6. Machine Learning Predictions.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment:  Machine Learning Prediction\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **60** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries for the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns\n# Preprocessing allows us to standarsize our data\nfrom sklearn import preprocessing\n# Allows us to split our data into training and testing data\nfrom sklearn.model_selection import train_test_split\n# Allows us to test parameters of classification algorithms and find the best one\nfrom sklearn.model_selection import GridSearchCV\n# Logistic Regression classification algorithm\nfrom sklearn.linear_model import LogisticRegression\n# Support Vector Machine classification algorithm\nfrom sklearn.svm import SVC\n# Decision Tree classification algorithm\nfrom sklearn.tree import DecisionTreeClassifier\n# K Nearest Neighbors classification algorithm\nfrom sklearn.neighbors import KNeighborsClassifier",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": "This function is to plot the confusion matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def plot_confusion_matrix(y,y_predict):\n    \"this function plots the confusion matrix\"\n    from sklearn.metrics import confusion_matrix\n\n    cm = confusion_matrix(y, y_predict)\n    ax= plt.subplot()\n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    ax.set_xlabel('Predicted labels')\n    ax.set_ylabel('True labels')\n    ax.set_title('Confusion Matrix'); \n    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed']) \n    plt.show() ",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "source": "## Load the dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Load the data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp1 = await fetch(URL1)\ntext1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\ndata = pd.read_csv(text1)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\nresp2 = await fetch(URL2)\ntext2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\nX = pd.read_csv(text2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  1\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y = data['Class'].to_numpy()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  2\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# students get this \ntransform = preprocessing.StandardScaler()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 7
    },
    {
      "cell_type": "code",
      "source": "X = transform.fit_transform(X)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  3\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>X_train, X_test, Y_train, Y_test</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 9
