Doctor Fee Prediction

Project Overview

• In today's healthcare landscape, data-driven approaches are pivotal for enhancing patient outcomes and addressing industry challenges. Leveraging comprehensive datasets, this project endeavors to employ machine learning techniques to analyze various attributes related to medical services. By developing predictive models for regression and classification tasks, we aim to gain insights into factors influencing medical service fees while accurately predicting fee categories. Through this effort, we aim to optimize healthcare services, fostering better decision-making and ultimately improving patient care.

Problem Statement

• The objective of this project is to leverage machine learning techniques to enhance the healthcare landscape by predicting medical service fees and catering to different audiences' needs. This project is structured into two milestones :

  1. (Milestone 1) Regression Task: Predicting Fee Prices - Developing a regression model to estimate the fee prices for medical services based the dataset attributes.

  2. (Milestone 2) Classification Task: Predicting Fee Categories - Categorizing Fee Prices into categories and building a classification model to predict medical service belong to which category (e.g., expensive, cheap, affordable)

Project Pipeline:

1. Exploratory Data Analysis (EDA): Conduct a deep analysis of the dataset to understand the distribution and relationships between the features.Observe and handle any errors within the features.Explore the distribution of fee prices and identify potential correlations with other features.

2. Data Preprocessing: Handle missing values, outliers, and any inconsistencies in the dataset. Perform data cleaning and normalization to ensure the quality and consistency of the data.

3. Feature Encoding: Encode categorical variables into numerical representations suitable for machine learning algorithms. Utilize techniques such as one-hot encoding or label encoding to transform categorical attributes into a format understandable by regression models.

4. Feature Selection: Identify and select relevant features that contribute most to predicting fee prices.Using techniques such as correlation analysis or feature importance ranking.

5. Model Building: Experimenting with multiple regression and classification algorithms through extensive model selection, tuning, and evaluation processes to identify the best-performing model.

6. Model Selection: After tuning and evaluating the models, selecting the best-performing regression and classification models based on its performance on the dataset. Subsequently, training the chosen models and assessing its performance on test data to analyze and gain insights from the results

Credits:

• Mohamed Samy
• Yomna Mohamed
• Nadine Haitham
• Mohamed Ashraf
• Youssef Tamer
• Ammar Mohamed