machine-learning-prediction-spacex.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# **Space X  Falcon 9 First Stage Landing Prediction**\n","## Machine Learning Prediction\n"]},{"cell_type":"markdown","metadata":{},"source":["### Objectives\n","\n","Performing 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","    \\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n","*   Find the method performs best using test data\n"]},{"cell_type":"markdown","metadata":{},"source":["## Libraries and Auxiliary Functions\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n","import 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\n","import 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.\n","import 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\n","import seaborn as sns\n","# Preprocessing allows us to standarsize our data\n","from sklearn import preprocessing\n","# Allows us to split our data into training and testing data\n","from sklearn.model_selection import train_test_split\n","# Allows us to test parameters of classification algorithms and find the best one\n","from sklearn.model_selection import GridSearchCV\n","# Logistic Regression classification algorithm\n","from sklearn.linear_model import LogisticRegression\n","# Support Vector Machine classification algorithm\n","from sklearn.svm import SVC\n","# Decision Tree classification algorithm\n","from sklearn.tree import DecisionTreeClassifier\n","# K Nearest Neighbors classification algorithm\n","from sklearn.neighbors import KNeighborsClassifier"]},{"cell_type":"markdown","metadata":{},"source":["This function is to plot the confusion matrix.\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"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'])"]},{"cell_type":"markdown","metadata":{},"source":["## Load the dataframe\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n","0             1  2010/06/04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n","1             2  2012/05/22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n","2             3  2013/03/01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n","3             4  2013/09/29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n","4             5  2013/12/03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n","\n","       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n","0    None None        1     False   False  False        NaN      1   \n","1    None None        1     False   False  False        NaN      1   \n","2    None None        1     False   False  False        NaN      1   \n","3  False Ocean        1     False   False  False        NaN      1   \n","4    None None        1     False   False  False        NaN      1   \n","\n","   ReusedCount Serial   Longitude   Latitude  Class  \n","0            0  B0003  -80.577366  28.561857      0  \n","1            0  B0005  -80.577366  28.561857      0  \n","2            0  B0007  -80.577366  28.561857      0  \n","3            0  B1003 -120.610829  34.632093      0  \n","4            0  B1004  -80.577366  28.561857      0  "],"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>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010/06/04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012/05/22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013/03/01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013/09/29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013/12/03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{},"execution_count":4}],"source":["data = pd.read_csv(\"dataset_part_2.csv\")\n","data.head()"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Orbit  LaunchSite  LandingPad  Serial\n","0       1           0           1       0\n","1       1           0           1       1\n","2       1           0           1       1\n","3       1           1           1       1\n","4       1           0           1       1\n","..    ...         ...         ...     ...\n","85      1           1           1       1\n","86      1           1           1       1\n","87      1           1           1       1\n","88      1           0           1       1\n","89      1           0           1       1\n","\n","[90 rows x 4 columns]"],"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>Orbit</th>\n      <th>LaunchSite</th>\n      <th>LandingPad</th>\n      <th>Serial</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 4 columns</p>\n</div>"},"metadata":{},"execution_count":19}],"source":["X = pd.read_csv('dataset_part_3.csv')\n","X.head(100)"]},{"cell_type":"markdown","metadata":{},"source":["Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n","assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["y = data['Class'].to_numpy() "]},{"cell_type":"markdown","metadata":{},"source":["Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code>"]},{"cell_type":"code","execution_count":57,"metadata":{},"outputs":[],"source":["transform = preprocessing.StandardScaler()\n","X = transform.fit_transform(X)"]},{"cell_type":"markdown","metadata":{},"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"]},{"cell_type":"markdown","metadata":{},"source":["### Spliting the data into training and test data\n","\n","Using the function train_test_split to split the data X and Y into training and test data. Setting 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","\n","<code>X_train, X_test, Y_train, Y_test</code>\n"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2, random_state=2)"]},{"cell_type":"markdown","metadata":{},"source":["we can see we only have 18 test samples.