    },
    {
      "cell_type": "markdown",
      "source": "we can see we only have 18 test samples.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_test.shape",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(18,)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  4\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}\nlr=LogisticRegression()\nlogreg_cv = GridSearchCV(lr,parameters,cv=10)\nlogreg_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})",
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 12
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  5\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "logreg_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  6\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 15
    },
    {
      "cell_type": "code",
      "source": "svm_cv = GridSearchCV(svm,parameters,cv=10)\nsvm_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})",
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  7\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svm_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 36
    },
    {
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')\ntree_cv = GridSearchCV(tree,parameters,cv=10)\ntree_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 45,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={'criterion': ['gini', 'entropy'],\n                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         'max_features': ['auto', 'sqrt'],\n                         'min_samples_leaf': [1, 2, 4],\n                         'min_samples_split': [2, 5, 10],\n                         'splitter': ['best', 'random']})",
            "text/html": "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 45
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'best'}\naccuracy : 0.875\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 46
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  9\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "tree_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 48,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8888888888888888"
          },
          "metadata": {}
        }
      ],
      "execution_count": 48
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 47
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  10\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n              'p': [1,2]}\n\nKNN = KNeighborsClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 25
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN,parameters,cv=10)\nknn_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         'p': [1, 2]})",
            "text/html": "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 27
