, tune the 'C' and 'penalty' hyperparameters of a logistic regression classifier using GridSearchCV on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Logistic Regression Parameter: {'C': 0.4393970560760795, 'penalty': 'l1'}\n",
      "Tuned Logistic Regression Accuracy: 0.7673913043478262\n"
     ]
    }
   ],
   "source": [
    "# Create the hyperparameter grid\n",
    "c_space = np.logspace(-5, 8, 15)\n",
    "param_grid = {'C': c_space, 'penalty': ['l1', 'l2']}\n",
    "\n",
    "# Create train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear')\n",
    "\n",
    "# Instantiate the GridSearchCV object: logreg_cv\n",
    "logreg_cv = GridSearchCV(logreg, param_grid, cv=5)\n",
    "\n",
    "# Fit it to the training data\n",
    "logreg_cv.fit(X_train, y_train)\n",
    "\n",
    "# Print the optimal parameters and best score\n",
    "print(\"Tuned Logistic Regression Parameter: {}\".format(logreg_cv.best_params_))\n",
    "print(\"Tuned Logistic Regression Accuracy: {}\".format(logreg_cv.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-24: Hold-out set in practice II: Regression\n",
    "Remember lasso and ridge regression from the previous chapter? Lasso used the L1 penalty to regularize, while ridge used the L2 penalty. There is another type of regularized regression known as the elastic net. In elastic net regularization, the penalty term is a linear combination of the L1 and L2 penalties:\n",
    "\n",
    "\\begin{align}\n",
    "a∗L1+b∗L2\n",
    "\\end{align}\n",
    "\n",
    "In scikit-learn, this term is represented by the 'l1_ratio' parameter: An 'l1_ratio' of 1 corresponds to an L1 penalty, and anything lower is a combination of L1 and L2.\n",
    "\n",
    "In this exercise, you will GridSearchCV to tune the 'l1_ratio' of an elastic net model trained on the Gapminder data. As in the previous exercise, use a hold-out set to evaluate your model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned ElasticNet l1 ratio: {'l1_ratio': 0.20689655172413793}\n",
      "Tuned ElasticNet R squared: 0.24021734578713896\n",
      "Tuned ElasticNet MSE: 0.16828884645725078\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Create the hyperparameter grid\n",
    "l1_space = np.linspace(0, 1, 30)\n",
    "param_grid = {'l1_ratio': l1_space}\n",
    "\n",
    "# Instantiate the ElasticNet regressor: elastic_net\n",
    "elastic_net = ElasticNet(tol=1)\n",
    "\n",
    "# Setup the GridSearchCV object: gm_cv\n",
    "gm_cv = GridSearchCV(elastic_net, param_grid, cv=5)\n",
    "\n",
    "# Fit it to the training data\n",
    "gm_cv.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set and compute metrics\n",
    "y_pred = gm_cv.predict(X_test)\n",
    "r2 = gm_cv.score(X_test, y_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(\"Tuned ElasticNet l1 ratio: {}\".format(gm_cv.best_params_))\n",
    "print(\"Tuned ElasticNet R squared: {}\".format(r2))\n",
    "print(\"Tuned ElasticNet MSE: {}\".format(mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-25: Exploring categorical features\n",
    "The Gapminder dataset that you worked with in previous chapters also contained a categorical 'Region' feature, which we dropped in previous exercises since you did not have the tools to deal with it. Now however, you do, so we have added it back in!\n",
    "\n",
    "Your job in this exercise is to explore this feature. Boxplots are particularly useful for visualizing categorical features such as this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read 'gapminder.csv' into a DataFrame: df\n",
    "df = pd.read_csv('gapminder_all.csv')\n",
    "\n",
    "# Create a boxplot of life expectancy per region\n",
    "df.boxplot('life', 'Region', rot=60)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-26: Creating dummy variables\n",
    "As Andy discussed in the video, scikit-learn does not accept non-numerical features. You saw in the previous exercise that the 'Region' feature contains very useful information that can predict life expectancy. For example, Sub-Saharan Africa has a lower life expectancy compared to Europe and Central Asia. Therefore, if you are trying to predict life expectancy, it would be preferable to retain the 'Region' feature. To do this, you need to binarize it by creating dummy variables, which is what you will do in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'population', 'fertility', 'HIV', 'CO2', 'BMI_male', 'GDP',\n",
      "       'BMI_female', 'life', 'child_mortality', 'Region_America',\n",
      "       'Region_East Asia & Pacific', 'Region_Europe & Central Asia',\n",
      "       'Region_Middle East & North Africa', 'Region_South Asia',\n",
      "       'Region_Sub-Saharan Africa'],\n",
      "      dtype='object')\n",
      "Index(['index', 'population', 'fertility', 'HIV', 'CO2', 'BMI_male', 'GDP',\n",
      "       'BMI_female', 'life', 'child_mortality', 'Region_East Asia & Pacific',\n",
      "       'Region_Europe & Central Asia', 'Region_Middle East & North Africa',\n",
