🩺 Healthcare Data Analysis – Project 1

This project explores a real-world healthcare dataset using Python and pandas. The data loaded, cleaned, and analyzed, then uses charts to uncover key patterns.

Steps Followed

1. Data Loading – Imported CSV file with patient info.
2. Exploration – Checked data shape, types, summary, and missing columns.
3. Data Cleaning – Handled missing values and fixed date formats.
4. Analysis & Visualization – Grouped and visualized by diagnosis, age, gender, and treatment.

Key Insights

Age & Cholesterol

• Cholesterol rises with age, peaking around 45–74.
• Older adults may need more heart health checkups.

Diagnosis by Gender

• Men had more Coronary Artery Disease.
• Women had more Hypertension and Hyperlipidemia.
• Healthy cases were fairly even across genders.

Treatment Plans

• Medication was most common.
• Some patients had no clear treatment plan — this needs better tracking.

BMI, Cholesterol, Age by Diagnosis

• Diabetes & Hypertension: Higher BMI and age.
• Coronary Disease: Older patients with high cholesterol.
• Healthy group had lower age, BMI, and cholesterol.

Conclusions

• Age and gender play a big role in patient health.
• Watch cholesterol and BMI in middle-aged and older adults.
• Clean medical records (e.g., treatment plan) are important.