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  11\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 28
    },
    {
      "cell_type": "code",
      "source": "knn_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ],
      "execution_count": 30
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 29
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "#All methods performed relatively the same.  \n#One could say that the decision tree model performed slightly better, but not necessarily significantly better.\n",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": "data = {'Logistic Regression':logreg_cv.best_score_, 'SVM':svm_cv.best_score_, 'Decision Tree':tree_cv.best_score_, \n        'KNN':knn_cv.best_score_}\ncourses = list(data.keys())\nvalues = list(data.values())\nplt.bar(courses, values)\nplt.xlabel(\"Model\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Model Accuracy Score\")\nplt.show()\n\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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O3PFS8LNnz2aq7fjx40b79u2NwoULG15eXkanTp2MkydPZrnPjhw5YoSGhhre3t6Gq6urUbZsWWPgwIFGWlpapvU+8sgjhpOTk3H8+PE77pdb7dy50xg2bJgRGBhoFC1a1MiXL5/h6+trdOrUydi2bVum/hs2bDBatGhhFCpUyPDw8DCqV69uvPfeezZ91qxZYzRq1Mhwd3c3PD09jTZt2hi7d++26XO3fWMYhrFgwQLjscceMzw8PAwPDw+jcuXKxsCBA429e/dma7sAR7AYxm3jjQCAf6xWrVoqWrSo4uPjHV0K8K/DnBsAyGG//PKLEhISFBoa6uhSgH8lRm4AIIf8+uuv2rp1qyZOnKjk5GQdPHgw03dCAch9jNwAQA756quvFBYWpuvXr+uLL74g2AAOwsgNAAAwFUZuAACAqRBuAACAqfzrPsQvIyNDJ0+eVKFChf7RtwsDAID7xzAMpaSkqFSpUn/7XW3/unBz8uRJ+fn5OboMAABwD44dO6aHHnrorn3+deHm5hcPHjt2TJ6eng6uBgAAZMelS5fk5+dn8wXCd/KvCzc3T0V5enoSbgAAeMBkZ0oJE4oBAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICpEG4AAICp5HN0AQDgSAGvfuPoEv61Do9r7egSYFKM3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFMh3AAAAFPhE4qBbOKTbB2HT7LFveA16ziOfs0SbnIYLybHcfSLCQCQN3BaCgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmIrDw8306dMVEBAgNzc3BQUFacuWLXftHxsbqxo1aqhAgQLy9fVV7969de7cuftULQAAyOscGm7i4uIUGRmpqKgobdu2TTVq1FBISIjOnDmTZf+NGzcqNDRU4eHh+u233zR//nxt2bJFffv2vc+VAwCAvMqh4WbSpEnq27evwsLCVLVqVc2cOVMFChTQ7Nmzs+y/adMmBQQEaPDgwSpTpowee+wxvfDCC3872gMAAP49HBZurl27pq1btyo4OPh/xTg5KTg4WJs2bcpymQYNGujYsWNavny5DMPQ6dOn9dVXX6lVq1Z3fJy0tDRdunTJ5gYAAMzLYeEmOTlZ6enp8vHxsWn38fHRqVOnslymUaNGio2NVZcuXeTi4qKSJUvKy8tL06dPv+PjjB07Vl5eXtabn59fjm4HAADIWxw+odgeu3fv1pAhQzRixAht3bpVK1eu1OHDh9WvX787LjN8+HBdvHjRejt27Nh9rBgAANxv+Rz1wMWLF5ezs7NOnz5t03769GmVLFkyy2XGjh2rRo0aadiwYZKk6tWry8PDQ40bN9bo0aPl6+ubaRlXV1e5urrm/AYAAIA8yWEjNy4uLqpdu7bi4+OtbRkZGYqPj1eDBg2yXObKlStycrIt2dnZWZJkGEbuFQsAAB4YDj0tFRkZqVmzZmnOnDlKTExU//79lZqaqrCwMEk3TimFhoZa+7dp00YLFy7UjBkzdPDgQW3cuFGDBw9WvXr1VKpUKUdtBgAAyEMcdlpKkrp06aKzZ89qxIgROnXqlGrWrKmVK1daJxknJSXp6NGj1v69evVSSkqK3n//fb300ksqXLiwmjVrpvHjxztqEwAAQB7j0HAjSREREYqIiMjyvpiYmExtgwYN0qBBg3K5KgAA8KB6oK6WAgAA+DuEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCoODzfTp09XQECA3NzcFBQUpC1btty1f1paml5//XX5+/vL1dVVAQEBmj179n2qFgAA5HX5HPngcXFxioyM1MyZMxUUFKQpU6YoJCREe/fuVYkSJbJcpnPnzjp9+rQ++eQTlS9fXklJScrIyLjPlQMAgLzKoeFm0qRJ6tu3r8LCwiRJM2fO1DfffKPZs2fr1VdfzdR/5cqVWrdunQ4ePKiiRYtKkgICAu5nyQAAII9z2Gmpa9euaevWrQoODv5fMU5OCg4O1qZNm7JcZunSpapTp44mTJig0qVLq2LFinr55Zd19erVOz5OWlqaLl26ZHMDAADm5bCRm+TkZKWnp8vHx8em3cfHR3v27MlymYMHD2rDhg1yc3PTokWLlJycrAEDBujcuXOKjo7OcpmxY8dq1KhROV4/AADImxw+odgeGRkZslgsio2NVb169dSqVStNmjRJc+bMuePozfDhw3Xx4kXr7dixY/e5agAAcD85bOSmePHicnZ21unTp23aT58+rZIlS2a5jK+vr0qXLi0vLy9rW5UqVWQYho4fP64KFSpkWsbV1VWurq45WzwAAMizHDZy4+Liotq1ays+Pt7alpGRofj4eDVo0CDLZRo1aqSTJ0/q8uXL1rbff/9dTk5Oeuihh3K9ZgAAkPc59LRUZGSkZs2apTlz5igxMVH9+/dXamqq9eqp4cOHKzQ01Nq/e/fuKlasmMLCwrR7926tX79ew4YNU+/eveXu7u6ozQAAAHmIQy8F79Kli86ePasRI0bo1KlTqlmzplauXGmdZJyUlKSjR49a+xcsWFCrV6/WoEGDVKdOHRUrVkydO3fW6NGjHbUJAAAgj3FouJGkiIgIRUREZHlfTExMprbKlStr9erVuVwVAAB4UD1QV0sBAAD8HcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFbvDTUBAgN58800dPXo0N+oBAAD4R+wON0OHDtXChQtVtmxZtWjRQvPmzVNaWlpu1AYAAGC3ewo3CQkJ2rJli6pUqaJBgwbJ19dXERER2rZtW27UCAAAkG33POcmMDBQ06ZN08mTJxUVFaWPP/5YdevWVc2aNTV79mwZhpGTdQIAAGRLvntd8Pr161q0aJGio6O1evVq1a9fX+Hh4Tp+/Lhee+01rVmzRnPnzs3JWgEAAP6W3eFm27Ztio6O1hdffCEnJyeFhoZq8uTJqly5srVP+/btVbdu3RwtFAAAIDvsDjd169ZVixYtNGPGDLVr10758+fP1KdMmTLq2rVrjhQIAABgD7vDzcGDB+Xv73/XPh4eHoqOjr7nogAAAO6V3ROKz5w5o82bN2dq37x5s3755ZccKQoAAOBe2R1uBg4cqGPHjmVqP3HihAYOHJgjRQEAANwru8PN7t27FRgYmKm9Vq1a2r17d44UBQAAcK/sDjeurq46ffp0pvakpCTly3fPV5YDAADkCLvDzX/+8x8NHz5cFy9etLZduHBBr732mlq0aJGjxQEAANjL7qGWd999V02aNJG/v79q1aolSUpISJCPj48+++yzHC8QAADAHnaHm9KlS2vnzp2KjY3Vjh075O7urrCwMHXr1i3Lz7wBAAC4n+5pkoyHh4eef/75nK4FAADgH7vnGcC7d+/W0aNHde3aNZv2p59++h8XBQAAcK/u6ROK27dvr127dslisVi//dtisUiS0tPTc7ZCAAAAO9h9tdSQIUNUpkwZnTlzRgUKFNBvv/2m9evXq06dOlq7dm0ulAgAAJB9do/cbNq0Sd99952KFy8uJycnOTk56bHHHtPYsWM1ePBgbd++PTfqBAAAyBa7R27S09NVqFAhSVLx4sV18uRJSZK/v7/27t2bs9UBAADYye6Rm0cffVQ7duxQmTJlFBQUpAkTJsjFxUUfffSRypYtmxs1AgAAZJvd4eb//u//lJqaKkl688039dRTT6lx48YqVqyY4uLicrxAAAAAe9gdbkJCQqz/L1++vPbs2aPz58+rSJEi1iumAAAAHMWuOTfXr19Xvnz59Ouvv9q0Fy1alGADAADyBLvCTf78+fXwww/zWTYAACDPsvtqqddff12vvfaazp8/nxv1AAAA/CN2z7l5//33tX//fpUqVUr+/v7y8PCwuX/btm05VhwAAIC97A437dq1y4UyAAAAcobd4SYqKio36gAAAMgRds+5AQAAyMvsHrlxcnK662XfXEkFAAAcye5ws2jRIpufr1+/ru3bt2vOnDkaNWpUjhUGAABwL+wON23bts3U1rFjRz3yyCOKi4tTeHh4jhQGAABwL3Jszk39+vUVHx+fU6sDAAC4JzkSbq5evapp06apdOnSObE6AACAe2b3aanbvyDTMAylpKSoQIEC+vzzz3O0OAAAAHvZHW4mT55sE26cnJzk7e2toKAgFSlSJEeLAwAAsJfd4aZXr165UAYAAEDOsHvOTXR0tObPn5+pff78+ZozZ06OFAUAAHCv7A43Y8eOVfHixTO1lyhRQmPGjMmRogAAAO6V3eHm6NGjKlOmTKZ2f39/HT16NEeKAgAAuFd2h5sSJUpo586dmdp37NihYsWK5UhRAAAA98rucNOtWzcNHjxY33//vdLT05Wenq7vvvtOQ4YMUdeuXXOjRgAAgGyz+2qpt956S4cPH1bz5s2VL9+NxTMyMhQaGsqcGwAA4HB2hxsXFxfFxcVp9OjRSkhIkLu7u6pVqyZ/f//cqA8AAMAudoebmypUqKAKFSrkZC0AAAD/mN1zbjp06KDx48dnap8wYYI6deqUI0UBAADcK7vDzfr169WqVatM7U8++aTWr1+fI0UBAADcK7vDzeXLl+Xi4pKpPX/+/Lp06ZLdBUyfPl0BAQFyc3NTUFCQtmzZkq3lNm7cqHz58qlmzZp2PyYAADAvu8NNtWrVFBcXl6l93rx5qlq1ql3riouLU2RkpKKiorRt2zbVqFFDISEhOnPmzF2Xu3DhgkJDQ9W8eXO7Hg8AAJif3ROK33jjDT3zzDM6cOCAmjVrJkmKj4/X3Llz9dVXX9m1rkmTJqlv374KCwuTJM2cOVPffPONZs+erVdfffWOy/Xr10/du3eXs7OzFi9ebO8mAAAAE7N75KZNmzZavHix9u/frwEDBuill17SiRMn9N1336l8+fLZXs+1a9e0detWBQcH/68YJycFBwdr06ZNd1wuOjpaBw8eVFRUVLYeJy0tTZcuXbK5AQAA87I73EhS69attXHjRqWmpurgwYPq3LmzXn75ZdWoUSPb60hOTlZ6erp8fHxs2n18fHTq1Kksl9m3b59effVVff7559YPEPw7Y8eOlZeXl/Xm5+eX7RoBAMCD557CjXTjqqmePXuqVKlSmjhxopo1a6affvopJ2uzkZ6eru7du2vUqFGqWLFitpcbPny4Ll68aL0dO3Ys12oEAACOZ9ecm1OnTikmJkaffPKJLl26pM6dOystLU2LFy+2ezJx8eLF5ezsrNOnT9u0nz59WiVLlszUPyUlRb/88ou2b9+uiIgISTe+9sEwDOXLl0+rVq2yzgG6laurq1xdXe2qDQAAPLiyPXLTpk0bVapUSTt37tSUKVN08uRJvffee/f8wC4uLqpdu7bi4+OtbRkZGYqPj1eDBg0y9ff09NSuXbuUkJBgvfXr10+VKlVSQkKCgoKC7rkWAABgHtkeuVmxYoUGDx6s/v3759jXLkRGRqpnz56qU6eO6tWrpylTpig1NdV69dTw4cN14sQJffrpp3JyctKjjz5qs3yJEiXk5uaWqR0AAPx7ZXvkZsOGDUpJSVHt2rUVFBSk999/X8nJyf/owbt06aJ3331XI0aMUM2aNZWQkKCVK1daJxknJSXp6NGj/+gxAADAv0u2w039+vU1a9YsJSUl6YUXXtC8efNUqlQpZWRkaPXq1UpJSbmnAiIiInTkyBGlpaVp8+bNNqeXYmJitHbt2jsuO3LkSCUkJNzT4wIAAHOy+2opDw8P9e7dWxs2bNCuXbv00ksvady4cSpRooSefvrp3KgRAAAg2+75UnBJqlSpkiZMmKDjx4/riy++yKmaAAAA7tk/Cjc3OTs7q127dlq6dGlOrA4AAOCe5Ui4AQAAyCsINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQINwAAwFQcHm6mT5+ugIAAubm5KSgoSFu2bLlj34ULF6pFixby9vaWp6enGjRooG+//fY+VgsAAPI6h4abuLg4RUZGKioqStu2bVONGjUUEhKiM2fOZNl//fr1atGihZYvX66tW7eqadOmatOmjbZv336fKwcAAHmVQ8PNpEmT1LdvX4WFhalq1aqaOXOmChQooNmzZ2fZf8qUKfrvf/+runXrqkKFChozZowqVKigr7/++j5XDgAA8iqHhZtr165p69atCg4O/l8xTk4KDg7Wpk2bsrWOjIwMpaSkqGjRonfsk5aWpkuXLtncAACAeTks3CQnJys9PV0+Pj427T4+Pjp16lS21vHuu+/q8uXL6ty58x37jB07Vl5eXtabn5/fP6obAADkbQ6fUHyv5s6dq1GjRunLL79UiRIl7thv+PDhunjxovV27Nix+1glAAC43/I56oGLFy8uZ2dnnT592qb99OnTKlmy5F2XnTdvnvr06aP58+fbnNbKiqurq1xdXf9xvQAA4MHgsJEbFxcX1a5dW/Hx8da2jIwMxcfHq0GDBndc7osvvlBYWJi++OILtW7d+n6UCgAAHiAOG7mRpMjISPXs2VN16tRRvXr1NGXKFKWmpiosLEzSjVNKJ06c0Keffirpxqmonj17aurUqQoKCrLOzXF3d5eXl5fDtgMAAOQdDg03Xbp00dmzZzVixAidOnVKNWvW1MqVK62TjJOSknT06FFr/48++kh//fWXBg4cqIEDB1rbe/bsqZiYmPtdPgAAyIMcGm4kKSIiQhEREVned3tgWbt2be4XBAAAHmgP7NVSAAAAWSHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAUyHcAAAAU3F4uJk+fboCAgLk5uamoKAgbdmy5a79165dq8DAQLm6uqp8+fKKiYm5P4UCAIAHgkPDTVxcnCIjIxUVFaVt27apRo0aCgkJ0ZkzZ7Lsf+jQIbVu3VpNmzZVQkKChg4dqj59+ujbb7+9z5UDAIC8yqHhZtKkSerbt6/CwsJUtWpVzZw5UwUKFNDs2bOz7D9z5kyVKVNGEydOVJUqVRQREaGOHTtq8uTJ97lyAACQVzks3Fy