Add autism

docs/machine-learning/autism-detection.md

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+# 🌟 Autism Spectrum Disorder (ASD) Detection using Machine Learning
+
+<div align="center">
+    <img src="https://github.com/user-attachments/assets/62cc5129-b502-4164-849b-8f74da079ee3" />
+</div>
+
+## 🎯 AIM
+To develop a machine learning model that predicts the likelihood of Autism Spectrum Disorder (ASD) based on behavioral and demographic features.
+
+## 🌊 DATASET LINK
+[Autism Screening Data](https://www.kaggle.com/code/konikarani/autism-prediction/data)  
+
+## 📚 KAGGLE NOTEBOOK
+[Autism Detection Kaggle Notebook](https://www.kaggle.com/code/thatarguy/autism-prediction-using-ml?kernelSessionId=224830771)
+
+??? Abstract "Kaggle Notebook"
+
+    <iframe src="https://www.kaggle.com/embed/thatarguy/autism-prediction-using-ml?kernelSessionId=224830771" height="800" style="margin: 0 auto; width: 100%; max-width: 950px;" frameborder="0" scrolling="auto" title="autism prediction using ml"></iframe>
+## ⚙️ TECH STACK
+
+| **Category**             | **Technologies**                            |
+|--------------------------|---------------------------------------------|
+| **Languages**            | Python                                     |
+| **Libraries/Frameworks** | Pandas, NumPy, Scikit-learn,      |
+| **Tools**                | Jupyter Notebook, VS Code                  |
+
+---
+
+## 🖍 DESCRIPTION
+!!! info "What is the requirement of the project?"
+    - The rise in Autism cases necessitates early detection.
+    - Traditional diagnostic methods are time-consuming and expensive.
+    - Machine learning can provide quick, accurate predictions to aid early intervention.
+
+??? info "How is it beneficial and used?"
+    - Helps doctors and researchers identify ASD tendencies early.
+    - Reduces the time taken for ASD screening.
+    - Provides a scalable and cost-effective approach.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+    - Collected and preprocessed the dataset.
+    - Explored different ML models for classification.
+    - Evaluated models based on accuracy and efficiency.
+
+
+---
+
+## 🔍 PROJECT EXPLANATION
+
+### 🧩 DATASET OVERVIEW & FEATURE DETAILS
+The dataset consists of **800 rows** and **22 columns**, containing information related to autism spectrum disorder (ASD) detection based on various parameters.
+
+
+| **Feature Name**    | **Description**                                    | **Datatype** |
+|---------------------|----------------------------------------------------|:-----------:|
+| `ID`               | Unique identifier for each record                   | `int64`     |
+| `A1_Score` - `A10_Score` | Responses to 10 screening questions (0 or 1) | `int64`     |
+| `age`              | Age of the individual                               | `float64`   |
+| `gender`           | Gender (`m` for male, `f` for female)               | `object`    |
+| `ethnicity`        | Ethnic background                                  | `object`    |
+| `jaundice`        | Whether the individual had jaundice at birth (`yes/no`) | `object`    |
+| `austim`          | Family history of autism (`yes/no`)                 | `object`    |
+| `contry_of_res`   | Country of residence                                | `object`    |
+| `used_app_before` | Whether the individual used a screening app before (`yes/no`) | `object`    |
+| `result`         | Score calculated based on the screening test        | `float64`   |
+| `age_desc`       | Age description (e.g., "18 and more")               | `object`    |
+| `relation`       | Relation of the person filling out the form          | `object`    |
+| `Class/ASD`      | ASD diagnosis label (`1` for ASD, `0` for non-ASD)   | `int64`     |
+
+This dataset provides essential features for training a model to detect ASD based on questionnaire responses and demographic information.
+
+
+---
+
+### 🛠 PROJECT WORKFLOW