      "       'Region_South Asia', 'Region_Sub-Saharan Africa'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Create dummy variables: df_region\n",
    "df_region = pd.get_dummies(df)\n",
    "\n",
    "# Print the columns of df_region\n",
    "print(df_region.columns)\n",
    "\n",
    "# Create dummy variables with drop_first=True: df_region\n",
    "df_region = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "# Print the new columns of df_region\n",
    "print(df_region.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-27: Regression with categorical features\n",
    "Having created the dummy variables from the 'Region' feature, you can build regression models as you did before. Here, you'll use ridge regression to perform 5-fold cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_region.drop('life', axis=1).values\n",
    "y = df_region['life'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85346079 0.8018539  0.83954719 0.77530847 0.87473417]\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a ridge regressor: ridge\n",
    "ridge = Ridge(alpha=0.5, normalize=True)\n",
    "\n",
    "# Perform 5-fold cross-validation: ridge_cv\n",
    "ridge_cv = cross_val_score(ridge, X, y, cv=5)\n",
    "\n",
    "# Print the cross-validated scores\n",
    "print(ridge_cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-28: Dropping missing data\n",
    "The voting dataset from Chapter 1 contained a bunch of missing values that we dealt with for you behind the scenes. Now, it's time for you to take care of these yourself!\n",
    "\n",
    "The unprocessed dataset has been loaded into a DataFrame df. Explore it in the IPython Shell with the .head() method. You will see that there are certain data points labeled with a '?'. These denote missing values. As you saw in the video, different datasets encode missing values in different ways. Sometimes it may be a '9999', other times a 0 - real-world data can be very messy! If you're lucky, the missing values will already be encoded as NaN. We use NaN because it is an efficient and simplified way of internally representing missing data, and it lets us take advantage of pandas methods such as .dropna() and .fillna(), as well as scikit-learn's Imputation transformer Imputer().\n",
    "\n",
    "In this exercise, your job is to convert the '?'s to NaNs, and then drop the rows that contain them from the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
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       "      <th>physician</th>\n",
       "      <th>salvador</th>\n",
       "      <th>religious</th>\n",
       "      <th>satellite</th>\n",
       "      <th>aid</th>\n",
       "      <th>missile</th>\n",
       "      <th>immigration</th>\n",
       "      <th>synfuels</th>\n",
       "      <th>education</th>\n",
       "      <th>superfund</th>\n",
       "      <th>crime</th>\n",
       "      <th>duty_free_exports</th>\n",
       "      <th>eaa_rsa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>republican</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>democrat</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>democrat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>democrat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            party infants water budget physician salvador religious satellite  \\\n",
       "index                                                                           \n",
       "0      republican       0     1      0         1        1         1         0   \n",
       "1      republican       0     1      0         1        1         1         0   \n",
       "2        democrat       ?     1      1         ?        1         1         0   \n",
       "3        democrat       0     1      1         0        ?         1         0   \n",
       "4        democrat       1     1      1         0        1         1         0   \n",
       "\n",
       "      aid missile immigration synfuels education superfund crime  \\\n",
       "index                                                              \n",
       "0       0       0           1        ?         1         1     1   \n",
       "1       0       0           0        0         1         1     1   \n",
       "2       0       0           0        1         0         1     1   \n",
       "3       0       0           0        1         0         1     0   \n",
       "4       0       0           0        1         ?         1     1   \n",
       "\n",
       "      duty_free_exports eaa_rsa  \n",
       "index                            \n",
       "0                     0       1  \n",
       "1                     0       ?  \n",
       "2                     0       0  \n",
       "3                     0       1  \n",
       "4                     1       1  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('house-votes-84_missing_data.csv', index_col=0)\n",
    "X = df.drop('party', axis=1).values\n",
    "y = df['party'].values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "party                0\n",
      "infants              0\n",
      "water                0\n",
      "budget               0\n",
      "physician            0\n",
      "salvador             0\n",
      "religious            0\n",
      "satellite            0\n",
      "aid                  0\n",
      "missile              0\n",
      "immigration          0\n",
      "synfuels             0\n",
      "education            0\n",
      "superfund            0\n",
      "crime                0\n",
      "duty_free_exports    0\n",
      "eaa_rsa              0\n",
      "dtype: int64\n",
      "Shape of Original DataFrame: (435, 17)\n",
      "Shape of DataFrame After Dropping All Rows with Missing Values: (435, 17)\n"
     ]
    }
   ],
   "source": [
    "# Convert '?' to NaN\n",
    "df[df == '?'] = 'NaN'\n",
    "\n",
    "# Print the number of NaNs\n",
    "print(df.isnull().sum())\n",
    "\n",
    "# Print shape of original DataFrame\n",
    "print(\"Shape of Original DataFrame: {}\".format(df.shape))\n",
    "\n",
    "# Drop missing values and print shape of new DataFrame\n",
    "df = df.dropna()\n",
    "\n",
    "# Print shape of new DataFrame\n",
    "print(\"Shape of DataFrame After Dropping All Rows with Missing Values: {}\".format(df.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-29: Imputing missing data in a ML Pipeline I\n",
    "As you've come to appreciate, there are many steps to building a model, from creating training and test sets, to fitting a classifier or regressor, to tuning its parameters, to evaluating its performance on new data. Imputation can be seen as the first step of this machine learning process, the entirety of which can be viewed within the context of a pipeline. Scikit-learn provides a pipeline constructor that allows you to piece together these steps into one process and thereby simplify your workflow.\n",
    "\n",
    "You'll now practice setting up a pipeline with two steps: the imputation step, followed by the instantiation of a classifier. You've seen three classifiers in this course so far: k-NN, logistic regression, and the decision tree. You will now be introduced to a fourth one - the Support Vector Machine, or SVM (https://scikit-learn.org/stable/modules/svm.html). For now, do not worry about how it works under the hood. It works exactly as you would expect of the scikit-learn estimators that you have worked with previously, in that it has the same .fit() and .predict() methods as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Imputer module\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Setup the Imputation transformer: imp\n",
    "imp = SimpleImputer(missing_values='?', strategy='most_frequent')\n",
    "\n",
    "# Instantiate the SVC classifier: clf\n",
    "clf = SVC()\n",
    "\n",
    "# Setup the pipeline with the required steps: steps\n",
    "steps = [('imputation', imp),\n",
    "        ('SVM', clf)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-30: Imputing missing data in a ML Pipeline II\n",
    "Having setup the steps of the pipeline in the previous exercise, you will now use it on the voting dataset to classify a Congressman's party affiliation. What makes pipelines so incredibly useful is the simple interface that they provide. You can use the .fit() and .predict() methods on pipelines just as you did with your classifiers and regressors!\n",
    "\n",
    "Practice this for yourself now and generate a classification report of your predictions. The steps of the pipeline have been set up for you, and the feature array X and target variable array y have been pre-loaded. Additionally, train_test_split and classification_report have been imported from sklearn.model_selection and sklearn.metrics respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    democrat       0.98      0.96      0.97        85\n",
      "  republican       0.94      0.96      0.95        46\n",
      "\n",
      "    accuracy                           0.96       131\n",
      "   macro avg       0.96      0.96      0.96       131\n",
      "weighted avg       0.96      0.96      0.96       131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Create the pipeline: pipeline\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Fit the pipeline to the train set\n",
    "pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels of the test set\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "# Compute metrics\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-31: Centering and scaling your data\n",
    "In the video, Hugo demonstrated how significantly the performance of a model can improve if the features are scaled. Note that this is not always the case: In the Congressional voting records dataset, for example, all of the features are binary. In such a situation, scaling will have minimal impact.\n",
    "\n",