7dk1bt25VcHDw/4pxclJwcLA2bdqU5TKbNm2y6S9JISEhd+wPAAD+ffI56oGTk5OVnp4uHx8fm3YfHx/t2bMny2VOnTqVZf9Lly7p6tWrcnd3z7RMWlqa0tLSrD9fvHhRknTp0qV/uglZyki7kivrxd/Lref0Jp5bx8nN55bn1XF4zZpXbjy3N9dpGMbf9nVYuLlfxo4dq1GjRmVq9/Pzc0A1yE1eUxxdAXILz6058byaV24+tykpKfLy8rprH4eFm+LFi8vZ2VmnT5+2aT99+rRKliyZ5TIlS5bMsr+np2eWozaSNHz4cEVGRlp/zsjI0Pnz51WsWDFZLJZ/uBXmcenSJfn5+enYsWPy9PR0dDnIQTy35sVza048r1kzDEMpKSkqVarU3/Z1WLhxcXFR7dq1FR8fr3bt2km6ETzi4+MVERGR5TINGjTQ8uXLbdpWr16tBg0a3PFxXF1d5erqatNWuHDhf1S7mXl6evJiMimeW/PiuTUnntfM/m7E5iaHXi0VGRmpWbNmac6cOUpMTFT//v2VmpqqsLAwSTdGXUJDQ639+/Xrp4MHD+q///2v9uzZow8++EBffvmlXnzxRUdtAgAAyGMcOuemS5cuOnv2rEaMGKFTp06pZs2aWrlypXXScFJSko4ePWrtX6ZMGX3zzTd68cUXNXXqVD300EP6+OOPFRIS4qhNAAAAeYzDJxRHRETc8TRUVp8+/MQTT2j79u25XNW/j6urq6KiojKdwsODj+fWvHhuzYnn9Z+zGNm5pgoAAOAB4fDvlgIAAMhJhBsAAGAqhBsAAGAqhJv7KCAgQFOmTLnn5WNiYviMnjv4p/sWuN/sOWY5vgH7EG7+v169elk/TDC3/Pzzz3r++eez1TerX2ZdunTR77//fs+PHxMTI4vFIovFIicnJ/n6+qpLly42l9s/qOzZtw+6s2fPqn///nr44Yfl6uqqkiVLKiQkROvWrVPx4sU1bty4LJd766235OPjo+vXr1uPhSpVqmTqN3/+fFksFgUEBOTyluQ9vXr1sr5G8ufPLx8fH7Vo0UKzZ89WRkZGjj6WPcdsbh/ft253Vrd/47GQk7L6+/LVV1/Jzc1NEydOtO7/21+7ixcvtvkk/bVr18piseiRRx5Renq6Td/ChQtneYXxvxXh5j7y9vZWgQIF7nl5d3d3lShR4h/V4OnpqaSkJJ04cUILFizQ3r171alTp3+0zuy4fv16rq7/n+7bB0mHDh20fft2zZkzR7///ruWLl2qJ554QhcvXtSzzz6r6OjoTMsYhqGYmBiFhoYqf/78kiQPDw+dOXNGmzZtsun7ySef6OGHH74v25IXtWzZUklJSTp8+LBWrFihpk2basiQIXrqqaf0119/5djj2HPM5vbxPXXqVCUlJVlvkhQdHW39+eeff7bpf+3atVyr5d/g448/Vo8ePTRjxgy99NJLkiQ3NzeNHz9ef/zxx98uf/DgQX366ae5XeYDjXCTTevWrVO9evXk6uoqX19fvfrqqza/6FJSUtSjRw95eHjI19dXkydP1hNPPKGhQ4da+9w6GmMYhkaOHGl9912qVCkNHjxY0o3P8jly5IhefPFF6zsnKevTUl9//bXq1q0rNzc3FS9eXO3bt7/rdlgsFpUsWVK+vr5q2LChwsPDtWXLFptvcF2yZIkCAwPl5uamsmXLatSoUTbbumfPHj322GNyc3NT1apVtWbNGlksFi1evFiSdPjwYVksFsXFxenxxx+Xm5ubYmNjJd14UVepUkVubm6qXLmyPvjgA+t6r127poiICPn6+srNzU3+/v4aO3bs3+6v2/etJB09elRt27ZVwYIF5enpqc6dO9t8L9nIkSNVs2ZNffbZZwoICJCXl5e6du2qlJSUu+4/R7tw4YJ++OEHjR8/Xk2bNpW/v7/q1aun4cOH6+mnn1Z4eLh+//13bdiwwWa5devW6eDBgwoPD7e25cuXT927d9fs2bOtbcePH9fatWvVvXv3+7ZNec3N0bDSpUsrMDBQr732mpYsWaIVK1bYvDO+cOGC+vTpI29vb3l6eqpZs2basWOHzbru9vrM7u+D2/tKOX98e3l5qWTJktabdGMk4ObPdevW1VtvvaXQ0FB5enpaR5E2bNigxo0by93dXX5+fho8eLBSU1Ot601LS9PLL7+s0qVLy8PDQ0FBQVq7dq1dz4fZTJgwQYMGDdK8efOsn8YvScHBwSpZsqT1d97dDBo0SFFRUUpLS8vNUh9ohJtsOHHihFq1aqW6detqx44dmjFjhj755BONHj3a2icyMlIbN27U0qVLtXr1av3www/atm3bHde5YMECTZ48WR9++KH27dunxYsXq1q1apKkhQsX6qGHHtKbb75p807qdt98843at2+vVq1aafv27YqPj1e9evWyvV1nzpzRokWL5OzsLGdnZ0nSDz/8oNDQUA0ZMkS7d+/Whx9+qJiYGL399tuSpPT0dLVr104FChTQ5s2b9dFHH+n111/Pcv2vvvqqhgwZosTERIWEhCg2NlYjRozQ22+/rcTERI0ZM0ZvvPGG5syZI0maNm2ali5dqi+//FJ79+5VbGysdTj8bvvrdhkZGWrbtq3Onz+vdevWafXq1Tp48KC6dOli0+/AgQNavHixli1bpmXLlmndunV3PKWTVxQsWFAFCxbU4sWLs/zFVq1aNdWtW9cmsEg33oU3bNhQlStXtmnv3bu3vvzyS