+!!! success "Project workflow"
+    ``` mermaid
+      graph LR
+        A[Start] --> B[Data Preprocessing];
+        B --> C[Feature Engineering];
+        C --> D[Model Training];
+        D --> E[Model Evaluation];
+        E --> F[Deployment];
+    ```
+
+=== "Step 1"
+    - Collected dataset and performed exploratory data analysis.
+
+=== "Step 2"
+    - Preprocessed data (handling missing values, encoding categorical data).
+
+=== "Step 3"
+    - Feature selection and engineering.
+
+=== "Step 4"
+    - Trained multiple classification models (Decision Tree, Random Forest, XGBoost).
+
+=== "Step 5"
+    - Evaluated models using accuracy, precision, recall, and F1-score.
+
+
+---
+
+### 🖥️ CODE EXPLANATION
+=== "Section 1: Data Preprocessing"
+    - Loaded dataset and handled missing values.
+
+=== "Section 2: Model Training"
+    - Implemented Logistic Regression and Neural Networks for classification.
+
+---
+
+### ⚖️ PROJECT TRADE-OFFS AND SOLUTIONS
+=== "Trade Off 1"
+    - **Accuracy vs. Model Interpretability**: Used a Random Forest model instead of a deep neural network for better interpretability.
+
+=== "Trade Off 2"
+    - **Speed vs. Accuracy**: Chose Logistic Regression for quick predictions in real-time applications.
+
+---
+
+## 🖼 SCREENSHOTS
+!!! tip "Visualizations and EDA of different features"
+
+    === "Age Distribution"
+        ![img](https://github.com/user-attachments/assets/412aa82d-0f7a-4c7a-bdca-30a553de36b4)
+
+??? example "Model performance graphs"
+
+    === "Confusion Matrix"
+        ![img](https://github.com/user-attachments/assets/71c5773c-fe1f-42bb-ab76-e1150f564507)
+
+??? example "Features Correlation"
+
+    === "Feature Correlation Heatmap"
+        ![img](https://github.com/user-attachments/assets/60d24749-2f2e-4222-9895-c46c29ea596e)
+
+
+---
+
+## 📉 MODELS USED AND THEIR EVALUATION METRICS
+|    Model   | Accuracy | Precision | Recall | F1-score |
+|------------|----------|-----------|--------|----------|
+| Decision Tree | 73%   | 0.71 | 0.73 | 0.72 |
+| Random Forest | 82%   | 0.82 | 0.82 | 0.82 |
+| XGBoost      | 81%   | 0.81 | 0.81 | 081 |
+
+---
+
+## ✅ CONCLUSION
+### 🔑 KEY LEARNINGS
+!!! tip "Insights gained from the data"
+    - Behavioral screening scores are the strongest predictors of ASD.
+    - Family history and neonatal jaundice also show correlations with ASD diagnosis.
+
+??? tip "Improvements in understanding machine learning concepts"
+    - Feature selection and engineering play a crucial role in medical predictions.
+    - Trade-offs between accuracy, interpretability, and computational efficiency need to be balanced.
+
+---
+
+### 🌍 USE CASES
+=== "Early ASD Screening"
+    - Helps parents and doctors identify ASD tendencies at an early stage.
+
+=== "Assistive Diagnostic Tool"
+    - Can support psychologists in preliminary ASD assessments before clinical diagnosis.
+
+

docs/machine-learning/index.md

@@ -97,5 +97,16 @@
       </div>
     </a>
   </figure>
+<!-- autism detection -->
+  <figure style="padding: 1rem; background: rgba(39, 39, 43, 0.5); border-radius: 10px; border: 1px solid rgba(76, 76, 82, 0.4); box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); transition: transform 0.2s ease-in-out; text-align: center; max-width: 320px; margin: auto;">
+    <a href="autism-detection" style="color: white; text-decoration: none; display: block;">
+      <img src="https://github.com/user-attachments/assets/2c1bdd07-f30a-4b1e-b0c5-74248ae0b700" alt="autism detcion using ml" style="width: 100%; height: 150px; object-fit: cover; border-radius: 8px; transition: transform 0.2s;" />
+      <div style="padding: 0.8rem;">
+        <h3 style="margin: 0; font-size: 18px;">Autism Detection</h3>
+        <p style="font-size: 14px; opacity: 0.8;">Predicting Autism Using Machine Learning</p>
+        <p style="font-size: 12px; opacity: 0.6;">📅 2025-02-26 | ⏱️ 8 mins</p>
+      </div>
+    </a>
+  </figure>
 
 </div>