development

import boto3 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import joblib import tempfile from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.feature_selection import SelectKBest, f_classif from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix # Define S3 details bucket_name="employee-data" file_key = 'inputfiles/employee_cleaned_data.csv' # Load dataset from S3 s3_client = boto3.client('s3') obj = s3_client.get_object(Bucket=bucket_name, Key=file_key) df = pd.read_csv(obj['Body']) # Data preprocessing df = df.drop(columns=['employee_id']) df['region'] = df['region'].str.extract('(d+)').astype(int) X = df.drop(columns=['turnover']) y = df['turnover'] # Column transformer preprocessor = ColumnTransformer( transformers=[ ('num', StandardScaler(), ['age', 'salary']), ('cat', OneHotEncoder(), ['department',…

In the rapidly evolving landscape of web development, the need for efficient data retrieval methods has never been greater. Traditional REST APIs, while foundational, often struggle with issues like over-fetching and under-fetching data. Enter GraphQL, a powerful query language developed by Facebook in 2012 that has transformed how developers interact with APIs. This blog will explore what GraphQL is, its advantages over REST, its architecture, and practical use cases. What is GraphQL? GraphQL is a query language for APIs and a runtime for executing those queries by leveraging existing data. Unlike REST, which typically exposes multiple endpoints for different resources,…