125ckXSjQDdsmVL66eE44ZmzZqpRo0aWrhwobWtU6dOOnPmjFasWKGtW7cqMDBQzZs31/nz5yXZ9/p8EI7vd999VzVq1ND27dv1xhtv6MCBA2rZsqU6dOignTt3Ki4uThs2bLD5UNaIiAht2rRJ8+bN086dO9WpUye1bNlS+/btu+c6HmSvvPKK3nrrLS1btizTG1FnZ2eNGTNG7733no4fP37X9QwdOlR//fWX3nvvvdws98FmwDAMw+jZs6fRtm3bLO977bXXjEqVKhkZGRnWtunTpxsFCxY00tPTjUuXLhn58+c35s+fb73/woULRoECBYwhQ4ZY2/z9/Y3JkycbhmEYEydONCpWrGhcu3Yty8e8te9N0dHRhpeXl/XnBg0aGD169Mj2NkZHRxuSDA8PD6NAgQKGJEOSMXjwYGuf5s2bG2PGjLFZ7rPPPjN8fX0NwzCMFStWGPny5TOSkpKs969evdqQZCxatMgwDMM4dOiQIcmYMmWKzXrKlStnzJ0716btrbfeMho0aGAYhmEMGjTIaNasmc1+vsme/bVq1SrD2dnZOHr0qPX+3377zZBkbNmyxTAMw4iKijIKFChgXLp0ydpn2LBhRlBQUJbrz0u++uoro0iRIoabm5vRsGFDY/jw4caOHTus98+cOdMoWLCgkZKSYhiGYVy6dMkoUKCA8fHHH1v73Hos1axZ05gzZ46RkZFhlCtXzliyZIkxefJkw9/f/35uVp5wt98DXbp0MapUqWIYhmH88MMPhqenp/Hnn3/a9ClXrpzx4YcfGobx96/Pe/19cD+O71tfzzcfv127djZ9wsPDjeeff96m7YcffjCcnJyMq1evGkeOHDGcnZ2NEydO2PRp3ry5MXz48GzVYRY9e/Y0XFxcDElGfHx8lvffPO7q169v9O7d2zAMw1i0aJFx65/p77//3pBk/PHHH8bMmTONokWLGhcuXDAMwzC8vLyM6OjoXN+WBwUjN9mQmJioBg0a2EzsatSokS5fvqzjx4/r4MGDun79us27Mi8vL1WqVOmO6+zUqZOuXr2qsmXLqm/fvlq0aJHd5/MTEhLUvHlzu5YpVKiQEhIS9Msvv2jixIkKDAy0jspI0o4dO/Tmm29aRwgKFiyovn37KikpSVeuXNHevXvl5+dnHbqWdMd3o3Xq1LH+PzU1VQcOHFB4eLjNukePHq0DBw5IujHpLiEhQZUqVdLgwYO1atUq6/L27K/ExET5+fnJz8/P2la1alUVLlxYiYmJ1raAgAAVKlTI+rOvr6/OnDmT3V3pMB06dNDJkye1dOlStWzZUmvXrlVgYKD1lEm3bt2Unp6uL7/8UpIUFxcnJyenTO/sb+rdu7eio6O1bt06paamqlWrVvdrUx4ohmFYfwfs2LFDly9fVrFixWyO50OHDlmPZ3tenw/C8X3r61m6sQ9iYmJstj8kJEQZGRk6dOiQdu3apfT0dFWsWNGmz7p166z76N+kevXqCggIUFRUlC5fvnzHfuPHj7d+mfTdhIeHq1ixYho/fnxOl2oKhBsH8fPz0969e/XBBx/I3d1dAwYMUJMmTeyaeOvu7m734zo5Oal8+fKqUqWKIiMjVb9+ffXv3996/+XLlzVq1CglJCRYb7t27dK+ffvk5uZm12N5eHjYrFeSZs2aZbPuX3/9VT/99JMkKTAwUIcOHdJbb72lq1evqnPnzurYsaOknNlft7s5sfYmi8WS41fE5BY3Nze1aNFCb7zxhn788Uf16tVLUVFRkm5MGu/YsaN1YnF0dLQ6d+6sggULZrmuHj166KefftLIkSP13HPPKV8+h3/lXJ6UmJioMmXKSLpxPPv6+tocywkJCdq7d6+GDRsmyb7X54NwfN/6epZu7IMXXnjBZvt37Nihffv2qVy5crp8+bKcnZ21detWmz6JiYmaOnXqPdfxoCpdurTWrl2rEydOqGXLlnec/9SkSROFhIRo+PDhd11fvnz59Pbbb2vq1Kk6efJkbpT8QCPcZEOVKlW0adMmGbd8DdfGjRtVqFAhPfTQQypbtqzy589vc0XBxYsX//aybXd3d7Vp00bTpk3T2rVrtWnTJu3atUuS5OLikulSv9tVr15d8fHx/2DLbsyLiYuLs84PCgwM1N69e1W+fPlMNycnJ1WqVEnHjh2zmbx4+5UUWfHx8VGpUqV08ODBTOu9+QdDuvGHuUuXLpo1a5bi4uK0YMEC6xyGu+2vW1WpUkXHjh3TsWPHrG27d+/WhQsXVLVq1XveV3lZ1apVbSZyhoeHa8OGDVq2bJl+/PFHm4nEtytatKiefvpprVu3Tr17974f5T5wvvvuO+3atUsdOnSQdON1curUKeXLly/T8Vy8eHFJ9r8+H7TjOzAwULt3787yd4WLi4tq1aql9PR0nTlzJtP9t478/pv4+/tr3bp1OnXq1F0Dzrhx4/T1119nupLxdp06ddIjjzyiUaNG5Ua5DzTeot3i4sWLSkhIsGkrVqyYBgwYoClTpmjQoEGKiIjQ3r17FRUVpcjISDk5OalQoULq2bOnhg0bpqJFi6pEiRKKioqSk5OTzamsW8XExCg9PV1BQUEqUKCAPv/8c7m7u8vf31/SjSHl9evXq2vXrnJ1dbX+wrxVVFSUmjdvrnLlyqlr167666+/tHz5cr3yyivZ3mY/Pz+1b99eI0aM0LJlyzRixAg99dRTevjhh9WxY0c5OTlpx44d+vXXXzV69