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How to use individual_deployment method in localstack

Best Python code snippet using localstack_python

cancer.py

Source:cancer.py

1import numpy as np2import pandas as pd3from sklearn import ensemble4from sklearn.model_selection import train_test_split5from sklearn.metrics import accuracy_score, classification_report, confusion_matrix6import joblib78df = pd.read_csv(r"C:\Users\Gauri\Desktop\HealthApp\individual_deployment\data\cancer.csv")9df.drop(df.columns[[0,-1]], axis=1, inplace=True)10# Split the features data and the target 11Xdata = df.drop(['diagnosis'], axis=1)12ydata = df['diagnosis']1314# Encoding the target value 15yenc = np.asarray([1 if c == 'M' else 0 for c in ydata])16cols = ['concave points_mean','area_mean','radius_mean','perimeter_mean','concavity_mean',]17Xdata = df[cols]18print(Xdata.columns)1920X_train, X_test, y_train, y_test = train_test_split(Xdata, yenc, 21                                                    test_size=0.3,22                                                    random_state=43)23print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))24print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))2526model = ensemble.RandomForestClassifier()27model.fit(X_train, y_train)28y_pred = model.predict(X_test)29print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))3031clf_report = classification_report(y_test, y_pred)32print('Classification report')33print("---------------------")34print(clf_report)35print("_____________________")36
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heart.py

Source:heart.py

1import numpy as np2import pandas as pd3from sklearn import ensemble4from sklearn.model_selection import train_test_split5from sklearn.metrics import accuracy_score, classification_report, confusion_matrix6import joblib78df = pd.read_csv("C:\Users\Gauri\Desktop\HealthApp\individual_deployment\data\heart.csv")910categorical_val = []11continous_val = []12for column in df.columns:13    if len(df[column].unique()) <= 10:14        categorical_val.append(column)15    else:16        continous_val.append(column)1718categorical_val.remove('target')19dataset = pd.get_dummies(df, columns = categorical_val)2021cols = ['cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang']       22X = df[cols]23y = dataset.target2425X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)2627print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))28print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))2930model = ensemble.RandomForestClassifier()31model.fit(X_train, y_train)32y_pred = model.predict(X_test)33print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))3435clf_report = classification_report(y_test, y_pred)36print('Classification report')37print("---------------------")38print(clf_report)39print("_____________________")40
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liver.py

Source:liver.py

1import numpy as np2import pandas as pd3from sklearn import ensemble4from sklearn.model_selection import train_test_split5from sklearn.metrics import accuracy_score, classification_report, confusion_matrix6import joblib78patients=pd.read_csv('C:\Users\Gauri\Desktop\HealthApp\individual_deployment\data\indian_liver_patient.csv')9patients['Gender']=patients['Gender'].apply(lambda x:1 if x=='Male' else 0)10patients=patients.fillna(0.94)1112X=patients[['Total_Bilirubin', 'Direct_Bilirubin',13       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',14       'Total_Protiens', 'Albumin', 'Albumin_and_Globulin_Ratio']]15y=patients['Dataset']1617X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=123)1819print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))20print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))2122model = ensemble.RandomForestClassifier()23model.fit(X_train, y_train)24y_pred = model.predict(X_test)25print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))2627clf_report = classification_report(y_test, y_pred)28print('Classification report')29print("---------------------")30print(clf_report)31print("_____________________")32
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