Create README.md

Author: psanyalaich

Detection Models/Stress Level Detection/README.md

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+# Stress Level Detection
+- The Stress Level Detection project aims to predict stress levels based on various physiological and demographic features using machine learning algorithms. 
+- The dataset used in this project contains information on individuals, including their age, heart rate, sleep hours, and gender.
+- The goal is to classify individuals into different stress levels using models such as Logistic Regression, Random Forest, and Support Vector Machines (SVM).
+
+## Prerequisites
+- Python
+- Pandas
+- NumPy
+- Seaborn
+- Matplotlib
+- Scikit-learn
+- Imbalanced-learn
+
+To install: `pip install pandas numpy seaborn matplotlib scikit-learn imbalanced-learn`
+
+## Dataset
+The dataset used for this project is a CSV file named `stress_data.csv`, which includes the following columns:
+- `Gender`: Gender of the individual (categorical)
+- `Age`: Age of the individual (numerical)
+- `HeartRate`: Heart rate of the individual (numerical)
+- `SleepHours`: Number of hours the individual sleeps (numerical)
+- `StressLevel`: Level of stress (categorical, target variable)
+
+# Usage
+- Mount your Google Drive to access the dataset.
+- Load the dataset using Pandas.
+- Perform data cleaning, including handling missing values.
+- Encode categorical variables and normalize numerical features.
+- Split the data into training, validation, and test sets.
+- Conduct exploratory data analysis (EDA) to visualize data distributions and correlations.
+- Train models using Logistic Regression, Random Forest, and SVM.
+- Evaluate the models using classification reports and accuracy scores.
+- Use SMOTE to address class imbalance and re-evaluate the models.
+
+# Results
+- Logistic Regression, Random Forest, and SVM models were trained and evaluated.
+- SMOTE was applied to balance the dataset, resulting in improved accuracy for the SVM model.
+
+# Conclusion
+This project demonstrates the process of detecting stress levels using machine learning techniques.