Gi1aNFC5cqVU8+ePTVhwgSlpKTo//7v/yTpjtt606hRozR48GB5eXmpZcuWSktL0y+//KI//vhDkZGRmjRpknx9fVWrVi05OTlp/vz5KlmypPXzG+62v24VHBysatWqqUePHpoyZYr++usvDRgwQI8//nimofUHzblz59SpUyf17t1b1atXV6FChfTLL79owoQJatu2rbVfkyZNVL58eYWGhqpy5cpq2LDhXdcbExOjDz74QMWKFcvtTcjz0tLSdOrUKaWnp+v06dNauXKlxo4dq6eeekqhoaGSbhxjDRo0ULt27TRhwgRVrFhRJ0+etE4irlOnjl2vzwfx+H7llVdUv359RUREqE+fPvLw8NDu3bu1evVqvf/++6pYsaJ69Oih0NBQTZw4UbVq1dLZs2cVHx+v6tWrq3Xr1vet1rzEz89Pa9euVdOmTRUSEqKVK1dm6nPz+Z02bdrfrm/cuHEKCQnJjVIfaIzc3GLt2rWqVauWzW3UqFEqXbq0li9fri1btqhGjRrq16+fwsPDrX/UJWnSpElq0KCBnnrqKQUHB6tRo0bWS56zUrhwYc2aNUuNGjVS9erVtWbNGn399dfWPy5vvvmmDh8+rHLlysnb2zvLdTzxxBOaP3++li5dqpo1a6pZs2basmWL3dv94osv6ptvvtGWLVsUEhKiZcuWadWqVapbt67q16+vyZMnW3/JOjs7a/Hixbp8+bLq1q2rPn36WK+W+rvTVn369NHHH3+s6OhoVatWTY8//rhiYmKsIzeFChXShAkTVKdOHdWtW1eHDx/W8uXL5eTk9Lf761YWi0VLlixRkSJF1KRJEwUHB6ts2bKKi4uze9/kNQULFlRQUJAmT56sJk2a6NFHH9Ubb7yhvn376v3337f2s1gs6t27t/74449sjca4u7sTbP6/lStXytfXVwEBAWrZsqW+//57TZs2TUuWLLFeVWixWLR8+XI1adJEYWFhqlixorp27aojR45YrzSz5/X5IB7f1atX17p16/T777+rcePGqlWrlkaMGKFSpUpZ+0RHRys0NFQvvfSSKlWqpHbt2unnn3/+V3+OkiQ99NBDWrt2rZKTkxUSEmLzURw3vfnmm9k6jdisWTM1a9YsRz+DyQwsxq3nWpBjUlNTVbp0aU2cOPGupwTMYOPGjXrssce0f/9+lStXztHlAAD+5TgtlUO2b9+uPXv2qF69erp48aLefPNNSbI5VWAWixYtUsGCBVWhQgXt379fQ4YMUaNGjQg2AIA8gXCTg959913t3btXLi4uql27tn744Ycs58o86FJSUvTKK6/o6NGjKl68uIKDgzVx4kRHlwUAgCROSwEAAJNhQjEAADAVwg0AADAVwg0AADAVwg0AADAVwg0A01u7dq0sFosuXLiQ7WUCAgI0ZcqUXKsJQO4h3ABwuF69eslisahfv36Z7hs4cKAsFot69ep1/wsD8EAi3ADIE/z8/DRv3jxdvXrV2vbnn39q7ty5//qP6wdgH8INgDwhMDBQfn5+WrhwobVt4cKFevjhh1WrVi1rW1pamgYPHqwSJUrIzc1Njz32WKZvpl++fLkqVqwod3d3NW3aVIcPH870eBs2bFDjxo3l7u4uPz8/DR482Oab1QE8uAg3APKM3r17Kzo62vrz7NmzFRYWZtPnv//9rxYsWKA5c+Zo27ZtKl++vEJCQnT+/HlJ0rFjx/TMM8+oTZs2SkhIUJ8+ffTqq6/arOPAgQNq2bKlOnTooJ07dyouLk4bNmxQRERE7m8kgFxHuAGQZzz77LPasGGDjhw5oiNHjmjjxo169tlnrfenpqZqxowZeuedd/Tkk0+qatWqmjVrltzd3fXJJ59IkmbMmKFy5cpp4sSJqlSpknr06JFpvs7YsWPVo0cPDR06VBUqVFDDhg01bdo0ffrpp/rzzz/v5yYDyAV8txSAPMPb21utW7dWTEyMDMNQ69atbb6f7cCBA7p+/boaNWpkbcufP7/q1aunxMRESVJiYqKCgoJs1tugQQObn3fs2KGdO3cqNjbW2mYYhjIyMnTo0CFVqVIlNzYPwH1CuAGQp/Tu3dt6emj69Om58hiXL1/WCy+8oMGDB2e6j8nLwIOPcAMgT2nZsqWuXbsmi8WikJAQm/vKlSsnFxcXbdy4Uf7+/pKk69ev6+eff9bQoUMlSVWqVNHSpUttlvvpp59sfg4MDNTu3btVvnz53NsQAA7DnBsAeYqzs7MSExO1e/duOTs729zn4eGh/v37a9iwYVq5cqV2796tvn376sqVKwoPD5ck9evXT/v27dOwYcO0d+9ezZ07VzExMTbreeWVV/Tjjz8qIiJCCQkJ2rdvn5YsWcKEYsAkCDcA8hxPT095enpmed+4cePUoUMHPffccwoMDNT+/fv17bffqkiRIpJunFZasGCBFi9erBo1amjmzJkaM2aMzTqqV6+udevW6ffff1fjxo1Vq1YtjRgxQqVKlcr1bQOQ+yyGYRiOLgIAACCnMHIDAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABMhXADAABM5f8BI4yFz50QHnwAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ],
      "execution_count": 57
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}