    "You will now explore scaling for yourself on a new dataset - White Wine Quality! Hugo used the Red Wine Quality dataset in the video. We have used the 'quality' feature of the wine to create a binary target variable: If 'quality' is less than 5, the target variable is 1, and otherwise, it is 0.\n",
    "\n",
    "Notice how some features seem to have different units of measurement. 'density', for instance, takes values between 0.98 and 1.04, while 'total sulfur dioxide' ranges from 9 to 440. As a result, it may be worth scaling the features here. Your job in this exercise is to scale the features and compute the mean and standard deviation of the unscaled features compared to the scaled features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('white-wine.csv')\n",
    "X = df.drop('quality', axis=1).values\n",
    "y = df['quality'].values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Unscaled Features: 18.432687072460002\n",
      "Standard Deviation of Unscaled Features: 41.54494764094571\n",
      "Mean of Scaled Features: 2.7314972981668206e-15\n",
      "Standard Deviation of Scaled Features: 0.9999999999999999\n"
     ]
    }
   ],
   "source": [
    "# Import scale\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "# Scale the features: X_scaled\n",
    "X_scaled = scale(X)\n",
    "\n",
    "# Print the mean and standard deviation of the unscaled features\n",
    "print(\"Mean of Unscaled Features: {}\".format(np.mean(X))) \n",
    "print(\"Standard Deviation of Unscaled Features: {}\".format(np.std(X)))\n",
    "\n",
    "# Print the mean and standard deviation of the scaled features\n",
    "print(\"Mean of Scaled Features: {}\".format(np.mean(X_scaled))) \n",
    "print(\"Standard Deviation of Scaled Features: {}\".format(np.std(X_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-32: Centering and scaling in a pipeline\n",
    "With regard to whether or not scaling is effective, the proof is in the pudding! See for yourself whether or not scaling the features of the White Wine Quality dataset has any impact on its performance. You will use a k-NN classifier as part of a pipeline that includes scaling, and for the purposes of comparison, a k-NN classifier trained on the unscaled data has been provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with Scaling: 0.5496598639455782\n",
      "Accuracy without Scaling: 0.47551020408163264\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary modules\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Setup the pipeline steps: steps\n",
    "steps = [('scaler', StandardScaler()),\n",
    "        ('knn', KNeighborsClassifier())]\n",
    "        \n",
    "# Create the pipeline: pipeline\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Create train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Fit the pipeline to the training set: knn_scaled\n",
    "knn_scaled = pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Instantiate and fit a k-NN classifier to the unscaled data\n",
    "knn_unscaled = KNeighborsClassifier().fit(X_train, y_train)\n",
    "\n",
    "# Compute and print metrics\n",
    "print('Accuracy with Scaling: {}'.format(knn_scaled.score(X_test, y_test)))\n",
    "print('Accuracy without Scaling: {}'.format(knn_unscaled.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-33: Bringing it all together I: Pipeline for classification\n",
    "It is time now to piece together everything you have learned so far into a pipeline for classification! Your job in this exercise is to build a pipeline that includes scaling and hyperparameter tuning to classify wine quality.\n",
    "\n",
    "You'll return to using the SVM classifier you were briefly introduced to earlier in this chapter. The hyperparameters you will tune are C and gamma. C controls the regularization strength. It is analogous to the C you tuned for logistic regression in Chapter 3, while gamma controls the kernel coefficient: Do not worry about this now as it is beyond the scope of this course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6081632653061224\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.24      0.39      0.30        28\n",
      "           5       0.61      0.62      0.61       289\n",
      "           6       0.66      0.66      0.66       447\n",
      "           7       0.62      0.54      0.58       177\n",
      "           8       0.47      0.42      0.44        38\n",
      "\n",
      "    accuracy                           0.61       980\n",
      "   macro avg       0.43      0.44      0.43       980\n",
      "weighted avg       0.61      0.61      0.61       980\n",
      "\n",
      "Tuned Model Parameters: {'SVM__C': 100, 'SVM__gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "# Setup the pipeline\n",
    "steps = [('scaler', StandardScaler()),\n",
    "         ('SVM', SVC())]\n",
    "\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Specify the hyperparameter space\n",
    "parameters = {'SVM__C':[1, 10, 100],\n",
    "              'SVM__gamma':[0.1, 0.01]}\n",
    "\n",
    "# Create train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)\n",
    "\n",
    "# Instantiate the GridSearchCV object: cv\n",
    "cv = GridSearchCV(pipeline, parameters, cv=3)\n",
    "\n",
    "# Fit to the training set\n",
    "cv.fit(X_train, y_train)\n",
    "\n",
    "# Predict the labels of the test set: y_pred\n",
    "y_pred = cv.predict(X_test)\n",
    "\n",
    "# Compute and print metrics\n",
    "print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise-34: Bringing it all together II: Pipeline for regression\n",
    "For this final exercise, you will return to the Gapminder dataset. Guess what? Even this dataset has missing values that we dealt with for you in earlier chapters! Now, you have all the tools to take care of them yourself!\n",
    "\n",
    "Your job is to build a pipeline that imputes the missing data, scales the features, and fits an ElasticNet to the Gapminder data. You will then tune the l1_ratio of your ElasticNet using GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>fertility</th>\n",
       "      <th>HIV</th>\n",
       "      <th>CO2</th>\n",
       "      <th>BMI_male</th>\n",
       "      <th>GDP</th>\n",
       "      <th>BMI_female</th>\n",
       "      <th>life</th>\n",
       "      <th>child_mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34811059.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.328945</td>\n",
       "      <td>24.59620</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>129.9049</td>\n",
       "      <td>75.3</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19842251.0</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.474353</td>\n",
       "      <td>22.25083</td>\n",
       "      <td>7103.0</td>\n",
       "      <td>130.1247</td>\n",
       "      <td>58.3</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40381860.0</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.785170</td>\n",
       "      <td>27.50170</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>118.8915</td>\n",
       "      <td>75.5</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2975029.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.804106</td>\n",
       "      <td>25.35542</td>\n",
       "      <td>7383.0</td>\n",
       "      <td>132.8108</td>\n",
       "      <td>72.5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21370348.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.1</td>\n",
       "      <td>18.016313</td>\n",
       "      <td>27.56373</td>\n",
       "      <td>41312.0</td>\n",
       "      <td>117.3755</td>\n",
       "      <td>81.5</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       population  fertility  HIV        CO2  BMI_male      GDP  BMI_female  \\\n",
       "index                                                                         \n",
       "0      34811059.0       2.73  0.1   3.328945  24.59620  12314.0    129.9049   \n",
       "1      19842251.0       6.43  2.0   1.474353  22.25083   7103.0    130.1247   \n",
       "2      40381860.0       2.24  0.5   4.785170  27.50170  14646.0    118.8915   \n",
       "3       2975029.0       1.40  0.1   1.804106  25.35542   7383.0    132.8108   \n",
       "4      21370348.0       1.96  0.1  18.016313  27.56373  41312.0    117.3755   \n",
       "\n",
       "       life  child_mortality  \n",
       "index                         \n",
       "0      75.3             29.5  \n",
       "1      58.3            192.0  \n",
       "2      75.5             15.4  \n",
       "3      72.5             20.0  \n",
       "4      81.5              5.2  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('gapminder_drop_region.csv', index_col=0)\n",
    "X = df.drop('life', axis=1).values\n",
    "y = df['life'].values\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned ElasticNet Alpha: {'elasticnet__l1_ratio': 0.4482758620689655}\n",
      "Tuned ElasticNet R squared: 0.8021554396102698\n"
     ]
    }
   ],
   "source": [
    "# Setup the pipeline steps: steps\n",
    "steps = [('imputation', SimpleImputer(missing_values=np.nan, strategy='mean')),\n",
    "         ('scaler', StandardScaler()),\n",
    "         ('elasticnet', ElasticNet(tol=1))]\n",
    "\n",
    "# Create the pipeline: pipeline \n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "# Specify the hyperparameter space\n",
    "parameters = {'elasticnet__l1_ratio':np.linspace(0,1,30)}\n",
    "\n",
    "# Create train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Create the GridSearchCV object: gm_cv\n",
    "gm_cv = GridSearchCV(pipeline, parameters, cv=3)\n",
    "\n",
    "# Fit to the training set\n",
    "gm_cv.fit(X_train, y_train)\n",
    "\n",
    "# Compute and print the metrics\n",
    "r2 = gm_cv.score(X_test, y_test)\n",
    "print(\"Tuned ElasticNet Alpha: {}\".format(gm_cv.best_params_))\n",
    "print(\"Tuned ElasticNet R squared: {}\".format(r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THE END!"
   ]
  }
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