Symptom Matcher

Table of Contents

• Project Overview
• Dataset
• Exploratory Data Analysis
• Random Forest Classifier
• Usage
• Conclusion

Project Overview

The Symptom Matcher project utilizes a dataset containing symptoms and disease labels to build a predictive model using the Random Forest Classifier. This model helps predict the disease based on the provided symptoms.

Primary Objectives

1. Data Loading: Load the dataset containing symptoms and disease labels.

2. Exploratory Data Analysis (EDA): Perform basic data exploration to understand the dataset's structure and characteristics.

3. Random Forest Classifier: Build a predictive model using the Random Forest Classifier algorithm to predict diseases based on symptoms.

4. User Interaction: Allow users to input their symptoms and use the model to predict potential diseases.

Dataset

The dataset used in this project contains the following columns:

• Symptoms: Various symptoms represented as Boolean values (0 or 1).
• Diseases: Labels representing different diseases.

Exploratory Data Analysis

The exploratory data analysis (EDA) phase includes examining basic statistics of the dataset and understanding the distribution of symptoms and diseases.

Random Forest Classifier

A Random Forest Classifier is employed to create a predictive model. The model is trained on the dataset, and its performance is evaluated using metrics such as F1-score, precision, recall, and accuracy.

Usage

To use the Symptom Matcher:

1. Load the dataset containing symptoms and disease labels.
2. Perform EDA to understand the dataset.
3. Train a Random Forest Classifier on the dataset.
4. Allow users to input their symptoms.
5. Use the trained model to predict potential diseases based on the provided symptoms.

Conclusion

The Symptom Matcher project provides a tool for predicting diseases based on symptoms using a Random Forest Classifier. Users can interact with the model by entering their symptoms to get potential disease predictions