\n"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(18,)"]},"metadata":{},"execution_count":22}],"source":["Y_test.shape"]},{"cell_type":"markdown","metadata":{},"source":["### Creating a logistic regression object using 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"]},{"cell_type":"code","execution_count":62,"metadata":{},"outputs":[],"source":["parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']} # l1 lasso l2 ridge\n","lr=LogisticRegression()\n","gscv = GridSearchCV(lr,parameters,scoring='accuracy',cv=10)\n","logreg_cv = gscv.fit(X_train,Y_train)"]},{"cell_type":"markdown","metadata":{},"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"]},{"cell_type":"code","execution_count":63,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.6678571428571429\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n","print(\"accuracy :\",logreg_cv.best_score_)"]},{"cell_type":"markdown","metadata":{},"source":["Calculating the accuracy on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":67,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy=   0.6666666666666666\n"]}],"source":["print('Accuracy=  ',logreg_cv.score(X_test,Y_test))"]},{"cell_type":"markdown","metadata":{},"source":["Lets look at the confusion matrix:\n"]},{"cell_type":"code","execution_count":68,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 359.93325 277.314375\" width=\"359.93325pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-07-05T01:12:38.974662</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     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the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Creating a support vector machine object then creating a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fitting the object to find the best parameters from the dictionary <code>parameters</code>.\n"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[],"source":["parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n","              'C': np.logspace(-3, 3, 5),\n","              'gamma':np.logspace(-3, 3, 5)}\n","svm = SVC()\n","gscv = GridSearchCV(svm,parameters,scoring='accuracy',cv=10)\n","svm_cv = gscv.fit(X_train,Y_train)"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["tuned hpyerparameters :(best parameters)  {'C': 0.001, 'gamma': 0.001, 'kernel': 'linear'}\naccuracy : 0.6678571428571429\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n","print(\"accuracy :\",svm_cv.best_score_)"]},{"cell_type":"markdown","metadata":{},"source":["Calculating the accuracy on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":75,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["accuracy:  0.6666666666666666\n"]}],"source":["print(\"accuracy: \",svm_cv.score(X_test,Y_test))"]},{"cell_type":"markdown","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":76,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 359.93325 277.314375\" width=\"359.93325pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" 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Creating a decision tree classifier object then  creating a <code>GridSearchCV</code> object <code>tree_cv</code> with cv = 10.  Fitting the object to find the best parameters from the dictionary <code>parameters</code>.\n"]},{"cell_type":"code","execution_count":78,"metadata":{},"outputs":[],"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","\n","tree = DecisionTreeClassifier()"]},{"cell_type":"code","execution_count":79,"metadata":{},"outputs":[],"source":["gscv = GridSearchCV(tree,parameters,scoring='accuracy',cv=10)\n","tree_cv = gscv.fit(X_train,Y_train)"]},{"cell_type":"code","execution_count":80,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'best'}\naccuracy : 0.6678571428571429\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n","print(\"accuracy :\",tree_cv.best_score_)"]},{"cell_type":"markdown","metadata":{},"source":["Calculating the accuracy of tree_cv on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":86,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["accuracy:  0.6666666666666666\n"]}],"source":["print(\"accuracy: \",tree_cv.score(X_test,Y_test))"]},{"cell_type":"markdown","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":87,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib 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= svm_cv.predict(X_test)\n","plot_confusion_matrix(Y_test,yhat)"]},{"cell_type":"markdown","metadata":{},"source":["Creating a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fitting the object to find the best parameters from the dictionary <code>parameters</code>.\n"]},{"cell_type":"code","execution_count":88,"metadata":{},"outputs":[],"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","\n","KNN = KNeighborsClassifier()"]},{"cell_type":"code","execution_count":89,"metadata":{},"outputs":[],"source":["gscv = GridSearchCV(KNN,parameters,scoring='accuracy',cv=10)\n","knn_cv = gscv.fit(X_train,Y_train)"]},{"cell_type":"code","execution_count":90,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["tuned hpyerparameters :(best parameters)  {'algorithm': 'brute', 'n_neighbors': 9, 'p': 1}\naccuracy : 0.6535714285714287\n"]}],"source":["print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n","print(\"accuracy :\",knn_cv.best_score_)"]},{"cell_type":"markdown","metadata":{},"source":["Calculating the accuracy of tree_cv on the test data using the method <code>score</code>:\n"]},{"cell_type":"code","execution_count":91,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["accuracy:  0.6666666666666666\n"]}],"source":["print(\"accuracy: \",knn_cv.score(X_test,Y_test))"]},{"cell_type":"markdown","metadata":{},"source":["We can plot the confusion matrix\n"]},{"cell_type":"code","execution_count":92,"metadata":{},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 353.57075 277.314375\" width=\"353.57075pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  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\n"},"metadata":{"needs_background":"light"}}],"source":["yhat = knn_cv.predict(X_test)\n","plot_confusion_matrix(Y_test,yhat)"]},{"cell_type":"markdown","metadata":{},"source":["### Finding the best algorithm"]},{"cell_type":"code","execution_count":125,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":["Best Algorithm is Tree with a score of 0.6678571428571429\nBest Params is : {'criterion': 'gini', 'max_depth': 2, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'best'}\n"]}],"source":["algorithms = {'KNN':knn_cv.best_score_,'Tree':tree_cv.best_score_,'LogisticRegression':logreg_cv.best_score_}\n","bestalgorithm = max(algorithms, key=algorithms.get)\n","print('Best Algorithm is',bestalgorithm,'with a score of',algorithms[bestalgorithm])\n","if bestalgorithm == 'Tree':\n","    print('Best Params is :',tree_cv.best_params_)\n","if bestalgorithm == 'KNN':\n","    print('Best Params is :',knn_cv.best_params_)\n","if bestalgorithm == 'LogisticRegression':\n","    print('Best Params is :',logreg_cv.best_params_)"]},{"cell_type":"markdown","metadata":{},"source":["#### Author : Ashlin Darius Govindasamy\n"]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3.8.5 64-bit"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"},"interpreter":{"hash":"c3ae22b844db142b40ee44c017e19b92c4d7208685f48a9abdf5417629f14980"}},"nbformat":4,"nbformat_minor":4}