Suspendisse interdum consectetur libero id. Fermentum leo vel orci porta non. Euismod viverra nibh cras pulvinar suspen.
Hiyma that uses predictive analytics to provide personalized healthcare services. Our platform utilizes cutting-edge machine learning models to help healthcare providers optimize care plans and improve outcomes. We empower healthcare providers to make more informed decisions and reduce costs while improving the quality of care. Our platform allows providers to collect, analyze and interpret patient data to determine the best course of action for each individual. We also provide medical experts with access to real-time insights and predictions to make data-driven decisions. We are committed to providing the highest quality healthcare services while leveraging the latest technology to ensure that healthcare providers are making the best possible decisions.
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet,
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet,
# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load the data into a Pandas DataFrame
df = pd.read_csv('healthcare_data.csv')
# Explore the data by displaying some basic statistics
print(df.describe())
# Visualize the data using a scatter plot
plt.scatter(df['age'], df['blood_pressure'])
plt.xlabel('Age')
plt.ylabel('Blood Pressure')
plt.show()
# Use the DataFrame groupby method to group the data by gender
grouped_data = df.groupby('gender')
# Calculate the average blood pressure for each group
avg_blood_pressure = grouped_data['blood_pressure'].mean()
# Print the results
print(avg_blood_pressure)Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet,
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet,