From 9bbc91c5cc8153fd15bc91c1a2f55f1377cb7b53 Mon Sep 17 00:00:00 2001
From: adwityac <adwityachakraborty@hotmail.com>
Date: Fri, 3 Jan 2025 17:03:21 +0530
Subject: [PATCH 1/2] Added sleep quality prediction model

---
 docs/ML/projects/sleep_quality_prediction.md | 277 +++++++++++++++++++
 1 file changed, 277 insertions(+)
 create mode 100644 docs/ML/projects/sleep_quality_prediction.md

diff --git a/docs/ML/projects/sleep_quality_prediction.md b/docs/ML/projects/sleep_quality_prediction.md
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+<h1>Sleep Quality Prediction</h1>
+
+<h2>AIM</h2>
+<p>To predict sleep quality based on lifestyle and health factors.</p>
+
+---
+
+<h2>DATASET LINK</h2>
+<p>
+    <a href="https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset">
+        Sleep Health and Lifestyle Dataset
+    </a>
+</p>
+
+---
+
+<h2>DESCRIPTION</h2>
+
+<h3>What is the requirement of the project?</h3>
+<ul>
+    <li>This project aims to predict the quality of sleep using various health and lifestyle metrics. Predicting sleep quality helps individuals and healthcare professionals address potential sleep-related health issues early.</li>
+</ul>
+
+<h3>Why is it necessary?</h3>
+<ul>
+    <li>Sleep quality significantly impacts physical and mental health. Early predictions can prevent chronic conditions linked to poor sleep, such as obesity, heart disease, and cognitive impairment.</li>
+</ul>
+
+<h3>How is it beneficial and used?</h3>
+<ul>
+    <li><strong>Individuals:</strong> Assess their sleep health and make lifestyle changes to improve sleep quality.</li>
+    <li><strong>Healthcare Professionals:</strong> Use the model as an auxiliary diagnostic tool to recommend personalized interventions.</li>
+</ul>
+
+<h3>How did you start approaching this project? (Initial thoughts and planning)</h3>
+<ul>
+    <li>Researching sleep health factors and existing literature.</li>
+    <li>Exploring and analyzing the dataset to understand feature distributions.</li>
+    <li>Preprocessing data for effective feature representation.</li>
+    <li>Iterating over machine learning models to find the optimal balance between accuracy and interpretability.</li>
+</ul>
+
+<h3>Mention any additional resources used</h3>
+<ul>
+    <li><strong>Research Paper:</strong> <a href="https://www.researchgate.net/publication/355188118_Long_Short_Term_Memory-based_Models_for_Sleep_Quality_Prediction_from_Wearable_Device_Time_Series_Data">Analyzing Sleep Patterns Using AI</a></li>
+    <li><strong>Public Notebook:</strong> <a href="https://www.kaggle.com/code/alexandermk04/sleep-quality-prediction-with-96-accuracy/notebook">Sleep Quality Prediction with 96% Accuracy</a></li>
+</ul>
+
+---
+
+<h2>LIBRARIES USED</h2>
+<ul>
+    <li>pandas</li>
+    <li>numpy</li>
+    <li>scikit-learn</li>
+    <li>matplotlib</li>
+    <li>seaborn</li>
+    <li>joblib</li>
+    <li>flask</li>
+</ul>
+
+---
+
+<h2>EXPLANATION</h2>
+
+<h3>DETAILS OF THE DIFFERENT FEATURES</h3>
+
+<table>
+    <thead>
+        <tr>
+            <th>Feature Name</th>
+            <th>Description</th>
+            <th>Type</th>
+            <th>Values/Range</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>Gender</td>
+            <td>Respondent's gender</td>
+            <td>Categorical</td>
+            <td>[Male, Female]</td>
+        </tr>
+        <tr>
+            <td>Age</td>
+            <td>Respondent's age</td>
+            <td>Numerical</td>
+            <td>Measured in years</td>
+        </tr>
+        <tr>
+            <td>Sleep Duration (hours)</td>
+            <td>Hours of sleep per day</td>
+            <td>Numerical</td>
+            <td>Measured in hours</td>
+        </tr>
+        <tr>
+            <td>Physical Activity Level</td>
+            <td>Daily physical activity in minutes</td>
+            <td>Numerical</td>
+            <td>Measured in minutes</td>
+        </tr>
+        <tr>
+            <td>Stress Level</td>
+            <td>Stress level on a scale</td>
+            <td>Numerical</td>
+            <td>1 to 5 (low to high)</td>
+        </tr>
+        <tr>
+            <td>BMI Category</td>
+            <td>Body Mass Index category</td>
+            <td>Categorical</td>
+            <td>[Underweight, Normal, Overweight, Obese]</td>
+        </tr>
+        <tr>
+            <td>Systolic Blood Pressure</td>
+            <td>Systolic blood pressure</td>
+            <td>Numerical</td>
+            <td>Measured in mmHg</td>
+        </tr>
+        <tr>
+            <td>Diastolic Blood Pressure</td>
+            <td>Diastolic blood pressure</td>
+            <td>Numerical</td>
+            <td>Measured in mmHg</td>
