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+# 🌟 Autism Spectrum Disorder (ASD) Detection using Machine Learning
+
+
+
+
+
+## 🎯 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"
+
+
+## ⚙️ 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"
+ 
+
+??? example "Model performance graphs"
+
+ === "Confusion Matrix"
+ 
+
+??? example "Features Correlation"
+
+ === "Feature Correlation Heatmap"
+ 
+
+
+---
+
+## 📉 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.
+
+
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