+        </tr>
+        <tr>
+            <td>Heart Rate (bpm)</td>
+            <td>Resting heart rate</td>
+            <td>Numerical</td>
+            <td>Beats per minute</td>
+        </tr>
+        <tr>
+            <td>Daily Steps</td>
+            <td>Average number of steps per day</td>
+            <td>Numerical</td>
+            <td>Measured in steps</td>
+        </tr>
+        <tr>
+            <td>Sleep Disorder</td>
+            <td>Reported sleep disorder</td>
+            <td>Categorical</td>
+            <td>[Yes, No]</td>
+        </tr>
+    </tbody>
+</table>
+
+---
+
+<h2>WHAT I HAVE DONE</h2>
+
+<h4>Step 1: Exploratory Data Analysis</h4>
+<ul>
+    <li>Summary statistics</li>
+    <li>Data visualization for numerical feature distributions</li>
+    <li>Target splits for categorical features</li>
+</ul>
+
+<h4>Step 2: Data Cleaning and Preprocessing</h4>
+<ul>
+    <li>Handling missing values</li>
+    <li>Label encoding categorical features</li>
+    <li>Standardizing numerical features</li>
+</ul>
+
+<h4>Step 3: Feature Engineering and Selection</h4>
+<ul>
+    <li>Merging features based on domain knowledge</li>
+    <li>Creating derived features such as "Activity-to-Sleep Ratio"</li>
+</ul>
+
+<h4>Step 4: Modeling</h4>
+<ul>
+    <li>Model trained: Decision Tree</li>
+    <li>Class imbalance handled using SMOTE</li>
+    <li>Metric for optimization: F1-score</li>
+</ul>
+
+<h4>Step 5: Result Analysis</h4>
+<ul>
+    <li>Visualized results using confusion matrices and classification reports</li>
+    <li>Interpreted feature importance for tree-based models</li>
+</ul>
+
+---
+
+<h2>MODELS USED AND THEIR ACCURACIES</h2>
+
+<table>
+    <thead>
+        <tr>
+            <th>Model</th>
+            <th>Accuracy (%)</th>
+            <th>F1-Score (%)</th>
+            <th>Precision (%)</th>
+            <th>Recall (%)</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>Decision Tree</td>
+            <td>74.50</td>
+            <td>75.20</td>
+            <td>73.00</td>
+            <td>77.50</td>
+        </tr>
+    </tbody>
+</table>
+
+---
+<h2>SCREENSHOTS</h2>
+
+
+<h2>CONCLUSION</h2>
+
+<h3>WHAT YOU HAVE LEARNED</h3>
+
+<div>
+    <h4>Insights gained from the data</h4>
+    <ul>
+        <li>Sleep Duration, Stress Level, and Physical Activity are the most indicative features for predicting sleep quality.</li>
+    </ul>
+</div>
+
+<div>
+    <h4>Improvements in understanding machine learning concepts</h4>
+    <ul>
+        <li>Learned and implemented preprocessing techniques like encoding categorical variables and handling imbalanced datasets.</li>
+        <li>Gained insights into deploying a machine learning model using Flask for real-world use cases.</li>
+    </ul>
+</div>
+
+<div>
+    <h4>Challenges faced and how they were overcome</h4>
+    <ul>
+        <li>Managing imbalanced classes: Overcame this by using SMOTE for oversampling the minority class.</li>
+        <li>Choosing a simple yet effective model: Selected Decision Tree for its interpretability and ease of deployment.</li>
+    </ul>
+</div>
+
+---
+
+<h3>USE CASES OF THIS MODEL</h3>
+
+<div>
+    <h4>Application 1</h4>
+    <p>
+        A health tracker app can integrate this model to assess and suggest improvements in sleep quality based on user inputs.
+    </p>
+</div>
+
+<div>
+    <h4>Application 2</h4>
+    <p>
+        Healthcare providers can use this tool to make preliminary assessments of patients' sleep health, enabling timely interventions.
+    </p>
+</div>
+
+---
+
+<h3>FEATURES PLANNED BUT NOT IMPLEMENTED</h3>
+
+<div>
+    <h4>Feature 1</h4>
+    <p>
+        Advanced models such as Random Forest, AdaBoost, and Gradient Boosting were not implemented due to the project's focus on simplicity and interpretability.
+    </p>
+</div>
+
+<div>
+    <h4>Feature 2</h4>
+    <p>
+        Integration with wearable device data for real-time predictions was not explored but remains a potential enhancement for future work.
+    </p>
+</div>
+
+
+

From 851bc8865d8099226a1cc5aa2ebeb779f56479af Mon Sep 17 00:00:00 2001
From: Adwitya Chakraborty <adwityachakraborty@hotmail.com>
Date: Fri, 3 Jan 2025 17:09:12 +0530
Subject: [PATCH 2/2] Update sleep_quality_prediction.md

---
 docs/ML/projects/sleep_quality_prediction.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/ML/projects/sleep_quality_prediction.md b/docs/ML/projects/sleep_quality_prediction.md
index 28fde334..b03ea561 100644
--- a/docs/ML/projects/sleep_quality_prediction.md
+++ b/docs/ML/projects/sleep_quality_prediction.md
@@ -207,7 +207,6 @@
 </table>
 
 ---
-<h2>SCREENSHOTS</h2>
 
 
 <h2>CONCLUSION</h2>